Recent advancements in autonomous vehicles (AVs) use Large Language Models (LLMs) to perform well in normal driving scenarios. However, ensuring safety in dynamic, high-risk environments and managing safety-critical long-tail events remain significant challenges. To address these issues, we propose SafeDrive, a knowledge- and data-driven risk-sensitive decision-making framework to enhance AV safety and adaptability. The proposed framework introduces a modular system comprising: (1) a Risk Module for quantifying multi-factor coupled risks involving driver, vehicle, and road interactions; (2) a Memory Module for storing and retrieving typical scenarios to improve adaptability; (3) a LLM-powered Reasoning Module for context-aware safety decision-making; and (4) a Reflection Module for refining decisions through iterative learning. By integrating knowledge-driven insights with adaptive learning mechanisms, the framework ensures robust decision-making under uncertain conditions. Extensive evaluations on real-world traffic datasets, including highways (HighD), intersections (InD), and roundabouts (RounD), validate the framework's ability to enhance decision-making safety (achieving a 100% safety rate), replicate human-like driving behaviors (with decision alignment exceeding 85%), and adapt effectively to unpredictable scenarios. SafeDrive establishes a novel paradigm for integrating knowledge- and data-driven methods, highlighting significant potential to improve safety and adaptability of autonomous driving in high-risk traffic scenarios. Project Page: https://mezzi33.github.io/SafeDrive/

翻译：自动驾驶车辆近期利用大型语言模型在常规驾驶场景中表现出色，但在动态高风险环境中确保安全性以及处理安全关键型长尾事件方面仍面临重大挑战。为解决这些问题，我们提出SafeDrive——一种基于知识与数据驱动的风险敏感型决策框架，旨在提升自动驾驶的安全性与适应性。该框架采用模块化系统设计，包含：（1）风险模块：用于量化涉及驾驶员、车辆与道路交互的多因素耦合风险；（2）记忆模块：用于存储和检索典型场景以提升适应性；（3）基于LLM的推理模块：实现情境感知的安全决策；（4）反思模块：通过迭代学习优化决策。通过将知识驱动的洞察与自适应学习机制相结合，该框架确保了不确定条件下的鲁棒决策。在真实交通数据集（包括高速公路（HighD）、交叉路口（InD）和环岛（RounD））上的大量评估验证了该框架在提升决策安全性（达成100%安全率）、复现类人驾驶行为（决策对齐度超过85%）以及有效适应不可预测场景方面的能力。SafeDrive建立了知识驱动与数据驱动方法融合的新范式，展现了提升高风险交通场景下自动驾驶安全性与适应性的巨大潜力。项目页面：https://mezzi33.github.io/SafeDrive/