Enhancing Autonomous Vehicle Training with Language Model Integration and Critical Scenario Generation

arXiv:2404.08570v1 Announce Type: cross
Abstract: This paper introduces CRITICAL, a novel closed-loop framework for autonomous vehicle (AV) training and testing. CRITICAL stands out for its ability to generate diverse scenarios, focusing on critical driving situations that target specific learning and performance gaps identified in the Reinforcement Learning (RL) agent. The framework achieves this by integrating real-world traffic dynamics, driving behavior analysis, surrogate safety measures, and an optional Large Language Model (LLM) component. It is proven that the establishment of a …

abstract arxiv autonomous autonomous vehicle cs.ai cs.lg cs.ro diverse driving framework generate integration language language model loop novel paper performance reinforcement reinforcement learning testing training type