Connected autonomous vehicles for improving mixed traffic efficiency in unsignalized intersections with deep reinforcement learning
Journal article, 2021

Human driven vehicles (HDVs) with selfish objectives cause low traffic efficiency in an un-signalized intersection. On the other hand, autonomous vehicles can overcome this inefficiency through perfect coordination. In this paper, we propose an intermediate solution, where we use vehicular communication and a small number of autonomous vehicles to improve the transportation system efficiency in such intersections. In our solution, two connected autonomous vehicles (CAVs) lead multiple HDVs in a double-lane intersection in order to avoid congestion in front of the intersection. The CAVs are able to communicate and coordinate their behavior, which is controlled by a deep reinforcement learning (DRL) agent. We design an altruistic reward function which enables CAVs to adjust their velocities flexibly in order to avoid queuing in front of the intersection. The proximal policy optimization (PPO) algorithm is applied to train the policy and the generalized advantage estimation (GAE) is used to estimate state values. Training results show that two CAVs are able to achieve significantly better traffic efficiency compared to similar scenarios without and with one altruistic autonomous vehicle.

traffic control

deep learning

reinforcement learning

automated vehicles

conencted vehicles

Author

Bile Peng

Furkan Keskin

Chalmers, Electrical Engineering, Communication and Antenna Systems, Communication Systems

Balázs Adam Kulcsár

Chalmers, Electrical Engineering, Systems and control, Automatic Control

Henk Wymeersch

Chalmers, Electrical Engineering, Communication and Antenna Systems, Communication Systems

Communications in Transportation Research

2772-4247 (ISSN)

IRIS: Inverse Reinforcement-Learning and Intelligent Swarm Algorithms for Resilient Transportation Networks

Chalmers, 2020-01-01 -- 2021-12-31.

Areas of Advance

Transport

Subject Categories

Transport Systems and Logistics

Vehicle Engineering

Control Engineering

More information

Created

12/1/2021