Computational modeling of driver pre-crash brake response, with and without off-road glances: Parameterization using real-world crashes and near-crashes
Journal article, 2021

When faced with an imminent collision threat, human vehicle drivers respond with braking in a manner which is stereotypical, yet modulated in complex ways by many factors, including the specific traffic situation and past driver eye movements. A computational model capturing these phenomena would have high applied value, for example in virtual vehicle safety testing methods, but existing models are either simplistic or not sufficiently validated. This paper extends an existing quantitative driver model for initiation and modulation of pre-crash brake response, to handle off-road glance behavior. The resulting models are fitted to time-series data from real-world naturalistic rear-end crashes and near-crashes. A stringent parameterization and model selection procedure is presented, based on particle swarm optimization and maximum likelihood estimation. A major contribution of this paper is the resulting first-ever fit of a computational model of human braking to real near-crash and crash behavior data. The model selection results also permit novel conclusions regarding behavior and accident causation: Firstly, the results indicate that drivers have partial visual looming perception during off-road glances; that is, evidence for braking is collected, albeit at a slower pace, while the driver is looking away from the forward roadway. Secondly, the results suggest that an important causation factor in crashes without off-road glances may be a reduced responsiveness to visual looming, possibly associated with cognitive driver state (e.g., drowsiness or erroneous driver expectations). It is also demonstrated that a model parameterized on less-critical data, such as near-crashes, may also accurately reproduce driver behavior in highly critical situations, such as crashes.

Brake response

Driver behavior

Naturalistic data



Driver model


Malin Svärd


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

Gustav M Markkula

University of Leeds

Jonas Bärgman

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

Trent Victor


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

Accident Analysis and Prevention

0001-4575 (ISSN)

Vol. 163 106433

Subject Categories

Infrastructure Engineering

Applied Psychology

Vehicle Engineering





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