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

• URL: http://arxiv.org/abs/2308.13183v1
• Date: Fri, 25 Aug 2023 05:25:01 GMT
• Title: STRIDE: Street View-based Environmental Feature Detection and Pedestrian Collision Prediction
• Authors: Cristina Gonz\'alez, Nicol\'as Ayobi, Felipe Escall\'on, Laura Baldovino-Chiquillo, Maria Wilches-Mogoll\'on, Donny Pasos, Nicole Ram\'irez, Jose Pinz\'on, Olga Sarmiento, D Alex Quistberg, Pablo Arbel\'aez
• Abstract summary: We introduce a built environment detection task in large-scale panoramic images and a detection-based pedestrian collision frequency prediction task. Our experiments demonstrate a significant correlation between object detection of built environment elements and pedestrian collision frequency prediction.
• Score: 1.002773173311891
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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