@article{ref1,
title="Deep reinforcement learning approach for automated vehicle mandatory lane changing",
journal="Transportation research record",
year="2023",
author="Ammourah, Rami and Talebpour, Alireza",
volume="2677",
number="2",
pages="712-724",
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.<p /> <p>Language: en</p>",
language="en",
issn="0361-1981",
doi="10.1177/03611981221108377",
url="http://dx.doi.org/10.1177/03611981221108377"
}