Attention-Based Hierarchical Deep Reinforcement Learning for Lane Change Behaviors in Autonomous Driving

Yilun Chen, Chiyu Dong, Praveen Palanisamy, Priyantha Mudalige, Katharina Muelling, John M. Dolan; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2019, pp. 0-0

Abstract


Performing safe and efficient lane changes is a crucial feature for creating fully autonomous vehicles. Recent advances have demonstrated successful lane following behavior using deep reinforcement learning, yet the interactions with other vehicles on road for lane changes are rarely considered. In this paper, we design a hierarchical Deep Reinforcement Learning (DRL) algorithm to learn lane change behaviors in dense traffic. By breaking down overall behavior to sub-policies, faster and safer lane change actions can be learned. We also apply temporal and spatial attention to the DRL architecture, which helps the vehicle focus more on surrounding vehicles and leads to smoother lane change behavior. We conduct our experiments in the TORCS simulator and the results outperform the state-of-art deep reinforcement learning algorithm in various lane change scenarios.

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[bibtex]
@InProceedings{Chen_2019_CVPR_Workshops,
author = {Chen, Yilun and Dong, Chiyu and Palanisamy, Praveen and Mudalige, Priyantha and Muelling, Katharina and Dolan, John M.},
title = {Attention-Based Hierarchical Deep Reinforcement Learning for Lane Change Behaviors in Autonomous Driving},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2019}
}