Increasing Traffic Throughput by Controlling Autonomous Vehicles at Low Penetration Rates

TL;DR

This paper demonstrates that controlling autonomous vehicles with an optimized policy can significantly improve traffic throughput and stability in mixed traffic environments, even at low AV penetration rates.

Contribution

It introduces a gradient-based optimization method to develop control policies for AVs that maximize traffic throughput and reduce oscillations in mixed traffic scenarios.

Findings

Optimized control policies increase traffic throughput by 28%.

Control reduces traffic oscillations and improves flow stability.

Method applicable at low autonomous vehicle penetration rates.

Abstract

Human drivers may behave in an imprecise/unstable manner, leading to traffic oscillations which are harmful to traffic throughput. Recent field experiments have shown that the control of a single autonomous vehicle (AV) can increase traffic throughput on a circular test track, as well as reduce traffic oscillations on straight roads. We consider a mixed traffic environment consisting of humans and autonomous vehicles, where the goal is to find a control policy for the autonomous vehicles which maximizes traffic throughput by preventing oscillations in speed. We formulate this problem as an optimization problem which can be solved using gradient based optimization. Numerical experiments on a circular road show that the optimized control policy improves traffic throughput by 28%.