Autonomous Vehicle (AV) requires rigorous testing in safety-critical scenarios for safety validation, yet its validation is hindered by the high cost of field testing and the lack of fidelity in current simulations for rare safety-critical events. Crash reports offer rich and authentic specifications of real-world accident dynamics, making them a promising resource for Large Language Models and Vision-Language models to generate high-fidelity scenarios. However, the existing models frequently deviate from actual accident characteristics due to context suppression. To address these limitations, this paper presents SG-CADVLM, a framework integrateing Context-Aware Decoding with multimodal input processing to generate safety-critical scenarios from crash reports. The framework mitigates the hallucination of VLMs while generating road geometry and vehicle trajectories simultaneously. The experimental results demonstrate that SG-CADVLM generates combined critical and high-risk scenarios at a rate of 88.1% compared to 31.2% for the baseline methods, representing a 182% improvement, while producing executable simulations for autonomous vehicle testing.

翻译：自动驾驶汽车需要在安全关键场景下进行严格测试以验证其安全性，然而高昂的实地测试成本以及现有仿真对罕见安全关键事件保真度的缺失，制约了其验证过程。碰撞报告提供了真实世界中事故动态的丰富且真实的规范描述，使其成为大型语言模型和视觉语言模型生成高保真场景的重要资源。然而，现有模型常因上下文抑制而偏离实际事故特征。针对这些局限，本文提出SG-CADVLM框架，该框架将上下文感知解码与多模态输入处理相结合，可从碰撞报告中生成安全关键场景。该框架在同时生成道路几何和车辆轨迹时抑制了视觉语言模型的幻觉。实验结果表明，SG-CADVLM生成关键高风险组合场景的比例达88.1%，而基线方法仅为31.2%，提升了182%；同时可生成适用于自动驾驶汽车测试的可执行仿真。