Zhouheng Li
I am currently a 3rd year Ph.D. student in the College of Control Science and Engineering at Zhejiang University, Hangzhou, China, under the supervision of Prof. Lei Xie and Prof. Hongye Su. Before joining Zhejiang University, I interned at Hikvision Research Institute in Hangzhou, where I applied optimization methods for robot motion planning and implemented the corresponding ROS components using Python and C++. β¨ Enjoyments of life: π² Board Games (Splendor, Seven Wonders: Duel, etc), π£ hiking, πΎ tennis, π ping-pong, πΊοΈ traveling.
π― Research
My ultimate goal is to develop embodied intelligent vehicles capable of seamlessly interacting with the physical world. (π Publications). To achieve this, my research focuses on decision-making methods powered by generative models and optimization-based trajectory planning methods designed for safety. Currently, I am exploring planning approaches for both single and multi-vehicle systems in autonomous racing and drifting, with a focus on the following key areas:
β Safe Decision-Making and planning Using Generative Models: Using energy-based models (EBMs) for decision-making, while ensuring safety through model-based planning methods.
β Integrated Trajectory Planning and Control: Aggressive vehicle motion is guaranteed by optimizing the velocity distribution within the MPC prediction horizon when planning racing trajectories.
β Learning-Based Parameter Tuning for Motion Planners: Leveraging post-race data to optimize planner performance and push the boundaries of the vehicle's racing capabilities.
I am also actively involved in applying these techniques to F1TENTH competition. Feel free to drop me emails (π¨ zh.li@zju.edu.cn) if you are interested in the topics mentioned above, I would be happy to discuss further collaborations.
ποΈ Recent demo
Implementation of the proposed motion planner on the F1TENTH platform. The maximum cornering velocity in the sharp U-turn is 11.5 km/h.

π₯ News
- [Mar. 2025] πππ Our paper about drift in extreme turning that utilizes mpc is accepted by IV 2025.
- [Mar. 2025] π₯π₯π₯ I have released the VPMPCC, a local trajectory planning method with velocity prediction for autonomous racing.
- [Feb. 2025] πππ Our paper about adaptive learning-based model predictive control (ALMPC) strategy that utilizes Bayesian optimization is accepted by the Journal Robotics and Autonomous Systems.
- [Jan. 2025] πππ Our paper about data-driven aggressive autonomous racing framework using velocity prediction MPCC and Bayesian optimization is accepted by ICRA 2025.
- [Nov. 2024] π₯π₯π₯ I have released the CiMPCC, a local trajectory planner for autonomous racing.
- [Oct. 2024] π₯π₯π₯ Our research on overtaking trajectory planning framework using spatio-temporal topology and reachable set analysis is now available.
- [Sep. 2024] πππ Our paper about rapid and safe trajectory planning for automated parking using path-velocity decomposition is accepted by the Journal Robotics and Autonomous Systems.
- [Jul. 2024] πππ Our paper about Curvature-Integrated MPCC for autonomous racing is accepted by ITSC 2024.
- [Mar. 2024] πππ Our paper about aggressive drifting for minimum time cornering based on MPC is accepted by IV 2024.
