6 citations · 10 across the 11 of their papers we have counts for
12 papers
Toward the Cognitive--Physical Limits of Embodied Intelligence through a World-Model-Centric Autonomous Racing Agent
Zitong Shan, Baichuan Lou, Yanxin Zhou +8
Embodied artificial intelligence aims to develop agents that perceive, reason, and act through continuous interaction with the physical world. However, most embodied systems are st…
SGTP: Sampling-based Game-Theoretic Planning for Real-Time Multi-Vehicle Autonomous Racing
Zhouheng Li, Fangguo Zhao, Mattia Piccinini +6
Autonomous multi-vehicle racing requires real-time planning of diverse competitive behaviors in intense interactions. Existing planners often struggle to balance strategic diversit…
Vision-Guided MPPI for Agile Drone Racing: Navigating Arbitrary Gate Poses via Neural Signed Distance Fields
Fangguo Zhao, Hanbing Zhang, Zhouheng Li +2
Autonomous drone racing requires the tight coupling of perception, planning, and control under extreme agility. However, recent approaches typically rely on precomputed spatial ref…
Enhancing Physical Consistency in Lightweight World Models
Dingrui Wang, Zhexiao Sun, Zhouheng Li +8
A major challenge in deploying world models is the trade-off between size and performance. Large world models can capture rich physical dynamics but require massive computing resou…
A Rapid Iterative Trajectory Planning Method for Automated Parking through Differential Flatness
Zhouheng Li, Lei Xie, Cheng Hu +1
As autonomous driving continues to advance, automated parking is becoming increasingly essential. However, significant challenges arise when implementing path velocity decompositio…
A Learning-based Planning and Control Framework for Inertia Drift Vehicles
Bei Zhou, Zhouheng Li, Lei Xie +2
Inertia drift is a transitional maneuver between two sustained drift stages in opposite directions, which provides valuable insights for navigating consecutive sharp corners for au…
Rapid and Safe Trajectory Planning over Diverse Scenes through Diffusion Composition
Wule Mao, Zhouheng Li, Yunhao Luo +2
Achieving safe, efficient, and kinematically feasible planning in dynamic environments remains a significant challenge, as planners must simultaneously handle moving obstacles, sen…
Adaptive Learning-based Model Predictive Control Strategy for Drift Vehicles
Bei Zhou, Cheng Hu, Jun Zeng +4
Drift vehicle control offers valuable insights to support safe autonomous driving in extreme conditions, which hinges on tracking a particular path while maintaining the vehicle st…
Reduce Lap Time for Autonomous Racing with Curvature-Integrated MPCC Local Trajectory Planning Method
Zhouheng Li, Lei Xie, Cheng Hu +1
The widespread application of autonomous driving technology has significantly advanced the field of autonomous racing. Model Predictive Contouring Control (MPCC) is a highly effect…
An Overtaking Trajectory Planning Framework Based on Spatio-temporal Topology and Reachable Set Analysis Ensuring Time Efficiency
Wule Mao, Zhouheng Li, Entao Sun +2
Generating overtaking trajectories in high-speed scenarios is typically addressed through hierarchical planning, which often suffers from local optima due to single initial solutio…
A Data-Driven Aggressive Autonomous Racing Framework Utilizing Local Trajectory Planning with Velocity Prediction
Zhouheng Li, Bei Zhou, Cheng Hu +2
The development of autonomous driving has boosted the research on autonomous racing. However, existing local trajectory planning methods have difficulty planning trajectories with…
Learning to Drift in Extreme Turning with Active Exploration and Gaussian Process Based MPC
Guoqiang Wu, Cheng Hu, Wangjia Weng +4
Extreme cornering in racing often leads to large sideslip angles, presenting a significant challenge for vehicle control. Conventional vehicle controllers struggle to manage this s…