MPNet: Multi-Stage Progressive Convolutional Neural Networks for Trajectory Prediction

Trajectory prediction is a continuing concern within autonomous vehicles. Psychological research shows that pedestrian traveling is a cyclic alternation. Pedestrians constantly interact with their surroundings, including social agents and physical environments, and plan paths to achieve goals. Nevertheless, most existing trajectory prediction methods are based on a single-stage design, which runs counter to traffic psychology principles. In this work, we present MPNet, a novel multi-stage progressive convolutional neural network that decomposes complicated trajectory prediction into multiple manageable components, where lightweight sub-networks handle each stage with a divide-and-conquer methodology. Specifically, our sub-networks are based on the encoder-decoder architecture, in which we capture interactive information and estimate goals to achieve trajectory prediction. The communication among sub-networks depends on a novel cross-stage fusion design. We introduce a feedback channel mutual attention mechanism and a cross-sub-network fusion unit to enable efficient information sharing across different stages. At each stage, we develop a symmetric gated supervision module to supervise future trajectory generation from coarse to fine. Extensive experiments demonstrate that MPNet achieves state-of-the-art performance, reducing Average Displacement Error (ADE) and Final Displacement Error (FDE) by 5.6% /7.4% on the ETH and UCY Datasets, 7.5% /7.7% on the Stanford Drone Dataset, and 7.1% /22.5% on the Intersection Drone Dataset, while maintaining comparable computational complexity. Additionally, our MPNet-Tiny variant reduces parameters by 90.5% and inference time by 1.83 seconds with competitive accuracy.