用大模型提升自动驾驶在高风险场景下的安全决策能力
SafeDrive: Knowledge- and Data-Driven Risk-Sensitive Decision-Making for Autonomous Vehicles with Large Language Models
- 分模块设计，融合风险评估与记忆检索实现动态决策
- 实测安全率达100%，决策与人类行为对齐超85%
- 适合研究自动驾驶安全与大模型应用的从业者
近年来，自动驾驶技术利用大语言模型（LLMs）在常规驾驶场景中表现良好，但在动态高风险环境及安全关键的长尾事件中仍面临挑战。为此，本文提出SafeDrive框架，通过知识驱动与数据驱动结合，提升自动驾驶的安全性与适应性。该框架包含四个模块：(1) 风险模块，量化驾驶员、车辆与道路交互的多因素耦合风险；(2) 记忆模块，存储与检索典型场景以增强适应性；(3) 基于LLM的推理模块，实现上下文感知的安全决策；(4) 反思模块，通过迭代学习优化决策。在真实交通数据集（HighD、InD、RounD）上的评估表明，该框架可实现100%安全率，决策与人类行为对齐度超过85%，并有效应对不可预测场景。SafeDrive为知识与数据驱动方法融合提供了新范式，显著提升了高风险交通场景下自动驾驶的安全性与适应性。
原文摘要 · Abstract (English)
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/
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