01403nas a2200145 4500008004100000245006100041210006100102260000800163520095900171100001901130700001901149700002001168700002301188856004601211 2021 eng d00aSafer Adaptive Cruise Control for Traffic Wave Dampening0 aSafer Adaptive Cruise Control for Traffic Wave Dampening bACM3 a

This project aims to develop an adaptive cruise controller for vehicles at low speeds in stop-and-go traffic. Current adaptive cruise controllers can use RADAR sensors to follow a vehicle at high speeds (greater than 18 mph), but reach their limits if the lead vehicle’s velocity dips below threshold, requiring the driver of the host vehicle to resume control over the car’s speed. Some cruise controllers adapt to stop-and-go traffic, but these are mostly experimental and have yet to see widespread commercial implementation. These experimental models often have issues because of their limited data; consequently, the acceleration and deceleration can be jarring and uncomfortable to passengers. In contrast, because of our reliable sensor data, and the sensor configuration unique to the CAT Vehicle, our cruise controller will be capable of following cars at low speeds and functioning continuously, even when the car is stopped. 

1 aBaschab, Emily1 aBall, Savannah1 aVazzana, Audrey1 aSprinkle, Jonathan uhttp://dx.doi.org/10.1145/3450267.345200302169nas a2200265 4500008004100000245008000041210006900121260001200190300001300202520135300215653002801568653002101596100001901617700002201636700002201658700001801680700001901698700001801717700003101735700002201766700002301788700002301811700002101834856004801855 2020 eng d00aAre commercially implemented adaptive cruise control systems string stable?0 aAre commercially implemented adaptive cruise control systems str c06/2020 a12 pages3 a

In this article, we assess the string stability of seven 2018 model year adaptive cruise control (ACC) equipped vehicles that are widely available in the US market. A total of seven distinct vehicle models from two different vehicle makes are analyzed using data collected from more than 1,200 miles of driving in designed car-following experiments with ACC engaged by the following vehicle. The data is used to identify the parameters of a linear second order delay differential equation model that approximates the behavior of the proprietary ACC systems. The string stability of the data fitted model associated with each vehicle is assessed, and the main finding is that all seven vehicle models have string unstable ACC systems. For one commonly available vehicle that offers ACC as a standard feature on all trim levels, we validate the string stability finding with a multi-vehicle platoon experiment in which all vehicles are the same year, make, and model. In the multi-vehicle platoon test, an initial disturbance of 6 mph is amplified by 19 mph to a 25 mph disturbance, at which point the last vehicle in the platoon is observed to disengage the ACC and return control to the human driver. The data collected the driving experiments is made available, representing the largest available driving dataset on ACC equipped vehicles.

10aAdaptive Cruise Control10aString Stability1 aGunter, George1 aGloudemans, Derek1 aStern, Raphael, E1 aMcQuade, Sean1 aBhadani, Rahul1 aBunting, Matt1 aMonache, Maria, Laura Dell1 aSeibold, Benjamin1 aSprinkle, Jonathan1 aPiccoli, Benedetto1 aWork, Daniel, B. uhttp://dx.doi.org/10.1109/TITS.2020.300068201576nas a2200193 4500008004100000245009200041210006900133520087400202653002501076653002101101653002901122653001501151653002601166100001601192700001801208700001901226700002301245856011401268 2020 eng d00aModeling Human Car-Following Behavior from Demonstration with Recurrent Neural Networks0 aModeling Human CarFollowing Behavior from Demonstration with Rec3 a

The validity of simulation testing for autonomous vehicles depends on the ability to accurately simulate human driving behavior. This project seeks to train a model on an individual’s driving data, and to test the ability of the model to predict trajectories that replicate the driver’s style by using the model in a realistic simulated environment. Specifically, we deployed Recurrent Neural Network (RNN) modeling techniques to create a black-box model of an individual’s driving behavior. We use our RNN-trained model to simulate a human-driven vehicle in the Robot Operating System (ROS) based CAT Vehicle simulator for autonomous vehicle validation. We hope this work is a step to improve testing environments for validating human behavior replicating car-following models and thereby improve testing environments for autonomous vehicles in general.

10acar-following models10adriving behavior10arecurrent neural network10asimulation10atrajectory prediction1 aJones, Iris1 aWalter, Megan1 aBhadani, Rahul1 aSprinkle, Jonathan uhttps://csl.arizona.edu/content/modeling-human-car-following-behavior-demonstration-recurrent-neural-networks01020nas a2200205 4500008004100000020002200041245010800063210006900171260005200240520028500292653001400577653003300591653001900624100001900643700001900662700002500681700002300706700001700729856006800746 2020 eng d a978-1-943580-80-400aProgramming the Kennedy Receiver for Capacity Maximization versus Minimizing One-shot Error Probability0 aProgramming the Kennedy Receiver for Capacity Maximization versu aOnlinebThe Optical Society of Americac09/20203 a

We find the capacity attained by the Kennedy receiver for coherent-state BPSK when the symbol prior $p$ and pre-detection displacement $\beta$ are optimized. The optimal $\beta$ is different than what minimizes error probability for single-shot BPSK state discrimination.

10aPhotonics10aPhotonics Information Theory10aQuantum Optics1 aBhadani, Rahul1 aGrace, Michael1 aDjordjevic, Ivan, B.1 aSprinkle, Jonathan1 aGuha, Saikat uhttps://www.osapublishing.org/abstract.cfm?uri=FiO-2020-JM6B.2902204nas a2200169 4500008004100000245006100041210006100102520163400163653002801797653001601825653002101841100001901862700001901881700002001900700002301920856009101943 2020 eng d00aSafer Adaptive Cruise Control for Traffic Wave Dampening0 aSafer Adaptive Cruise Control for Traffic Wave Dampening3 a

Our goal is to develop an adaptive cruise controller for vehicles at low speeds in stop-and-go traffic. Current adaptive cruise controllers can use radar sensors to follow a vehicle at high speeds (greater than 18 mph), but reach their limits if the lead vehicle’s velocity dips below threshold, requiring the driver of the host vehicle to resume control over the car’s speed. Some cruise controllers adapt to stop-and-go traffic, but these are mostly experimental and have yet to see widespread commercial implementation. These experimental models often have issues because of their limited data; consequently, the acceleration and deceleration can be jarring and uncomfortable to passengers. In contrast, because of our reliable sensor data, and the sensor configuration unique to the CAT Vehicle, our cruise controller will be capable of following cars at low speeds and functioning continuously, even when the car is stopped.

This project has the potential to interest automobile companies who could implement this technology in future automobiles. If our technology were to be implemented in future automobiles, it would make driving considerably more convenient for drivers. This technology could also potentially reduce the number of traffic accidents, as well as making drivers feel safer when navigating traffic. However, if errors were to occur, they could potentially put the car’s passengers at risk, as well as the passengers in nearby vehicles.

Our project had a time frame of ten weeks during which we were able to model an adaptive cruise controller and test it in a simulation.

10aAdaptive Cruise Control10astop-and-go10aVehicle Autonomy1 aBaschab, Emily1 aBall, Savannah1 aVazzana, Audrey1 aSprinkle, Jonathan uhttps://csl.arizona.edu/content/safer-adaptive-cruise-control-traffic-wave-dampening-000571nas a2200133 4500008004100000245011400041210006900155260000700224100002200231700002100253700001900274700002300293856012100316 2020 eng d00aSafety and Stability Analysis of the FollowerStopper Traffic Wave Dampening Controller (Late-Breaking Poster)0 aSafety and Stability Analysis of the FollowerStopper Traffic Wav c071 aKreienkamp, Chris1 aFishbein, Daniel1 aBhadani, Rahul1 aSprinkle, Jonathan uhttps://csl.arizona.edu/content/safety-and-stability-analysis-followerstopper-traffic-wave-dampening-controller-late01771nas a2200157 4500008004100000245007400041210006900115260001200184300001300196490000700209520129700216100002201513700002301535700001901558856003601577 2019 eng d00aAutomated Model-based Optimization of Data-Adaptable Embedded Systems0 aAutomated Modelbased Optimization of DataAdaptable Embedded Syst c02/2020 a22 pages0 v193 a

This paper presents a modeling and optimization framework that enables developers to model an application's data sources, tasks, and exchanged data tokens; specify application requirements through high-level design metrics and fuzzy logic based optimization rules; and define an estimation framework to automatically optimize the application at runtime. We demonstrate the modeling and optimization process via an example application for video-based vehicle tracking and collision avoidance. We analyze the benefits of runtime optimization by comparing the performance of static point solutions to dynamic solutions over five distinct execution scenarios, showing improvements of up to 74% for dynamic over static configurations.

1 aLizarraga, Adrian1 aSprinkle, Jonathan1 aLysecky, Roman uhttps://doi.org/10.1145/337214202086nas a2200265 4500008004100000020002200041245009400063210006900157260004400226300001400270520121200284100003101496700002001527700001601547700002201563700001901585700002201604700002301626700002101649700002301670700002701693700002401720700002701744856004901771 2019 eng d a978-3-030-25446-900aFeedback Control Algorithms for the Dissipation of Traffic Waves with Autonomous Vehicles0 aFeedback Control Algorithms for the Dissipation of Traffic Waves aChambSpringer International Publishing a275–2993 a

This article considers the problem of traffic control in which an autonomous vehicle is used to regulate human-piloted traffic to dissipate stop-and-go traffic waves. We first investigated the controllability of well-known microscopic traffic flow models, namely, (i) the Bando model (also known as the optimal velocity model), (ii) the follow-the-leader model, and (iii) a combined optimal velocity follow-the-leader model. Based on the controllability results, we proposed three control strategies for an autonomous vehicle to stabilize the other, human-piloted traffics. We subsequently simulate the control effects on the microscopic models of human drivers in numerical experiments to quantify the potential benefits of the controllers. Based on the simulations, finally, we conduct a field experiment with 22 human drivers and a fully autonomous-capable vehicle, to assess the feasibility of autonomous vehicle-based traffic control on real human-piloted traffic. We show that both in simulation and in the field test that an autonomous vehicle is able to dampen waves generated by 22 cars, and that as a consequence, the total fuel consumption of all vehicles is reduced by up to 20{%}.

1 aMonache, Maria, Laura Dell1 aLiard, Thibault1 aRat, Anaïs1 aStern, Raphael, E1 aBhadani, Rahul1 aSeibold, Benjamin1 aSprinkle, Jonathan1 aWork, Daniel, B.1 aPiccoli, Benedetto1 aBlondin, Maude, Josée1 aPardalos, Panos, M.1 aSáez, Javier, Sanchis uhttps://doi.org/10.1007/978-3-030-25446-9_1202407nas a2200157 4500008004100000020002200041245005700063210005600120260001000176300001200186520191000198100001902108700001802127700002302145856008102168 2019 eng d a978-1-119-55239-000aModel-based engineering with application to autonomy0 aModelbased engineering with application to autonomy bWiley a255-2853 a

In this chapter we focus on where models fit into the verification and validation design cycle of autonomous cyber-physical systems. These systems typically make decisions through myriad of sensing loops, have implementations in multiple languages, and may have their logic represented in several different kinds of formal models. The use of code generation, along with software-in-the-loop and hardware-in-the-loop simulation (discussed further in Section 4), permits system designers to apply various agile techniques for the validation and verification of systems as requirements are implemented, tested, and demonstrated. The work in this chapter explores such a design cycle with application to autonomous driving. Examples are given for the implementation of various components that describe vehicle dynamics, control models, system identification, sensor/data acquisition, etc., which can be functionally de- scribed in models, and explored in simulation before utilizing code generation to deploy final solutions. The integration of simulation tools during functional design, software-in-the-loop testing, and hardware-in-the-loop testing, permits regression evaluation of use case scenarios. In addition to functional testing, we also describe how high-level domain- specific models can be used to include verification-in-the-loop toolboxes as part of the design cycle. All the examples in this chapter are based on an autonomous Ford Escape, which has a Robotic Operating System (ROS) API for its control and the integration of autonomous components—however, the results are applicable to other event-based and time-triggered middleware platforms. The implementation models in use include Simulink, MATLAB, StateFlow, and other domain-specific languages that specify high-level behaviors.

1 aBhadani, Rahul1 aBunting, Matt1 aSprinkle, Jonathan uhttps://csl.arizona.edu/content/model-based-engineering-application-autonomy02237nas a2200205 4500008004100000245009600041210006900137260001200206490000700218520158800225100001601813700002101829700001501850700003101865700002301896700002201919700002301941700002101964856004601985 2019 eng d00aQuantifying air quality benefits resulting from few autonomous vehicles stabilizing traffic0 aQuantifying air quality benefits resulting from few autonomous v c02/20190 v673 a

It is anticipated that in the near future, the penetration rate of vehicles with some autonomous capabilities (e.g., adaptive cruise control, lane following, full automation, etc.) will increase on roadways. This work investigates the potential reduction of vehicular emissions caused by the whole traffic stream, when a small number of autonomous vehicles (e.g., 5% of the vehicle fleet) are designed to stabilize the traffic flow and dampen stop-and-go waves. To demonstrate this, vehicle velocity and acceleration data are collected from a series of field experiments that use a single autonomous-capable vehicle to dampen traffic waves on a circular ring road with 20–21 human-piloted vehicles. From the experimental data, vehicle emissions (hydrocarbons, carbon monoxide, carbon dioxide, and nitrogen oxides) are estimated using the MOVES emissions model. This work finds that vehicle emissions of the entire fleet may be reduced by between 15% (for carbon dioxide) and 73% (for nitrogen oxides) when stop-and-go waves are reduced or eliminated by the dampening action of the autonomous vehicle in the flow of human drivers. This is possible if a small fraction (∼5%) of vehicles are autonomous and designed to actively dampen traffic waves. However, these reductions in emissions apply to driving conditions under which stop-and-go waves are present. Less significant reductions in emissions may be realized from a deployment of AVs in a broader range of traffic conditions.

1 aChen, Yuche1 aChurchill, Miles1 aWu, Fangyu1 aMonache, Maria, Laura Dell1 aPiccoli, Benedetto1 aSeibold, Benjamin1 aSprinkle, Jonathan1 aWork, Daniel, B. uhttps://doi.org/10.1016/j.trd.2018.12.00802015nas a2200253 4500008004100000245009900041210006900140250000700209260003000216520122200246653002401468653001901492653001501511653001201526100001901538700002101557700002201578700002201600700001501622700002301637700002301660700002101683856005701704 2019 eng d00aReal-Time Distance Estimation and Filtering of Vehicle Headways for Smoothing of Traffic Waves0 aRealTime Distance Estimation and Filtering of Vehicle Headways f a10 aMontreal, Canadac04/20193 a

In this paper, we describe an experience report and field deployment of real-time filtering algorithms used with a robotic vehicle to smooth emergent traffic waves. When smoothing these waves in simulation, a common approach is to implement controllers that utilize headway, relative velocity and even acceleration from smooth ground truth information, rather than from realistic data. As a result, many results may be limited in their impact when considering the dynamics of the vehicle under control and the discretized nature of the laser data as well as its periodic arrival. Our approach discusses trade-offs in estimation accuracy to provide both distance and velocity estimates, with ground-truth hardware-in-the-loop tests with a robotic car. The contribution of the work enabled an experiment with 21 vehicles, including the robotic car closing the loop at up to 8.0 m/s with this filtered estimate, stressing the importance of an algorithm that can deliver real-time results with acceptable accuracy for the safety of the drivers in the experiment.

