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.

本文介绍了一种新的基准，用于研究建筑环境元素对行人碰撞预测的影响和关系，旨在增强自动驾驶系统对环境的认知，积极预防行人受伤。我们引入了大规模全景图像中的建筑环境检测任务和基于检测的行人碰撞频率预测任务。我们提出了一种基准方法，将碰撞预测模块纳入先进的检测模型中，同时解决这两个任务。我们的实验证明了建筑环境元素的目标检测与行人碰撞频率预测之间的显著相关性。我们的结果是理解建筑环境条件与行人安全之间相互依赖关系的基石。

STRIDE：基于街景的环境特征检测与行人碰撞预测