González Cristina, Ayobi Nicolás, Escallón Felipe, Baldovino-Chiquillo Laura, Wilches-Mogollón Maria, Pasos Donny, Ramírez Nicole, Pinzón Jose, Sarmiento Olga, Quistberg D Alex, Arbeláez Pablo
Center for Research and Formation in Artificial Intelligence, Universidad de los Andes, Colombia.
School of Engineering, Universidad de los Andes, Colombia.
IEEE Int Conf Comput Vis Workshops. 2023 Oct;2023:3222-3234. doi: 10.1109/iccvw60793.2023.00347. Epub 2023 Dec 25.
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.
本文介绍了一种新颖的基准，用于研究建筑环境要素对行人碰撞预测的影响及关系，旨在增强自动驾驶系统中的环境意识，以积极预防行人受伤。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。我们提出了一种基线方法，将碰撞预测模块纳入最先进的检测模型中，以同时处理这两项任务。我们的实验表明，建筑环境要素的目标检测与行人碰撞频率预测之间存在显著相关性。我们的研究结果是迈向理解建筑环境条件与行人安全之间相互依存关系的垫脚石。
