Citation

Ammourah R, Talebpour A. Transp. Res. Rec. 2023; 2677(2): 712-724.

Copyright

(Copyright © 2023, Transportation Research Board, National Research Council, National Academy of Sciences USA, Publisher SAGE Publishing)

DOI

10.1177/03611981221108377

PMID

unavailable

Abstract

This paper proposes a reinforcement learning-based framework for mandatory lane changing of automated vehicles in a non-cooperative environment. The objective is to create a reinforcement learning (RL) agent that is able to perform lane-changing maneuvers successfully and efficiently and with minimal impact on traffic flow in the target lane. For this purpose, this study utilizes the double deep Q-learning algorithm structure, which takes relevant traffic states as input and outputs the optimal actions (policy) for the automated vehicle. We put forward a realistic approach for dealing with this problem where, for instance, actions selected by the automated vehicle include steering angles and acceleration/deceleration values. We show that the RL agent is able to learn optimal policies for the different scenarios it encounters and performs the lane-changing task safely and efficiently. This work illustrates the potential of RL as a flexible framework for developing superior and more comprehensive lane-changing models that take into consideration multiple aspects of the road environment and seek to improve traffic flow as a whole.

Language: en