How Would Surround Vehicles Move? A Unified Framework for Maneuver Classification and Motion Prediction

TLDR

Reliable prediction of surround vehicle motion is a critical requirement for path planning for autonomous vehicles. The paper proposes a unified framework for surround vehicle maneuver classification and motion prediction that exploits multiple cues, including vehicle motion estimates, typical freeway traffic patterns, and inter‑vehicle interactions. The framework integrates vehicle motion estimates, typical freeway traffic patterns, and inter‑vehicle interactions, and its components are evaluated through ablative importance analysis and execution‑time profiling. The framework achieves reported maneuver classification accuracy and trajectory prediction errors on real freeway sensor data, and case studies illustrate its performance in complex traffic scenarios.

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