Pedestrian Behavior Maps for Safety Advisories: CHAMP Framework and
  Real-World Data Analysis
Ross Greer Samveed Desai Lulua Rakla Akshay Gopalkrishnan Afnan Alofi Mohan Trivedi
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.