@article {454, title = {Quantifying air quality benefits resulting from few autonomous vehicles stabilizing traffic}, journal = {Transportation Research Part D: Transport and Environment}, volume = {67}, year = {2019}, month = {02/2019}, chapter = {351}, abstract = {

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{\textendash}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.

}, doi = {10.1016/j.trd.2018.12.008}, url = {https://doi.org/10.1016/j.trd.2018.12.008}, author = {Yuche Chen and Miles Churchill and Fangyu Wu and Maria Laura Dell Monache and Benedetto Piccoli and Benjamin Seibold and Jonathan Sprinkle and Daniel B. Work} } @proceedings {455, title = {Real-Time Distance Estimation and Filtering of Vehicle Headways for Smoothing of Traffic Waves}, year = {2019}, month = {04/2019}, edition = {10}, address = {Montreal, Canada}, abstract = {

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.

}, keywords = {autonomous vehicles, Digital Filter, simulation, Traffic}, doi = {10.1145/3302509.3314026}, url = {https://dl.acm.org/citation.cfm?doid=3302509.3314026}, author = {Rahul Bhadani and Matthew Bunting and Benjamin Seibold and Raphael E Stern and Shumo Cui and Jonathan Sprinkle and Benedetto Piccoli and Daniel B. Work} } @article {wu2019tracking, title = {Tracking vehicle trajectories and fuel rates in phantom traffic jams: Methodology and data}, journal = {Transportation Research Part C: Emerging Technologies}, volume = {99}, year = {2019}, pages = {82{\textendash}109}, publisher = {Elsevier}, doi = {10.1016/j.trc.2018.12.012}, url = {https://doi.org/10.1016/j.trc.2018.12.012}, author = {Fangyu Wu and Raphael E Stern and Shumo Cui and Maria Laura Dell Monache and Rahul Bhadani and Matthew Bunting and Miles Churchill and Nathaniel Hamilton and Benedetto Piccoli and Benjamin Seibold and Jonathan Sprinkle and Daniel B. Work} } @booklet {464, title = {The Arizona Ring Experiments Dataset (ARED)}, year = {2018}, url = {http://hdl.handle.net/1803/9358}, author = {Fangyu Wu and Raphael E Stern and Shumo Cui and Maria Laura Dell Monache and Rahul Bhadani and Matthew Bunting and Miles Churchill and Nathaniel Hamilton and Fangyu Wu and Benedetto Piccoli and Benjamin Seibold and Jonathan Sprinkle and Daniel B. Work} } @article {272, title = {Dissipation of stop-and-go waves via control of autonomous vehicles: Field experiments}, journal = {Transportation Research Part C}, volume = {89}, year = {2018}, month = {04/2018}, type = {Journal}, chapter = {205-221}, keywords = {autonomous vehicles, cyber physical systems}, doi = {10.1016/j.trc.2018.02.005}, author = {Raphael E Stern and Shumo Cui and Maria Laura Dell Monache and Rahul Bhadani and Matthew Bunting and Miles Churchill and Nathaniel Hamilton and Hannah Pohlmann and Fangyu Wu and Benedetto Piccoli and Benjamin Seibold and Jonathan Sprinkle and Daniel B. Work} } @conference {450, title = {{A LiDAR Error Model for Cooperative Driving Simulations}}, booktitle = {IEEE Vehicular Network Conference}, year = {2018}, month = {12/2018}, publisher = {IEEE}, organization = {IEEE}, address = {Taipei, Taiwan}, abstract = {

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.