 

10aautonomous vehicles10aDigital Filter10asimulation10aTraffic1 aBhadani, Rahul1 aBunting, Matthew1 aSeibold, Benjamin1 aStern, Raphael, E1 aCui, Shumo1 aSprinkle, Jonathan1 aPiccoli, Benedetto1 aWork, Daniel, B. uhttps://dl.acm.org/citation.cfm?doid=3302509.331402601438nas a2200181 4500008004100000245005300041210005300094260004700147520082100194653002401015653002001039653003101059100002201090700002101112700001901133700002301152856008101175 2019 eng d00aSafety and stability analysis of FollowerStopper0 aSafety and stability analysis of FollowerStopper aTucsonbThe University of Arizonac08/20193 a

In this paper, we demonstrate that the velocity controller, FollowerStopper, is safe and string unstable. FollowerStopper is a controller that is meant to be implemented on an autonomous vehicle or in an adaptive cruise control (ACC) system. Through mathematical proof, simulation in Simulink, and hardware in the loop implementation on a real autonomous vehicle through the Robot Operating System (ROS) and Gazebo, several results are achieved. It is found that an autonomous vehicle controlled by FollowerStopper will never crash. FollowerStopper will dissipate larger traffic waves from human-driven vehicles but will amplify smaller velocity perturbations that are created within the controller. Given the maximum LiDAR range of 81 m, FollowerStopper will never command a velocity greater than 13.69 m/s

10aautonomous vehicles10acontrol systems10aintelligent transportation1 aKreienkamp, Chris1 aFishbein, Daniel1 aBhadani, Rahul1 aSprinkle, Jonathan uhttp://csl.arizona.edu/content/safety-and-stability-analysis-followerstopper00795nas a2200253 4500008004100000245009500041210006900136260001300205300001300218490000700231100001500238700002200253700001500275700003100290700001900321700002100340700002100361700002400382700002300406700002200429700002300451700002100474856004600495 2019 eng d00aTracking vehicle trajectories and fuel rates in phantom traffic jams: Methodology and data0 aTracking vehicle trajectories and fuel rates in phantom traffic  bElsevier a82–1090 v991 aWu, Fangyu1 aStern, Raphael, E1 aCui, Shumo1 aMonache, Maria, Laura Dell1 aBhadani, Rahul1 aBunting, Matthew1 aChurchill, Miles1 aHamilton, Nathaniel1 aPiccoli, Benedetto1 aSeibold, Benjamin1 aSprinkle, Jonathan1 aWork, Daniel, B. uhttps://doi.org/10.1016/j.trc.2018.12.01201253nas a2200241 4500008004100000245008200041210006900123520049300192653002800685653002100713100001900734700001300753700002200766700002100788700003100809700001900840700001800859700001900877700002300896700002200919700002300941856004700964 2019 eng d00aWiP Abstract: String stability of commercial adaptive cruise control vehicles0 aWiP Abstract String stability of commercial adaptive cruise cont3 aIn this work, we conduct a series of car-following experiments with seven different ACC vehicles and use the collected data to model the car-following behavior of each vehicle. Using a linear stability analysis, the string stability of each tested vehicle is analyzed. Addition- ally, platoon experiments with platoons of up to eight identical vehicles are conducted to validate the stability findings. Previously, only one commercial ACC system has been evaluated for string stability. 10aAdaptive Cruise Control10aString Stability1 aGunter, George1 aYang, Y.1 aStern, Raphael, E1 aWork, Daniel, B.1 aMonache, Maria, Laura Dell1 aBhadani, Rahul1 aBunting, Matt1 aLysecky, Roman1 aSprinkle, Jonathan1 aSeibold, Benjamin1 aPiccoli, Benedetto uhttps://dl.acm.org/citation.cfm?id=331332500669nas a2200229 4500008004100000245004800041210004200089100001500131700002200146700001500168700003100183700001900214700002100233700002100254700002400275700001500299700002300314700002200337700002300359700002100382856003600403 2018 eng d00aThe Arizona Ring Experiments Dataset (ARED)0 aArizona Ring Experiments Dataset ARED1 aWu, Fangyu1 aStern, Raphael, E1 aCui, Shumo1 aMonache, Maria, Laura Dell1 aBhadani, Rahul1 aBunting, Matthew1 aChurchill, Miles1 aHamilton, Nathaniel1 aWu, Fangyu1 aPiccoli, Benedetto1 aSeibold, Benjamin1 aSprinkle, Jonathan1 aWork, Daniel, B. uhttp://hdl.handle.net/1803/935800631nas a2200169 4500008004100000245010500041210006900146260001200215490000800227653002400235653001500259653001200274100001900286700002300305700002100328856011200349 2018 eng d00a{The CAT Vehicle Testbed: A Simulator with Hardware in the Loop for Autonomous Vehicle Applications}0 aCAT Vehicle Testbed A Simulator with Hardware in the Loop for Au c04/20180 v26910aautonomous vehicles10asimulation10atestbed1 aBhadani, Rahul1 aSprinkle, Jonathan1 aBunting, Matthew uhttps://csl.arizona.edu/content/cat-vehicle-testbed-simulator-hardware-loop-autonomous-vehicle-applications02161nas a2200205 4500008004100000245009600041210006900137260004100206490000700247520150000254653002401754653000801778653001201786100001901798700002301817700002201840700002301862700002101885856004901906 2018 eng d00aDissipation of Emergent Traffic Waves in Stop-and-Go Traffic Using a Supervisory Controller0 aDissipation of Emergent Traffic Waves in StopandGo Traffic Using aFontainbleau, Miami Beach, USAbIEEE0 v573 a

This paper presents the use of a quadratic band controller in an autonomous vehicle (AV) to regulate emergent traffic waves resulting from traffic congestion. The controller dampens the emergent traffic waves through modulating its velocity according to the relative distance and velocity of the immediately preceding vehicle in the flow. At the same time, it prevents any collision within the range specified by the design parameters. The approach is based on a configurable quadratic band that allows smooth transitions between (i) no modification to the desired velocity; (ii) braking to match the speed of the preceding vehicle; and (iii) braking to avoid collision with the lead vehicle. By assuming that the lead vehicle's velocity will be oscillatory, the controller's smooth transition between modes permits any vehicle following the AV to have a smoother reference velocity. The configurable quadratic band allows design parameters, such as actuator and computation delays as well as the dynamics of vehicle deceleration, to be taken into account when constructing the controller. Experimental data, software-in-the-loop distributed simulation, and results from physical platform performance in an experiment with 21 human-driven vehicles are presented. Analysis shows that the design parameters used in constructing the quadratic band controller are met, and assumptions regarding the oscillatory nature of emergent traffic waves are valid. 

10aautonomous vehicles10aCPS10aTraffic1 aBhadani, Rahul1 aPiccoli, Benedetto1 aSeibold, Benjamin1 aSprinkle, Jonathan1 aWork, Daniel, B. uhttps://ieeexplore.ieee.org/document/861970000876nas a2200277 4500008004100000245009100041210006900132260001200201490000700213653002400220653002700244100002200271700001500293700003100308700001900339700002100358700002100379700002400400700002100424700001500445700002300460700002200483700002300505700002100528856004900549 2018 eng d00aDissipation of stop-and-go waves via control of autonomous vehicles: Field experiments0 aDissipation of stopandgo waves via control of autonomous vehicle c04/20180 v8910aautonomous vehicles10acyber physical systems1 aStern, Raphael, E1 aCui, Shumo1 aMonache, Maria, Laura Dell1 aBhadani, Rahul1 aBunting, Matthew1 aChurchill, Miles1 aHamilton, Nathaniel1 aPohlmann, Hannah1 aWu, Fangyu1 aPiccoli, Benedetto1 aSeibold, Benjamin1 aSprinkle, Jonathan1 aWork, Daniel, B. uhttps://csl.arizona.edu/stern2017dissipation01584nas a2200241 4500008004100000245006200041210005800103260003400161520086600195653002401061653000701085653000801092653001001100653002201110653001101132653000801143100002001151700002201171700001901193700002101212700002301233856008601256 2018 eng d00a{A LiDAR Error Model for Cooperative Driving Simulations}0 aLiDAR Error Model for Cooperative Driving Simulations aTaipei, TaiwanbIEEEc12/20183 a

Cooperative driving and vehicular network simulations have done huge steps toward high realism. They have become essential tools for performance evaluation of any kind of vehicular networking application. Yet, cooperative vehicular applications will not be built on top of wireless networking alone, but rather fusing together different data sources including sensors like radars, LiDARs, or cameras. So far, these sensors have been assumed to be ideal, i.e., without any measurement error. This paper analyzes a set of estimated distance traces obtained with a LiDAR sensor and develops a stochastic error model that can be used in cooperative driving simulations. After implementing the model within the PLEXE simulation framework, we show the impact of the model on a set of cooperative driving control algorithms.

10aautonomous vehicles10aAV10aCAV10aLiDAR10aself-driving cars10asensor10aVNC1 aSegata, Michele1 aCigno, Renato, Lo1 aBhadani, Rahul1 aBunting, Matthew1 aSprinkle, Jonathan uhttps://csl.arizona.edu/content/lidar-error-model-cooperative-driving-simulations00954nas a2200301 4500008004100000020002200041245010200063210006900165260002700234300001000261653002400271653001700295100002100312700002200333700001500355700002100370700001500391700002100406700002200427700002300449700001900472700002100491700002300512700003100535700002400566700001500590856004700605 2017 eng d a978-1-4503-4976-500aControlling for Unsafe Events in Dense Traffic Through Autonomous Vehicles: Invited Talk Abstract0 aControlling for Unsafe Events in Dense Traffic Through Autonomou aNew York, NY, USAbACM a7–710aSugiyama experiment10aTraffic flow1 aWork, Daniel, B.1 aStern, Raphael, E1 aWu, Fangyu1 aChurchill, Miles1 aCui, Shumo1 aPohlmann, Hannah1 aSeibold, Benjamin1 aPiccoli, Benedetto1 aBhadani, Rahul1 aBunting, Matthew1 aSprinkle, Jonathan1 aMonache, Maria, Laura Dell1 aHamilton, Nathaniel1 aHaulcy, R. uhttp://doi.acm.org/10.1145/3055378.305538001801nas a2200229 4500008004100000245006000041210005800101260009100159520099000250653002301240653001901263100001901282700002401301700001901325700002301344700002101367700002001388700002301408700003101431700002201462856008701484 2017 eng d00aA Fuzzy based approach to Dampen Emergent Traffic Waves0 aFuzzy based approach to Dampen Emergent Traffic Waves aThe University of ArizonabCAT Vehicle Research Experience for Undergraduatesc01/20173 a

Adaptive Cruise Control (ACC) and Traffic Aware Cruise Control (TACC) are recent advancements in cruise control design that allow a semi-autonomous vehicle to slow itself when approaching vehicles. The issue with these technologies is that they focus on keeping the distance from a leading vehicle constant. This may lead to unwanted dynamics in the following traffic flow, could result in the creation of traveling waves.  This paper focuses on maintaining a reference velocity based on the relative position of the preceding vehicle instead of slowing down to maintain a certain following distance. Doing so could reduce the amount of braking the vehicles behind the autonomous vehicle will do. With this kind of technology implemented, the number and duration of traffic jams could be greatly reduced. Simulation results and tests run on the University of Arizona's Cognitive Autonomous Test (CAT) Vehicle illustrate the feasibility and success of this new controller.

10aAutonomous Systems10aControl System1 aHaulcy, R'mani1 aHamilton, Nathaniel1 aBhadani, Rahul1 aSprinkle, Jonathan1 aWork, Daniel, B.1 aRisso, Nathalie1 aPiccoli, Benedetto1 aMonache, Maria, Laura Dell1 aSeibold, Benjamin uhttps://csl.arizona.edu/content/fuzzy-based-approach-dampen-emergent-traffic-waves00497nas a2200145 4500008004100000245005900041210005900100260000900159300001000168100002500178700002000203700002200223700002300245856008300268 2017 eng d00aFuzzy Control of an Autonomous Car using a Smart Phone0 aFuzzy Control of an Autonomous Car using a Smart Phone bIEEE a1–51 aOlson, Elizabeth, A.1 aRisso, Nathalie1 aJohnson, Adam, M.1 aSprinkle, Jonathan uhttps://csl.arizona.edu/content/fuzzy-control-autonomous-car-using-smart-phone02032nas a2200277 4500008004100000022001400041245005800055210005700113300001900170490000700189520121400196653002201410653003101432653002301463653003401486100002101520700001801541700001901559700002901578700002701607700002301634700001601657700002301673700001901696856003901715 2017 eng d a1539-908700aTask Transition Scheduling for Data-Adaptable Systems0 aTask Transition Scheduling for DataAdaptable Systems a105:1–105:280 v163 a

Data-adaptable embedded systems operate on a variety of data streams, which requires a large degree of configurability and adaptability to support runtime changes in data stream inputs. Data-adaptable reconfigurable embedded systems, when decomposed into a series of tasks, enable a flexible runtime implementation in which a system can transition the execution of certain tasks between hardware and software while simultaneously continuing to process data during the transition. Efficient runtime scheduling of task transitions is needed to optimize system throughput and latency of the reconfiguration and transition periods. In this article, we provide an overview of a runtime framework enabling the efficient transition of tasks between software and hardware in response to changes in system inputs. We further present and analyze several runtime transition scheduling algorithms and highlight the latency and throughput tradeoffs for two data-adaptable systems. To evaluate the task transition selection algorithms, a case study was performed on an adaptable JPEG2000 implementation as well as three other synchronous dataflow systems characterized by transition latency and communication load.

