@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} } @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} } @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.} }