Destination intention estimation-based convolutional encoder-decoder for pedestrian trajectory multimodality forecast

Forecasting pedestrian trajectory is a vital area of research in smart urban mobility, which can be applied to intelligent transportation and intelligent surveillance. Current approaches employ conditional variational autoencoders to model future trajectory multimodality. However, these methods generate multi-modal trajectories for one single destination, ignoring the trajectory multimodality caused by the uncertainty of the pedestrians' destination intention. Besides, they can lead to mode collapse and training instability. To address this issue, we propose a novel destination intention estimation-based convolutional encoder-decoder framework for multimodal trajectory forecast. Specially, we design a destination intention estimator to forecast pedestrian future destination intentions at the last time step. Then, we devise a trajectory decoder module to forecast pedestrian trajectories at each time step with the assistance of the destination intentions. To evaluate our method, we perform experiments on publicly available benchmark datasets and demonstrate that our proposed method achieves the superior results compared with state-of-the-art approaches.