10aData adaptability10ahardware/software codesign10amodel-based design10aruntime transition scheduling1 aSandoval, Nathan1 aMackin, Casey1 aWhitsitt, Sean1 aGopinath, Vijay, Shankar1 aMahadevan, Sachidanand1 aMilakovich, Andrew1 aMerry, Kyle1 aSprinkle, Jonathan1 aLysecky, Roman uhttp://doi.acm.org/10.1145/304749800507nas a2200145 4500008004100000245010700041210006900148260001200217300001200229490000700241100001500248700002300263700002700286856004800313 2016 eng d00aComputationally-Aware Switching Criteria for Hybrid Model Predictive Control Of Cyber-Physical Systems0 aComputationallyAware Switching Criteria for Hybrid Model Predict c04/2016 a479-4900 v131 aZhang, Kun1 aSprinkle, Jonathan1 aSanfelice, Ricardo, G. uhttp://dx.doi.org/10.1109/TASE.2016.252334106037nas a2200253 4500008004100000245005300041210005300094520525700147100002205404700001505426700002105441700002105462700003105483700002305514700002105537700001505558700002205573700002405595700002105619700001905640700002105659700002305680856008005703 2016 eng d00aDampening traffic waves with autonomous vehicles0 aDampening traffic waves with autonomous vehicles3 aIn congested traffic, minor disturbances or fluctuations in the velocity of a single vehicle may induce dynamically evolving traffic waves such as stop-and-go waves. These waves cause vehicles upstream to slow down or stop before accelerating back to the desired speed, resulting in increases in fuel consumption and risk of collisions. This work postulates that by intelligently controlling a small number (e.g., 1-5%) of autonomous vehicles (AVs) soon to be present in the traffic flow, it is possible to dampen or completely remove these speed fluctuations in the entire traffic stream. By only making small changes to the way the AV drives compared to human drivers near the dynamic wave, we can significantly improves the smoothness of the overall traffic flow, and reduces fuel consumption of all vehicles on the road. Due to the inherent instability of dense traffic flow, small disturbances in the speed of individual vehicles can generate large-scale disturbances in the traffic stream in the form of waves. Uncontrolled, these waves will propagate indefinitely until the traffic density decreases and the instability dissipates. This phenomenon was first experimentally demonstrated in the famous ring road experiment of Sugiyama et al., 2008. In that experiment, 22 vehicles were driven on a circular track to demonstrate that the uniform initial traffic flow (uniform speed and spacing) quickly devolves into a stop-and-go wave with vehicles at one side of the track at a complete standstill, while vehicles at the other side of the track are racing to keep up with the vehicle in front of them. To learn effective AV control strategies to dampen these traffic waves we must accurately simulate traffic in the specific conditions under which these waves arise. This will allow us to study how traffic responds to different control mechanisms implemented by the AV mathematically from a control prospective as well as in simulation. In this work, a microscopic car following model is used to simulate traffic with a mix of human-controlled and autonomous vehicles. We model human traffic flow using the combined optimal-velocity follow-the-leader (OV-FTL). This model is calibrated using trajectories of vehicles under human driving behavior such that the macroscopic quantities (average velocity, wave growth time, wave propagation speed) match those quantities observed in the Sugiyama experiment. Note that thus, the microscopic model is calibrated to reproduce real traffic waves, which is not commonly done in the traffic modelling community. Using linear stability theory, this model is shown to be unstable, since it has positive-valued eigenvalues. These manifest themselves in the form of stop-and-go waves when the calibrated model is used to simulated individual vehicle’s trajectories in time. In order to calibrate more realistic models of human drivers in dense, unstable, traffic conditions, field experiments are conducted using between 12 and 22 vehicles at the University of Illinois in Urbana, Illinois to re-create the traffic waves observed in the Sugiyama experiment, and probe the state space of traffic conditions under which such traffic instabilities will arise. This data is then used to calibrate more realistic models of human driving behavior that cover a broader range of traffic conditions. All vehicles used in the experiments are equipped with onboard diagnostics (OBD-II) scanners to record the vehicle’s velocity, engine speed, fuel rate, and fuel consumption throughout the experiment. This provides additional data that allows us to compare fuel consumption in traffic with stop-and-go waves to uniformly-flowing traffic. Furthermore, the trajectory of each vehicle is tracked using a 360-degree panoramic camera located at the center of the circular track. To begin, this research addresses the case of the 22-vehicle system recorded in the Sugiyama experiment and augment it by replacing one of the vehicles with an AV, which provides actuation in the system since it can be controlled to drive arbitrarily smoothly within the constraints set by the vehicle immediately in front of it. To apply linear stability theory, this augmented system is then linearized about an equilibrium traffic flow. We then use a feedback controller and pole placement to stabilize the system, and prevent traffic waves from emerging. Results from simulating the stabilized system in time indicate that a single AV using realistic control gains is able to dampen traffic waves in a 22-vehicle system without decreasing the average speed. The societal implications of this work are broad since most drivers experience delays and increased fuel consumption due to unstable and non-uniformly flowing traffic. While complete automation of the entire vehicle fleet may be many years away, in the short term, it is likely that some vehicles will be capable of driving autonomously in the near future. This research demonstrates then even with only a small percentage of vehicles driving autonomously, it is possible alter the traffic flow and prevent instabilities from arising. This results are lower fuel consumption and a shorter driving time not only for the autonomous vehicles, but all vehicles in the traffic stream.1 aStern, Raphael, E1 aWu, Fangyu1 aChurchill, Miles1 aWork, Daniel, B.1 aMonache, Maria, Laura Dell1 aPiccoli, Benedetto1 aPohlmann, Hannah1 aCui, Shumo1 aSeibold, Benjamin1 aHamilton, Nathaniel1 aHaulcy, R’mani1 aBhadani, Rahul1 aBunting, Matthew1 aSprinkle, Jonathan uhttps://csl.arizona.edu/content/dampening-traffic-waves-autonomous-vehicles00564nas a2200133 4500008004100000245010100041210006900142260001700211300001200228100002200240700001900262700002300281856012600304 2016 eng d00aModel-based Fuzzy Logic Classifier Synthesis for Optimization of Data-Adaptable Embedded Systems0 aModelbased Fuzzy Logic Classifier Synthesis for Optimization of  aHartford, CT a293-3021 aLizarraga, Adrian1 aLysecky, Roman1 aSprinkle, Jonathan uhttps://csl.arizona.edu/content/model-based-fuzzy-logic-classifier-synthesis-optimization-data-adaptable-embedded-systems01989nas a2200145 4500008004100000245006500041210006300106260000900169300001200178520154200190100002201732700001901754700002301773856004701796 2016 eng d00aModel-driven Optimization of Data-Adaptable Embedded Systems0 aModeldriven Optimization of DataAdaptable Embedded Systems bIEEE a293-3023 a

Complex sensing and decision applications such as object tracking and classification, video surveillance, unmanned aerial vehicle flight decisions, and others operate on vast data streams with dynamic characteristics. As the availability and quality of the sensed data changes, the underlying models and decision algorithms should continually adapt in order to meet desired high-level requirements. Due to the complexity of such dynamic data-driven systems, traditional design time techniques are often incapable of producing a solution that remains optimal in the face of dynamically changing data, algorithms, and even availability of computational resources. To assist developers of these systems, we present a modeling and optimization methodology that enables developers to capture application task flows and data sources, define associated quality metrics with data types, specify each algorithm’s data and quality requirements, and define a data quality estimation framework to optimize the application at runtime. We demonstrate each facet of the modeling and optimization process via a video-based vehicle tracking and collision avoidance application, and show how such an approach results in efficient design space exploration when selecting the optimal set of algorithm modalities. When searching for an application configuration within 1% to 5% of optimal, our model-guided approach can achieve speedups of up to 9.3X versus a standard genetic algorithm and speedups of up to 80X relative to a brute force algorithm.

1 aLizarraga, Adrian1 aLysecky, Roman1 aSprinkle, Jonathan uhttp://dx.doi.org/10.1109/COMPSAC.2016.15600460nas a2200121 4500008004100000245006500041210006300106260000900169100002200178700001900200700002300219856009600242 2016 eng d00aModel-Driven Optimization of Data-Adaptable Embedded Systems0 aModelDriven Optimization of DataAdaptable Embedded Systems bIEEE1 aLizarraga, Adrian1 aLysecky, Roman1 aSprinkle, Jonathan uhttps://csl.arizona.edu/content/model-driven-optimization-data-adaptable-embedded-systems-002203nas a2200193 4500008004100000245004600041210004600087260002400133300001000157520163100167100002201798700002101820700001701841700001701858700001701875700002301892700001601915856007801931 2016 eng d00aPower Efficient Vehicular Ad Hoc Networks0 aPower Efficient Vehicular Ad Hoc Networks aReston, VAc03/2016 a26-313 aInter-vehicular communication is a growing plat- form for improving roadway safety. The highly mobile nature of Vehicle to Vehicle communications causes rapid changes in network topologies and propagation conditions. Since the advent of Vehicular Ad-Hoc Networks (VANETs), over fifty routing protocols with attendant topologies have been proposed. Despite these protocols’ merits, many of them are not optimized for power management and frequency reuse. Our approach utilizes the one dimensional dynamic of divided highways to simplify the routing problem and reduce energy consumption. Since each car is aware of only two types of connections, up-road and down-road, we can form low power, line of sight links between adjacent vehicles. We also utilize a fuzzy logic algorithm that predicts the location of up-road cars to reduce interference from request for link signals. Once these links have been established, up-road vehicles send data down-road for a length of time based on the relative speed of the two vehicles. After this time period has expired the down-road vehicle must request additional information, restarting the timer. Data sent through the network will include information on up-road vehicles, and when required, messages such as accident notifications, alerts, and traffic warnings. Through simulation, we show that our approach to VANETs maintains its update frequency despite bumper to bumper traffic and uses two to five orders of magnitude less power than an IEEE 802.11 network with clustering and 1 mW transmit power. Overall, the network performs well and is a viable improvement to the standard.1 aHolcomb, Sterling1 aKnowlton, Audrey1 aGuerra, Juan1 aAsadi, Hamed1 aVolos, Haris1 aSprinkle, Jonathan1 aBose, Tamal uhttps://csl.arizona.edu/content/power-efficient-vehicular-ad-hoc-networks01614nas a2200145 4500008004100000245005400041210005400095260001200149520114500161100002301306700001901329700001501348700002201363856008301385 2016 eng d00aRobust Control of Autonomous Vehicle Trajectories0 aRobust Control of Autonomous Vehicle Trajectories c07/20163 a

In this paper we describe a robust treatment of tracking trajectories with an autonomous vehicle. In employing autonomous behaviors for traffic control there will inevitably be disturbances introduced through model error, non-planar surfaces, sensor noise, and delay in both sensing and actuation. We describe how we address these issues through robust control techniques. The trajectories we follow include position and orientation as part of their specification: but the most interesting aspect of these trajectories is the time-varying description of the state. This is opposed to a traditional approach of following a trajectory at any speed (with expected error in all dimensions of the state vector), as long as the speed does not exceed a maximum value. However, for traffic control to reduce traffic waves, most of the dampening approaches are time-varying trajectories. With this in mind, it becomes necessary to consider the delay of following the reference trajectory, and how this may affect drivers in the flow. We include simulation data demonstrating the results, as well as data from a full-sized robotic Ford Escape.

1 aSprinkle, Jonathan1 aBhadani, Rahul1 aCui, Shumo1 aSeibold, Benjamin uhttps://csl.arizona.edu/content/robust-control-autonomous-vehicle-trajectories00533nas a2200145 4500008004100000245010200041210006900143260003200212300001200244100002100256700001900277700002100296700002300317856004700340 2016 eng d00aA Safe Autonomous Vehicle Trajectory Domain Specific Modeling Language For Non-Expert Development0 aSafe Autonomous Vehicle Trajectory Domain Specific Modeling Lang aAmsterdam, NetherlandsbACM a42–481 aBunting, Matthew1 aZeleke, Yegeta1 aMcKeever, Kennon1 aSprinkle, Jonathan uhttp://doi.acm.org/10.1145/3023147.302315400598nas a2200181 4500008004100000245007100041210006900112300000800181100002200189700002100211700001500232700002100247700002200268700003100290700002300321700002300344856004900367 2016 eng d00aWiP Abstract: Stabilizing traffic with a single autonomous vehicle0 aWiP Abstract Stabilizing traffic with a single autonomous vehicl a1-11 aStern, Raphael, E1 aWork, Daniel, B.1 aCui, Shumo1 aPohlmann, Hannah1 aSeibold, Benjamin1 aMonache, Maria, Laura Dell1 aPiccoli, Benedetto1 aSprinkle, Jonathan uhttp://dx.doi.org/10.1109/ICCPS.2016.747913004605nas a2200169 4500008004100000245006000041210006000101260002700161520403200188100001904220700001504239700003004254700002404284700001704308700002304325856008704348 2015 eng d00aAdaptive Multifactor Routing with Constrained Data Sets0 aAdaptive Multifactor Routing with Constrained Data Sets aSan Diego, CAc03/20153 a

Autonomous Vehicles can benefit greatly from the use of cellular infrastructure. Consequently, it may be desirable at times to consider the availability of this infrastructure when planning autonomous vehicle routes. In order to make such decisions it is necessary to have up-to-date knowledge of signal strength in surrounding areas. We consider the quality of routing possible when using incomplete knowledge of signal strength along a route. As our motivation for how signal strength information would be constrained we consider Vehicle to Vehicle communications. Such communications offer great promise in creating real-time signal maps through a decentralized data collection and aggregation process. One might envision such a process involving the transfer of signal reception information between cars within an area. Such a process, while low-latency and low-cost, could suffer from limited data availability beyond relatively short ranges. In order to route based on signal strength we employ a weighting formula to combine distance and signal strength into a single cost quantity. Then, we apply this formula to a city grid map with signal strength information. We replace the distance values with the formula’s aggregate cost values. The cost values are then presented to a shortest path routing algorithm to determine the lowest cost path. Finally, we simulate a vehicle which regularly updates a signal map of its surroundings and continuously updates its route in response. The costs of its chosen path and the cost of the ideal path are then recorded. In order to rigorously test our routing formula’s performance with varying degrees of information we employ a Matlab program that randomly generates thousands of city signal maps to run the routing formula and algorithms on. The routing algorithms are run with route signal knowledge between 0% and 100%. We chart the average ratio between partial knowledge and full knowledge path costs. We consider the performance of a variety of algorithms and conclude that using Dijkstra we may produce routes that are 95% optimal using a signal knowledge window only 1/10 of our total route size. These results indicate the potential for excellent routing even when V2V communication can only offer highly constrained data sets. However, the use of random maps potentially weakens our results as real world signal maps tend to be patterned and non-random. Such patterns are extremely problematic as there is great potential for scenarios that do not occur frequently in a randomly generated map and that require extensive map knowledge. For example, the need to find an exit to a signal dead-zone One might view our randomized maps as presenting the signal knowledge limited algorithm a series of local minima optimization problems. In a random map with extremely high signal value variance there is likely to be a variety of immediately visible signal dead-zones and strong signal zones that all have small sizes and are consistently intermixed. As a result, a knowledge limited algorithm can easily avoid a high cost path by moving between small strong signal zones while avoiding the interspersed dead zones with little advanced knowledge. We expect that the most relevant metric for signal formula routing performance may be the ratio between the median size of extreme signal zones and the size of the signal knowledge window. This ratio directly determines the ability of the path cost minimization algorithm to solve path cost as a series of local minima. In order to test the potential value of this metric we tweak our random map generation algorithm to assign a random signal values to increasing numbers of intersections. For example, we might assign each random value to 4x4 intersection grids. Then, we would apply that single value to 8x8 grids. This modification allows us to directly alter the size of extreme signal zones and test the performance of the routing algorithm as extreme signal zones outsize the size of the signal knowledge window

