@article {459, title = {Are commercially implemented adaptive cruise control systems string stable?}, journal = {IEEE Transactions on Intelligent Transportation Systems}, year = {2020}, month = {06/2020}, pages = {12 pages}, abstract = {

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.

}, keywords = {Adaptive Cruise Control, String Stability}, doi = {10.1109/TITS.2020.3000682}, url = {http://dx.doi.org/10.1109/TITS.2020.3000682}, author = {George Gunter and Derek Gloudemans and Raphael E Stern and Sean McQuade and Rahul Bhadani and Matt Bunting and Maria Laura Dell Monache and Benjamin Seibold and Jonathan Sprinkle and Benedetto Piccoli and Daniel B. Work} } @inbook {492, title = {Hyperparameter Optimization using Grid Search for use in Monocular Depth Estimation}, booktitle = {CAT Vehicle 2020 Technical Reports}, year = {2020}, abstract = {

Autonomous vehicles uses a myriad of sensors to observe and monitor its surroundings. Current re- search incorporates static digital images that are used to train and optimize deep neural networks. These deep neural networks are used to identify and determine the distances of objects from the autonomous vehicle. Unfortunately, the hyperparameters used to train these networks were chosen arbitrarily. We explored the hyperparameter space through a two-step grid search in order to recognize the optimal combination of losses before continuing to train the rest of the system to understand the trade offs be- tween constant parameters. Specifically, the research focuses on finding the optimal relation between the left-right consistency, appearance, disparity losses in generating accurate feature edges in our output images. The trade off between costs allows for the neural network an increased performance at edge detection.

}, keywords = {Autonomous Driving, Grid Search, Neural Networks, Optimization}, author = {Calvin Barrett and Tomohide Bessho} } @inbook {493, title = {Real-Time Traffic Lights Identification using YOLOv3 Algorithm For Autonomous Vehicles}, booktitle = {CAT Vehicle 2020 Technical Reports}, year = {2020}, abstract = {

Safety has always been a priority in automobile manufacture. Traffic light detection plays a major role in regards to safety in autonomous vehicles. Previ- ous methods involved utilizing a combination of image processing and training a neural network model. Those methods were not fully successful as they pre- sented limitations such as trouble detecting yellow and arrow traffic lights, the inability to identify a traffic light from a few pixels due to long distances, and the obstruction of traffic lights causing failure of detection. We propose to identify traffic lights and their states in both urban and suburban areas by developing a deep learning model utilizing a YOLOv3 model. Additionally, we will use the large dataset of traffic light images from the {\textquoteright}Bosch Small Traffic Lights Dataset{\textquoteright} to train the YOLOv3. Our proposed method will be successful because a better processed dataset indicates traffic lights in busier environments, and a balanced dataset allows the model to be better trained at identifying traffic signals and their status. The success of our proposed method will benefit autonomous ve- hicles{\textquoteright} feasibility by improving safety at intersections. The risks of this method include missing potential traffic signals which could lead to potential accidents. We will use a camera that will record the data that will later be processed. The mid-term check for success is accurate detection of traffic lights in ROS simula- tions, and the final check for success is the successful test drive of University of Arizona{\textquoteright}s CAT Vehicle by detecting traffic lights at intersections.

}, keywords = {Autonomous Driving, Neural Networks, Object Detection}, author = {Rachel Kozel and Naeemah Robert} } @article {491, title = {Safer Adaptive Cruise Control for Traffic Wave Dampening}, year = {2020}, abstract = {

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{\textquoteright}s velocity dips below threshold, requiring the driver of the host vehicle to resume control over the car{\textquoteright}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{\textquoteright}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.

