  Making accurate motion prediction of the surrounding traffic agents such as
pedestrians, vehicles, and cyclists is crucial for autonomous driving. Recent
data-driven motion prediction methods have attempted to learn to directly
regress the exact future position or its distribution from massive amount of
trajectory data. However, it remains difficult for these methods to provide
multimodal predictions as well as integrate physical constraints such as
traffic rules and movable areas. In this work we propose a novel two-stage
motion prediction framework, Trajectory Proposal Network (TPNet). TPNet first
generates a candidate set of future trajectories as hypothesis proposals, then
makes the final predictions by classifying and refining the proposals which
meets the physical constraints. By steering the proposal generation process,
safe and multimodal predictions are realized. Thus this framework effectively
mitigates the complexity of motion prediction problem while ensuring the
multimodal output. Experiments on four large-scale trajectory prediction
datasets, i.e. the ETH, UCY, Apollo and Argoverse datasets, show that TPNet
achieves the state-of-the-art results both quantitatively and qualitatively.
