You Shall Not Pass: Warning Drivers of Unsafe Overtaking Maneuvers on Country Roads by Predicting Safe Sight Distance

TL;DR

This paper presents a sensor-based assistant system that predicts safe overtaking distances on country roads to enhance safety without relying on vehicle-to-vehicle communication.

Contribution

It introduces a novel prediction model using in-car sensors and map data, validated through a VR user study comparing two UI designs for overtaking assistance.

Findings

Both UIs increased patient driving and safety.

Monitoring-focused UI scored higher in usability and less distraction.

Model predictions sometimes deviated from actual driving behavior.

Abstract

Overtaking on country roads with possible opposing traffic is a dangerous maneuver and many proposed assistant systems assume car-to-car communication and sensors currently unavailable in cars. To overcome this limitation, we develop an assistant that uses simple in-car sensors to predict the required sight distance for safe overtaking. Our models predict this from vehicle speeds, accelerations, and 3D map data. In a user study with a Virtual Reality driving simulator (N=25), we compare two UI variants (monitoring-focused vs scheduling-focused). The results reveal that both UIs enable more patient driving and thus increase overall driving safety. While the monitoring-focused UI achieves higher System Usability Score and distracts drivers less, the preferred UI depends on personal preference. Driving data shows predictions were off at times. We investigate and discuss this in a comparison of our models to actual driving behavior and identify crucial model parameters and assumptions that significantly improve model predictions.