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This paper introduces a novel benchmark to study the impact and relationshipof built environment elements on pedestrian collision prediction, intending toenhance environmental awareness in autonomous driving systems to preventpedestrian injuries actively. We introduce a built environment detection taskin large-scale panoramic images and a detection-based pedestrian collisionfrequency prediction task. We propose a baseline method that incorporates acollision prediction module into a state-of-the-art detection model to tackleboth tasks simultaneously. Our experiments demonstrate a significantcorrelation between object detection of built environment elements andpedestrian collision frequency prediction. Our results are a stepping stonetowards understanding the interdependencies between built environmentconditions and pedestrian safety.

STRIDE: Street View-based Environmental Feature Detection and Pedestrian Collision Prediction