Title: A Transferable Pedestrian Motion Prediction Model for Intersections with Different Geometries,
Abstract: This paper presents a novel framework for accurate pedestrian intent
prediction at intersections. Given some prior knowledge of the curbside
geometry, the presented framework can accurately predict pedestrian
trajectories, even in new intersections that it has not been trained on. This
is achieved by making use of the contravariant components of trajectories in
the curbside coordinate system, which ensures that the transformation of
trajectories across intersections is affine, regardless of the curbside
geometry. Our method is based on the Augmented Semi Nonnegative Sparse Coding
(ASNSC) formulation and we use that as a baseline to show improvement in
prediction performance on real pedestrian datasets collected at two
intersections in Cambridge, with distinctly different curbside and crosswalk
geometries. We demonstrate a 7.2% improvement in prediction accuracy in the
case of same train and test intersections. Furthermore, we show a comparable
prediction performance of TASNSC when trained and tested in different
intersections with the baseline, trained and tested on the same intersection.