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.

翻译：本文提出了一个新的基准，用以研究建筑环境元素对行人碰撞预测的影响及其关系，旨在增强自动驾驶系统中的环境感知能力，从而主动预防行人伤害。我们在大规模全景图像中引入了建筑环境检测任务，以及基于检测的行人碰撞频率预测任务。提出了一种基线方法，该方法将碰撞预测模块集成到当前最先进的检测模型中，以同时处理这两个任务。实验表明，建筑环境元素的目标检测与行人碰撞频率预测之间存在显著相关性。该结果为进一步理解建筑环境条件与行人安全之间的相互依赖关系奠定了基础。