# Prediction of Highway Lane Changes Based on Prototype Trajectories

## TL;DR

This paper presents a statistical method utilizing prototype trajectories for early lane change detection and trajectory prediction in highway scenarios, enhancing safety and efficiency in automated driving.

## Contribution

It introduces a novel approach combining clustering of real traffic data and mixture models for improved maneuver recognition and trajectory prediction.

## Key findings

- Enhanced accuracy in early lane change detection
- Improved trajectory prediction performance over previous methods
- Effective use of prototype trajectories for uncertainty-aware predictions

## Abstract

The vision of automated driving is to increase both road safety and efficiency, while offering passengers a convenient travel experience. This requires that autonomous systems correctly estimate the current traffic scene and its likely evolution. In highway scenarios early recognition of cut-in maneuvers is essential for risk-aware maneuver planning. In this paper, a statistical approach is proposed, which advantageously utilizes a set of prototypical lane change trajectories to realize both early maneuver detection and uncertainty-aware trajectory prediction for traffic participants. Generation of prototype trajectories from real traffic data is accomplished by Agglomerative Hierarchical Clustering. During clustering, the alignment of the cluster prototypes to each other is optimized and the cohesion of the resulting prototype is limited when two clusters merge. In the prediction stage, the similarity of observed vehicle motion and typical lane change patterns in the data base is evaluated to construct a set of significant features for maneuver classification via Boosted Decision Trees. The future trajectory is predicted combining typical lane change realizations in a mixture model. B-splines based trajectory adaptations guarantee continuity during transition from actually observed to predicted vehicle states. Quantitative evaluation results demonstrate the proposed concept's improved performance for both maneuver and trajectory prediction compared to a previously implemented reference approach.