发表机构
C-DRiVeS Lab: Cognitive Driving Research in Vehicular Systems; German University in Cairo（C-DRiVeS实验室：车辆系统认知驾驶研究实验室; 开罗德国大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种结合计算机视觉、LLM和AR的自动化流水线，将安全驾驶场景转换为安全关键场景，在nuScenes上实现97.52%分类准确率，生成逼真测试场景。
AI 中文摘要
测试自动驾驶系统（ADS）需要真实的安全关键场景，但从真实驾驶中收集此类数据成本高昂且不安全。本文提出了一种自动化流水线，通过结合计算机视觉、大语言模型（LLMs）和增强现实（AR），将安全驾驶场景转换为安全关键场景。该系统检测并跟踪道路使用者，提取包括距离、速度、运动方向和碰撞时间（TTC）在内的安全特征，并评估场景的临界性。安全场景由LLM进行修改，生成逼真的碰撞诱导对象和行为，并通过AR集成到原始场景中。所提出的流水线在nuScenes数据集上进行了评估，实现了97.52%的安全分类准确率，并成功生成了诸如行人横穿、后方超车车辆和突然停车事件等逼真场景。结果表明，该方法为自动生成安全关键场景以支持自动驾驶系统的测试和验证提供了一种有效且灵活的方法。
英文摘要
Testing Autonomous Driving Systems (ADS) requires realistic safety-critical scenarios, but collecting such data from real-world driving is costly and unsafe. This paper presents an automated pipeline that transforms safe driving scenes into safety-critical scenarios by combining computer vision, Large Language Models (LLMs), and Augmented Reality (AR). The system detects and tracks road users, extracts safety features including distance, velocity, motion direction, and Time-to-Collision (TTC), and assesses scene criticality. Safe scenes are modified by an LLM, which generates realistic collision-inducing objects and behaviors that are integrated into the original scene using AR. The proposed pipeline was evaluated on the nuScenes dataset, achieving 97.52% safety classification accuracy and successfully generating realistic scenarios such as pedestrian crossings, rear overtaking vehicles, and sudden-stop events. The results demonstrate an effective and flexible approach for automated generation of safety-critical scenarios to support the testing and validation of autonomous driving systems.