π Selected Publications
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A Data-Driven Aggressive Autonomous Racing Framework Utilizing Local Trajectory Planning with Velocity Prediction
Zhouheng Li, Bei Zhou, Cheng Hu, Lei Xie, Hongye Su
Li Z, Zhou B, Hu C, et al. A Data-Driven Aggressive Autonomous Racing Framework Utilizing Local Trajectory Planning with Velocity Prediction[J]. arXiv preprint arXiv:2410.11570, 2024.@article{li2024data, title={A Data-Driven Aggressive Autonomous Racing Framework Utilizing Local Trajectory Planning with Velocity Prediction}, author={Li, Zhouheng and Zhou, Bei and Hu, Cheng and Xie, Lei and Su, Hongye}, journal={arXiv preprint arXiv:2410.11570}, year={2024} } -
Reduce Lap Time for Autonomous Racing with Curvature-Integrated MPCC Local Trajectory Planning Method
Zhouheng Li, Lei Xie, Cheng Hu, Hongye Su
Li Z, Xie L, Hu C, et al. Reduce Lap Time for Autonomous Racing with Curvature-Integrated MPCC Local Trajectory Planning Method[J]. arXiv preprint arXiv:2502.03695, 2025.@article{li2025reduce, title={Reduce Lap Time for Autonomous Racing with Curvature-Integrated MPCC Local Trajectory Planning Method}, author={Li, Zhouheng and Xie, Lei and Hu, Cheng and Su, Hongye}, journal={arXiv preprint arXiv:2502.03695}, year={2025} } -
A rapid iterative trajectory planning method for automated parking through differential flatness
Zhouheng Li, Lei Xie, Cheng Hu, Hongye Su
Li Z, Xie L, Hu C, et al. A rapid iterative trajectory planning method for automated parking through differential flatness[J]. Robotics and Autonomous Systems, 2024, 182: 104816.@article{li2024rapid, title={A rapid iterative trajectory planning method for automated parking through differential flatness}, author={Li, Zhouheng and Xie, Lei and Hu, Cheng and Su, Hongye}, journal={Robotics and Autonomous Systems}, volume={182}, pages={104816}, year={2024}, publisher={Elsevier} } -
Adaptive Learning-based Model Predictive Control Strategy for Drift Vehicles
Bei Zhou, Cheng Hu, Jun Zeng, Zhouheng Li, Johannes Betz, Lei Xie, Hongye Su
Zhou B, Hu C, Zeng J, et al. Adaptive learning-based model predictive control strategy for drift vehicles[J]. Robotics and Autonomous Systems, 2025: 104941.@article{zhou2025adaptive, title={Adaptive learning-based model predictive control strategy for drift vehicles}, author={Zhou, Bei and Hu, Cheng and Zeng, Jun and Li, Zhouheng and Betz, Johannes and Xie, Lei and Su, Hongye}, journal={Robotics and Autonomous Systems}, pages={104941}, year={2025}, publisher={Elsevier} } -
An aggressive cornering framework for autonomous vehicles combining trajectory planning and drift control
Wangjia Weng, Cheng Hu, Zhouheng Li, Hongye Su, Lei Xie
Weng W, Hu C, Li Z, et al. An aggressive cornering framework for autonomous vehicles combining trajectory planning and drift control[C]//2024 IEEE Intelligent Vehicles Symposium (IV). IEEE, 2024: 2749-2755.@inproceedings{weng2024aggressive, title={An aggressive cornering framework for autonomous vehicles combining trajectory planning and drift control}, author={Weng, Wangjia and Hu, Cheng and Li, Zhouheng and Su, Hongye and Xie, Lei}, booktitle={2024 IEEE Intelligent Vehicles Symposium (IV)}, pages={2749--2755}, year={2024}, organization={IEEE} } - An Overtaking Trajectory Planning Framework Based on Spatio-temporal Topology and Reachable Set Analysis Ensuring Time EfficiencyMao W, Li Z, Xie L, et al. An Overtaking Trajectory Planning Framework Based on Spatio-temporal Topology and Reachable Set Analysis Ensuring Time Efficiency[J]. arXiv preprint arXiv:2410.22643, 2024.@article{mao2024overtaking, title={An Overtaking Trajectory Planning Framework Based on Spatio-temporal Topology and Reachable Set Analysis Ensuring Time Efficiency}, author={Mao, Wule and Li, Zhouheng and Xie, Lei and Su, Hongye}, journal={arXiv preprint arXiv:2410.22643}, year={2024} }
Wule Mao, Zhouheng Li, Lei Xie, Hongye Su
π Competitions
- 4th place in 18TH F1TENTH autonomous grand prix by IV 2024
2024 IEEE Intelligent Vehicles Symposium (IV 2024), June 3rd - 5th 2024, Jeju Shinhwa World, Jeju Island, Korea
Zhouheng Li, Cheng Hu, Bei Zhou, Yonghao Fu, Guoqiang Wu, Yangyang Xie
π» Internships
- 2021.12 - 2022.09, Hikvision Research Institute (HRI), Hangzhou, mentored by Researcher Weiwen Hu and Dr. Hao Zhu.