}, keywords = {autonomous vehicles, AV, CAV, LiDAR, self-driving cars, sensor, VNC}, author = {Michele Segata and Renato Lo Cigno and Rahul Bhadani and Matthew Bunting and Jonathan Sprinkle} } @conference {Work:2017:CUE:3055378.3055380, title = {Controlling for Unsafe Events in Dense Traffic Through Autonomous Vehicles: Invited Talk Abstract}, booktitle = {Proceedings of the 1st International Workshop on Safe Control of Connected and Autonomous Vehicles}, year = {2017}, pages = {7{\textendash}7}, publisher = {ACM}, organization = {ACM}, address = {New York, NY, USA}, keywords = {Sugiyama experiment, Traffic flow}, isbn = {978-1-4503-4976-5}, doi = {10.1145/3055378.3055380}, url = {http://doi.acm.org/10.1145/3055378.3055380}, author = {Daniel B. Work and Raphael E Stern and Fangyu Wu and Miles Churchill and Shumo Cui and Hannah Pohlmann and Benjamin Seibold and Benedetto Piccoli and Rahul Bhadani and Matthew Bunting and Jonathan Sprinkle and Maria Laura Dell Monache and Nathaniel Hamilton and Haulcy, R.} } @conference {sprinkle298, title = {Dampening traffic waves with autonomous vehicles}, booktitle = {ITRL Conference on Integrated Transport: Connected and Automated Transport Systems}, year = {2016}, abstract = {In 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{\textquoteright}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{\textquoteright}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.}, author = {Raphael E Stern and Fangyu Wu and Miles Churchill and Daniel B. Work and Maria Laura Dell Monache and Benedetto Piccoli and Hannah Pohlmann and Shumo Cui and Benjamin Seibold and Nathaniel Hamilton and R{\textquoteright}mani Haulcy and Rahul Bhadani and Matthew Bunting and Jonathan Sprinkle} } @conference {sprinkle284, title = {Robust Control of Autonomous Vehicle Trajectories}, booktitle = {AIMS}, year = {2016}, month = {07/2016}, abstract = {

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.

}, author = {Jonathan Sprinkle and Rahul Bhadani and Shumo Cui and Benjamin Seibold} } @conference { cps2016wip, title = {WiP Abstract: Stabilizing traffic with a single autonomous vehicle}, booktitle = {2016 ACM/IEEE 7th International Conference on Cyber-Physical Systems (ICCPS)}, year = {2016}, pages = {1-1}, doi = {10.1109/ICCPS.2016.7479130}, url = {http://dx.doi.org/10.1109/ICCPS.2016.7479130}, author = {Raphael E Stern and Daniel B. Work and Shumo Cui and Hannah Pohlmann and Benjamin Seibold and Maria Laura Dell Monache and Benedetto Piccoli and Jonathan Sprinkle} } @conference {sprinkle274, title = {Intersection Management via the Opportunistic Organization of Platoons by Route}, booktitle = {WinnComm 2016}, year = {2015}, abstract = {Intersections 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{\textquoteright}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.}, author = {Alberto Heras and Lykes Claytor and Haris Volos and Hamed Asadi and Jonathan Sprinkle and Tamal Bose} } @conference {sprinkle258, title = {A Heterogeneity Based Method to Identify Major Variability Components}, booktitle = {International Conference on Electronics, Communications, and Networks}, year = {2014}, month = {12/2014}, address = {Beijing}, author = {Fahd Shaikh and Wei He and Jonathan Sprinkle and K. Chen and Janet Roveda} } @mastersthesis {238, title = {Design Validation of Multi-Mode Systems}, volume = {Ph.D.}, year = {2013}, month = {12/2013}, pages = {175}, school = {University of Arizona}, type = {mastersPh.D. dissertation}, address = {Tucson, AZ}, abstract = {

Cyber-Physical Systems (CPS) are involved with both physical processes and com- putational processes. The interaction of physical components and computational components makes it difficult to analyze, design and verify this type of systems. The problem becomes more complex when an input or decision must be initiated by a human. For this dissertation, Cyber-Physical Systems with a human operator in the loop are called Embedded Human Systems (EHS). To ensure the safety of EHS such as traffic control systems, space shuttle control systems, nuclear power plant control systems and so on, it is critically important for human operators to fully understand both physical and computational processes. However, humans are usually easily overwhelmed by concurrent information, so this assumption becomes intractable when it comes to complex EHS with timing constraints.

This dissertation proposes a domain specific modeling language that takes ad- vantage of hybrid system abstraction to retain important system behaviors and automatically generates self-configured system verification software. The verifica- tion software effectively reduces the computation time through parallel scheduling algorithm, and if the computation process detects a violation of the design require- ments, verification can be halted without wasting computation resources. The mod- eling environment also allows a user to conveniently set design constraints to avoid flaws early in the prototype process, and reuse the available model for a family of different platforms. Several verification results of different platforms are shown to demonstrate the efficiency and reusability of the modeling environment.

