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

TL;DR

This paper presents a new benchmark and method for detecting environmental features in street view images and predicting pedestrian collision risk, aiming to improve autonomous driving safety.

Contribution

It introduces a built environment detection task and a collision prediction model integrated into a detection framework, advancing pedestrian safety analysis.

Findings

Object detection correlates with collision frequency

Baseline method effectively predicts pedestrian collisions

Built environment features influence pedestrian safety

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.