4 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…
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…
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…
Safe Control and Learning Using Generalized Action Governor
Peiyuan Fang, Weiqi Zhang, Lu Xiong +7
This paper introduces the Generalized Action Governor (AG), a supervisory scheme that augments a nominal closed-loop system with the capability to enforce state and input constrain…