VLM-C4L：基于视觉语言模型的自动驾驶连续核心数据集学习与边缘案例优化
VLM-C4L: Continual Core Dataset Learning with Corner Case Optimization via Vision-Language Models for Autonomous Driving
AI总结：
提出VLM-C4L框架，利用视觉语言模型动态优化边缘案例数据集并结合核心数据回放，实现自动驾驶持续学习，在Waymo和CODA上验证有效性。
摘要：
随着自动驾驶的广泛采用和部署，处理复杂环境已成为一个不可避免的挑战。由于极端场景数据集的稀缺性和多样性，当前的自动驾驶模型难以有效管理边缘案例。这一局限性构成了显著的安全风险，根据美国国家公路交通安全管理局（NHTSA）的数据，自动驾驶车辆系统在美国每年涉及数百起报告的碰撞事故，这些事故发生在诸如阳光眩光和雾等边缘案例中，并导致了一些致命事故。此外，为了持续维持一个稳健可靠的自动驾驶系统，模型不仅需要在常规场景中表现良好，还需要适应新出现的场景，尤其是那些偏离常态的边缘案例。这需要一种学习机制，能够在不降低先前获取能力的情况下增量整合新知识。然而，据我们所知，目前尚无现有的持续学习方法能够确保自动驾驶中一致且可扩展的边缘案例学习。为解决这些局限性，我们提出了VLM-C4L，一个持续学习框架，引入视觉语言模型（VLMs）来动态优化和增强边缘案例数据集，VLM-C4L结合了VLM引导的高质量数据提取与核心数据回放策略，使模型能够从多样化的边缘案例中增量学习，同时保持在先前常规场景上的性能，从而确保真实世界自动驾驶中的长期稳定性和适应性。我们在大规模真实世界自动驾驶数据集上评估了VLM-C4L，包括Waymo和边缘案例数据集CODA。
英文摘要：
With the widespread adoption and deployment of autonomous driving, handling complex environments has become an unavoidable challenge. Due to the scarcity and diversity of extreme scenario datasets, current autonomous driving models struggle to effectively manage corner cases. This limitation poses a significant safety risk, according to the National Highway Traffic Safety Administration (NHTSA), autonomous vehicle systems have been involved in hundreds of reported crashes annually in the United States, occurred in corner cases like sun glare and fog, which caused a few fatal accident. Furthermore, in order to consistently maintain a robust and reliable autonomous driving system, it is essential for models not only to perform well on routine scenarios but also to adapt to newly emerging scenarios, especially those corner cases that deviate from the norm. This requires a learning mechanism that incrementally integrates new knowledge without degrading previously acquired capabilities. However, to the best of our knowledge, no existing continual learning methods have been proposed to ensure consistent and scalable corner case learning in autonomous driving. To address these limitations, we propose VLM-C4L, a continual learning framework that introduces Vision-Language Models (VLMs) to dynamically optimize and enhance corner case datasets, and VLM-C4L combines VLM-guided high-quality data extraction with a core data replay strategy, enabling the model to incrementally learn from diverse corner cases while preserving performance on previously routine scenarios, thus ensuring long-term stability and adaptability in real-world autonomous driving. We evaluate VLM-C4L on large-scale real-world autonomous driving datasets, including Waymo and the corner case dataset CODA.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
