PIT: Progressive Interaction Transformer for Pedestrian Crossing Intention Prediction

For autonomous driving, one of the major challenges is to predict pedestrian crossing intention in ego-view. Pedestrian intention depends not only on their intrinsic goals but also on the stimulation of surrounding traffic elements. Considering the influence of other traffic elements on pedestrian intention, recent work introduced more traffic element information into the model to successfully improve performance. However, it is still difficult to effectively capture and fully exploit the potential dynamic spatio-temporal interactions among the target pedestrian and its surrounding traffic elements for accurate reasoning. In this work, inspired by neuroscience that human drivers tend to make continuous sensory-motor driving decisions by progressive visual stimulation, we propose a model termed Progressive Interaction Transformer (PIT) for pedestrian crossing intention prediction. Local pedestrian, global environment, and ego-vehicle motion are considered simultaneously in the proposed PIT. In particular, the temporal fusion block and self-attention mechanism are introduced to jointly and progressively model the dynamic spatio-temporal interactions among the three parties, allowing it to capture richer information and make prediction in a similar way to human drivers. Experimental results demonstrate that PIT achieves higher performance compared with other state-of-the-arts and preserves real-time inference.