02015nas a2200253 4500008004100000245009900041210006900140250000700209260003000216520122200246653002401468653001901492653001501511653001201526100001901538700002101557700002201578700002201600700001501622700002301637700002301660700002101683856005701704 2019 eng d00aReal-Time Distance Estimation and Filtering of Vehicle Headways for Smoothing of Traffic Waves0 aRealTime Distance Estimation and Filtering of Vehicle Headways f a10 aMontreal, Canadac04/20193 a

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.

 

10aautonomous vehicles10aDigital Filter10asimulation10aTraffic1 aBhadani, Rahul1 aBunting, Matthew1 aSeibold, Benjamin1 aStern, Raphael, E1 aCui, Shumo1 aSprinkle, Jonathan1 aPiccoli, Benedetto1 aWork, Daniel, B. uhttps://dl.acm.org/citation.cfm?doid=3302509.331402601613nas a2200145 4500008004100000245005400041210005400095260001200149520114500161100002301306700001901329700001501348700002201363856008201385 2016 eng d00aRobust Control of Autonomous Vehicle Trajectories0 aRobust Control of Autonomous Vehicle Trajectories c07/20163 a

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.

1 aSprinkle, Jonathan1 aBhadani, Rahul1 aCui, Shumo1 aSeibold, Benjamin uhttp://csl.arizona.edu/content/robust-control-autonomous-vehicle-trajectories