}, author = {Diyang Chu} } @conference { c:whitsitt-ecbs-2012, title = {On the Extraction and Analysis of a Social Network with Partial Organizational Observation}, booktitle = {Proceedings of the 19th IEEE International Conference and Workshops on Engineering of Computer-Based Systems}, year = {2012}, pages = {249-256}, abstract = {

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.

}, doi = {10.1109/ECBS.2012.33}, url = {http://dx.doi.org/10.1109/ECBS.2012.33}, author = {Sean Whitsitt and Abishek Gopalan and Sangman Cho and Jonathan Sprinkle and Srinivasan Ramasubramanian and Liana Suantak and Jerzy Rozenblit} } @article { j:bart-eceasst-2012, title = {A generic in-place transformation-based approach to structured model co-evolution}, journal = {Electronic Communications of the European Association of Software Science and Technology (EASST)}, volume = {42}, year = {2012}, month = {April}, pages = {1{\textendash}13}, publisher = {ECEASST}, abstract = {In 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.}, url = {http://journal.ub.tu-berlin.de/eceasst/article/view/608}, author = {Bart Meyers and Manuel Wimmer and Antonio Cicchetti and Jonathan Sprinkle} } @article { j:smc-a-camera-controller, title = {Model-Based Configuration of a Heterogeneous Human-in-the-loop Command and Control Simulation Environment}, journal = {tbd}, volume = {tbd}, number = {tbd}, year = {2012}, pages = {(in preparation)}, author = {Diyang Chu and Jacob Gulotta and Jonathan Sprinkle and Himanshu Neema and Harmon Nine and Nicholas Kottenstette and Graham Hemingway and Janos Sztipanovits} } @conference { w:whitsitt-neat-2011, title = {Constrained data acquisition for mobile citizen science applications}, booktitle = {Proceedings of the compilation of the co-located workshops on DSM{\textquoteright}11, TMC{\textquoteright}11, AGERE!{\textquoteright}11, AOOPES{\textquoteright}11, NEAT{\textquoteright}11, \& VMIL{\textquoteright}11}, series = {SPLASH {\textquoteright}11 Workshops}, year = {2011}, pages = {267{\textendash}272}, publisher = {ACM}, organization = {ACM}, address = {New York, NY, USA}, abstract = {

The popularity and ubiquity of personal mobile computing devices{\textendash}-coupled with their powerful sensing capabilities{\textendash}-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.

}, keywords = {citizen scientist, domain-specific modeling, iphone, mobile phone programming}, isbn = {978-1-4503-1183-0}, doi = {10.1145/2095050.2095095}, url = {http://dx.doi.org/10.1145/2095050.2095095}, author = {Sean Whitsitt and Armando Barreto and Maribel Hudson and Hussain Al-Helal and Diyang Chu and Kamel Didan and Jonathan Sprinkle} } @conference {c:whitsitt-prose-2010, title = {Citizen Science in Support of Vegetation Index and Phenology Research}, booktitle = {Phenology Research and Observations of Southwest Ecosystems Symposium (PROSE)}, year = {2010}, month = {October}, abstract = {

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.

