7 papers
Beyond Safe Execution: Safe Online Reinforcement Learning for Autonomous Driving with Value Calibration and Adaptive Guidance
Zeqiao Li, Yijing Wang, Haoyu Wang +2
Online reinforcement learning (RL) enables driving policies to adapt to changing deployment conditions, but requires safe exploration throughout training. Safety filtering alone do…
Posterior-Averaged Bellman Flow Matching for Distributional Critics
Zeqiao Li, Yijing Wang, Haoyu Wang +2
Distributional critics model the full return distribution, providing richer signals for exploration and risk-sensitive control. Recent work has shown that flow-based critics can mo…
Boundary-Aligned Contribution Routing for Robust Optical--SAR Object Detection
Haifa Zhang, Yijing Wang, Haoyu Wang +2
Optical imagery provides rich appearance cues, whereas synthetic aperture radar (SAR) offers observations that are less sensitive to illumination and weather, making optical--SAR f…
Can BEV Perception Gracefully Degrade under Sensor Failures?
Haifa Zhang, Yijing Wang, Haoyu Wang +2
Despite the remarkable success of multi-modal bird's-eye view (BEV) perception in autonomous driving, current systems exhibit a critical vulnerability: existing fusion mechanisms a…
Boosting Maximum Entropy Reinforcement Learning via One-Step Flow Matching
Zeqiao Li, Yijing Wang, Haoyu Wang +2
Diffusion policies are expressive yet incur high inference latency. Flow Matching (FM) enables one-step generation, but integrating it into Maximum Entropy Reinforcement Learning (…
From Learning to Mastery: Achieving Safe and Efficient Real-World Autonomous Driving with Human-In-The-Loop Reinforcement Learning
Li Zeqiao, Wang Yijing, Wang Haoyu +4
Autonomous driving with reinforcement learning (RL) has significant potential. However, applying RL in real-world settings remains challenging due to the need for safe, efficient,…
Confidence-Guided Human-AI Collaboration: Reinforcement Learning with Distributional Proxy Value Propagation for Autonomous Driving
Li Zeqiao, Wang Yijing, Wang Haoyu +4
Autonomous driving promises significant advancements in mobility, road safety and traffic efficiency, yet reinforcement learning and imitation learning face safe-exploration and di…