Predicting Lane Keeping Behavior of Visually Distracted Drivers Using Inverse Suboptimal Control

TL;DR

This paper introduces an inverse suboptimal control method to predict lane keeping behavior of visually distracted drivers, enabling situation-dependent risk assessment and outperforming traditional models in real traffic data.

Contribution

The study presents a novel inverse suboptimal control approach for predicting distracted driving behavior, incorporating driving context and improving prediction accuracy over existing models.

Findings

Lower prediction error across speed and track variations

Better generalization to unseen driving speeds

Outperforms baseline behavior models in real traffic data

Abstract

Driver distraction strongly contributes to crash-risk. Therefore, assistance systems that warn the driver if her distraction poses a hazard to road safety, promise a great safety benefit. Current approaches either seek to detect critical situations using environmental sensors or estimate a driver's attention state solely from her behavior. However, this neglects that driving situation, driver deficiencies and compensation strategies altogether determine the risk of an accident. This work proposes to use inverse suboptimal control to predict these aspects in visually distracted lane keeping. In contrast to other approaches, this allows a situation-dependent assessment of the risk posed by distraction. Real traffic data of seven drivers are used for evaluation of the predictive power of our approach. For comparison, a baseline was built using established behavior models. In the evaluation our method achieves a consistently lower prediction error over speed and track-topology variations. Additionally, our approach generalizes better to driving speeds unseen in training phase.