}, keywords = {Adaptive Cruise Control, stop-and-go, Vehicle Autonomy}, author = {Emily Baschab and Savannah Ball and Audrey Vazzana}, editor = {Jonathan Sprinkle} } @conference {458, title = {Adaptive HSL Filters and Inverse Perspective Transforms in Lane Detection for Autonomous Driving}, booktitle = {CAT Vehicle REU}, year = {2019}, month = {03/2019}, keywords = {autonomous vehicles, computer vision, image processing}, doi = {10.13140/RG.2.2.18919.60327}, url = {http://doi.org/10.13140/RG.2.2.18919.60327}, author = {Hannah Mason and Landon Bentley and Joe MacInnes and Rahul Bhadani and Tamal Bose} } @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} } @conference {457, title = {A Pseudo-Derivative Method for Sliding Window Path Mapping in Robotics-Based Image Processing}, year = {2019}, publisher = {CAT Vehicle Research Experience for Undergraduates}, organization = {CAT Vehicle Research Experience for Undergraduates}, type = {Undergraduate Research}, address = {Accepted at IEEE International Conference on Robotic Computing}, abstract = {

A sliding window technique in robotics-based image processing applications is a common approach to path mapping from extracted features. Mapping a path inside an image requires finding a series of points representing the path. Previous approaches find these points by sliding a window along the path in fixed increments across one image dimension. After each slide, the center of the window in the other dimension is adjusted so that the window maximally covers the path in that area. This approach, however, fails to map paths that experience sharp curvature since the windows slide along only one dimension. The method proposed herein uses a pseudo-derivative approach to sliding windows that improves upon the traditional technique by dynamically adjusting the windows along both image dimensions during each slide. In this method, the directional components of a vector representing the previous slide are used as a naive estimation to perform the current slide. If this fails to map the path, the vector direction is used to enlarge the window dimensions. The method was tested in the domain of autonomous vehicles as an approach for detecting road lane markings. The algorithm proved more successful than previous sliding window approaches on perspective mapped lane images.

}, keywords = {autonomous vehicles, image processing}, doi = {10.13140/RG.2.2.35628.10885}, author = {Landon Bentley and Joe MacInnes and Rahul Bhadani and Tamal Bose} } @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} } @conference {470, title = {Reinforcement Learning for Autonomous Driving using CAT Vehicle Testbed}, booktitle = {CAT Vehicle Research Experience for Undergraduates}, year = {2019}, month = {08/2019}, publisher = {The University of Arizona}, organization = {The University of Arizona}, type = {Technical Report}, address = {Tucson}, abstract = {

We discuss a deep reinforcement learning implementation using the CAT Vehicle Testbed and discuss the merits of simulation-based deep reinforcement learning. After implementing a simple 3 layered neural network which learned using deep Q-learning, we found some challenges associated with ROS and Gazebo. In spite of these challenges, we demonstrate that our reinforcement learning architecture can teach a car to avoid obstacles. With these preliminary results, we discuss what new things we can do and how we can implement more advanced methods.

}, keywords = {Autonomous Systems, Machine Learning}, url = {http://csl.arizona.edu/content/reinforcement-learning-autonomous-driving-using-cat-vehicle-testbed}, author = {John Nguyen and Hoang Huynh and Eric Av and Rahul Bhadani and Tamal Bose} } @conference {471, title = {Safety and stability analysis of FollowerStopper}, 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 = {

