Towards Incorporating Contextual Knowledge into the Prediction of Driving Behavior

TL;DR

This paper explores how incorporating external contextual information, specifically traffic density, can significantly enhance the accuracy of vehicle behavior prediction in autonomous driving systems.

Contribution

It introduces a framework for integrating external conditions into motion prediction models and demonstrates the impact of traffic density on prediction accuracy.

Findings

Traffic density greatly influences prediction performance.

The approach achieves over 97% AUC in maneuver prediction.

Median lateral prediction error is 0.18 meters at 5 seconds.

Abstract

Predicting the behavior of surrounding traffic participants is crucial for advanced driver assistance systems and autonomous driving. Most researchers however do not consider contextual knowledge when predicting vehicle motion. Extending former studies, we investigate how predictions are affected by external conditions. To do so, we categorize different kinds of contextual information and provide a carefully chosen definition as well as examples for external conditions. More precisely, we investigate how a state-of-the-art approach for lateral motion prediction is influenced by one selected external condition, namely the traffic density. Our investigations demonstrate that this kind of information is highly relevant in order to improve the performance of prediction algorithms. Therefore, this study constitutes the first step towards the integration of such information into automated vehicles. Moreover, our motion prediction approach is evaluated based on the public highD data set showing a maneuver prediction performance with areas under the ROC curve above 97% and a median lateral prediction error of only 0.18m on a prediction horizon of 5s.