Users’ response to critical situations in automated driving: rear-ends, sideswipes, and false warnings
Journal article, 2021

Although an automated vehicle may operate for an extended time, it may suddenly request a user's intervention in critical situations (i.e., beyond the system's operational design domain). Despite the proliferation of studies to understand how users resume control in such critical situations, a systematic analysis of the whole response process is necessary. We analyzed the visual-motor response process of distracted users to front and lateral vehicle conflicts. We also investigated the effect of false warnings and expectations. In a driving simulator experiment (high fidelity, fixed-based, within-subject design), 45 participants performed a visual-manual distracting task until an audio warning was issued. The response process was modeled with Bayesian generalized linear mixed effects models. The models incorporate the carryover effect (up to the 2nd order), typical side effect of within-subjects experiments. Reaction times were modeled with a shifted-Wald distribution; response choices with a softmax regression. The warning was effective at capturing visual attention and prompting the resumption of control, but it did not directly initiate an intervention. Glance location and the choice and timing of evasive maneuver depended on driving context and on previous experience. Analysis of the whole response process yields more relevant information on the effect of warnings for transition of control than a single measure of intervention time. Furthermore, the carryover effect should not be discounted, because trial randomization can only partially alleviate the problem. We provided results in a format that can be used as a reference for future studies and for computational models of driver behavior.

reaction time

take over request

Bayesian data analysis

collision warning.

response chain


Alberto Morando

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

Trent Victor

Volvo Cars

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

Klaus Bengler

Technical University of Munich

Marco Dozza

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

IEEE Transactions on Intelligent Transportation Systems

1524-9050 (ISSN) 1558-0016 (eISSN)

Vol. 22 5 2809-2822 9016367

Human Factors of Automated Driving (HFAUTO)

European Commission (EC) (EC/FP7/605817), 2013-11-01 -- 2017-10-31.

Subject Categories

Mechanical Engineering


Areas of Advance




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4/5/2022 6