SafeDrive: Knowledge- and data-driven risk-sensitive decision-making for autonomous vehicles with Large Language Models
提出SafeDrive框架，通过风险模块、记忆模块、大语言模型推理模块和反思模块，在动态高风险场景中实现100%安全率和超过85%的人类驾驶行为匹配，提升自动驾驶安全性与适应性。
Recent advancements in autonomous vehicles (AVs) leverage 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 remains a significant challenge. 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 comprehensive quantification of 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 characterized by dynamic and high-risk scenarios, 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. The proposed framework of SafeDrive establishes a novel paradigm for integrating knowledge- and data-driven methods, highlighting significant potential to improve the safety and adaptability of autonomous driving in long-tail or high-risk traffic scenarios. Project page: https://mezzi33.github.io/SafeDrive/.