Mind wandering and driving: responsibility case-control study

Galèra, Cédric; Orriols, Ludivine; M’Baïlara, Katia; Laborey, Magali; Contrand, Benjamin; Ribéreau-Gayon, Régis; Masson, F.; Bakiri, Sarah; Gabaude, Catherine; Fort, Alexandra; Maury, Bertrand; Lemercier, Céline; Cours, Maurice; Bouvard, M.; Lagarde, Emmanuel

doi:10.1136/bmj.e8105

Cited by 129 publications

(113 citation statements)

References 17 publications

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“…Subjects 4,7,13) and less in others (e.g. Subjects 5,6). Also, we observe that the correlation related features (Feature Set 2) do not increase the detection efficiency when used in combination with Feature Set 1, and also result in lower results when used alone.…”

Section: Subject Independent Cognitive Load Detectionmentioning

confidence: 57%

“…Many studies show that driver distraction is one of the most important causes of traffic accidents, along with alcohol use and speeding. A study conducted in France showed that 17% of 453 accidents that resulted in admittance to the emergency room was caused by a high mental distraction of the responsible driver [6]. More recent studies from the same group has shown that induced distractive thoughts led to less micro-regulation of both speed and lateral position and narrowed visual scanning of the driving scene [7], that mind wandering is the cause of 8% of close to 1000 accidents according to emergency room interviews with the drivers [8] and that it affects 85.2% of the drivers especially in situations requiring less attention from the driver such as an everyday commute or a monotonous motorway [9].…”

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confidence: 99%

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Action Units and Their Cross-Correlations for Prediction of Cognitive Load during Driving

Yüce

Gao

Cuendet

et al. 2017

IEEE Trans. Affective Comput.

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Abstract-Driving requires the constant coordination of many body systems and full attention of the person. Cognitive distraction (subsidiary mental load) of the driver is an important factor that decreases attention and responsiveness, which may result in human error and accidents. In this paper, we present a study of facial expressions of such mental diversion of attention. First, we introduce a multi-camera database of 46 people recorded while driving a simulator in two conditions, baseline and induced cognitive load using a secondary task. Then, we present an automatic system to differentiate between the two conditions, where we use features extracted from Facial Action Unit (AU) values and their cross-correlations in order to exploit recurring synchronization and causality patterns. Both the recording and detection system are suitable for integration in a vehicle and a real-world application, e.g. an early warning system. We show that when the system is trained individually on each subject we achieve a mean accuracy and F-score of ∼ 95%, and for the subject independent tests ∼ 68% accuracy and ∼ 66% F-score, with person-specific normalization to handle subject dependency. Based on the results, we discuss the universality of the facial expressions of such states and possible real-world uses of the system.

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Section: Subject Independent Cognitive Load Detectionmentioning

confidence: 57%

mentioning

confidence: 99%

Action Units and Their Cross-Correlations for Prediction of Cognitive Load during Driving

Yüce

Gao

Cuendet

et al. 2017

IEEE Trans. Affective Comput.

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“…Authors from France stated that their two observational epidemiological studies were the first to assess the risk of RTC related to driver's distraction in the Emergency Departments. They found that distraction caused 8 to 17% of RTCs 11,12 .…”

Section: Discussionmentioning

confidence: 99%

“…Thinking deeply beyond the driving task will distract the driver and increase the risk of RTCs. An observational study has shown that more than half of the drivers who had a RTC had some degree of mind wandering before collisions 12 . Similar to our study, a survey from Sweden showed that 3.2% of drivers who were involved with RTC's were distracted by deep thinking 16 .…”

Section: Discussionmentioning

confidence: 99%

Distraction-related road traffic collisions

Eid¹,

Abu‐Zidan²

2017

Afr H. Sci.

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Objectives: We aimed to prospectively study distraction-related road traffic collision injuries, their contributory factors, severity, and outcome. Methods: Data were prospectively collected on all hospitalized road traffic collision trauma patients in Al-Ain City who were drivers at the collision time over one and half years. Driver's inattentive behaviors preceding the collision were collected by interviewing the admitted drivers. Results: There were 444 drivers, 330 of them were fully oriented patients, out of them only 44 (13%) were distracted. Nineteen (5.8%) drivers were distracted by using mobile phones, 12 (3.6%) were pre-occupied with deep thinking, six (1.8%) were talking with other passengers, four (1.2%) were picking things in the vehicle, and three (0.9%) were using entertainment systems. The maximum distraction occurred during the time of 6 am -12 noon when the traffic was crowded. There were no significant differences between distracted and non-distracted drivers in demographical and physiological factors, injured regions, and outcomes. Conclusion: Distraction of alert drivers causes 13% of road traffic collisions in Al-Ain city. About 40 percent of the distracted drivers involved in road traffic collisions (RTC) were using mobile phones. Our study supports the ban of use of cell phones while driving.

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“…There is a growing body of research investigating the concept of MW, suggesting potential causes that could foster such a phenomenon. Only one epidemiological study has focused on this issue in a critical driving context (Galéra et al, 2012), and it revealed the harmful effects of MW in increasing the risk of a car crash. Experimental studies rather consider that driver would adduce in MW (Lemercier et al, 2014).…”

mentioning

confidence: 99%