Using naturalistic and driving simulator data to model driver responses to unintentional lane departures
Journal article, 2024

Unintentional lane departures on straight roads cause many road fatalities each year. The objective of this study was to explore and model drivers’ recovery steering maneuvers in unintentional drift situations, to enable the prospective safety benefit assessment of lane departure warning and avoidance systems through counterfactual simulations. The timing and amplitude of the steering adjustments drivers make to avoid lane departure were studied over three data sets with different origins, consisting of both naturalistic data and experimental data from a driving simulator study. With respect to timing, the main finding was that visually distracted drivers often initiate the corrective steering response prior to looking back towards the road, demonstrating that lane-keeping information in the visual periphery is sufficient to trigger the response. As for steering amplitude, the observed amplitudes were correlated against different lane departure risk metrics from the literature, resulting in a model capable of accounting for human behavior across all three data sets with good performance. Surprisingly, a very simple model (which describes the steering amplitude as increasing quadratically with the vehicle's orientation to the road) predicted the amplitude of the primary corrective steering adjustment better than models based on more complex lane departure risk metrics. This result indicates that drivers scale the amplitude of their steering adjustment to the steering input needed to get the vehicle back in the lane already at first response. However, it was possible to obtain a similar model fit using a more complex threshold model, with different dynamics depending on the vehicle's current orientation to the road. We discuss how these findings can be applied to models of human steering for safety benefit assessment.

Lane departure

Safety benefit

Steering

Intermittent control

Driver model

Author

Malin Svärd

Chalmers, Mechanics and Maritime Sciences (M2), Vehicle Safety

Gustav M Markkula

University of Leeds

Mikael Ljung Aust

Volvo

Jonas Bärgman

Chalmers, Mechanics and Maritime Sciences (M2), Vehicle Safety

Transportation Research Part F: Traffic Psychology and Behaviour

1369-8478 (ISSN)

Vol. 100 361-387

Improved quantitative driver behavior models and safety assessment methods for ADAS and AD (QUADRIS)

VINNOVA (2020-05156), 2021-04-01 -- 2024-03-31.

Subject Categories

Applied Psychology

Vehicle Engineering

Control Engineering

DOI

10.1016/j.trf.2023.11.021

More information

Latest update

1/11/2024