Prediction by Anticipation: An Action-Conditional Prediction Method based on Interaction Learning

TL;DR

This paper introduces a probabilistic prediction method for autonomous driving that models interactions as a latent process, improving long-term prediction accuracy and generalization across diverse traffic scenarios.

Contribution

It proposes a novel action-conditional prediction framework based on interaction learning, utilizing a variational Bayesian model to better capture vehicle interactions.

Findings

Significant accuracy improvements over state-of-the-art methods.

Enhanced generalization to various driving situations.

Validated on NGSIM I-80 and Argoverse datasets.

Abstract

In autonomous driving (AD), accurately predicting changes in the environment can effectively improve safety and comfort. Due to complex interactions among traffic participants, however, it is very hard to achieve accurate prediction for a long horizon. To address this challenge, we propose prediction by anticipation, which views interaction in terms of a latent probabilistic generative process wherein some vehicles move partly in response to the anticipated motion of other vehicles. Under this view, consecutive data frames can be factorized into sequential samples from an action-conditional distribution that effectively generalizes to a wider range of actions and driving situations. Our proposed prediction model, variational Bayesian in nature, is trained to maximize the evidence lower bound (ELBO) of the log-likelihood of this conditional distribution. Evaluations of our approach with prominent AD datasets NGSIM I-80 and Argoverse show significant improvement over current state-of-the-art in both accuracy and generalization.