自动驾驶视觉问答：以事件为中心的行车记录仪理解视觉语言模型基准测试
AUTOPILOT VQA: Benchmarking Vision-Language Models for Incident-Centric Dashcam Understanding
AI总结：
针对评估视觉语言模型对安全关键事件推理能力的挑战，提出AUTOPILOT-VQA基准，通过围绕真实驾驶事件的结构化问题评估系统，涵盖多安全相关类别，推动向安全意识推理发展，为自动驾驶系统评估提供标准，助力相关视觉语言系统开发。
摘要：
视觉语言模型、大语言模型和多模态大语言模型的进展改善了自动驾驶任务，但评估这些模型能否可靠推理安全关键事件仍具挑战。为此提出AUTOPILOT-VQA，一个以事件为中心的行车记录仪视频理解视觉问答基准。该数据集通过围绕真实驾驶事件及近事件设计的结构化问题评估不同系统，涵盖多种安全相关类别。通过要求模型回答关于上下文场景属性和事件级事件细节的有根据问题，推动从目标识别向时间上有根据、安全意识推理发展。数据集作为AUTOPILOT CVPR 2026竞赛一部分发布，为评估自动驾驶系统在不同场景下的可靠性提供标准化基准，支持开发更具可解释性、鲁棒性和安全意识的视觉语言系统。
英文摘要：
Recent advances in Vision-Language Models, Large Language Models, and Multimodal Large Language Models have improved autonomous driving tasks such as scene understanding, decision making, trajectory prediction, and visual question answering. However, evaluating whether these models can reliably reason about safety-critical incidents remains challenging. To address this gap, we present AUTOPILOT-VQA, an incident-centric visual question answering benchmark for dashcam video understanding. The dataset evaluates different systems through structured questions designed around real-world driving incidents and near-incidents. The benchmark covers diverse safety-relevant categories, including weather and lighting conditions, traffic environment, road layout, road surface state, signage, involved entities, accident occurrence, impact location, and avoidability-related reasoning. By requiring models to answer grounded questions about both contextual scene properties and event-level incident details, AUTOPILOT-VQA moves beyond object recognition toward temporally grounded, safety-aware reasoning. The dataset is released as part of the AUTOPILOT CVPR 2026 competition and provides a standardized benchmark for assessing the reliability of autonomous driving systems in different scenarios. Our benchmark support developments for more interpretable, robust, and safety-conscious vision-language systems for real-world autonomous driving.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
