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Predicting the behavior of road users, particularly pedestrians, is vital forsafe motion planning in the context of autonomous driving systems.Traditionally, pedestrian behavior prediction has been realized in terms offorecasting future trajectories. However, recent evidence suggests thatpredicting higher-level actions, such as crossing the road, can help improvetrajectory forecasting and planning tasks accordingly. There are a number ofexisting datasets that cater to the development of pedestrian action predictionalgorithms, however, they lack certain characteristics, such as bird's eye viewsemantic map information, 3D locations of objects in the scene, etc., which arecrucial in the autonomous driving context. To this end, we propose a newpedestrian action prediction dataset created by adding per-frame 2D/3D boundingbox and behavioral annotations to the popular autonomous driving dataset,nuScenes. In addition, we propose a hybrid neural network architecture thatincorporates various data modalities for predicting pedestrian crossing action.By evaluating our model on the newly proposed dataset, the contribution ofdifferent data modalities to the prediction task is revealed. The dataset isavailable at https://github.com/huawei-noah/PePScenes.

PePScenes: A Novel Dataset and Baseline for Pedestrian Action Prediction in 3D