@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} }