12 citations · 29 across the 17 of their papers we have counts for
17 papers
Vision-Augmented On-Track System Identification for Autonomous Racing via Attention-Based Priors and Iterative Neural Correction
Zhiping Wu, Cheng Hu, Yiqin Wang +2
Operating autonomous vehicles at the absolute limits of handling requires precise, real-time identification of highly non-linear tire dynamics. However, traditional online optimiza…
Robust Spatiotemporal Motion Planning for Multi-Agent Autonomous Racing via Topological Gap Identification and Accelerated MPC
Mingyi Zhang, Cheng Hu, Yiqin Wang +3
High-speed multi-agent autonomous racing demands robust spatiotemporal planning and precise control under strict computational limits. Current methods often oversimplify interactio…
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…
MCTR: Midpoint Corrected Triangulation for Autonomous Racing via Digital Twin Simulation in CARLA
Junhao Ye, Cheng Hu, Yiqin Wang +6
In autonomous racing, reactive controllers eliminate the computational burden of the full See-Think-Act autonomy stack by directly mapping sensor inputs to control actions. This by…
Residual Koopman Model Predictive Control for Enhanced Vehicle Dynamics with Small On-Track Data Input
Yonghao Fu, Cheng Hu, Haokun Xiong +6
In vehicle trajectory tracking tasks, the simplest approach is the Pure Pursuit (PP) Control. However, this single-point preview tracking strategy fails to consider vehicle model c…
Safe Reinforcement Learning with a Predictive Safety Filter for Motion Planning and Control: A Drifting Vehicle Example
Bei Zhou, Baha Zarrouki, Mattia Piccinini +3
Autonomous drifting is a complex and crucial maneuver for safety-critical scenarios like slippery roads and emergency collision avoidance, requiring precise motion planning and con…
Efficient and Real-Time Motion Planning for Robotics Using Projection-Based Optimization
Xuemin Chi, Hakan Girgin, Tobias Löw +6
Generating motions for robots interacting with objects of various shapes is a complex challenge, further complicated by the robot geometry and multiple desired behaviors. While cur…
Enhancing Autonomous Driving Systems with On-Board Deployed Large Language Models
Nicolas Baumann, Cheng Hu, Paviththiren Sivasothilingam +4
Neural Networks (NNs) trained through supervised learning struggle with managing edge-case scenarios common in real-world driving due to the intractability of exhaustive datasets c…
GP-enhanced Autonomous Drifting Framework using ADMM-based iLQR
Yangyang Xie, Cheng Hu, Nicolas Baumann +3
Autonomous drifting is a complex challenge due to the highly nonlinear dynamics and the need for precise real-time control, especially in uncertain environments. To address these l…
FSDP: Fast and Safe Data-Driven Overtaking Trajectory Planning for Head-to-Head Autonomous Racing Competitions
Cheng Hu, Jihao Huang, Wule Mao +7
Generating overtaking trajectories in autonomous racing is a challenging task, as the trajectory must satisfy the vehicle's dynamics and ensure safety and real-time performance run…
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…
RLPP: A Residual Method for Zero-Shot Real-World Autonomous Racing on Scaled Platforms
Edoardo Ghignone, Nicolas Baumann, Cheng Hu +4
Autonomous racing presents a complex environment requiring robust controllers capable of making rapid decisions under dynamic conditions. While traditional controllers based on tir…
Predictive Spliner: Data-Driven Overtaking in Autonomous Racing Using Opponent Trajectory Prediction
Nicolas Baumann, Edoardo Ghignone, Cheng Hu +6
Head-to-head racing against opponents is a challenging and emerging topic in the domain of autonomous racing. We propose Predictive Spliner, a data-driven overtaking planner that l…
Learning-Based On-Track System Identification for Scaled Autonomous Racing in Under a Minute
Onur Dikici, Edoardo Ghignone, Cheng Hu +5
Accurate tire modeling is crucial for optimizing autonomous racing vehicles, as state-of-the-art (SotA) model-based techniques rely on precise knowledge of the vehicle's parameters…
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…