STRIDE: Street View-based Environmental Feature Detection and Pedestrian
  Collision Prediction
Cristina González Nicolás Ayobi Felipe Escallón Laura Baldovino-Chiquillo Maria Wilches-Mogollón Donny Pasos Nicole Ramírez Jose Pinzón Olga Sarmiento D Alex Quistberg Pablo Arbeláez
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.