How would surround vehicles move? A Unified Framework for Maneuver Classification and Motion Prediction
This addresses the critical need for reliable motion prediction in autonomous driving, but appears incremental as it builds on existing methods with a unified approach.
The paper tackles the problem of predicting surrounding vehicle motion for autonomous vehicles by proposing a unified framework that uses multiple cues, achieving results in maneuver classification accuracy and trajectory prediction error on real freeway data.
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