CASNSC: A context-based approach for accurate pedestrian motion prediction at intersections

Nikita Japuria, Golnaz Habibi, Jonathan P. How

Abstract:

Intention recognition of pedestrians is crucial to safe and reliable working of
autonomous vehicles, when serving as, for instance, indoor service robots or
self-driving cars in busy urban scenes. Previously, Chen et al. [2016] combined
Markovian-based and clustering-based approaches to learn motion primitives and
subsequently predict pedestrian trajectories by modeling the transition between
learned primitives as a Gaussian Process (GP). This work further develops their
approach by incorporating semantic features from the environment (relative distance
to curbside and status of pedestrian traffic lights) for more confident prediction of
pedestrian trajectories at intersections. Adding the environmental context, when
available, not only makes prediction more robust but can also provide increased
flexibility of prediction in new environments. We test our algorithm on real data.
The results show 26% improvement in prediction accuracy as compared to previous
work, on incorporation of new features.

Keywords: Pedestrian intention, Self-driving cars, Gaussian Process