Polar Collision Grids: Effective Interaction Modelling for Pedestrian
  Trajectory Prediction in Shared Space Using Collision Checks
Mahsa Golchoubian Moojan Ghafurian Kerstin Dautenhahn Nasser Lashgarian Azad
Predicting pedestrians' trajectories is a crucial capability for autonomous
vehicles' safe navigation, especially in spaces shared with pedestrians.
Pedestrian motion in shared spaces is influenced by both the presence of
vehicles and other pedestrians. Therefore, effectively modelling both
pedestrian-pedestrian and pedestrian-vehicle interactions can increase the
accuracy of the pedestrian trajectory prediction models. Despite the huge
literature on ways to encode the effect of interacting agents on a pedestrian's
predicted trajectory using deep-learning models, limited effort has been put
into the effective selection of interacting agents. In the majority of cases,
the interaction features used are mainly based on relative distances while
paying less attention to the effect of the velocity and approaching direction
in the interaction formulation. In this paper, we propose a heuristic-based
process of selecting the interacting agents based on collision risk
calculation. Focusing on interactions of potentially colliding agents with a
target pedestrian, we propose the use of time-to-collision and the approach
direction angle of two agents for encoding the interaction effect. This is done
by introducing a novel polar collision grid map. Our results have shown
predicted trajectories closer to the ground truth compared to existing methods
(used as a baseline) on the HBS dataset.