5 papers
Cooperative Autonomous Driving in Diverse Behavioral Traffic: A Heterogeneous Graph Reinforcement Learning Approach
Qi Liu, Xueyuan Li, Zirui Li +1
Navigating heterogeneous traffic environments with diverse driving styles poses a significant challenge for autonomous vehicles (AVs) due to their inherent complexity and dynamic i…
Red-Team Multi-Agent Reinforcement Learning for Emergency Braking Scenario
Yinsong Chen, Kaifeng Wang, Xiaoqiang Meng +3
Current research on decision-making in safety-critical scenarios often relies on inefficient data-driven scenario generation or specific modeling approaches, which fail to capture…
Dynamic Residual Safe Reinforcement Learning for Multi-Agent Safety-Critical Scenarios Decision-Making
Kaifeng Wang, Yinsong Chen, Qi Liu +2
In multi-agent safety-critical scenarios, traditional autonomous driving frameworks face significant challenges in balancing safety constraints and task performance. These framewor…
Multilevel Graph Reinforcement Learning for Consistent Cognitive Decision-making in Heterogeneous Mixed Autonomy
Xin Gao, Zhaoyang Ma, Xueyuan Li +2
In the realm of heterogeneous mixed autonomy, vehicles experience dynamic spatial correlations and nonlinear temporal interactions in a complex, non-Euclidean space. These complexi…
A Nested Graph Reinforcement Learning-based Decision-making Strategy for Eco-platooning
Xin Gao, Xueyuan Li, Hao Liu +3
Platooning technology is renowned for its precise vehicle control, traffic flow optimization, and energy efficiency enhancement. However, in large-scale mixed platoons, vehicle het…