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

}, keywords = {autonomous vehicles, control systems, intelligent transportation}, url = {http://csl.arizona.edu/content/safety-and-stability-analysis-followerstopper}, author = {Chris Kreienkamp and Daniel Fishbein and Rahul Bhadani and Jonathan Sprinkle} } @conference {468, title = {A Sliding Window for Path Mapping based on a Pseudo-Derivative Method in Autonomous Navigation}, booktitle = {IEEE Vehicular Technology Conference, VTC-2019, Fall}, year = {2019}, month = {11/2019}, publisher = {IEEE}, organization = {IEEE}, address = {Honolulu, Hawaii}, keywords = {autonomous vehicles}, author = {Landon Bentley and Joe MacInnes and Hannah Mason and Rahul Bhadani and Tamal Bose} } @conference {460, title = {WiP Abstract: String stability of commercial adaptive cruise control vehicles}, booktitle = {International Conference on Cyber-Physical Systems}, year = {2019}, abstract = {In 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. }, keywords = {Adaptive Cruise Control, String Stability}, doi = {10.1145/3302509.3313325}, url = {https://dl.acm.org/citation.cfm?id=3313325}, author = {George Gunter and Y. Yang and Raphael E Stern and Daniel B. Work and Maria Laura Dell Monache and Rahul Bhadani and Matt Bunting and Roman Lysecky and Jonathan Sprinkle and Benjamin Seibold and Benedetto Piccoli} } @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} } @proceedings {449, title = {Dissipation of Emergent Traffic Waves in Stop-and-Go Traffic Using a Supervisory Controller}, volume = {57}, year = {2018}, publisher = {IEEE}, address = {Fontainbleau, Miami Beach, USA}, abstract = {

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{\textquoteright}s velocity will be oscillatory, the controller{\textquoteright}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.\ 

}, keywords = {autonomous vehicles, CPS, Traffic}, doi = {10.1109/CDC.2018.8619700}, url = {https://ieeexplore.ieee.org/document/8619700}, author = {Rahul Bhadani 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 {469, title = {A Fuzzy based approach to Dampen Emergent Traffic Waves}, booktitle = {CAT Vehicle Research Experience for Undergraduates}, year = {2017}, month = {01/2017}, publisher = {CAT Vehicle Research Experience for Undergraduates}, organization = {CAT Vehicle Research Experience for Undergraduates}, address = {The University of Arizona}, abstract = {

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{\textquoteright}s Cognitive Autonomous Test (CAT) Vehicle illustrate the feasibility and success of this new controller.

}, keywords = {Autonomous Systems, Control System}, author = {R{\textquoteright}mani Haulcy and Nathaniel Hamilton and Rahul Bhadani and Jonathan Sprinkle and Daniel B. Work and Nathalie Risso and Benedetto Piccoli and Maria Laura Dell Monache and Benjamin Seibold} } @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} } @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 {234, title = {Mobile Device Software: Model-Based Architectures and Examples}, booktitle = {Engineering of Computer Based Systems (ECBS), 2013 20th IEEE International Conference and Workshops on the}, year = {2013}, month = {April}, pages = {215}, keywords = {API, application program interfaces, canonical UML models, Computational modeling, computer science education, Computers, Conferences, high-level software concepts, mobile computing, mobile device apps, mobile device programming, mobile device software, Mobile handsets, model-based architectures, model-based design, model-based examples, object-oriented programming, Software, software architecture, software engineering, terminology points, Tutorials, unclear starting points, Unified modeling language}, doi = {10.1109/ECBS.2013.28}, author = {Jonathan Sprinkle} } @conference {231, title = {Model-Based Software Synthesis for Self-Reconfigurable Sensor Network in Water Monitoring}, booktitle = {Engineering of Computer Based Systems (ECBS), 2013 20th IEEE International Conference and Workshops on the}, year = {2013}, month = {April}, pages = {40-48}, keywords = {accelerometers, Code Generation, communication task, computation task, Computational modeling, concurrent engineering, concurrent tasks specification, control tasks, cyber-physical systems, domain-specific modeling language, drifters, embedded programming, environmental monitoring (geophysics), environmental science computing, floating sensor testbed, formal specification, Global Positioning System, GPS sensors, hand-written code, Instruction sets, mobile computing, mobile phone, mobile radio, mobile sensing platforms, model-based software synthesis, model-integrated computing, program compilers, Programming, self-reconfigurable sensor network, Smart phones, software synthesis, ubiquitous mobile device, Unified modeling language, water flow monitoring, wireless sensor networks}, doi = {10.1109/ECBS.2013.34}, url = {http://dx.doi.org/10.1109/ECBS.2013.34}, author = {Kun Zhang and Jonathan Sprinkle} }