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.

在共享空间中，预测行人轨迹对于自动驾驶车辆的安全导航至关重要。我们提出了一种基于启发式的选择相互作用代理的过程，通过计算碰撞风险，使用时间间隔和接近方向角来编码交互效果，并引入了新的极坐标碰撞格网图。实验结果表明，与现有方法相比，我们的方法在 HBS 数据集上的预测轨迹更接近于真实情况。