4 papers
Contingency Planning for Safety-Critical Autonomous Vehicles: A Review and Perspectives
Lei Zheng, Luyao Zhang, Peiqi Yu +4
Contingency planning is the architectural capability that enables autonomous vehicles (AVs) to anticipate and mitigate discrete, high-impact hazards, such as sensor outages and adv…
Bilevel Multi-Armed Bandit-Based Hierarchical Reinforcement Learning for Interaction-Aware Self-Driving at Unsignalized Intersections
Zengqi Peng, Yubin Wang, Lei Zheng +1
In this work, we present BiM-ACPPO, a bilevel multi-armed bandit-based hierarchical reinforcement learning framework for interaction-aware decision-making and planning at unsignali…
Occlusion-Aware Contingency Safety-Critical Planning for Autonomous Driving
Lei Zheng, Rui Yang, Minzhe Zheng +3
Ensuring safe driving while maintaining travel efficiency for autonomous vehicles in dynamic and occluded environments is a critical challenge. This paper proposes an occlusion-awa…
LearningFlow: Automated Policy Learning Workflow for Urban Driving with Large Language Models
Zengqi Peng, Yubin Wang, Xu Han +2
Recent advancements in reinforcement learning (RL) demonstrate the significant potential in autonomous driving. Despite this promise, challenges such as the manual design of reward…