  Accurate prediction of pedestrian crossing behaviors by autonomous vehicles
can significantly improve traffic safety. Existing approaches often model
pedestrian behaviors using trajectories or poses but do not offer a deeper
semantic interpretation of a person's actions or how actions influence a
pedestrian's intention to cross in the future. In this work, we follow the
neuroscience and psychological literature to define pedestrian crossing
behavior as a combination of an unobserved inner will (a probabilistic
representation of binary intent of crossing vs. not crossing) and a set of
multi-class actions (e.g., walking, standing, etc.). Intent generates actions,
and the future actions in turn reflect the intent. We present a novel
multi-task network that predicts future pedestrian actions and uses predicted
future action as a prior to detect the present intent and action of the
pedestrian. We also designed an attention relation network to incorporate
external environmental contexts thus further improve intent and action
detection performance. We evaluated our approach on two naturalistic driving
datasets, PIE and JAAD, and extensive experiments show significantly improved
and more explainable results for both intent detection and action prediction
over state-of-the-art approaches. Our code is available at:
https://github.com/umautobots/pedestrian_intent_action_detection.
