Lane-Keeping Guardian with Safety Filter: Experimental Validation

In this paper, a control barrier function (CBF) is constructed for the lane-keeping problem which is applicable to both human-driven and automated vehicles. Based on the resulting CBF, a safety filter is developed that prevents the vehicle from crossing the lane boundaries, while only modifying the nominal steering input when necessary. The effectiveness of the proposed control approach is demonstrated in a series of numerical simulations and real vehicle experiments with a human driver. The experimental results show that the safety filter can successfully keep the vehicle inside the lane boundaries by seamlessly modifying the steering input of the human driver in a minimally invasive manner.

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Publication Details

Published
2025-06-22
DOI
https://doi.org/10.1109/iv64158.2025.11097430
Primary Topic
Autonomous Vehicle Technology and Safety
Type
article
Field-Weighted Citation Impact
0.00
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article

Lane-Keeping Guardian with Safety Filter: Experimental Validation

Illés Vörös, Gábor Orosz, Makoto Suminaka, John M. Talbot et al.
Autonomous Vehicle Technology and Safety
article

Lane-Keeping Guardian with Safety Filter: Experimental Validation

Illés Vörös, Gábor Orosz, Makoto Suminaka, John M. Talbot, James Dallas, John Subosits, Xiao Li, Ilya Kolmanovsky
article en

Abstract

In this paper, a control barrier function (CBF) is constructed for the lane-keeping problem which is applicable to both human-driven and automated vehicles. Based on the resulting CBF, a safety filter is developed that prevents the vehicle from crossing the lane boundaries, while only modifying the nominal steering input when necessary. The effectiveness of the proposed control approach is demonstrated in a series of numerical simulations and real vehicle experiments with a human driver. The experimental results show that the safety filter can successfully keep the vehicle inside the lane boundaries by seamlessly modifying the steering input of the human driver in a minimally invasive manner.

University of Michigan (US), Toyota Research Institute
Openalex Percentile: Top 79%
Autonomous Vehicle Technology and Safety
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Lane-Keeping Guardian with Safety Filter: Experimental Validation — Illés Vörös, Gábor Orosz, et al. · (2025) | TGRS Research Map | TGRS