1 aMiller, Torger1 aRoss, Cody1 aBarbosa, Matheus, Marques1 aHirzallah, Mohammed1 aVolos, Haris1 aSprinkle, Jonathan uhttps://csl.arizona.edu/content/adaptive-multifactor-routing-constrained-data-sets00458nas a2200121 4500008004100000245010100041210006900142300001200211100001500223700002300238700002700261856004800288 2015 eng d00aComputationally-Aware Control of Autonomous Vehicles: A Hybrid Model Predictive Control Approach0 aComputationallyAware Control of Autonomous Vehicles A Hybrid Mod a503-5171 aZhang, Kun1 aSprinkle, Jonathan1 aSanfelice, Ricardo, G. uhttp://dx.doi.org/10.1007/s10514-015-9469-500550nas a2200145 4500008004100000020002200041245007200063210006800135260002700203100001800230700002300248700002500271700001700296856009100313 2015 eng d a978-1-4503-3903-200a{DSM 2015: Proceedings of the Workshop on Domain-Specific Modeling}0 aDSM 2015 Proceedings of the Workshop on DomainSpecific Modeling aNew York, NY, USAbACM1 aGray, Jeffrey1 aSprinkle, Jonathan1 aTolvanen, Juka-Pekka1 aRossi, Matti uhttps://csl.arizona.edu/content/dsm-2015-proceedings-workshop-domain-specific-modeling00647nas a2200193 4500008004100000020002200041245008400063210006900147260002700216300001200243653002300255653002700278653001700305100002100322700001900343700002100362700002300383856004700406 2015 eng d a978-1-4503-3903-200aExperience Report: Constraint-based Modeling of Autonomous Vehicle Trajectories0 aExperience Report Constraintbased Modeling of Autonomous Vehicle aNew York, NY, USAbACM a17–2210aAutonomous Systems10acyber-physical systems10ametamodeling1 aMcKeever, Kennon1 aZeleke, Yegeta1 aBunting, Matthew1 aSprinkle, Jonathan uhttp://doi.acm.org/10.1145/2846696.284670602019nas a2200145 4500008004100000245007800041210006900119260003000188300001200218520153300230100001501763700002301778700002701801856004501828 2015 eng d00aA Hybrid Model Predictive Controller for Path Planning and Path Following0 aHybrid Model Predictive Controller for Path Planning and Path Fo aSeattle, WAbACMc04/2015 a139-1483 a

The use of nonlinear model-predictive methods for path planning and following has the advantage of concurrently solving problems of obstacle avoidance, feasible trajectory selection, and trajectory following, while obeying constraints on control inputs and state values. 
However, such approaches are computationally intensive, and may not be guaranteed to return a result in bounded time when performing a nonconvex optimization. This problem is an interesting application to cyber-physical systems due to their reliance on computation to carry out complex control. The computational burden can be addressed through model reduction, at a cost of potential (bounded) model error over the prediction horizon. In this paper we introduce a metric called uncontrollable divergence, and discuss how the selection of the model to use for the predictive controller can be addressed by evaluating this metric, which reveals the divergence between predicted and true states caused by return time and model mismatch. A map of uncontrollable divergence plotted over the state space gives the criterion to judge where reduced models can be tolerated when high update rate is preferred (e.g. at high speed and small steering angles), and where high-fidelity models are required to avoid obstacles or make tighter curves (e.g. at large steering angles). With this metric, we design a hybrid controller that switches at runtime between predictive controllers in which respective models are deployed. 

1 aZhang, Kun1 aSprinkle, Jonathan1 aSanfelice, Ricardo, G. uhttp//dx.doi.org/10.1145/2735960.273596603427nas a2200157 4500008004100000245008400041210006900125520286200194100001903056700001903075700001703094700001703111700002303128700001603151856010203167 2015 eng d00aIntersection Management via the Opportunistic Organization of Platoons by Route0 aIntersection Management via the Opportunistic Organization of Pl3 aIntersections behave as a pathway for traffic redirection and play an important role in the general movement of traffic, because of this, traffic congestion is not desirable and methods to remove congestion or its effects have been implemented. Generally, congestion occurs when a group of vehicles that is large relative to available road space attempts to move through an area that cannot redirect the group effectively. The are of this paper’s focus is the intersection. Some methods currently implemented to control congestion are roadway expansion, the use of adaptive traffic lights, and the use of public transportation. Good short term solutions, they only postpone the occurrence of congestion and are also costly. One way to deal with congestion is have vehicles cooperate with each other and their intersections through means of wireless communication. The vehicles can then be organized by a management system in such a way that vehicle throughput at an intersection can be maintained even at high levels of traffic congestion. A requirement of the system proposed in this paper is that all vehicles behave autonomously in order to obtain easily predictable behavior. The following describes the results of such a simulation of such a system. The data gathered over four simulations show that a higher platoon depth causes a general decrease in delay. The results between signalized and un-signalized simulations were vastly different with the un-signalized having significantly better results. The delay at a platoon depth of 9, for the unsignalized intersection, was -1.8, this brings the ideal delay to 98.2 seconds down from 100 seconds. This was due to the way intersection merging and gap threading is performed and is valid for several reasons. Because this method of intersection management desires to decrease the delay in traffic as much as possible, it would not make sense to slow down a platoon in order to perform a maneuver. The signalized method performed significantly worse than the un-signalized due to how the signalized method was implemented. Only one traffic light was green at a time, hence, whenever a platoon was stopped at intersection, that platoon would be guaranteed a delay of at most 56 seconds, which is how long a red light lasts for one signal. Though inefficient signal timing was used, we hypothesize that the behavior of a right of way system, such as a traffic light, is fundamentally flawed. By delegating the right of way to only a few lanes at a time, it is guaranteed that some vehicles will be stopped and will therefore experience delay. In order to show that the capacity of signalized intersection management through the use of traffic lights falls short when compared to un-signalized autonomous intersection management, better traffic light control methods will need to be tested in the future.1 aHeras, Alberto1 aClaytor, Lykes1 aVolos, Haris1 aAsadi, Hamed1 aSprinkle, Jonathan1 aBose, Tamal uhttps://csl.arizona.edu/content/intersection-management-opportunistic-organization-platoons-route00654nas a2200193 4500008004100000020002200041245008100063210006900144260002700213653002000240653003000260653001700290653002300307100001800330700002300348700002500371700001700396856004700413 2015 eng d a978-1-4503-3722-900aWorkshop Preview of the 15th Workshop on Domain Specific Modeling (DSM 2015)0 aWorkshop Preview of the 15th Workshop on Domain Specific Modelin aNew York, NY, USAbACM10aCode Generation10adomain-specific languages10ametamodeling10amodeling languages1 aGray, Jeffrey1 aSprinkle, Jonathan1 aTolvanen, Juka-Pekka1 aRossi, Matti uhttp://doi.acm.org/10.1145/2814189.283320401370nas a2200133 4500008004100000245009700041210006900138260000900207300000800216520092800224100001501152700002301167856004601190 2014 eng d00aA Closed-loop Model-based Design Approach Based On Automatic Verification and Transformation0 aClosedloop Modelbased Design Approach Based On Automatic Verific c2014 a1-63 a

Domain-specific modeling languages effectively constrain struc- tural concepts, but constraints that are not easily captured with structural constraints are still important to fix at design time. In practice these kinds of constraints are implicitly left to be carried out by the domain modelers. This paper explores the process of in- corporating system behavioral (not just structural) constraints into a DSML, and studies the way of generating feasible transformation solutions if those constraints fail, based on a transformation library constructed in advance. Our approach is to carry out the verifica- tion process through code generation, but utilize the results of veri- fication as an input to a model transformation generator. The output transformation then operates on the original model. As a case study, we applied the approach to finite state machine (FSM) models that control a cyber-physical system.

1 aZhang, Kun1 aSprinkle, Jonathan uhttp://dx.doi.org/10.1145/2688447.268844800460nas a2200133 4500008004100000245009900041210006900140300000600209490000700215100001400222700001900236700002300255856004800278 2014 eng d00aA Data-Driven Linear Approximation of HVAC Utilization for Predictive Control and Optimization0 aDataDriven Linear Approximation of HVAC Utilization for Predicti a10 v991 aQin, Xiao1 aLysecky, Susan1 aSprinkle, Jonathan uhttp://dx.doi.org/10.1109/TCST.2014.233287301888nas a2200145 4500008004100000245010500041210006900146260000900215300001000224520140100234100001901635700002301654700001901677856004601696 2014 eng d00aGenerating Model Transformations for Mending Dynamic Constraint Violations in Cyber Physical Systems0 aGenerating Model Transformations for Mending Dynamic Constraint  c2014 a35-403 a

Cyber physical systems by definition involve design constraints addressing the computation and communication necessary to control physical systems. These systems have been modeled using domain specific modeling languages, but some limitations exist in the continued application of such a modeling approach to more complex, or safety-critical, systems. Specifically, it is well known how to formulate constraints in a domain-specific modeling language in order to prevent users from building invalid structures, but existing constraint-based techniques do not take into consideration design requirements that may require analysis in the physical domain (i.e. dynamic constraints). Those analysis results, when interpreted by a domain expert, can inform changes to the model: unfortunately, this process does not scale. This paper presents an approach to integrating dynamic constraints that cannot be enforced using structural model constraints. The technique uses expert blocks to analyze systems and generates model transformations specific to the system using the results of those analyses to fix constraint violations. The paper describes a Dynamic Constraint Feedback (DCF) methodology for integrating this technique into existing systems from a generic perspective. Specific examples in this paper are derived from the domain of data adaptable reconfigurable embedded systems (DARES).

1 aWhitsitt, Sean1 aSprinkle, Jonathan1 aLysecky, Roman uhttp://dx.doi.org/10.1145/2688447.268845400535nas a2200145 4500008004100000245007400041210006900115260002100184100001700205700001200222700002300234700001300257700001800270856010100288 2014 eng d00aA Heterogeneity Based Method to Identify Major Variability Components0 aHeterogeneity Based Method to Identify Major Variability Compone aBeijingc12/20141 aShaikh, Fahd1 aHe, Wei1 aSprinkle, Jonathan1 aChen, K.1 aRoveda, Janet uhttps://csl.arizona.edu/content/heterogeneity-based-method-identify-major-variability-components01760nas a2200133 4500008004100000245008300041210006900124260003600193300001400229520129400243100001901537700002301556856004701579 2014 eng d00aA Hybrid Controller for Autonomous Vehicle Lane Changing with Epsilon Dragging0 aHybrid Controller for Autonomous Vehicle Lane Changing with Epsi aPortland, OregonbIEEEc06/2014 a5307-53123 a

Trajectory control for a ground vehicle typically utilizes the error from the desired path or trajectory (i.e., crosstrack error) to produce velocity and steering commands. If an obstacle is in the path, previous techniques have synthesized a new trajectory that avoids the obstacles, and the vehicle directly follows this new path. This approach has drawbacks at high velocity, because the synthesized trajectory must satisfy the stability criteria of the vehicle. This paper introduces a technique which we call epsilon dragging The approach modifies the existing trajectory by some value ε in order to avoid an obstacle at high speeds, while preserving the original trajectory as the desired path. Epsilon dragging is performed by inducing an additional error to the crosstrack error of the vehicle; this induced error can be bounded in order to stay within the velocity/turnrate profile that governs safe behavior at high speeds. The paper provides a method to construct epsilon such that a vehicle can avoid an obstacle at high speeds without the need to verify the trajectory’s curvature before it is synthesized. The technique is demonstrated in completing a lane-change maneuver at different velocities, and verifying that the velocity/turnrate profiles are not exceeded.

1 aWhitsitt, Sean1 aSprinkle, Jonathan uhttp://dx.doi.org/10.1109/ACC.2014.685945001650nas a2200193 4500008004100000022001400041245012000055210006900175260001200244300001700256520102900273100001401302700001301316700001901329700001801348700001901366700002301385856004801408 2014 eng d a1868-396700aA Modular Framework to Enable Rapid Evaluation and Exploration of Energy Management Methods in Smart Home Platforms0 aModular Framework to Enable Rapid Evaluation and Exploration of  c04/2014 aOnline First3 a

Numerous efforts focus on developing smart grid and smart home plat- forms to provide monitoring, management, and optimization solutions. In order to more effectively manage energy resources, a holistic view is needed; however the involved platforms are complex and require integration of a multitude of parameters such as the end-user behavior, underlying hardware components, environment, etc., many of which operate on varying time scale at various levels of detail. A general and modular framework is presented to enable designers to focus on modeling, simulating, analyzing, or optimizing specific sub-components without requiring a detailed imple- mentation across all levels. We incorporate two case studies in which the proposed framework is utilized to help an end user evaluate platform configurations given an energy usage model, as well as integrate an energy optimization module to investigate rescheduling of appliance usage times in an effort to lower cost. 

1 aQin, Xiao1 aLin, Lin1 aLysecky, Susan1 aRoveda, Janet1 aSon, Young-Jun1 aSprinkle, Jonathan uhttp://dx.doi.org/10.1007/s12667-014-0121-900631nas a2200181 4500008004100000245014200041210006900183260001200252300001200264490000700276100002100283700002100304700001600325700001700341700002300358700001800381856005000399 2014 eng d00aMotorized mobility scooters: The Use of Training/Intervention and Technology for Improving Driving Skills in Aging Adults - A Mini-Review0 aMotorized mobility scooters The Use of TrainingIntervention and  c06/2014 a357-3650 v601 aToosizadeh, Nima1 aBunting, Matthew1 aHowe, Carol1 aMohler, Jane1 aSprinkle, Jonathan1 aNajafi, Bijan uhttp://www.karger.com/Article/FullText/35676600541nas a2200145 4500008004100000245009900041210006900140260003700209100001900246700002100265700001900286700001800305700002300323856004900346 2013 eng d00aEfficient Reconfiguration Methods to Enable Rapid Deployment of Runtime Reconfigurable Systems0 aEfficient Reconfiguration Methods to Enable Rapid Deployment of  aPacific Grove, CAbIEEEc11/20131 aLysecky, Roman1 aSandoval, Nathan1 aWhitsitt, Sean1 aMackin, Casey1 aSprinkle, Jonathan uhttp://dx.doi.org/10.1109/ACSSC.2013.681040101875nas a2200205 4500008004100000245006800041210006500109260002600174300001000200520125300210653002401463653002001487100002001507700001701527700001601544700002101560700001901581700002301600856004601623 2013 eng d00aGenerating a {ROS/JAUS} Bridge for an Autonomous Ground Vehicle0 aGenerating a ROSJAUS Bridge for an Autonomous Ground Vehicle aIndianapolis, INbACM a13-183 a

Robotic systems have truly benefitted from standardized middleware that can componentize the development of new capabilities for a robot. The popularity of these robotic middleware systems has resulted in sizable libraries of components that are now available to roboticists. However, many robotic systems (such as autonomous vehicles) must adhere to externally defined standards that are not blessed with such a large repository of components. Due to the real-time and safety concerns that accompany the domain of unmanned systems, it is not trivial to interface these middleware systems, and previous attempts to do so have succeeded at the cost of ad hoc design and implementation. This paper describes a domain-specific approach to the synthesis of a bridge between the popular Robotic Operating System (ROS) and the Joint Architecture for Unmanned Systems (JAUS). The domain-specific nature of the approach permits the bridge to be limited in scope by the application’s specific messages (and their attribute mappings between JAUS/ROS), resulting in smaller code size and overhead than would be incurred by a generic solution. Our approach is validated by tests performed on an unmanned vehicle with and without the JAUS/ROS bridge.

