TIP: Task-Informed Motion Prediction for Intelligent Vehicles

TL;DR

This paper introduces a task-informed motion prediction model for autonomous vehicles that optimizes predictions based on downstream task utility, leading to significantly improved task performance over traditional task-agnostic methods.

Contribution

The proposed model jointly reasons about prediction accuracy and task utility, enabling better support for various downstream decision-making tasks in autonomous driving.

Findings

Significantly improved task performance on Waymo dataset

Effective support for multiple downstream tasks

Outperforms task-agnostic baselines

Abstract

When predicting trajectories of road agents, motion predictors usually approximate the future distribution by a limited number of samples. This constraint requires the predictors to generate samples that best support the task given task specifications. However, existing predictors are often optimized and evaluated via task-agnostic measures without accounting for the use of predictions in downstream tasks, and thus could result in sub-optimal task performance. In this paper, we propose a task-informed motion prediction model that better supports the tasks through its predictions, by jointly reasoning about prediction accuracy and the utility of the downstream tasks, which is commonly used to evaluate the task performance. The task utility function does not require the full task information, but rather a specification of the utility of the task, resulting in predictors that serve a wide range of downstream tasks. We demonstrate our approach on two use cases of common decision making tasks and their utility functions, in the context of autonomous driving and parallel autonomy. Experiment results show that our predictor produces accurate predictions that improve the task performance by a large margin in both tasks when compared to task-agnostic baselines on the Waymo Open Motion dataset.