Pedestrian Behavior Maps for Safety Advisories: CHAMP Framework and Real-World Data Analysis

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.

I. Introduction

According to a report from the National Highway Traffic Safety Administration [1], 6,516 pedestrians died and 54,769 were injured in traffic crashes in the United States in 2020. From 2000 to 2020, there has been an increase by 42% in the number of pedestrian fatalities on public roadways despite developments in vehicle and road safety. The majority of pedestrian traffic deaths occurred in urban areas (80%) and on open roads (75%) rather than at intersections (25%). The report data showed that the primary factor of pedestrian traffic deaths (50%) was the failure to yield right of way. Due to the prioritization of infrastructure designed for the convenience of cars over the last few decades, safe and convenient pedestrian infrastructure has been dramatically reduced. As a result, out- side of urban areas with high walk-scores, pedestrian activity is sparsely distributed. However, in most cases, pedestrian patterns emerge that may or may not align with existing pedestrian paths or marked crossings. In these areas, it can be difficult for new drivers, or drivers unfamiliar with certain neighborhoods to understand where to expect pedestrians.