10aautonomous vehicles10aCode Generation1 aMorley, Patrick1 aWarren, Alex1 aRabb, Ethan1 aBunting, Matthew1 aWhitsitt, Sean1 aSprinkle, Jonathan uhttp://dx.doi.org/10.1145/2541928.254193101315nas a2200421 4500008004100000245009200041210006900133260001000202300001200212653001700224653001500241653001300256653001400269653001600283653001700299653002200316653002100338653001300359653001900372653003100391653003200422653002300454653001500477653002500492653002800517653003600545653002200581653001900603653001800622653001900640653002100659653001400680100002100694700001800715700001900733700002300752856011800775 2013 eng d00aHow You Can Learn to Stop Worrying and Love Reconfigurable Embedded Systems: A Tutorial0 aHow You Can Learn to Stop Worrying and Love Reconfigurable Embed cApril a213-21410aC++ language10aC/C++ code10acodesign10aComputers10aConferences10adata streams10aembedded hardware10aembedded systems10aHardware10ahardware tasks10ahardware-software codesign10aimage processing algorithms10aJPEG2000 standards10amiddleware10amiddleware framework10amodeling infrastructure10areconfigurable embedded systems10aruntime behaviors10asoftware tasks10asoftware tool10asoftware tools10aTransform coding10aTutorials1 aSandoval, Nathan1 aMackin, Casey1 aLysecky, Roman1 aSprinkle, Jonathan uhttps://csl.arizona.edu/content/how-you-can-learn-stop-worrying-and-love-reconfigurable-embedded-systems-tutorial01289nas a2200397 4500008004100000245006700041210006500108260001000173300000800183653000800191653003500199653002500234653002700259653003100286653001400317653001600331653003300347653002100380653002300401653003000424653002700454653002000481653003000501653002300531653002500554653003200579653001300611653002600624653002500650653002300675653001400698653002800712653003000740100002300770856009800793 2013 eng d00aMobile Device Software: Model-Based Architectures and Examples0 aMobile Device Software ModelBased Architectures and Examples cApril a21510aAPI10aapplication program interfaces10acanonical UML models10aComputational modeling10acomputer science education10aComputers10aConferences10ahigh-level software concepts10amobile computing10amobile device apps10amobile device programming10amobile device software10aMobile handsets10amodel-based architectures10amodel-based design10amodel-based examples10aobject-oriented programming10aSoftware10asoftware architecture10asoftware engineering10aterminology points10aTutorials10aunclear starting points10aUnified modeling language1 aSprinkle, Jonathan uhttps://csl.arizona.edu/content/mobile-device-software-model-based-architectures-and-examples00397nas a2200121 4500008004100000245006000041210006000101300001000161100001900171700002300190700001900213856004300232 2013 eng d00aModel Based Development with the Skeleton Design Method0 aModel Based Development with the Skeleton Design Method a12-191 aWhitsitt, Sean1 aSprinkle, Jonathan1 aLysecky, Roman uhttp://dx.doi.org/10.1109/ECBS.2013.1601741nas a2200541 4500008004100000245009400041210006900135260001000204300001000214653001900224653002000243653002300263653002100286653002700307653002700334653003500361653001800396653002700414653003800441653001300479653002500492653004200517653003600559653002800595653002500623653003000648653001600678653002200694653002100716653002100737653001700758653001700775653002900792653003500821653003100856653002200887653001600909653003900925653001700964653002300981653002901004653003001033653002601063653002901089100001501118700002301133856004301156 2013 eng d00aModel-Based Software Synthesis for Self-Reconfigurable Sensor Network in Water Monitoring0 aModelBased Software Synthesis for SelfReconfigurable Sensor Netw cApril a40-4810aaccelerometers10aCode Generation10acommunication task10acomputation task10aComputational modeling10aconcurrent engineering10aconcurrent tasks specification10acontrol tasks10acyber-physical systems10adomain-specific modeling language10adrifters10aembedded programming10aenvironmental monitoring (geophysics)10aenvironmental science computing10afloating sensor testbed10aformal specification10aGlobal Positioning System10aGPS sensors10ahand-written code10aInstruction sets10amobile computing10amobile phone10amobile radio10amobile sensing platforms10amodel-based software synthesis10amodel-integrated computing10aprogram compilers10aProgramming10aself-reconfigurable sensor network10aSmart phones10asoftware synthesis10aubiquitous mobile device10aUnified modeling language10awater flow monitoring10awireless sensor networks1 aZhang, Kun1 aSprinkle, Jonathan uhttp://dx.doi.org/10.1109/ECBS.2013.3400328nas a2200121 4500008004100000245003200041210003200073300001200105490000700117100001900124700002300143856004000166 2013 eng d00aModeling Autonomous Systems0 aModeling Autonomous Systems a396-4130 v101 aWhitsitt, Sean1 aSprinkle, Jonathan uhttp://dx.doi.org/10.2514/1.I01003900511nas a2200145 4500008004100000245009600041210006900137300001200206100002100218700001800239700001900257700001900276700002300295856004700318 2013 eng d00aRuntime Hardware/Software Task Transition Scheduling for Runtime-Adaptable Embedded Systems0 aRuntime HardwareSoftware Task Transition Scheduling for RuntimeA a342-3451 aSandoval, Nathan1 aMackin, Casey1 aWhitsitt, Sean1 aLysecky, Roman1 aSprinkle, Jonathan uhttp://dx.doi.org/10.1109/FPT.2013.671838201894nas a2200529 4500008004100000245015900041210006900200260001000269300001000279653002800289653002200317653003800339653003600377653002400413653001800437653001600455653002900471653002900500653004500529653002100574653003500595653001600630653001100646653001300657653002600670653003100696653004400727653003100771653004700802653002600849653003300875653001500908653002300923653003400946653002400980653001201004653003701016653002001053653003301073653003501106100002101141700001801162700001901180700001901199700002301218856012301241 2013 eng d00aSystem Throughput Optimization and Runtime Communication Middleware Supporting Dynamic Software-Hardware Task Migration in Data Adaptable Embedded Systems0 aSystem Throughput Optimization and Runtime Communication Middlew cApril a59-6810acombinatorial explosion10aData adaptability10adata adaptable design methodology10adata adaptable embedded systems10adata configurations10adata handling10aData models10adata profile correlation10adesign time optimization10adynamic software-hardware task migration10aembedded systems10aField programmable gate arrays10aFIFO queues10aFiring10aHardware10ahardware accelerators10ahardware-software codesign10ahardware-software communication wrapper10ahardware/software codesign10ahardware/software communication middleware10aheuristic programming10aheuristic search methodology10amiddleware10amodel-based design10aPareto optimal configurations10aPareto optimisation10aRuntime10aruntime communication middleware10asearch problems10asimulation-based methodology10asystem throughput optimization1 aSandoval, Nathan1 aMackin, Casey1 aWhitsitt, Sean1 aLysecky, Roman1 aSprinkle, Jonathan uhttps://csl.arizona.edu/content/system-throughput-optimization-and-runtime-communication-middleware-supporting-dynamic00599nas a2200193 4500008004100000020002200041245005000063210004500113260002700158653002000185653003000205653001700235653002300252100002500275700002300300700001700323700001800340856004700358 2012 eng d a978-1-4503-1563-000aThe 12th Workshop on Domain-specific Modeling0 a12th Workshop on Domainspecific Modeling aNew York, NY, USAbACM10aCode Generation10adomain-specific languages10ametamodeling10amodeling languages1 aTolvanen, Juka-Pekka1 aSprinkle, Jonathan1 aRossi, Matti1 aGray, Jeffrey uhttp://doi.acm.org/10.1145/2384716.238478401296nas a2200181 4500008004100000245011100041210006900152300001000221520067900231653002200910653003100932653002300963100002300986700002001009700001901029700002301048856004301071 2012 eng d00aAutomated Software Generation and Hardware Coprocessor Synthesis for Data-Adaptable Reconfigurable Systems0 aAutomated Software Generation and Hardware Coprocessor Synthesis a15-233 a

We present an overview of a data-adaptable reconfigurable embedded systems design methodology. The paper presents a novel paradigm for hardware/software code sign and reconfigurable computing driven by data-adaptability. The data-adaptable approach allows designers to directly model the data configurability of the target application, thereby enabling a solution that permits dynamic reconfiguration based on the data profile of the incoming data stream. This approach permits low-power, small form-factor hardware implementations of algorithms that might otherwise consume significant resources, or perhaps exceed the available space of the reconfigurable hardware.

10aData adaptability10ahardware/software codesign10amodel-based design1 aMilakovich, Andrew1 aGopinath, Vijay1 aLysecky, Roman1 aSprinkle, Jonathan uhttp://dx.doi.org/10.1109/ECBS.2012.1601891nas a2200181 4500008004100000245009500041210006900136300001200205520129700217100001901514700002101533700001701554700002301571700003201594700001901626700002101645856004301666 2012 eng d00aOn the Extraction and Analysis of a Social Network with Partial Organizational Observation0 aExtraction and Analysis of a Social Network with Partial Organiz a249-2563 a

The behavior of an organization may be inferred based on the behavior of its members, their contacts, and their connectivity. One approach to organizational analysis is the construction and interpretation of a social network graph, where entities of an organization (persons, vehicles, locations, events, etc.) are nodes, and edges represent varying kinds of connectivity between entities. This paper describes a transformation based approach to the extraction of a social network graph, where the original data comprising (partial) observation of the organization are embedded on a graph with a different ontology, and with many entities and edges that are unrelated to the organization of interest. Social network extraction allows the inference of implied relationships, and the selection of relationships relevant for intended analysis techniques. The analysis of the resulting social network graph is based on organizational and individual analysis, in order to permit an advanced user to draw conclusions regarding the behavior of the organization, based on established social network graph metrics. The results of the paper include a discussion of the complexity of analysis, and how the observation data graph is pruned in order to scale the application of analysis algorithms.

1 aWhitsitt, Sean1 aGopalan, Abishek1 aCho, Sangman1 aSprinkle, Jonathan1 aRamasubramanian, Srinivasan1 aSuantak, Liana1 aRozenblit, Jerzy uhttp://dx.doi.org/10.1109/ECBS.2012.3301451nas a2200169 4500008004100000245008600041210006900127260001900196300001100215490000700226520090600233100001701139700001901156700002301175700002301198856006001221 2012 eng d00aA generic in-place transformation-based approach to structured model co-evolution0 ageneric inplace transformationbased approach to structured model bECEASSTcApril a1–130 v423 aIn MDE not only models but also metamodels are subject to evolution. More specifically, they need to be adapted to correct errors, support new and/or update language features. The direct consequence of such evolutionary steps comprises the problem of managing the co-evolution of existing model instances, which may no longer conform to the new metamodel version. This model migration is intrinsically complex and results in a time-consuming and error-prone process if no adequate support is provided. For tackling this problem, we introduce a new technique to guide the user in solving migration issues in a step-wise manner. The aims are manifold, notably the simplification of the migration specification, the reduction of the effort for the evolver, the control of user intervention, and the optimization of the migration execution itself by allowing in-place adaptation of the existing instances.1 aMeyers, Bart1 aWimmer, Manuel1 aCicchetti, Antonio1 aSprinkle, Jonathan uhttp://journal.ub.tu-berlin.de/eceasst/article/view/60800970nas a2200145 4500008004100000245007300041210006900114300001200183520044300195100001700638700001200655700002300667700003200690856010200722 2012 eng d00aIdentifying key components of variability using Energy based Control0 aIdentifying key components of variability using Energy based Con a2 pages3 aThis paper proposes a method to estimate the reliability of a circuit based on the energy distribution of that circuit. A circuit with a widespread energy distribution is more unreliable in comparison with one with uniform energy distribution. A gate within a circuit that has a major contribution to energy distribution will have a greater impact on the variability within the circuit as opposed to a component with a minor contribution.1 aShaikh, Fahd1 aHe, Wei1 aSprinkle, Jonathan1 aWang-Roveda, Janet, Meiling uhttps://csl.arizona.edu/content/identifying-key-components-variability-using-energy-based-control00591nas a2200145 4500008004100000020002200041245008400063210006900147260002700216100002100243700002200264700002300286700002500309856011100334 2012 eng d a978-1-4503-1798-600aME ’12: Proceedings of the 6th International Workshop on Models and Evolution0 aME 12 Proceedings of the 6th International Workshop on Models an aNew York, NY, USAbACM1 aTamzalit, Dalila1 aSchätz, Bernhard1 aSprinkle, Jonathan1 aPierantonio, Alfonso uhttps://csl.arizona.edu/content/me-%E2%80%9912-proceedings-6th-international-workshop-models-and-evolution00402nas a2200109 4500008004100000245005900041210005800100300002100158490000800179100002300187856008200210 2012 eng d00aMetamodel-Based Metrics for Complexity of Using a DSML0 aMetamodelBased Metrics for Complexity of Using a DSML a(in preparation)0 vTBD1 aSprinkle, Jonathan uhttps://csl.arizona.edu/content/metamodel-based-metrics-complexity-using-dsml00729nas a2200193 4500008004100000245011000041210006900151300002100220490000800241100001600249700001900265700002300284700002000307700001700327700002700344700002200371700002400393856011800417 2012 eng d00aModel-Based Configuration of a Heterogeneous Human-in-the-loop Command and Control Simulation Environment0 aModelBased Configuration of a Heterogeneous Humanintheloop Comma a(in preparation)0 vtbd1 aChu, Diyang1 aGulotta, Jacob1 aSprinkle, Jonathan1 aNeema, Himanshu1 aNine, Harmon1 aKottenstette, Nicholas1 aHemingway, Graham1 aSztipanovits, Janos uhttps://csl.arizona.edu/content/model-based-configuration-heterogeneous-human-loop-command-and-control-simulation00600nas a2200145 4500008004100000020002200041245009000063210006900153260002700222100002400249700002000273700002300293700002300316856011500339 2012 eng d a978-1-4503-1805-100a{MPM ’12}: Proceedings of the 6th International Workshop on Multi-Paradigm Modeling0 aMPM 12 Proceedings of the 6th International Workshop on MultiPar aNew York, NY, USAbACM1 aHardebolle, Cécile1 aSyriani, Eugene1 aSprinkle, Jonathan1 aMészáros, Tamás uhttps://csl.arizona.edu/content/mpm-%E2%80%9912-proceedings-6th-international-workshop-multi-paradigm-modeling00443nas a2200133 4500008004100000245007200041210006900113260001200182300000800194100001900202700002300221700001900244856004600263 2012 eng d00aAn Overseer Control Methodology for Data Adaptable Embedded Systems0 aOverseer Control Methodology for Data Adaptable Embedded Systems c08/2012 a1-61 aWhitsitt, Sean1 aSprinkle, Jonathan1 aLysecky, Roman uhttp://dx.doi.org/10.1145/2508443.250844800388nas a2200109 4500008004100000245006800041210006600109300001400175100001900189700002300208856004700231 2012 eng d00aA Passenger Comfort Controller for an Autonomous Ground Vehicle0 aPassenger Comfort Controller for an Autonomous Ground Vehicle a3380-33851 aWhitsitt, Sean1 aSprinkle, Jonathan uhttp://dx.doi.org/10.1109/CDC.2012.642604900510nas a2200133 4500008004100000245006500041210006400106260003600170100002500206700002300231700001700254700001800271856008700289 2012 eng d00aProceedings of the 12th Workshop on Domain-specific Modeling0 aProceedings of the 12th Workshop on Domainspecific Modeling aNew York, NY, USAbACMc10/20121 aTolvanen, Juka-Pekka1 aSprinkle, Jonathan1 aRossi, Matti1 aGray, Jeffrey uhttps://csl.arizona.edu/content/proceedings-12th-workshop-domain-specific-modeling00519nas a2200157 4500008004100000245009200041210006900133300001200202490000700214100001600221700002300237700001900260700002000279700002400299856003800323 2012 eng d00aReachability Calculations for Vehicle Safety during Manned/Unmanned Vehicle Interaction0 aReachability Calculations for Vehicle Safety during MannedUnmann a138-1520 v351 aDing, Jerry1 aSprinkle, Jonathan1 aTomlin, Claire1 aSastry, Shankar1 aPaunicka, James, L. uhttp://dx.doi.org/10.2514/1.5370600706nas a2200205 4500008004100000020002200041245008400063210006900147260002700216653002700243653001700270653002900287653001900316653002800335100002400363700002000387700002300407700002300430856004700453 2012 eng d a978-1-4503-1805-100aSummary of the 6th International Workshop on Multi-Paradigm Modeling (MPM’12)0 aSummary of the 6th International Workshop on MultiParadigm Model aNew York, NY, USAbACM10aheterogeneous modeling10ametamodeling10amodel driven engineering10amulti-modeling10amulti-paradigm modeling1 aHardebolle, Cécile1 aSyriani, Eugene1 aSprinkle, Jonathan1 aMészáros, Tamás uhttp://doi.acm.org/10.1145/2508443.250844401522nas a2200145 4500008004100000245008600041210006900127300001200196490000700208520105100215100001901266700002301285700002001308856004801328 2012 eng d00aSwitched and Symmetric Pursuit/Evasion Games With Online Model Predictive Control0 aSwitched and Symmetric PursuitEvasion Games With Online Model Pr a604-6200 v203 a

