Joint Intention and Trajectory Prediction Based on Transformer

Although autonomous driving technology has made tremendous progress in recent years, it is still challenging to predict the intentions and trajectories of pedestrians. The state-of-the-art methods suffer from two problems. (1) Existing works consider these two tasks separately, ignoring the connection between them. (2) The selection and integration of inputs for these tasks are not well designed. In this paper, these two tasks are taken into consideration in a unified model. In this way, the information provided by the labels of each other is shared, improving the performance of both tasks. Besides, in addition to the bounding boxes and speeds, orientation and road semantic segmentation features are taken into consideration to show the potential intention and road context of the pedestrian. And all the inputs are weighted by an attention module before integration. Meanwhile, a Transformer encoder is applied in our method to extract the temporal information from the fused feature sequence. Our method outperforms all previous models for both trajectory prediction and intention prediction tasks on the JAAD dataset and PIE dataset.