How would surround vehicles move? A Unified Framework for Maneuver Classification and Motion Prediction

TL;DR

This paper introduces a unified framework for classifying maneuvers and predicting the motion of surrounding vehicles in autonomous driving, leveraging multiple cues for improved accuracy and real-world applicability.

Contribution

It presents a novel integrated approach combining vehicle motion, traffic patterns, and interactions for enhanced maneuver classification and trajectory prediction.

Findings

High maneuver classification accuracy achieved.

Mean and median trajectory prediction errors reported.

Ablative analysis highlights key cues for prediction.

Abstract

Reliable prediction of surround vehicle motion is a critical requirement for path planning for autonomous vehicles. In this paper we propose a unified framework for surround vehicle maneuver classification and motion prediction that exploits multiple cues, namely, the estimated motion of vehicles, an understanding of typical motion patterns of freeway traffic and inter-vehicle interaction. We report our results in terms of maneuver classification accuracy and mean and median absolute error of predicted trajectories against the ground truth for real traffic data collected using vehicle mounted sensors on freeways. An ablative analysis is performed to analyze the relative importance of each cue for trajectory prediction. Additionally, an analysis of execution time for the components of the framework is presented. Finally, we present multiple case studies analyzing the outputs of our model for complex traffic scenarios