This paper describes a supervisory controller for pursuit and evasion of two fixed-wing autonomous aircraft. Novel contributions of the work include the real-time use of model- predictive control, specifically nonlinear model predictive tracking control, for predictions of the vehicle under control, as well as predictions for the adversarial aircraft. In addition to this inclusion, the evasive controller is a hybrid system, providing switching criteria to change modes to become a pursuer based on the current and future state of the vehicle under control, and that of the adversarial aircraft. Results of the controller for equally matched platforms in actual flight tests against a US Air Force trained F-15 test pilot are given. Extensive simulation analysis of the symmetric games is provided, including regressive analysis based on initial conditions of height advantage, and relative velocity vectors, and in particular the effect of allowing the evading aircraft to switch modes between "evader" and "pursuer" during the game.

1 aEklund, Mikael1 aSprinkle, Jonathan1 aSastry, Shankar uhttp://dx.doi.org/10.1109/TCST.2011.213643500599nas a2200193 4500008004100000020002200041245005000063210004500113260002700158653002000185653003000205653001700235653002300252100002500275700002300300700001700323700001800340856004700358 2011 eng d a978-1-4503-0942-400aThe 11th Workshop on Domain-specific Modeling0 a11th Workshop on Domainspecific Modeling aNew York, NY, USAbACM10aCode Generation10adomain-specific languages10ametamodeling10amodeling languages1 aTolvanen, Juka-Pekka1 aSprinkle, Jonathan1 aRossi, Matti1 aGray, Jeffrey uhttp://doi.acm.org/10.1145/2048147.204822700599nas a2200193 4500008004100000020002200041245005000063210004500113260002700158653002000185653003000205653001700235653002300252100002500275700002300300700001800323700001700341856004700358 2011 eng d a978-1-4503-1183-000aThe 11th Workshop on Domain-specific Modeling0 a11th Workshop on Domainspecific Modeling aNew York, NY, USAbACM10aCode Generation10adomain-specific languages10ametamodeling10amodeling languages1 aTolvanen, Juka-Pekka1 aSprinkle, Jonathan1 aGray, Jeffrey1 aRossi, Matti uhttp://doi.acm.org/10.1145/2095050.209505201221nas a2200193 4500008004100000020002200041245009800063210006900161260002700230300001200257520057400269653001900843653002900862653002100891653002900912100001700941700002300958856004600981 2011 eng d a978-1-4503-1183-000a{autoVHDL: a domain-specific modeling language for the auto-generation of VHDL core wrappers}0 aautoVHDL a domainspecific modeling language for the autogenerati aNew York, NY, USAbACM a71–763 a

Reconfigurable embedded hardware is a staple of many applications in defense technology and applied engineering. The integration of various embedded hardware "cores" (i.e., the computing units) is complicated by the unintended complexities inherent in the consistent and correct construction of communication pathways–-specified using VHDL. This paper presents a domain-specific modeling approach to reducing this complexity. The results include demonstration of the tool, where generated VHDL code with complex data and processing requirements is simulated.

10acode synthesis10adomain-specific modeling10aembedded systems10areconfigurable computing1 aJones, Erica1 aSprinkle, Jonathan uhttp://dx.doi.org/10.1145/2095050.209506301728nas a2200253 4500008004100000020002200041245007300063210006900136260002700205300001400232520095300246653002201199653002901221653001101250653002901261100001901290700002101309700002001330700002201350700001601372700001701388700002301405856004601428 2011 eng d a978-1-4503-1183-000aConstrained data acquisition for mobile citizen science applications0 aConstrained data acquisition for mobile citizen science applicat aNew York, NY, USAbACM a267–2723 a

The popularity and ubiquity of personal mobile computing devices–-coupled with their powerful sensing capabilities–-allow their application in the structured collection of data for societal benefit and science applications. Citizen scientists are willing users and active contributors to scientific research and applications, but if they gather data in an unconstrained or ad hoc manner, their efforts may be of little scientific value. In this paper, we present a user interface for a mobile device which is properly constrained to permit the gathering of valid scientific data. This helps to achieve the goal that any individual with a basic familiarity of the device (but not of the science) should be able to obtain useful data with little learning required. As a use case for this concept, we present a mobile application that allows users to collect location-stamped images to supplement satellite data for climate change research.

10acitizen scientist10adomain-specific modeling10aiphone10amobile phone programming1 aWhitsitt, Sean1 aBarreto, Armando1 aHudson, Maribel1 aAl-Helal, Hussain1 aChu, Diyang1 aDidan, Kamel1 aSprinkle, Jonathan uhttp://dx.doi.org/10.1145/2095050.209509501419nas a2200181 4500008004100000245008300041210006900124260003200193300001000225520082600235100002701061700002001088700001901108700002301127700002101150700002301171856004301194 2011 eng d00aHardware/Software Communication Middleware for Data Adaptable Embedded Systems0 aHardwareSoftware Communication Middleware for Data Adaptable Emb bIEEE Computer Society Press a34-433 a

Recent trends toward increased flexibility and configurability in emerging applications present demanding challenges for implementing systems that incorporate such capabilities. The resulting application configuration space is generally much larger than any one hardware implementation can support. We provide an overview of a new data-adaptive approach to the rapid design and implementation of such highly configurable applications. In support of this data-adaptable approach, we present and detail an efficient and flexible hardware/software communication middleware to support the seamless communication between hardware and software tasks at runtime. We highlight the flexibility of this interface and present an initial case study and results demonstrating the performance capabilities and area requirements.

1 aMahadevan, Sachidanand1 aGopinath, Vijay1 aLysecky, Roman1 aSprinkle, Jonathan1 aRozenblit, Jerzy1 aMarcellin, Michael uhttp://dx.doi.org/10.1109/ECBS.2011.1201544nas a2200133 4500008004100000245007600041210006900117260001000186300001400196520111500210100001901325700002301344856004301367 2011 eng d00aMessage Modeling for the Joint Architecture for Unmanned Systems (JAUS)0 aMessage Modeling for the Joint Architecture for Unmanned Systems cApril a251–2593 aThe Joint Architecture for Unmanned Systems (JAUS) is a standard for sensing, control, and computational communication of components for unmanned systems. This paper presents a modeling environment capable of producing a domain-specific prototype of the software necessary for inter-computer communications. A metamodel is used to provide the domain-specific modeling language to model both the messages used in JAUS, and the shell interfaces for components that transmit and receive those messages. The produced artifacts are C and C++ code that can be used in unmanned systems and simulations of such systems, including tests that validate the structure and behavior of the generated code. The generated code is compatible with standard JAUS implementations, and is validated using the OpenJAUS open source API and framework. Future work describes the second spiral of features and behaviors (currently in the design phase). The case study and test environment for the software generated by this project is an autonomous ground vehicle, modeled on a Ford Escape Hybrid that is used in laboratory experiments.1 aWhitsitt, Sean1 aSprinkle, Jonathan uhttp://dx.doi.org/10.1109/ECBS.2011.1701775nas a2200133 4500008004100000245009700041210006900138260000900207300001200216520132800228100002301556700001901579856004301598 2011 eng d00aOn the Mitigation of MultiCore-Induced Behavioral Deviations of an Autonomous Ground Vehicle0 aMitigation of MultiCoreInduced Behavioral Deviations of an Auton bIEEE a159-1683 a

Complex systems such as autonomous vehicles frequently utilize a distributed network of computers for sensing, control, and supervisory tasks. A common way to abstract the deployment of the computational nodes that implement the system’s behavior is through the utilization of middleware, which treats each atomic processing element as a component. Multiple components may execute on a single node, and nodes are typically heterogeneous in their processing power. For component implementations that use an event-driven model of computation, however, significant behavioral deviations may occur when a single-core computational node is replaced with a multicore node, especially if that computational node is running more than one component. This paper discusses the observed behavioral deviations through a series of simulations with identical initial conditions, performed on various single core and multicore processing platforms. In addition to the empirical demonstration, the paper provides a technique to mitigate the behavioral deviations by inserting a time-triggered buffer between a key set of components, enforcing a loosely time-triggered execution even though the system is still defined through event-triggered components. This preserves existing legacy code, but provides a time-triggered execution.

1 aSprinkle, Jonathan1 aEames, Brandon uhttp://dx.doi.org/10.1109/ECBS.2011.2901646nas a2200145 4500008004100000245006300041210006300104260001000167300001200177520120600189100002001395700002301415700001901438856004301457 2011 eng d00aModeling of Data Adaptable Reconfigurable Embedded Systems0 aModeling of Data Adaptable Reconfigurable Embedded Systems cApril a276-2853 a

Many applications require high flexibility, high configurability and high processing speeds. The physical constraints of a highly flexible system’s hardware implementation preclude a hardware solution that satisfies all configuration options. Similarly for pure software implementations, even if configurability is satisfied, process efficiency will be sacrificed. Thus for applications of any significant size, there can be no single hardware or software configuration that can efficiently support all the configurability options of the applications. The Data-Adaptable Reconfigurable Embedded System (DARES) approach tackles this problem through combination of the hardware-software co-design and reconfigurable computing methodologies. Data-adaptability means that as data streams change, the system is reconfigured along the baselines defined within the system’s specifications. In this project we use the concepts of Model-Integrated Computing to implement a domain-specific modeling language for the DARES approach. The language captures all the configurability options of the application task(s), performs design-space exploration, and provides a template for source code generation.

1 aGopinath, Vijay1 aSprinkle, Jonathan1 aLysecky, Roman uhttp://dx.doi.org/10.1109/ECBS.2011.3100496nas a2200133 4500008004100000245006500041210006400106260002200170100002500192700002300217700001700240700001800257856008700275 2011 eng d00aProceedings of the 11th Workshop on Domain-Specific Modeling0 aProceedings of the 11th Workshop on DomainSpecific Modeling aPortland, ORbACM1 aTolvanen, Juka-Pekka1 aSprinkle, Jonathan1 aRossi, Matti1 aGray, Jeffrey uhttps://csl.arizona.edu/content/proceedings-11th-workshop-domain-specific-modeling00597nas a2200133 4500008004100000245011300041210006900154260004700223300000800270100002300278700001800301700002000319856012400339 2011 eng d00aProceedings of the 18th IEEE International Conference and Workshops on Engineering of Computer-Based Systems0 aProceedings of the 18th IEEE International Conference and Worksh aLas Vegas, NVbIEEE Computer Societyc2011 a2921 aSprinkle, Jonathan1 aSterritt, Roy1 aBreitman, Karin uhttps://csl.arizona.edu/content/proceedings-18th-ieee-international-conference-and-workshops-engineering-computer-based01670nas a2200133 4500008004100000245010200041210006900143260001000212300001200222520121600234100002001450700002301470856004301493 2011 eng d00aSimplification of Semantically-Rich Model Transformations Through Generated Transformation Blocks0 aSimplification of SemanticallyRich Model Transformations Through cApril a260-2683 aThis paper demonstrates a novel concept for the simplification of model transformations in which composite or complex objects are inserted into an existing model through a well-defined interface. The technique utilizes a model transformation from the domain of the modeling language into the domain of model transformation languages. The user specifies these semantically rich blocks using the original domain-specific modeling language. Then, a transformation generates the necessary model transformation graph to create an instance of the semantically rich, user-defined pattern. Users insert these generated patterns into their customized transformations. The approach is helpful for endogenous transformations in which existing objects may be refactored. It will also serve as a teaching tool for users who are unfamiliar with model transformations: specifically how to represent a newly-created model in the transformation domain. Finally, the approach is designed to reduce specification errors of model transformations in which new (semantically rich) blocks are inserted at key points, as the correctness of the semantically rich blocks is guaranteed, based on their construction in the original domain.1 aHudson, Maribel1 aSprinkle, Jonathan uhttp://dx.doi.org/10.1109/ECBS.2011.2801192nas a2200169 4500008004100000020002200041245006500063210006500128260002700193300001400220520064500234653002000879653002900899653002500928100002300953856004600976 2011 eng d a978-1-4503-1183-000aTeaching students to learn to learn mobile phone programming0 aTeaching students to learn to learn mobile phone programming aNew York, NY, USAbACM a261–2663 aThis paper describes experiences of the instructor of a course dealing with mobile phone programming. This instance of the course (offered yearly since 2010) reuses the academic content of a traditional software engineering course, but requires mobile phone application development for concrete deliverables that exemplify competency of the academic concepts of the course. The paper describes the tradeoffs between teaching the material vs. students learning the material, group dynamics and constraints, as well as technical recommendations for faculty who are considering offering a course that concentrates on mobile phone applications.10alearning styles10amobile phone programming10asoftware engineering1 aSprinkle, Jonathan uhttp://dx.doi.org/10.1145/2095050.209509402360nas a2200157 4500008004100000022001400041245006200055210006000117260002000177300001100197490000600208520189800214100002302112700001902135856004802154 2011 eng d a1614-504600aTime-Triggered Buffers for Event-Based Middleware Systems0 aTimeTriggered Buffers for EventBased Middleware Systems bSpringer London a9–220 v73 a

Application developers utilizing event-based middleware have sought to leverage domain-specific modeling for the advantages of intuitive specification, code synthesis, and support for design evolution. For legacy and cyber-physical systems, the use of event-based middleware may mean that changes in computational platform can result anomalous system behavior, due to the presence of implicit temporal dependencies. These anomalies are a function not of the component implementation, but of the model of computation employed for supporting system composition. In order to address these behavioral anomalies, the paper presents an approach where time-based blocks are inserted into the system to account for the temporal dependencies. An advantage of capturing the system composition in a domain-specific modeling language is the ability to efficiently refactor an application to include time-triggered, event-based schedulers. This paper describes how an existing event-based component topology can be modified to permit a time triggered model of computation, with no changes to the existing component software. Further, the time-triggered components can be deployed alongside standard publish/subscribe methodologies. This strategy is beneficial to the maintenance of existing legacy systems upon upgrade, since the current operational mode could be maintained with minimal changes to the legacy software even under changes to the target platform which alter execution speed. These time-triggered layers are discussed in three permutations: fully triggered, start triggered and release triggered. A discussion is provided regarding the limitations of each approach, and a brief example is given. The example shows how to apply these triggering approaches without the modification of existing components, but instead through the insertion of triggered buffers between legacy components.

