Predictive models for autonomous vehicles based on object interactions

Abstract

Techniques are discussed herein for training and executing machine learning (ML) prediction models used to control autonomous vehicles in driving environments. In various examples, ML prediction models configured to output joint trajectory predictions for multiple objects in an environment may be trained by evaluating the interactions between the objects represented by the predicted trajectories. A training component may train an ML prediction model using a standard loss function based on the accuracy of the predicted trajectories relative to the ground truth trajectories, and based on an auxiliary loss determined by the agent-to-agent interactions represented by the predicted trajectories. The auxiliary loss may be determined by various techniques, including using a classification model trained to receive and classify sets of object trajectories in a generative adversarial network (GAN), and/or determining a divergence loss based on an alternative ML prediction model that masks object interactions, thereby increasing reliance on object interactions in the training of the ML prediction model.