Automated Lane Change Decision Making using Deep Reinforcement Learning in Dynamic and Uncertain Highway Environment
Robotics
2019-09-26 v1 Artificial Intelligence
Machine Learning
Systems and Control
Systems and Control
Machine Learning
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.
Cite
@article{arxiv.1909.11538,
title = {Automated Lane Change Decision Making using Deep Reinforcement Learning in Dynamic and Uncertain Highway Environment},
author = {Ali Alizadeh and Majid Moghadam and Yunus Bicer and Nazim Kemal Ure and Ugur Yavas and Can Kurtulus},
journal= {arXiv preprint arXiv:1909.11538},
year = {2019}
}
Comments
Accepted to IEEE Intelligent Transportation Systems Conference - ITSC 2019