1 aSprinkle, Jonathan1 aEames, Brandon uhttp://dx.doi.org/10.1007/s11334-010-0139-700486nas a2200133 4500008004100000245005900041210005200100260002700152100001700179700002500196700002300221700001800244856009000262 2010 eng d00a10th Workshop on Domain-Specific Modeling ({DSM}’10)0 a10th Workshop on DomainSpecific Modeling DSM 10 bOOPSLA/SPLASHcOctober1 aRossi, Matti1 aTolvanen, Juka-Pekka1 aSprinkle, Jonathan1 aKelly, Steven uhttps://csl.arizona.edu/content/10th-workshop-domain-specific-modeling-dsm%E2%80%991000559nas a2200169 4500008004100000020002200041245008700063210006900150260002700219300001600246653001400262653001100276653001400287653001900301100002300320856004600343 2010 eng d a978-1-4503-0549-500aAnalysis of a metamodel to estimate complexity of using a domain-specific language0 aAnalysis of a metamodel to estimate complexity of using a domain aNew York, NY, USAbACM a13:1–13:610ametamodel10ametric10ausability10auser interface1 aSprinkle, Jonathan uhttp://dx.doi.org/10.1145/2060329.206035902506nas a2200193 4500008004100000245007400041210006900115260001200184520185900196100001902055700002102074700001902095700002202114700002002136700001602156700002302172700001702195856010002212 2010 eng d00aCitizen Science in Support of Vegetation Index and Phenology Research0 aCitizen Science in Support of Vegetation Index and Phenology Res cOctober3 a

Vegetation indices (VIs) are simple transformations of images into proxy measures of greenness and vegetation health and change over time. They are also used to derive information about the land surface phenology status, providing extensive spatial coverage and direct support for global ecosystem models. These measurements however contain large uncertainties and errors. A new suite of mobile devices, equipped with geo-location, image capture, and transmission capabilities could aid with vegetation phenology observations and documentation. The iPhone, with its wide distribution and array of sensors, can contribute significantly to the field of citizen science. In this project we are developing an end-to-end system for the collection, processing, and visualization of land surface vegetation phenology. The system consists of a client-server application and a Google Earth based visualization model. The client side (an iPhone app) intuitively guides the observer to capture up to three images per location: a close-up image of leaves, flowers, or fruits, an individual plant image, and a panoramic landscape image. The iPhone automatically embeds location, orientation, date/time, and other metadata with the images and allows the observer to add text comments. The images are then transmitted to the server, where they are validated, post-processed, archived, and made available to the interactive visualization system. The images are separated into primary colors and processed into a greenness index comparable to the classical VI. These measurements are then plotted against satellite based VI time series to aid in their validation and the characterization of the location phenology. With this effort we hope to recruit global observers into contributing to the field of land surface vegetation change detection and characterization.

1 aWhitsitt, Sean1 aBarreto, Armando1 aRam, Sundaresh1 aAl-Helal, Hussain1 aHudson, Maribel1 aChu, Diyang1 aSprinkle, Jonathan1 aDidan, Kamel uhttps://csl.arizona.edu/content/citizen-science-support-vegetation-index-and-phenology-research00416nas a2200157 4500008004100000245001800041210001800059260001300077300001200090490000900102100002300111700002000134700002100154700001900175856006400194 2010 eng d00aMetamodelling0 aMetamodelling bSpringer a59–780 v61001 aSprinkle, Jonathan1 aRumpe, Bernhard1 aVangheluwe, Hans1 aKarsai, Gábor uhttp://www.springer.com/computer/swe/book/978-3-642-16276-300494nas a2200169 4500008004100000245003500041210003500076260001300111300001400124490000900138100002700147700002000174700002200194700002300216700002100239856006400260 2010 eng d00aModel Evolution and Management0 aModel Evolution and Management bSpringer a243–2720 v61001 aLevendovszky, Tíhamer1 aRumpe, Bernhard1 aSchätz, Bernhard1 aSprinkle, Jonathan1 aVangheluwe, Hans uhttp://www.springer.com/computer/swe/book/978-3-642-16276-300426nas a2200121 4500008004100000245008200041210006900123260001600192300001400208100002300222700001600245856004300261 2010 eng d00aModeling Languages Applied to Decision Controllers for Embedded Human Systems0 aModeling Languages Applied to Decision Controllers for Embedded  bIEEEcMarch a129–1361 aSprinkle, Jonathan1 aChu, Diyang uhttp://dx.doi.org/10.1109/EASe.2010.2400514nas a2200157 4500008004100000245009300041210006900134260000900203300001200212490000600224100001600230700002300246700001800269700002200287856004700309 2010 eng d00aSimulations and Flight Experiments of Transition Maneuvers of a {VTOL} Micro Air Vehicle0 aSimulations and Flight Experiments of Transition Maneuvers of a  cJune a69–890 v21 aChu, Diyang1 aSprinkle, Jonathan1 aRandall, Ryan1 aShkarayev, Sergey uhttp://dx.doi.org/10.1260/1756-8293.2.2.6900371nas a2200121 4500008004100000245004900041210004500090260001600135300001100151100002200162700002300184856004200207 2010 eng d00a{UAV} Search : Maximizing Target Acquisition0 aUAV Search Maximizing Target Acquisition bIEEEcMarch a9–191 aAl-Helal, Hussain1 aSprinkle, Jonathan uhttp://dx.doi.org/10.1109/ECBS.2010.900450nas a2200133 4500008004100000245006700041210005800108260002000166100001700186700002300203700001800226700002500244856004700269 2009 eng d00a9th {OOPSLA} Workshop on Domain-Specific Modeling ({DSM}’09)0 a9th OOPSLA Workshop on DomainSpecific Modeling DSM 09 bOOPSLAcOctober1 aRossi, Matti1 aSprinkle, Jonathan1 aGray, Jeffrey1 aTolvanen, Juka-Pekka uhttp://hsepubl.lib.hse.fi/FI/publ/hse/b10800509nas a2200145 4500008004100000245007700041210006900118260001700187300001300204100001600217700002300233700001800256700002200274856006700296 2009 eng d00aAutomatic Control of {VTOL} Micro Air Vehicle During Transition Maneuver0 aAutomatic Control of VTOL Micro Air Vehicle During Transition Ma bAIAAcAugust a16 pages1 aChu, Diyang1 aSprinkle, Jonathan1 aRandall, Ryan1 aShkarayev, Sergey uhttp://pdf.aiaa.org/preview/CDReadyMGNC09_1998/PV2009_5875.pdf00915nas a2200265 4500008004100000245010900041210006900150260007600219100002200295700001800317700001800335700001800353700002300371700002200394700001600416700001800432700002200450700001900472700002600491700002300517700001700540700001600557700002100573856005500594 2009 eng d00aA Community Report of the 2008 High Confidence Transportation Cyber-Physical Systems {(HCTCPS)} Workshop0 aCommunity Report of the 2008 High Confidence Transportation Cybe aHigh Confidence Transportation Cyber-Physical Systems (HCTCPS)cJuly 221 aPoovendran, Radha1 aRajkumar, Raj1 aCorman, David1 aPaunicka, Jim1 aMilam, William, P.1 aPrasad, Venkatesh1 aWang, Shige1 aBarhorst, Jim1 aGill, Christopher1 aGupta, Sandeep1 aSampigethaya, Krishna1 aSprinkle, Jonathan1 aStuart, Doug1 aWolf, Wayne1 aMangharam, Rahul uhttp://www.ee.washington.edu/research/nsl/aar-cps/01470nas a2200133 4500008004100000245006600041210006500107260006900172520092000241100002301161700001801184700001901202856011501221 2009 eng d00aFundamental Limitations in Domain-Specific Language Evolution0 aFundamental Limitations in DomainSpecific Language Evolution a1230 E. Speedway Blvd., Bldg. 104bUniversity of ArizonacAugust3 a

In this paper we address language engineering issues surrounding domain-specific modeling languages (DSMLs). By definition, such languages track the domain, meaning that changes to the domain require changes to the DSML in order to provide an intuitive specification of domain-specific programs or models. For this work, our primary focus is on fundamental limitations that affect the preservation of semantics during domain model evolution. We specifically address fundamental limitations in semantics-preserving transformations, and/or the implementation of algorithms that specify such transformations. This work has profound implications for language engineers who are planning for the maintenance of models, or designing model transformations for the purpose of preserving semantics. We provide a brief representative example from the discipline of hybrid systems, where such results can be interpreted.

1 aSprinkle, Jonathan1 aGray, Jeffrey1 aMernik, Marjan uhttp://www.ece.arizona.edu/ sprinkjm/wiki/uploads/Publications/sprinkle-tse2009-domainevolution-submitted.pdf00578nas a2200169 4500008004100000022001400041245008600055210006900141260004900210300001000259490000700269100002300276700001900299700002500318700002400343856004100367 2009 eng d a0740-745900aGuest Editors’ Introduction: What Kinds of Nails Need a Domain-Specific Hammer?0 aGuest Editors Introduction What Kinds of Nails Need a DomainSpec aLos Alamitos, CA, USAbIEEE Computer Society a15-180 v261 aSprinkle, Jonathan1 aMernik, Marjan1 aTolvanen, Juka-Pekka1 aSpinellis, Diomidis uhttp://dx.doi.org/10.1109/MS.2009.9200452nas a2200121 4500008004100000245009100041210006900132260001200201300001400213100002300227700001900250856006100269 2009 eng d00aModel-Based Autosynthesis of Time-Triggered Buffers for Event-Based Middleware Systems0 aModelBased Autosynthesis of TimeTriggered Buffers for EventBased cOctober a119–1241 aSprinkle, Jonathan1 aEames, Brandon uhttp://www.dsmforum.org/events/DSM09/Papers/Sprinkle.pdf02005nas a2200265 4500008004100000022004100041245008200082210006900164260004400233300001200277490000600289520117100295100002301466700001901489700002301508700002601531700001701557700002001574700001601594700001901610700001801629700002401647700002001671856004801691 2009 eng d a1619-1366 (Print) 1619-1374 (Online)00aModel-based design: a report from the trenches of the {DARPA} Urban Challenge0 aModelbased design a report from the trenches of the DARPA Urban  bSpringer Berlin / HeidelbergcSeptember a551-5660 v83 aThe impact of model-based design on the software engineering community is impressive, and recent research in model transformations, and elegant behavioral specifications of systems has the potential to revolutionize the way in which systems are designed. Such techniques aim to raise the level of abstraction at which systems are specified, to remove the burden of producing application-specific programs with general-purpose programming. For complex real-time systems, however, the impact of model-driven approaches is not nearly so widespread. In this paper, we present a perspective of model-based design researchers who joined with software experts in robotics to enter the DARPA Urban Challenge, and to what extent model-based design techniques were used. Further, we speculate on why, according to our experience and the testimonies of many teams, the full promises of model-based design were not widely realized for the competition. Finally, we present some thoughts for the future of model-based design in complex systems such as these, and what advancements in modeling are needed to motivate small-scale projects to use model-based design in these domains.1 aSprinkle, Jonathan1 aEklund, Mikael1 aGonzalez, Humberto1 aGrøtli, Esten, Ingar1 aUpcroft, Ben1 aMakarenko, Alex1 aUther, Will1 aMoser, Michael1 aFitch, Robert1 aDurrant-Whyte, Hugh1 aSastry, Shankar uhttp://dx.doi.org/10.1007/s10270-009-0116-500461nas a2200121 4500008004100000245009000041210006900131300001300200490000700213100002200220700002300242856007400265 2009 eng d00aSynthesizing Executable Simulations from Structural Models of Component-Based Systems0 aSynthesizing Executable Simulations from Structural Models of Co a10 pages0 v211 aSchuster, Andreas1 aSprinkle, Jonathan uhttp://eceasst.cs.tu-berlin.de/index.php/eceasst/article/view/289/28000684nas a2200205 4500008004100000020002200041245007600063210006900139260004900208300000900257100001900266700001600285700001500301700002200316700001900338700001800357700002000375700002300395856006000418 2009 eng d a978-0-7695-3602-600aUsing Integrative Models in an Advanced Heterogeneous System Simulation0 aUsing Integrative Models in an Advanced Heterogeneous System Sim aLos Alamitos, CA, USAbIEEE Computer Society a3-101 aGulotta, Jacob1 aChu, Diyang1 aYu, Ximing1 aAl-Helal, Hussain1 aPatki, Tapasya1 aHansen, Jason1 aHudson, Maribel1 aSprinkle, Jonathan uhttp://doi.ieeecomputersociety.org/10.1109/ECBS.2009.4200473nas a2200133 4500008004100000245006700041210005800108260002000166100001800186700002300204700001700227700002500244856007000269 2008 eng d00a8th {OOPSLA} Workshop on Domain-Specific Modeling ({DSM}’08)0 a8th OOPSLA Workshop on DomainSpecific Modeling DSM 08 bOOPSLAcOctober1 aGray, Jeffrey1 aSprinkle, Jonathan1 aRossi, Matti1 aTolvanen, Juka-Pekka uhttp://www.dsmforum.org/events/DSM08/Papers/DSM08-proceedings.pdf00465nas a2200145 4500008004100000245006100041210006100102260001300163300001400176100001600190700002300206700002000229700002300249856004700272 2008 eng d00aReachability Calculations for Automated Aerial Refueling0 aReachability Calculations for Automated Aerial Refueling cDecember a3706-37121 aDing, Jerry1 aSprinkle, Jonathan1 aSastry, Shankar1 aTomlin, Claire, J. uhttp://dx.doi.org/10.1109/CDC.2008.473899801437nas a2200181 4500008004100000245007400041210006900115260004700184520082200231100002301053700001901076700002301095700002601118700002301144700001901167700002001186856004901206 2008 eng d00aRecovering Models of a Four-Wheel Vehicle Using Vehicular System Data0 aRecovering Models of a FourWheel Vehicle Using Vehicular System  bUniversity of California, BerkeleycAugust3 a

This paper discusses efforts to parameterize the actuation models of a four-wheel automobile for the purposes of closed-loop control. As a novelty, the authors used the equipment already available or in use by the vehicle, rather than expensive equipment used solely for the purpose of system identification. After rudimentary measurements were taken of wheelbase, axle width, etc., the vehicle was driven and data were captured using a controller area network (CAN) interface. Based on this captured data, we were able to estimate the feasibility of certain closed-loop controllers, and the models they assumed (i.e., linear, or nonlinear) for control. Examples were acceleration and steering. This work served to inform the separation of differences in simulation and vehicle behavior during vehicle testing.