}, author = {Sean Whitsitt and Armando Barreto and Sundaresh Ram and Hussain Al-Helal and Maribel Hudson and Diyang Chu and Jonathan Sprinkle and Kamel Didan} } @conference { c:sprinkle-ease-2010, title = {Modeling Languages Applied to Decision Controllers for Embedded Human Systems}, booktitle = {Seventh IEEE International Conference and Workshops on Engineering of Autonomic and Autonomous Systems (EAsE 2010)}, year = {2010}, month = {March}, pages = {129{\textendash}136}, publisher = {IEEE}, organization = {IEEE}, doi = {10.1109/EASe.2010.24}, url = {http://dx.doi.org/10.1109/EASe.2010.24}, author = {Jonathan Sprinkle and Diyang Chu} } @article { j:chu-ijmav-2010, title = {Simulations and Flight Experiments of Transition Maneuvers of a {VTOL} Micro Air Vehicle}, journal = {International Journal of Micro Air Vehicles}, volume = {2}, number = {2}, year = {2010}, note = {{ISSN} 1756-8293}, month = {June}, pages = {69{\textendash}89}, doi = {10.1260/1756-8293.2.2.69}, url = {http://dx.doi.org/10.1260/1756-8293.2.2.69}, author = {Diyang Chu and Jonathan Sprinkle and Ryan Randall and Sergey Shkarayev} } @conference { c:chu-gnc-2009, title = {Automatic Control of {VTOL} Micro Air Vehicle During Transition Maneuver}, booktitle = {AIAA Guidance, Navigation and Control Conference}, year = {2009}, note = {{AIAA-2009-5875}}, month = {August}, pages = {16 pages}, publisher = {AIAA}, organization = {AIAA}, url = {http://pdf.aiaa.org/preview/CDReadyMGNC09_1998/PV2009_5875.pdf}, author = {Diyang Chu and Jonathan Sprinkle and Ryan Randall and Sergey Shkarayev} } @article {HCTCPS, title = {A Community Report of the 2008 High Confidence Transportation Cyber-Physical Systems {(HCTCPS)} Workshop}, year = {2009}, month = {July 22}, address = {High Confidence Transportation Cyber-Physical Systems (HCTCPS)}, url = {http://www.ee.washington.edu/research/nsl/aar-cps/}, author = {Radha Poovendran and Raj Rajkumar and David Corman and Jim Paunicka and William P. Milam and K Venkatesh Prasad and Shige Wang and Jim Barhorst and Christopher Gill and Sandeep Gupta and Krishna Sampigethaya and Jonathan Sprinkle and Doug Stuart and Wayne Wolf and Rahul Mangharam} } @conference { c:gulotta-ecbs-2009, title = {Using Integrative Models in an Advanced Heterogeneous System Simulation}, booktitle = {IEEE International Conference on the Engineering of Computer-Based Systems}, year = {2009}, pages = {3-10}, publisher = {IEEE Computer Society}, organization = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, isbn = {978-0-7695-3602-6}, doi = {10.1109/ECBS.2009.42}, url = {http://doi.ieeecomputersociety.org/10.1109/ECBS.2009.42}, author = {Jacob Gulotta and Diyang Chu and Ximing Yu and Hussain Al-Helal and Tapasya Patki and Jason Hansen and Maribel Hudson and Jonathan Sprinkle} } @article {j:NordstromLNCS2003, title = {ANEMIC: Automatic Interface Enabler for Model Integrated Computing}, journal = {Lecture Notes in Computer Science}, volume = {2830}, year = {2003}, note = {ISBN: 3-540-20102-5}, month = {November}, pages = {138{\textendash}150}, doi = {10.1007/978-3-540-39815-8_9}, url = {http://dx.doi.org/10.1007/978-3-540-39815-8_9}, author = {Steve Nordstrom and Shweta Shetty and Kumar Guarav Chhokra and Jonathan Sprinkle and Brandon Eames and {\'A}kos L{\'e}deczi} } @conference {c:NordstromGPCE, title = {ANEMIC: Automatic Interface Enabler for Model Integrated Computing}, booktitle = {Generative Programming and Component Engineering (GPCE {\textquoteright}03)}, year = {2003}, month = {September}, url = {http://www.isis.vanderbilt.edu/publications/archive/Nordstrom_SG_9_22_2003_ANEMIC__Au.pdf}, author = {Steve Nordstrom and Shweta Shetty and Kumar Guarav Chhokra and Jonathan Sprinkle and Brandon Eames and {\'A}kos L{\'e}deczi} } @article {tr:ANTSTechReport, title = {Computer-aided aircraft maintenance scheduling}, number = {ISIS-02-303}, year = {2002}, month = {November}, publisher = {Vanderbilt University}, type = {Tech. Rep.}, address = {2015 Terrace Place}, author = {Christopher P. van Buskirk and Benoit Dawant and G{\'a}bor Karsai and Jonathan Sprinkle and Gabor Szokoli and Karlkim Suwanmongkol and Russ Currer} }