Automated Lane Change Decision Making using Deep Reinforcement Learning in Dynamic and Uncertain Highway Environment

Ali Alizadeh, Majid Moghadam, Yunus Bicer, Nazim Kemal Ure, Ugur Yavas, Can Kurtulus

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

98 Citations (Scopus)

Abstract

Autonomous lane changing is a critical feature for advanced autonomous driving systems, that involves several challenges such as uncertainty in other driver's behaviors and the trade-off between safety and agility. In this work, we develop a novel simulation environment that emulates these challenges and train a deep reinforcement learning agent that yields consistent performance in a variety of dynamic and uncertain traffic scenarios. Results show that the proposed data-driven approach performs significantly better in noisy environments compared to methods that rely solely on heuristics.

Original languageEnglish
Title of host publication2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1399-1404
Number of pages6
ISBN (Electronic)9781538670248
DOIs
Publication statusPublished - Oct 2019
Event2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019 - Auckland, New Zealand
Duration: 27 Oct 201930 Oct 2019

Publication series

Name2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019

Conference

Conference2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019
Country/TerritoryNew Zealand
CityAuckland
Period27/10/1930/10/19

Bibliographical note

Publisher Copyright:
© 2019 IEEE.

Fingerprint

Dive into the research topics of 'Automated Lane Change Decision Making using Deep Reinforcement Learning in Dynamic and Uncertain Highway Environment'. Together they form a unique fingerprint.

Cite this