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, [1] combined Markovianbased 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 [2] . The results show 12.5% improvement in prediction accuracy and a 2.65× 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.
In REF context features from the environment (such as traffic light status and distance to curbside) are incorporated into a Gaussian Process based framework for pedestrian trajectory prediction.
Context-Aware Pedestrian Motion Prediction In Urban Intersections
ArXiv
ArXiv
Mathematics Computer Science