Multimodal Transformer Networks for Pedestrian Trajectory Prediction

We consider the problem of forecasting the future locations of pedestrians in an ego-centric view of a moving vehicle. Current CNNs or RNNs are flawed in capturing the high dynamics of motion between pedestrians and the ego-vehicle, and suffer from the massive parameter usages due to the inefficiency of learning long-term temporal dependencies. To address these issues, we propose an efficient multimodal transformer network that aggregates the trajectory and ego-vehicle speed variations at a coarse granularity and interacts with the optical flow in a fine-grained level to fill the vacancy of highly dynamic motion. Specifically, a coarse-grained fusion stage fuses the information between trajectory and ego-vehicle speed modalities to capture the general temporal consistency. Meanwhile, a fine-grained fusion stage merges the optical flow in the center area and pedestrian area, which compensates the highly dynamic motion of ego-vehicle and target pedestrian. Besides, the whole network is only attention-based that can efficiently model long-term sequences for better capturing the temporal variations. Our multimodal transformer is validated on the PIE and JAAD datasets and achieves state-of-the-art performance with the most light-weight model size. The codes are available at https://github.com/ericyinyzy/MTN_trajectory.