2024/12/17 by Zhiyuan Zhou, Zhou, Zhiyuan, Heye Huang +9 · 6 citations
Computer Science · Decision Sciences · Psychology · #Artificial Intelligence (cs.AI) #Artificial intelligence #Computer science #Emerging Technologies (cs.ET) #Explainable Artificial Intelligence (XAI) #FOS: Computer and information sciences #Human-Automation Interaction and Safety #Risk and Safety Analysis #Robotics (cs.RO)
paper · pdf · doi:10.48550/arxiv.2412.13238
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2024/12/17 · openalex created_date 2024/12/21 · openalex updated_date 2026/07/28
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/