PedFormer: Pedestrian Behavior Prediction via Cross-Modal Attention Modulation and Gated Multitask Learning. (arXiv:2210.07886v1 [cs.CV])

Predicting pedestrian behavior is a crucial task for intelligent driving
systems. Accurate predictions require a deep understanding of various
contextual elements that potentially impact the way pedestrians behave. To
address this challenge, we propose a novel framework that relies on different
data modalities to predict future trajectories and crossing actions of
pedestrians from an ego-centric perspective. Specifically, our model utilizes a
cross-modal Transformer architecture to capture dependencies between different
data types. The output of the Transformer is augmented with representations …

arxiv attention behavior multitask learning prediction