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Estimating the out-of-the-loop phenomenon from visual strategies during highly automated driving

Abstract : During highly automated driving, drivers no longer physically control the vehicle but they might need to monitor the driving scene. This is true for SAE level 2, where monitoring the external environment is required; it is also true for level 3, where drivers must react quickly and safely to a take-over request. Without such monitoring, even if only partial, drivers are considered out-of-the-loop (OOTL) and safety may be compromised. The OOTL phenomenon may be particularly important for long automated driving periods during which mind wandering can occur. This study scrutinized drivers’ visual behaviour for 18 min of highly automated driving. Intersections between gaze and 13 areas of interest (AOIs) were analysed, considering both static and dynamic indicators. An estimation of self-reported mind wandering based on gaze behaviour was performed using partial least squares (PLS) regression models. The outputs of the PLS regressions allowed defining visual strategies associated with good monitoring of the driving scene. This information may enable online estimation of the OOTL phenomenon based on a driver’s spontaneous visual behaviour.
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Submitted on : Friday, October 9, 2020 - 7:02:42 AM
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Damien Schnebelen, Camilo Charron, Franck Mars. Estimating the out-of-the-loop phenomenon from visual strategies during highly automated driving. Accident Analysis & Prevention, Elsevier, 2020, 148, pp.105776. ⟨10.1016/j.aap.2020.105776⟩. ⟨hal-02962111⟩



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