Trajectory prediction is a safety-critical tool for autonomous vehicles to plan and execute
actions. Our work addresses two key challenges in trajectory prediction, learning multimodal
outputs, and better predictions by imposing constraints using driving knowledge. Recent
methods have achieved strong performances using Multi-Choice Learning objectives like
winner-takes-all (WTA) or best-of-many. But the impact of those methods in learning diverse
hypotheses is under-studied as such objectives highly depend on their initialization for …

A method for driving path prediction is provided. The method concatenates past trajectory
features and lane centerline features in a channel dimension at an agent's respective
location in a top view map to obtain concatenated features thereat. The method obtains
convolutional features derived from the top view map, the concatenated features, and a
single representation of the training scene the vehicle and agent interactions. The method
extracts hypercolumn descriptor vectors which include the convolutional features from the …