Long-term Prediction of Vehicle Behavior using Short-term Uncertainty-aware Trajectories and High-definition Maps

TL;DR

This paper presents a unified approach combining learned, uncertainty-aware vehicle trajectories with lane-based paths to improve long-term and short-term motion prediction accuracy for autonomous driving, validated on real-world data and onboard testing.

Contribution

It introduces a novel framework that unifies rule-based and learned prediction methods, enhancing accuracy across different prediction horizons.

Findings

Outperformed existing state-of-the-art methods in real-world tests.

Improved long-term and short-term prediction accuracy.

Successfully validated onboard in a self-driving vehicle.

Abstract

Motion prediction of surrounding vehicles is one of the most important tasks handled by a self-driving vehicle, and represents a critical step in the autonomous system necessary to ensure safety for all the involved traffic actors. Recently a number of researchers from both academic and industrial communities have focused on this important problem, proposing ideas ranging from engineered, rule-based methods to learned approaches, shown to perform well at different prediction horizons. In particular, while for longer-term trajectories the engineered methods outperform the competing approaches, the learned methods have proven to be the best choice at short-term horizons. In this work we describe how to overcome the discrepancy between these two research directions, and propose a method that combines the disparate approaches under a single unifying framework. The resulting algorithm fuses learned, uncertainty-aware trajectories with lane-based paths in a principled manner, resulting in improved prediction accuracy at both shorter- and longer-term horizons. Experiments on real-world, large-scale data strongly suggest benefits of the proposed unified method, which outperformed the existing state-of-the-art. Moreover, following offline evaluation the proposed method was successfully tested onboard a self-driving vehicle.