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

要旨

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.

受賞
Honorable Mention
著者
Adrian Bauske
University of Bayreuth, Bayreuth, Germany
Arthur Fleig
University of Leipzig, Leipzig, Germany
DOI

10.1145/3706598.3713768

論文URL

https://dl.acm.org/doi/10.1145/3706598.3713768

動画

会議: CHI 2025

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2025.acm.org/)

セッション: Privacy and Safety

G316+G317
7 件の発表
2025-04-29 23:10:00
2025-04-30 00:40:00
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