@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 {476, title = {Feedback Control Algorithms for the Dissipation of Traffic Waves with Autonomous Vehicles}, booktitle = {Computational Intelligence and Optimization Methods for Control Engineering}, year = {2019}, pages = {275{\textendash}299}, publisher = {Springer International Publishing}, organization = {Springer International Publishing}, address = {Cham}, abstract = {

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

}, isbn = {978-3-030-25446-9}, doi = {10.1007/978-3-030-25446-9_12}, url = {https://doi.org/10.1007/978-3-030-25446-9_12}, author = {Maria Laura Dell Monache and Liard, Thibault and Rat, Ana{\"\i}s and Raphael E Stern and Rahul Bhadani and Benjamin Seibold and Jonathan Sprinkle and Daniel B. Work and Benedetto Piccoli}, editor = {Blondin, Maude Jos{\'e}e and Pardalos, Panos M. and Sanchis S{\'a}ez, Javier} } @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} } @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} } @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} } @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 {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 {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 {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} }