CurricuVLM: Towards Safe Autonomous Driving via Personalized Safety-Critical Curriculum Learning with Vision-Language Models
Journal:
arXiv
Published Date:
Feb 21, 2025
Abstract
Ensuring safety in autonomous driving systems remains a critical challenge,
particularly in handling rare but potentially catastrophic safety-critical
scenarios. While existing research has explored generating safety-critical
scenarios for autonomous vehicle (AV) testing, there is limited work on
effectively incorporating these scenarios into policy learning to enhance
safety. Furthermore, developing training curricula that adapt to an AV's
evolving behavioral patterns and performance bottlenecks remains largely
unexplored. To address these challenges, we propose CurricuVLM, a novel
framework that leverages Vision-Language Models (VLMs) to enable personalized
curriculum learning for autonomous driving agents. Our approach uniquely
exploits VLMs' multimodal understanding capabilities to analyze agent behavior,
identify performance weaknesses, and dynamically generate tailored training
scenarios for curriculum adaptation. Through comprehensive analysis of unsafe
driving situations with narrative descriptions, CurricuVLM performs in-depth
reasoning to evaluate the AV's capabilities and identify critical behavioral
patterns. The framework then synthesizes customized training scenarios
targeting these identified limitations, enabling effective and personalized
curriculum learning. Extensive experiments on the Waymo Open Motion Dataset
show that CurricuVLM outperforms state-of-the-art baselines across both regular
and safety-critical scenarios, achieving superior performance in terms of
navigation success, driving efficiency, and safety metrics. Further analysis
reveals that CurricuVLM serves as a general approach that can be integrated
with various RL algorithms to enhance autonomous driving systems. The code and
demo video are available at: https://zihaosheng.github.io/CurricuVLM/.