1 aSprinkle, Jonathan1 aEklund, Mikael1 aGonzalez, Humberto1 aGrøtli, Esten, Ingar1 aSanketi, Pannag, R1 aMoser, Michael1 aSastry, Shankar uhttp://chess.eecs.berkeley.edu/pubs/405.html00697nas a2200157 4500008004100000245011000041210006900151260005700220100002300277700002300300700002400323700002300347700002300370700002000393856012600413 2008 eng d00aTransitioning Control and Sensing Technologies from Fully-autonomous Driving to Driver Assistance Systems0 aTransitioning Control and Sensing Technologies from Fullyautonom bTechnical University, BraunschweigcFebruary 13–141 aGonzalez, Humberto1 aGrøtli, Esten, I.1 aTempleton, Todd, R.1 aBiermeyer, Jan, O.1 aSprinkle, Jonathan1 aSastry, Shankar uhttps://csl.arizona.edu/content/transitioning-control-and-sensing-technologies-fully-autonomous-driving-driver-assistance00534nas a2200157 4500008004100000245007600041210006900117260001800186300001000204100001900214700002200233700001900255700001800274700002300292856006100315 2008 eng d00aUsing Integrative Modeling for Advanced Heterogeneous System Simulation0 aUsing Integrative Modeling for Advanced Heterogeneous System Sim cOctober 19-20 a80-851 aPatki, Tapasya1 aAl-Helal, Hussain1 aGulotta, Jacob1 aHansen, Jason1 aSprinkle, Jonathan uhttp://www.dsmforum.org/events/DSM08/Papers/14-Patki.pdf00478nas a2200133 4500008004100000245006700041210005800108260004200166100002300208700001800231700001700249700002500266856005300291 2007 eng d00a7th {OOPSLA} Workshop on Domain-Specific Modeling ({DSM}’07)0 a7th OOPSLA Workshop on DomainSpecific Modeling DSM 07 aJyväskylä, FinlandbOOPSLAcOctober1 aSprinkle, Jonathan1 aGray, Jeffrey1 aRossi, Matti1 aTolvanen, Juka-Pekka uhttp://www.dsmforum.org/events/DSM07/Papers.html00872nas a2200253 4500008004100000245007200041210006900113260010100182100001700283700001900300700002100319700001900340700001900359700002200378700001800400700001900418700001900437700002300456700002600479700002000505700002100525700002300546856004900569 2007 eng d00aDARPA Urban Challenge Technical Paper: Sydney-Berkeley Driving Team0 aDARPA Urban Challenge Technical Paper SydneyBerkeley Driving Tea bUniversity of Sydney; University of Technology, Sydney; University of California, BerkeleycJune1 aUpcroft, Ben1 aMoser, Michael1 aMakarenko, Alexi1 aJohnson, David1 aDonikan, Ashod1 aAlempijevic, Alen1 aFitch, Robert1 aUther, William1 aBiermeyer, Jan1 aGonzalez, Humberto1 aGrøtli, Esten, Ingar1 aTempleton, Todd1 aSrini, Vason, P.1 aSprinkle, Jonathan uhttp://chess.eecs.berkeley.edu/pubs/379.html00528nas a2200181 4500008004100000245002900041210002800070260002300098300001500121100001800136700002500154700001800179700002300197700001900220700002300239700002300262856006100285 2007 eng d00aDomain-Specific Modeling0 aDomainSpecific Modeling bChapman & Hall/CRC a7-1–7-201 aGray, Jeffrey1 aTolvanen, Juka-Pekka1 aKelly, Steven1 aGokhale, Aniruddha1 aNeema, Sandeep1 aSprinkle, Jonathan1 aFishwick, Paul, A. uhttps://csl.arizona.edu/content/domain-specific-modeling00420nas a2200133 4500008004100000245005300041210005300094260001400147100001900161700002300180700002000203700002000223856004300243 2007 eng d00aTransitioning Intelligence to Embedded Platforms0 aTransitioning Intelligence to Embedded Platforms bNATOcMay1 aEklund, Mikael1 aSprinkle, Jonathan1 aTempleton, Todd1 aSastry, Shankar uhttp://handle.dtic.mil/100.2/ADA47869100492nas a2200121 4500008004100000245006700041210005800108260004200166100002500208700001800233700002300251856009600274 2006 eng d00a6th {OOPSLA} Workshop on Domain-Specific Modeling ({DSM}’06)0 a6th OOPSLA Workshop on DomainSpecific Modeling DSM 06 aJyväskylä, FinlandbOOPSLAcOctober1 aTolvanen, Juka-Pekka1 aGray, Jeffrey1 aSprinkle, Jonathan uhttps://csl.arizona.edu/content/6th-oopsla-workshop-domain-specific-modeling-dsm%E2%80%990600290nas a2200085 4500008004100000245003600041210003600077100002300113856006800136 2006 eng d00aModel Based Systems Engineering0 aModel Based Systems Engineering1 aSprinkle, Jonathan 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4500008004100000245004900041210004700090260000900137300001600146100002300162700001900185700002000204856004700224 2005 eng d00aDeciding to Land a {UAV} Safely in Real Time0 aDeciding to Land a UAV Safely in Real Time cJune a3506–35111 aSprinkle, Jonathan1 aEklund, Mikael1 aSastry, Shankar uhttp://dx.doi.org/10.1109/ACC.2005.147051600504nas a2200145 4500008004100000245007700041210006900118260001000187300001400197100002300211700002300234700002100257700003700278856004300315 2005 eng d00aFault Tolerant Data Flow Modeling Using the Generic Modeling Environment0 aFault Tolerant Data Flow Modeling Using the Generic Modeling Env cApril a229–2351 aMcKelvin, Mark, L.1 aSprinkle, Jonathan1 aPinello, Claudio1 aSangiovanni-Vincentelli, Alberto uhttp://dx.doi.org/10.1109/ECBS.2005.3800368nas a2200121 4500008004100000245005100041210005100092260001000143300001200153490000600165100002300171856005200194 2005 eng d00aGenerative Components for Hybrid Systems Tools0 aGenerative Components for Hybrid Systems 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4500008004100000245004700041210004500088250001300133260000900146100002300155856005600178 2005 eng d00aUser’s Guide to the PublicationsDatabase0 aUser s Guide to the PublicationsDatabase av.5.7.11 cJuly1 aSprinkle, Jonathan uhttp://www.eecs.berkeley.edu/ sprinkle/work/pubdb/00537nas a2200133 4500008004100000245009400041210006900135100001700204700001700221700002300238700001500261700001500276856011200291 2005 eng d00a{Using smart sensors and a camera phone to detect and verify the fall of elderly persons}0 aUsing smart sensors and a camera phone to detect and verify the 1 aHansen, T.R.1 aEklund, J.M.1 aSprinkle, Jonathan1 aBajcsy, R.1 aSastry, S. uhttps://csl.arizona.edu/content/using-smart-sensors-and-camera-phone-detect-and-verify-fall-elderly-persons00512nas a2200145 4500008004100000245010600041210006900147260001000216300001000226100002300236700002000259700001900279700002000298856004800318 2005 eng d00aUsing the Hybrid Systems Interchange Format to Input Design Models to Verification & Validation Tools0 aUsing the Hybrid Systems Interchange Format to Input Design Mode cMarch a1–61 aSprinkle, Jonathan1 aShakernia, Omid1 aMiller, Robert1 aSastry, Shankar uhttp://dx.doi.org/10.1109/AERO.2005.155959500458nas a2200121 4500008004100000245006700041210005800108260004200166100002500208700002300233700001700256856006300273 2004 eng d00a4th {OOPSLA} Workshop on Domain-Specific Modeling ({DSM}’04)0 a4th OOPSLA Workshop on DomainSpecific Modeling DSM 04 aJyväskylä, FinlandbOOPSLAcOctober1 aTolvanen, Juka-Pekka1 aSprinkle, Jonathan1 aRossi, Matti uhttp://www.dsmforum.org/events/DSM04/Proceedings-DSM04.zip00421nas a2200133 4500008004100000245006500041210006200106260000900168300001200177490000700189100002300196700001900219856004900238 2004 eng d00aA Domain-Specific Visual Language for Domain Model Evolution0 aDomainSpecific Visual Language for Domain Model Evolution cJune a291-3070 v151 aSprinkle, Jonathan1 aKarsai, Gábor uhttp://dx.doi.org/10.1016/j.jvlc.2004.01.00600546nas a2200157 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bReprinted in J. of Obj. Tech.cOctober1 aSprinkle, Jonathan uhttp://www.jot.fm/issues/issue_2005_04/article500366nas a2200109 4500008004100000245006100041210006100102260000800163300001400171100002300185856004800208 2004 eng d00aImproving CBS Tool Development with Technological Spaces0 aImproving CBS Tool Development with Technological Spaces cMay a218–2241 aSprinkle, Jonathan uhttp://dx.doi.org/10.1109/ECBS.2004.131670200325nas a2200121 4500008004100000245003100041210003000072260001300102300001200115490000700127100002300134856004600157 2004 eng d00aModel-Integrated Computing0 aModelIntegrated Computing cFebruary a28–300 v231 aSprinkle, Jonathan uhttp://dx.doi.org/10.1109/MP.2004.126693700444nas a2200121 4500008004100000245005600041210005400097260001700151100002300168700001700191700002000208856009400228 2004 eng d00aA Paradigm for Teaching Modeling Environment Design0 aParadigm for Teaching Modeling Environment Design bACMcOctober1 aSprinkle, Jonathan1 aDavis, James1 aNordstrom, Greg 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4500008004100000245005100041210005000092260005600142100003300198700001900231700001900250700002300269700001900292700002600311700001700337856008300354 2002 eng d00aComputer-aided aircraft maintenance scheduling0 aComputeraided aircraft maintenance scheduling a2015 Terrace PlacebVanderbilt UniversitycNovember1 avan Buskirk, Christopher, P.1 aDawant, Benoit1 aKarsai, Gábor1 aSprinkle, Jonathan1 aSzokoli, Gabor1 aSuwanmongkol, Karlkim1 aCurrer, Russ uhttps://csl.arizona.edu/content/computer-aided-aircraft-maintenance-scheduling00534nas a2200145 4500008004100000245006900041210006900110260001300179100002300192700002000215700002700235700001400262700001900276856009300295 2002 eng d00aDomain Evolution in Visual Languages Using Graph Transformations0 aDomain Evolution in Visual Languages Using Graph Transformations cNovember1 aSprinkle, Jonathan1 aAgrawal, Aditya1 aLevendovszky, Tíhamer1 aShi, Feng1 aKarsai, Gábor 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d00aModel Integrated Program Synthesis of Agent Negotiation Protocols0 aModel Integrated Program Synthesis of Agent Negotiation Protocol aNashville, TN 37203bVanderbilt UniversitycAugust1 aSprinkle, Jonathan uhttps://csl.arizona.edu/content/model-integrated-program-synthesis-agent-negotiation-protocols00404nas a2200145 4500008004100000245003100041210003100072260001200103300001400115490000600129100002300135700003300158700001900191856004800210 2000 eng d00aModeling Agent Negotiation0 aModeling Agent Negotiation cOctober a454–4590 v11 aSprinkle, Jonathan1 avan Buskirk, Christopher, P.1 aKarsai, Gábor uhttp://dx.doi.org/10.1109/ICSMC.2000.88503400432nas a2200133 4500008004100000245005900041210005900100260001200159100001700171700002300188700002000211700001900231856004800250 2000 eng d00aTowards a Standard for Model Specification and Storage0 aTowards a Standard for Model Specification and Storage cOctober1 aDeva, Dinesh1 aSprinkle, Jonathan1 aNordstrom, Greg1 aMaroti, Miklos uhttp://dx.doi.org/10.1109/ICSMC.2000.88501800382nas a2200109 4500008004000000245005100040210005100091260001400142100001500156700002300171856007800194 0 engd00aAutomobile Localization with Commodity Sensors0 aAutomobile Localization with Commodity Sensors cSubmitted1 aZhang, Kun1 aSprinkle, Jonathan uhttps://csl.arizona.edu/content/automobile-localization-commodity-sensors00501nas a2200121 4500008004000000245009300040210006900133260001400202300002100216653000900237100002300246856011000269 0 engd00aA Domain-Specific Modeling Environment Applied to the Design of an Embedded Human System0 aDomainSpecific Modeling Environment Applied to the Design of an  cSubmitted a(in preparation)10adsml1 aSprinkle, Jonathan uhttps://csl.arizona.edu/content/domain-specific-modeling-environment-applied-design-embedded-human-system01453nas a2200157 4500008004000000245006600040210006500106260001400171300002100185490001000206520092200216100002301138700001801161700001901179856009701198 0 engd00aFundamental Limitations in Domain-Specific Language Evolution0 aFundamental Limitations in DomainSpecific Language Evolution cSubmitted a(in preparation)0 v(tbd)3 a

In this paper we address language engineering issues surrounding domain-specific modeling languages (DSMLs). By definition, such languages track the domain, meaning that changes to the domain require changes to the DSML in order to provide an intuitive specification of domain-specific programs or models. For this work, our primary focus is on fundamental limitations that affect the preservation of semantics during domain model evolution. We specifically address fundamental limitations in semantics-preserving transformations, and/or the implementation of algorithms that specify such transformations. This work has profound implications for language engineers who are planning for the maintenance of models, or designing model transformations for the purpose of preserving semantics. We provide a brief representative example from the discipline of hybrid systems, where such results can be interpreted.

1 aSprinkle, Jonathan1 aGray, Jeffrey1 aMernik, Marjan uhttps://csl.arizona.edu/content/fundamental-limitations-domain-specific-language-evolution-0