Title: Context-Aware Pedestrian Motion Prediction In Urban Intersections,
Abstract: This paper presents a novel context-based approach for pedestrian motion
prediction in crowded, urban intersections, with the additional flexibility of
prediction in similar, but new, environments. Previously, Chen et. al. combined
Markovian-based and clustering-based approaches to learn motion primitives in a
grid-based world and subsequently predict pedestrian trajectories by modeling
the transition between learned primitives as a Gaussian Process (GP). This work
extends that prior approach by incorporating semantic features from the
environment (relative distance to curbside and status of pedestrian traffic
lights) in the GP formulation for more accurate predictions of pedestrian
trajectories over the same timescale. We evaluate the new approach on
real-world data collected using one of the vehicles in the MIT Mobility On
Demand fleet. The results show 12.5% improvement in prediction accuracy and a
2.65 times reduction in Area Under the Curve (AUC), which is used as a metric
to quantify the span of predicted set of trajectories, such that a lower AUC
corresponds to a higher level of confidence in the future direction of
pedestrian motion.