  Accurate understanding and prediction of human behaviors are critical
prerequisites for autonomous vehicles, especially in highly dynamic and
interactive scenarios such as intersections in dense urban areas. In this work,
we aim at identifying crossing pedestrians and predicting their future
trajectories. To achieve these goals, we not only need the context information
of road geometry and other traffic participants but also need fine-grained
information of the human pose, motion and activity, which can be inferred from
human keypoints. In this paper, we propose a novel multi-task learning
framework for pedestrian crossing action recognition and trajectory prediction,
which utilizes 3D human keypoints extracted from raw sensor data to capture
rich information on human pose and activity. Moreover, we propose to apply two
auxiliary tasks and contrastive learning to enable auxiliary supervisions to
improve the learned keypoints representation, which further enhances the
performance of major tasks. We validate our approach on a large-scale in-house
dataset, as well as a public benchmark dataset, and show that our approach
achieves state-of-the-art performance on a wide range of evaluation metrics.
The effectiveness of each model component is validated in a detailed ablation
study.
