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

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.

Cite

Text

Chen et al. "Attention-Based Hierarchical Deep Reinforcement Learning for Lane Change Behaviors in Autonomous Driving." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2019. doi:10.1109/CVPRW.2019.00172

Markdown

[Chen et al. "Attention-Based Hierarchical Deep Reinforcement Learning for Lane Change Behaviors in Autonomous Driving." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2019.](https://mlanthology.org/cvprw/2019/chen2019cvprw-attentionbased/) doi:10.1109/CVPRW.2019.00172

BibTeX

@inproceedings{chen2019cvprw-attentionbased,
  title     = {{Attention-Based Hierarchical Deep Reinforcement Learning for Lane Change Behaviors in Autonomous Driving}},
  author    = {Chen, Yilun and Dong, Chiyu and Palanisamy, Praveen and Mudalige, Priyantha and Muelling, Katharina and Dolan, John M.},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  year      = {2019},
  pages     = {1326-1334},
  doi       = {10.1109/CVPRW.2019.00172},
  url       = {https://mlanthology.org/cvprw/2019/chen2019cvprw-attentionbased/}
}