Reinforcement Learning for Reducing Congestion in Mixed Autonomy Highways

Abstract

This thesis focuses on assessing the impact of AVs on improving traffic flow in mixed autonomy collision-free highways. Roads were simulated using micro-traffic models, with adjustments designed to simulate a future world in which Automatic Braking Systems will be good enough to completely prevent rear-end and lane-change collisions on highways. We combined Deep Neural Networks with Reinforcement Learning (Deep Q Networks) to model the AVs, and this approach was tested on single and double lane highways. Among many other salient issues, our results show that replacing only 5% of human-drivers with AVs can, remarkably, improve average road speeds by up to 10% on single lane roads, up to 24% on double lane roads, and also significantly reduce the percentage of fully-stopped cars on average, for most situations. Furthermore, these trends, for the most part, continue to amplify as higher percentages of the human drivers are replaced with AVs.