  In this paper, we address the challenge of fine-grained video event
understanding in traffic scenarios, vital for autonomous driving and safety.
Traditional datasets focus on driver or vehicle behavior, often neglecting
pedestrian perspectives. To fill this gap, we introduce the WTS dataset,
highlighting detailed behaviors of both vehicles and pedestrians across over
1.2k video events in hundreds of traffic scenarios. WTS integrates diverse
perspectives from vehicle ego and fixed overhead cameras in a
vehicle-infrastructure cooperative environment, enriched with comprehensive
textual descriptions and unique 3D Gaze data for a synchronized 2D/3D view,
focusing on pedestrian analysis. We also pro-vide annotations for 5k publicly
sourced pedestrian-related traffic videos. Additionally, we introduce
LLMScorer, an LLM-based evaluation metric to align inference captions with
ground truth. Using WTS, we establish a benchmark for dense video-to-text
tasks, exploring state-of-the-art Vision-Language Models with an instance-aware
VideoLLM method as a baseline. WTS aims to advance fine-grained video event
understanding, enhancing traffic safety and autonomous driving development.
