Peide Cai

Ph.D. candidate, Department of Electronic & Computer Engineering, HKUST

I am a fourth-year Ph.D. student at the Robotics and Multi-perception Lab in Robotics Institute, Hong Kong Univerisity of Science and Technology, under the supervision of Prof. Ming Liu. Before that, I received my bachelor’s degree in Control Science & Engineering at Zhejiang University in 2018.

My research interests mainly include deep reinforcement learning (DRL), imitation learning (IL), autonomous driving, and robotics.

Complex and various traffic scenarios are hard to model manually, therefore, learning to drive from data is a promising solution. My research focus is to develop stronger AI for autonomous driving, which can automatically learn (end-to-end) control policies with deep learning techniques such as DRL and IL. My ultimate goal is to make the AI driving agent more applicable to daily life and deploy it to complex and challenging driving scenarios.


News

Apr 1, 2021

Successful end to the first HKUST-Kaisa Autonomous RC Car Racing Competition (25 teams, 90+ participants). [News] & [Code] & [Video]. I am the PI of this project.


Selected publications

  1. DQ-GAT: Towards Safe and Efficient Autonomous Driving with Deep Q-Learning and Graph Attention Networks
    Peide Cai, Hengli Wang, Yuxiang Sun, and Ming Liu
    Under Review , 2022
  2. Vision-Based Autonomous Car Racing Using Deep Imitative Reinforcement Learning
    Peide Cai, Hengli Wang, Huaiyang Huang, Yuxuan Liu, and Ming Liu
    IEEE Robotics and Automation Letters (RA-L) &
    IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS​)
    , 2021
  3. DiGNet: Learning Scalable Self-Driving Policies for Generic Traffic Scenarios with Graph Neural Networks
    Peide Cai, Hengli Wang, Yuxiang Sun, and Ming Liu
    IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS​) , 2021
  4. VTGNet: A Vision-based Trajectory Generation Network for Autonomous Vehicles in Urban Environments
    Peide Cai, Yuxiang Sun, Hengli Wang, and Ming Liu
    IEEE Transactions on Intelligent Vehicles (T-IV) , 2020
  5. Probabilistic End-to-End Vehicle Navigation in Complex Dynamic Environments With Multimodal Sensor Fusion
    Peide Cai, Sukai Wang, Yuxiang Sun, and Ming Liu
    IEEE Robotics and Automation Letters (RA-L) &
    IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS​)
    , 2020
  6. High-Speed Autonomous Drifting With Deep Reinforcement Learning
    Peide Cai*, Xiaodong Mei*, Lei Tai, Yuxiang Sun, and Ming Liu
    IEEE Robotics and Automation Letters (RA-L) &
    IEEE International Conference on Robotics and Automation (ICRA)
    , 2020
  7. Vision-Based Trajectory Planning via Imitation Learning for Autonomous Vehicles
    Peide Cai, Yuxiang Sun, Yuying Chen, and Ming Liu
    In IEEE Intelligent Transportation Systems Conference (ITSC) , 2019
SEE ALL PUBLICATIONS

Service

Reviewer for IEEE Robotics and Automation Letters (RA-L)
Reviewer for IEEE International Conference on Robotics and Automation (ICRA)
Reviewer for IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)