Pedestrian Behavior Maps for Safety Advisories: CHAMP Framework and Real-World Data Analysis
This work addresses pedestrian safety for autonomous and assisted driving systems, offering an incremental improvement over existing visual detection methods by leveraging map-based aggregation.
The paper tackles pedestrian collision prevention by developing an online map-based system that learns common pedestrian locations from repeated passes, addressing limitations like night-time or occlusion in visual detectors. It demonstrates the system's ability to generate safety advisories using real-world data, reporting performance metrics such as precision and recall.
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.