works on

From the 2 of 10 linked papers with an AI index.

most citedA Survey of Reinforcement Learning-Based Motion Planning for Autonomous Driving: Lessons Learned from a Driving Task Perspective

1 citations · 1 across the 3 of their papers we have counts for

collaborators

10 papers

cs.RO2026

Expert Knowledge-driven Reinforcement Learning for Autonomous Racing via Trajectory Guidance and Dynamics Constraints

Bo Leng, Weiqi Zhang, Zhuoren Li +4

The paper introduces TraD‑RL, a reinforcement‑learning framework for autonomous racing that uses expert racing lines for state augmentation and reward shaping, and incorporates veh…

cs.RO2026

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…

cs.LG20261 cited

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…

cs.RO2026

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…

cs.RO2026

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…

cs.CV2026

An interactive enhanced driving dataset for autonomous driving

Haojie Feng, Peizhi 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…