5 citations · 8 across the 11 of their papers we have counts for
12 papers
A Risk-Sensitive and Uncertainty-Aware Decision-Making and Control Framework for Safe and Robust Autonomous Driving
Zhuoren Li, Ran Yu, Weiqi Zhang +4
Reinforcement learning (RL) has demonstrated considerable potential for autonomous driving decision-making. However, its deployment in urban autonomous driving, particularly at hig…
SSP: An Event-Matched Syn2Sim2Phy Cross-Domain Evaluation Framework for Autonomous Driving VLA Models
Haojie Feng, Peizhi Zhang, Xinrui Zhang +7
Vision-language-action (VLA) models for autonomous driving jointly produce scene interpretation, language-based reasoning, and driving trajectories. Existing evaluations often use…
Comparison-Based Ordinal Learning for Proactive Driving Risk Assessment
Zhuoren Li, Yi Zhong, Weiqi Zhang +4
Real-time driving risk assessment provides an essential basis for proactive safety by identifying and quantifying the danger of ongoing road interactions before adverse outcomes oc…
FeaXDrive: Feasibility-aware Trajectory-Centric Diffusion Planning for End-to-End Autonomous Driving
Baoyun Wang, Zhuoren Li, Ran Yu +6
End-to-end diffusion planning has shown strong potential for autonomous driving, but the physical feasibility of generated trajectories remains insufficiently addressed. In particu…
Expert Knowledge-driven Reinforcement Learning for Autonomous Racing via Trajectory Guidance and Dynamics Constraints
Bo Leng, Weiqi Zhang, Zhuoren Li +4
Reinforcement learning has shown significant potential for autonomous racing, but it still faces challenges such as training instability, inefficient exploration, and unsafe action…
An interactive enhanced driving dataset for autonomous driving
Haojie Feng, Xinrui Zhang, Mengjie Tian +8
Driving interaction data are important for training and evaluating autonomous drivingVision-Language-Action (VLA) models, but existing datasets contain limited denseinteraction sam…
Multi-Timescale Hierarchical Reinforcement Learning for Unified Behavior and Control of Autonomous Driving
Guizhe Jin, Zhuoren Li, Bo Leng +3
Reinforcement Learning (RL) is increasingly used in autonomous driving (AD) and shows clear advantages. However, most RL-based AD methods overlook policy structure design. An RL po…
HCRMP: A LLM-Hinted Contextual Reinforcement Learning Framework for Autonomous Driving
Zhiwen Chen, Bo Leng, Zhuoren Li +4
Integrating Large Language Models (LLMs) with Reinforcement Learning (RL) can enhance autonomous driving (AD) performance in complex scenarios. However, current LLM-Dominated RL me…
Uncertainty-Aware Safety-Critical Decision and Control for Autonomous Vehicles at Unsignalized Intersections
Ran Yu, Zhuoren Li, Lu Xiong +2
Reinforcement learning (RL) has demonstrated potential in autonomous driving (AD) decision tasks. However, applying RL to urban AD, particularly in intersection scenarios, still fa…
Risk-Aware Reinforcement Learning for Autonomous Driving: Improving Safety When Driving through Intersection
Bo Leng, Ran Yu, Wei Han +3
Applying reinforcement learning to autonomous driving has garnered widespread attention. However, classical reinforcement learning methods optimize policies by maximizing expected…
A Survey of Reinforcement Learning-Based Motion Planning for Autonomous Driving: Lessons Learned from a Driving Task Perspective
Zhuoren Li, Guizhe Jin, Ran Yu +8
Reinforcement learning (RL), with its ability to explore and optimize policies in complex, dynamic decision-making tasks, has emerged as a promising approach to addressing motion p…
Hybrid Action Based Reinforcement Learning for Multi-Objective Compatible Autonomous Driving
Guizhe Jin, Zhuoren Li, Bo Leng +3
Reinforcement Learning (RL) has shown excellent performance in solving decision-making and control problems of autonomous driving, which is increasingly applied in diverse driving…