Zhouheng Li

Zhouheng Li

Ph.D. student at ZJU.

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. I will be visiting the Kempner Institute at Harvard University's John A. Paulson School of Engineering and Applied Sciences (SEAS) starting in December 2025, supervised by Prof. Yilun Du. During the visit, my research will focus on safe, high-mobility trajectory planning methods with out-of-distribution generalization ability. ✨ Enjoyments of life: 🎲 Board Games (Splendor, Seven Wonders: Duel, etc), 👣 hiking, 🎾 tennis, 🏓 ping-pong, 🗺️ traveling.

🎯 Research

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:
⭐ Safe Decision-Making and planning Using Generative Models: Using energy-based models (EBMs) for decision-making, while ensuring safety through model-based planning methods.
⭐ Integrated Trajectory Planning and Control: Aggressive vehicle motion is guaranteed by optimizing the velocity distribution within the MPC prediction horizon when planning racing trajectories.
⭐ 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 Roboracer competition. If any of these topics caught your interest, feel free to drop me emails (📨 zh_li@zju.edu.cn). I enjoy collaborating on interesting projects and making amazing things happen together!

🏎️ Spotlights

Navigation scenarios focus on decision-making and planning challenges that are constrained by multiple factors. Racing scenarios, on the other hand, involve integrated trajectory planning and control.

Navigation

Static Model replay
Dynamic Model replay
Composed Result replay
This diffusion composition approach guarantees safe, collision-free trajectory planning in unseen scenes for safety. It can make test-time decisions to generate safe behaviors, such as accelerating to bypass or decelerating to avoid obstacles, thus allowing safe trajecoty planning for diverse scenes without retraining.
Web

Racing

Implementation of the proposed VPMPCC planner on the F1TENTH platform. The safe and aggressive cornering velocity with in the sharp U-turn is 11.5 km/h.

racing


🔥 News

📝 Selected Publications

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

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

    Code

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

    Zhouheng Li, Lei Xie, Cheng Hu, Hongye Su

    Code

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

    Zhouheng Li, Lei Xie, Cheng Hu, Hongye Su

    Code

  • Adaptive Learning-based Model Predictive Control Strategy for Drift Vehicles

    Bei Zhou, Cheng Hu, Jun Zeng, Zhouheng Li, Johannes Betz, Lei Xie, Hongye Su

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

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

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

    Wule Mao, Zhouheng Li, Lei Xie, Hongye Su

🏁 Competitions