GMP: Goal-Based Multimodal Motion Prediction for Automated Vehicles

To reliably and safely navigate dynamic urban environments, connected automated vehicles should anticipate the future motion of surrounding traffic agents, which can have fundamental implications on road safety, traffic management, and network communications in vehicular networks. This requires considering the inherent uncertainty in agents' behavior, making motion prediction challenging. To tackle this issue, we propose conditioning the agents' future motions on both context information and potential multimodal goals. We design a novel Goal-based Motion Prediction approach (GMP) for multimodal motion prediction. By encoding both interactions between agents using temporal convolutions and dynamic and static context information using graph attention, our method estimates the distribution of target goals, efficiently takes the inherent uncertainty in the behavior of agents into account, and generates precise multimodal trajectories. Experimental results indicate that GMP outperforms several benchmarks on the Argoverse Motion Forecasting dataset.