使用视觉语言模型（VLMs）进行路怒推理：任务定义与评估数据集
Road Rage Reasoning with Vision-language Models (VLMs): Task Definition and Evaluation Dataset
AI总结：
本文定义路怒推理任务，构建精细标注测试集与指标评估主流VLMs，发现其在视觉场景理解和文本空间关系理解上存在明显不足。
摘要：
路怒由交通拥堵和攻击性驾驶等与驾驶相关的刺激所触发，对道路安全构成重大威胁。以往关于路怒调控的研究主要集中在反应抑制上，缺乏主动预防能力。随着视觉语言模型（VLMs）的出现，对触发事件进行视觉推理，并在驾驶员愤怒升级前开展基于对话的安抚，已成为可能。为此，我们提出路怒推理任务，并提供一个精细标注的测试数据集和评估指标，以评估当前主流VLMs在场景理解、事件识别和路怒推理方面的能力。结果表明，当前VLMs在视觉模态中的场景理解，以及文本模态中对物体间空间关系的理解方面存在显著不足。提升VLMs在这些领域的性能，将极大惠及以先行因素为中心的路怒调控等下游任务。
英文摘要：
Road rage, triggered by driving-related stimuli such as traffic congestion and aggressive driving, poses a significant threat to road safety. Previous research on road rage regulation has primarily focused on response suppression, lacking proactive prevention capabilities. With the advent of Vision-Language Models (VLMs), it has become possible to reason about trigger events visually and then engage in dialog-based comforting before drivers' anger escalates. To this end, we propose the road rage reasoning task, along with a finely annotated test dataset and evaluation metrics, to assess the capabilities of current mainstream VLMs in scene understanding, event recognition, and road rage reasoning. The results indicate that current VLMs exhibit significant shortcomings in scene understanding within the visual modality, as well as in comprehending the spatial relationships between objects in the textual modality. Improving VLMs' performance in these areas will greatly benefit downstream tasks like antecedent-focused road rage regulation.
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。
