Abstract
The complexity of interactions between pedestrians poses a challenge to
pedestrian trajectory prediction, and existing trajectory prediction
methods based on data-driven models lack interpretation for modeling
interactions between pedestrians. To address this problem, an improved
avoidance force algorithm is proposed to model the interaction of
pedestrian forces explicitly. Multiple socially acceptable pedestrian
trajectory information is generated by using the prior knowledge of
observed trajectory and the avoidance force algorithm.The avoidance
force trajectories are evaluated by an attention network to generate
confidence scores; the avoidance force trajectories are selected based
on the confidence scores;The final accurate trajectories are refined
using Teacher-forcing. Compared to Social-Implicit, ours experimental
results conducted on the ETH and UCY datasets show that the proposed
method improves the average displacement error (ADE) and final
displacement error (FDE) by 6\% and
16\%, respectively.