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STRIDE: Street View-based Environmental Feature Detection and Pedestrian Collision Prediction
Conference proceeding   Open access

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

Cristina Gonzalez, Nicolas Ayobi, Felipe Escallon, Laura Baldovino-Chiquillo, Maria Wilches-Mogollon, Donny Pasos, Nicole Ramirez, Jose Pinzon, Olga Sarmiento, D. Alex Quistberg, …
IEEE International Conference on Computer Vision workshops, v 2023, pp 3222-3234
01 Oct 2023
DOI: https://doi.org/10.1109/ICCVW60793.2023.00347
PMID: 39104779
url
https://pmc.ncbi.nlm.nih.gov/articles/PMC11298792/pdf/nihms-2008470.pdfView
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Abstract

Autonomous Driving Benchmark testing Collision Prediction Feature extraction Frequency estimation Panoramic Images Pedestrian Safety Pedestrians Predictive models Situational Awareness Visualization Transportation
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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