STRIDE: Street View-based Environmental Feature Detection and Pedestrian Collision Prediction
This work addresses pedestrian safety for autonomous driving systems, but it is incremental as it builds on existing detection models and introduces a new benchmark.
The paper tackles the problem of predicting pedestrian collisions by analyzing built environment elements from street view images, and demonstrates a significant correlation between object detection of these elements and collision frequency prediction.
This paper introduces a novel benchmark to study the impact and relationship of built environment elements on pedestrian collision prediction, intending to enhance environmental awareness in autonomous driving systems to prevent pedestrian injuries actively. We introduce a built environment detection task in large-scale panoramic images and a detection-based pedestrian collision frequency prediction task. We propose a baseline method that incorporates a collision prediction module into a state-of-the-art detection model to tackle both tasks simultaneously. Our experiments demonstrate a significant correlation between object detection of built environment elements and pedestrian collision frequency prediction. Our results are a stepping stone towards understanding the interdependencies between built environment conditions and pedestrian safety.