01697nas a2200181 4500008004100000245006200041210006200103260004700165520109000212653002401302653000901326653001501335100002001350700001601370700001901386700002101405856008901426 2019 eng d00aDomain Specific Modeling Language for Test World Creation0 aDomain Specific Modeling Language for Test World Creation aTucsonbThe University of Arizonac08/20193 a
It is often necessary to use a 3D physics simulator in order to model and test complex robotic systems. Verifying certain behaviors of systems in the real world can be costly, time-consuming, and even dangerous, while simulations are relatively cheap and fast. However, creating simulated environments to test robotic behaviors can take up quite a lot of time and processing power. In order for behaviors to be tested in a variety of scenarios, multiple environments must be created, causing verification time to increase. This paper presents a domain-specific modeling language that can be used to speed up this process. This modeling language can be used in WebGME to generate multiple world and launch files in Gazebo, a 3D dynamics simulator. These world files can then be used to test various behaviors of complex robots such as the CAT Vehicle (Cognitive and Autonomous Test Vehicle) in a variety of simulated environments. This model language can save valuable testing time by quickly creating usable test files for complex physics based models such as the CAT Vehicle.
10aautonomous vehicles10adsml10asimulation1 aAlexander, Jill1 aPyryt, Alex1 aBhadani, Rahul1 aBunting, Matthew uhttp://csl.arizona.edu/content/domain-specific-modeling-language-test-world-creation02015nas 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 aIn 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=3302509.331402600795nas 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.2018.12.01200669nas 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-applications00876nas 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-simulations01115nas a2200133 4500008004100000245007600041210006900117260006500186520061900251100002100870700001600891700002100907856005300928 2018 eng d00aUser-Friendly Method to Optimize the Network of a Cyber-Physical System0 aUserFriendly Method to Optimize the Network of a CyberPhysical S aKnoxville, Tennesseebthe American Physical Societyc11/20183 aMany cyber-physical systems (CPS), such as self-driving cars, require numerous components working together, which can result in a complex network. This research focused on user-friendly network optimization methods on the autonomous vehicle at the University of Arizona. The method verified that the network operated under cost, bandwidth, latency (time delay in the transfer of information) and processing power constraints. Operating within these constraints ensures that the system is safe and efficient. The optimization method discussed in this poster can be customized for any cyber-physical system.
1 aHarris, Samantha1 aWelch, Levi1 aBunting, Matthew uhttp://meetings.aps.org/link/BAPS.2018.SES.D05.100954nas 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/10.1145/3055378.305538006037nas 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-vehicles00533nas 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/10.1145/3023147.302315400647nas 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/10.1145/2846696.284670600631nas 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/35676601875nas 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 aRobotic 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/10.1145/2541928.254193100528nas a2200133 4500008004100000245008500041210006900126260000900195100001900204700002100223700001900244700002000263856011100283 2011 eng d00aToward ultra high speed locomotors: Design and test of a cheetah robot hind limb0 aToward ultra high speed locomotors Design and test of a cheetah bIEEE1 aLewis, Anthony1 aBunting, Matthew1 aSalemi, Behnam1 aHoffmann, Heiko uhttps://csl.arizona.edu/content/toward-ultra-high-speed-locomotors-design-and-test-cheetah-robot-hind-limb