Impact of the driver’s visual engagement on situation awareness and takeover quality

Marti, Paul; Jallais, Christophe; Koustanaï, Arnaud; Guillaume, Anne; Mars, Franck

doi:10.1016/j.trf.2022.04.018

Cited by 9 publications

(2 citation statements)

References 28 publications

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“…Among physiological parameters, eye-tracking data such as pupil size, duration and count of fixations and saccades, blink rate, gaze direction, and gaze dispersion appear most frequently [33,40,42,43,55,[57][58][59][60], followed by heart rate (HR) or heart rate variability (root mean square of successive differences) [31,42,57,[61][62][63] and skin conductance (SC), e.g., phasic SC, count and magnitude of SC appearances above threshold, and SC response time [31,42,61]. A few studies also featured an electroencephalogram (EEG), usually relying on theta to beta waves ratio (TBR) [52,64].…”

Section: Related Work On Common Take-over Parametersmentioning

confidence: 99%

“…Although physiological data seem to have great potential in research, there is still little consensus on their actual meaning and potential use cases [65]. In addition to physiological measurements, authors have measured drivers' mental workload during a TO by performing the n-back task [66], the detection-response task (DRT) [62,67], or the non-driving task itself [29], as well as through questionnaires such as NASA's task load index (TLX) [57,68,69].…”

Section: Related Work On Common Take-over Parametersmentioning

confidence: 99%

See 1 more Smart Citation

Determining Key Parameters with Data-Assisted Analysis of Conditionally Automated Driving

Gruden

Jakus

2023

Applied Sciences

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In conditionally automated driving, a vehicle issues a take-over request when it reaches the functional limits of self-driving, and the driver must take control. The key driving parameters affecting the quality of the take-over (TO) process have yet to be determined and are the motivation for our work. To determine these parameters, we used a dataset of 41 driving and non-driving parameters from a previous user study with 216 TOs while performing a non-driving-related task on a handheld device in a driving simulator. Eight take-over quality aspects, grouped into pre-TO predictors (attention), during-TO predictors (reaction time, solution suitability), and safety performance (off-road drive, braking, lateral acceleration, time to collision, success), were modeled using multiple linear regression, support vector machines, M5’, 1R, logistic regression, and J48. We interpreted the best-suited models by highlighting the most influential parameters that affect the overall quality of a TO. The results show that these are primarily maximal acceleration (88.6% accurate prediction of collisions) and the TOR-to-first-brake interval. Gradual braking, neither too hard nor too soft, as fast as possible seems to be the strategy that maximizes the overall TO quality. The position of the handheld device and the way it was held prior to TO did not affect TO quality. However, handling the device during TO did affect driver attention when shorter attention times were observed and drivers held their mobile phones in only one hand. In the future, automatic gradual braking maneuvers could be considered instead of immediate full TOs.

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