It is critical for vehicles to prevent any collisions with pedestrians. Current methods for pedestrian collision prevention focus on integrating visual pedestrian detectors with Automatic Emergency Braking (AEB) systems which can trigger warnings and apply brakes as a pedestrian enters a vehicle's path. Unfortunately, pedestrian-detection-based systems can be hindered in certain situations such as night-time or when pedestrians are occluded. Our system addresses such issues using an online, map-based pedestrian detection aggregation system where common pedestrian locations are learned after repeated passes of locations. Using a carefully collected and annotated dataset in La Jolla, CA, we demonstrate the system's ability to learn pedestrian zones and generate advisory notices when a vehicle is approaching a pedestrian despite challenges like dark lighting or pedestrian occlusion. Using the number of correct advisories, false advisories, and missed advisories to define precision and recall performance metrics, we evaluate our system and discuss future positive effects with further data collection. We have made our code available at https://github.com/s7desai/ped-mapping, and a video demonstration of the CHAMP system at https://youtu.be/dxeCrS_Gpkw.

翻译：防止车辆与行人碰撞至关重要。当前的行人防碰撞方法主要侧重于将视觉行人检测器与自动紧急制动系统（AEB）集成，当行人进入车辆路径时可触发警告并施加制动。然而，基于行人检测的系统在特定场景（如夜间或行人被遮挡时）可能受到限制。我们的系统通过在线地图式行人检测聚合机制解决了此类问题，该系统在重复经过特定位置后可学习行人的常见出现区域。利用在加州拉霍亚精心采集并标注的数据集，我们证明了该系统能够在黑暗光照或行人遮挡等挑战性条件下，识别行人活动区域并在车辆接近行人时生成预警通知。通过定义精确率与召回率性能指标（基于正确预警、误报和漏报次数），我们评估了系统性能，并讨论了进一步数据收集带来的积极影响。相关代码已开源至 https://github.com/s7desai/ped-mapping，CHAMP系统的演示视频见 https://youtu.be/dxeCrS_Gpkw。