Motion Prediction using Trajectory Sets and Self-Driving Domain Knowledge

Understand

Predicting the future motion of vehicles has been studied using various techniques, including stochastic policies, generative models, and regression.

• Recent work has shown that classification over a trajectory set, which approximates possible motions, achieves state-of-the-art performance and avoids issues like mode collapse.
• However, map information and the physical relationships between nearby trajectories is not fully exploited in this formulation.
• We build on classification-based approaches to motion prediction by adding an auxiliary loss that penalizes off-road predictions.