@conference {472, title = {Domain Specific Modeling Language for Test World Creation}, booktitle = {CAT Vehicle Research Experience for Undergraduates}, year = {2019}, month = {08/2019}, publisher = {The University of Arizona}, organization = {The University of Arizona}, address = {Tucson}, abstract = {

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.

}, keywords = {autonomous vehicles, dsml, simulation}, url = {http://csl.arizona.edu/content/domain-specific-modeling-language-test-world-creation}, author = {Jill Alexander and Alex Pyryt and Rahul Bhadani and Matthew Bunting} } @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} } @conference {448, title = {{The CAT Vehicle Testbed: A Simulator with Hardware in the Loop for Autonomous Vehicle Applications}}, booktitle = {Proceedings 2nd International Workshop on Safe Control of Autonomous Vehicles (SCAV 2018), Porto, Portugal, Electronic Proceedings in Theoretical Computer Science }, volume = {269}, year = {2018}, month = {04/2018}, type = {Workshop}, keywords = {autonomous vehicles, simulation, testbed}, doi = {10.4204/EPTCS.269.4}, author = {Rahul Bhadani and Jonathan Sprinkle and Matthew Bunting} } @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 {473, title = {User-Friendly Method to Optimize the Network of a Cyber-Physical System}, booktitle = {85th Annual Meeting of the APS Southeastern Section}, year = {2018}, month = {11/2018}, publisher = {the American Physical Society}, organization = {the American Physical Society}, address = {Knoxville, Tennessee}, abstract = {

Many 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.

}, url = {http://meetings.aps.org/link/BAPS.2018.SES.D05.1}, author = {Samantha Harris and Levi Welch and Matthew Bunting} } @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 {sprinkle279, title = {A Safe Autonomous Vehicle Trajectory Domain Specific Modeling Language For Non-Expert Development}, booktitle = {Proceedings of the International Workshop on Domain-Specific Modeling (DSM 2016)}, year = {2016}, pages = {42{\textendash}48}, publisher = {ACM}, organization = {ACM}, address = {Amsterdam, Netherlands}, doi = {10.1145/3023147.3023154}, url = {http://doi.acm.org/10.1145/3023147.3023154}, author = {Matthew Bunting and Yegeta Zeleke and Kennon McKeever and Jonathan Sprinkle} } @conference {McKeever:2015:ERC:2846696.2846706, title = {Experience Report: Constraint-based Modeling of Autonomous Vehicle Trajectories}, booktitle = {Proceedings of the Workshop on Domain-Specific Modeling}, year = {2015}, pages = {17{\textendash}22}, publisher = {ACM}, organization = {ACM}, address = {New York, NY, USA}, keywords = {Autonomous Systems, cyber-physical systems, metamodeling}, isbn = {978-1-4503-3903-2}, doi = {10.1145/2846696.2846706}, url = {http://doi.acm.org/10.1145/2846696.2846706}, author = {Kennon McKeever and Yegeta Zeleke and Matthew Bunting and Jonathan Sprinkle} } @article {213, title = {Motorized mobility scooters: The Use of Training/Intervention and Technology for Improving Driving Skills in Aging Adults - A Mini-Review}, journal = {Gerontology}, volume = {60}, year = {2014}, month = {06/2014}, pages = {357-365}, doi = {10.1159/000356766}, url = {http://www.karger.com/Article/FullText/356766}, author = {Nima Toosizadeh and Matthew Bunting and Carol Howe and Jane Mohler and Jonathan Sprinkle and Bijan Najafi} } @conference {211, title = {Generating a {ROS/JAUS} Bridge for an Autonomous Ground Vehicle}, booktitle = {Proceedings of the 2013 ACM workshop on Domain-specific modeling (DSM {\textquoteright}13)}, year = {2013}, pages = {13-18}, publisher = {ACM}, organization = {ACM}, address = {Indianapolis, IN}, abstract = {

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{\textquoteright}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.

}, keywords = {autonomous vehicles, Code Generation}, doi = {10.1145/2541928.2541931}, url = {http://dx.doi.org/10.1145/2541928.2541931}, author = {Patrick Morley and Alex Warren and Ethan Rabb and Matthew Bunting and Sean Whitsitt and Jonathan Sprinkle} } @conference {220, title = {Toward ultra high speed locomotors: Design and test of a cheetah robot hind limb}, booktitle = {Robotics and Automation (ICRA), 2011 IEEE International Conference on}, year = {2011}, publisher = {IEEE}, organization = {IEEE}, author = {Lewis, M Anthony and Matthew Bunting and Salemi, Behnam and Hoffmann, Heiko} }