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From the 2 of 9 linked papers with an AI index.

most citedHybrid Action Based Reinforcement Learning for Multi-Objective Compatible Autonomous Driving

2 citations · 3 across the 5 of their papers we have counts for

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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.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.RO20262 cited

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.RO2025

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…

cs.RO2025

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…