4 papers
Orchestrate, Generate, Reflect: A VLM-Based Multi-Agent Collaboration Framework for Automated Driving Policy Learning
Zengqi Peng, Yusen Xie, Yubin Wang +3
The advancement of foundation models fosters new initiatives for policy learning in achieving safe and efficient autonomous driving. However, a critical bottleneck lies in the manu…
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…
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…
CALMM-Drive: Confidence-Aware Autonomous Driving with Large Multimodal Model
Ruoyu Yao, Yubin Wang, Haichao Liu +4
Decision-making and motion planning constitute critical components for ensuring the safety and efficiency of autonomous vehicles (AVs). Existing methodologies typically adopt two p…