Deep Reinforcement Learning Approach for Automated Vehicle Mandatory Lane Changing

Rami Ammourah, Alireza Talebpour

Research output: Chapter in Book/Report/Conference proceedingChapter

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.

Original languageEnglish (US)
Title of host publicationTransportation Research Record
PublisherSAGE Publishing
Pages712-724
Number of pages13
Volume2677
Edition2
DOIs
StatePublished - Feb 2023

Keywords

  • automated/autonomous vehicles
  • automated/autonomous/connected vehicles
  • operations

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Mechanical Engineering

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