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++. Prior to that, I earned my bachelor's degree from ZJGSU.✨ Enjoyments of life: 🎲 Board Games (Splendor, Seven Wonders: Duel, etc), 👣 hiking, 🎾 tennis, 🏓 ping-pong, 🗺️ traveling. ​

My ultimate goal is to develop embodied intelligent vehicles capable of seamlessly interacting with the physical world. (📝 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:
⭐ Integrated Trajectory Planning and Control: Aggressive vehicle motion is guaranteed by optimizing the velocity distribution within the MPC prediction horizon when planning racing trajectories.
⭐ Safe Decision-Making and planning Using Generative Models: Using energy-based models (EBMs) for decision-making, while ensuring safety through model-based planning methods.
⭐ Learning-Based Parameter Tuning for Motion Planners: Leveraging post-race autonomous data to optimize planner performance and push the limits of racing capability.
I am also actively involved in applying these techniques to F1TENTH competition. Feel free to drop me emails (📨 zh.li@zju.edu.cn) if you are interested in the topics mentioned above, I would be happy to discuss further collaborations.

Implementation of the proposed motion planner on the F1TENTH platform. The maximum cornering velocity in the sharp U-turn is 11.5 km/h.

• Under review

  

  A Data-Driven Aggressive Autonomous Racing Framework Utilizing Local Trajectory Planning with Velocity Prediction
  

  Zhouheng Li, Bei Zhou, Cheng Hu, Lei Xie, Hongye Su

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  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.

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  @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} }

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• ITSC 2024

  

  Reduce Lap Time for Autonomous Racing with Curvature-Integrated MPCC Local Trajectory Planning Method
  

  Zhouheng Li, Lei Xie, Cheng Hu, Hongye Su

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• RAS

  

  A rapid iterative trajectory planning method for automated parking through differential flatness
  

  Zhouheng Li, Lei Xie, Cheng Hu, Hongye Su

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  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.

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  @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} }

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• An Overtaking Trajectory Planning Framework Based on Spatio-temporal Topology and Reachable Set Analysis Ensuring Time Efficiency

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  Mao 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.

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  @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} }

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  Wule Mao, Zhouheng Li, Lei Xie, Hongye Su

• Learning to Race in Extreme Turning Scene with Active Exploration and Gaussian Process Regression-based MPC

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  Wu G, Hu C, Weng W, et al. Learning to Race in Extreme Turning Scene with Active Exploration and Gaussian Process Regression-based MPC[J]. arXiv preprint arXiv:2410.05740, 2024.

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  @article{wu2024learning, title={Learning to Race in Extreme Turning Scene with Active Exploration and Gaussian Process Regression-based MPC}, author={Wu, Guoqiang and Hu, Cheng and Weng, Wangjia and Li, Zhouheng and Fu, Yonghao and Xie, Lei and Su, Hongye}, journal={arXiv preprint arXiv:2410.05740}, year={2024} }

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  Guoqiang Wu, Cheng Hu, Wangjia Weng, Zhouheng Li, Yonghao Fu, Lei Xie, Hongye Su

• An aggressive cornering framework for autonomous vehicles combining trajectory planning and drift control

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  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.

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  @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} }

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  Wangjia Weng, Cheng Hu, Zhouheng Li, Hongye Su, Lei Xie

• 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
  

• 2021.12 - 2022.09, Hikvision Research Institute (HRI), Hangzhou, mentored by Researcher Weiwen Hu and Dr. Hao Zhu.