Adaptations in attention allocation: Implications for takeover in an automated vehicle

Miller, Erika E.; Boyle, Linda Ng

doi:10.1016/j.trf.2019.08.016

Cited by 5 publications

(7 citation statements)

References 17 publications

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“…Payre et al ( 30 ) found that drivers had more eye glances toward the secondary tasks while driving with combined ACC and LKA in a driving simulator. Simulator studies by Miller and Boyle ( 27 ) and Shen and Neyens ( 36 ) found that drivers engaged more and had longer glances toward the secondary task while driving with LKS/LKA or ACC + LKA systems respectively. Carsten et al ( 4 ) ( η 2 of ACC group = 0.17 and LKA group = 0.36), Dogan et al ( 17 ) ( η 2 = 0.17), and Naujoks et al ( 29 ) conducted driving simulator studies with ACC and LKA and found that drivers increased their secondary task engagement during ADAS drives.…”

Section: Discussionmentioning

confidence: 99%

“…Shen and Neyens ( 36 ) also found, in an experimental simulator study, that driving with ACC and LKA was associated with improved secondary task performance. Miller and Boyle conducted a series of simulator studies with LKS/LKA ( 25 – 27 ) and their results showed some improvement in secondary task performance as measured by miss rate ( 25 ), by response times and miss rates as participants gained experience over drives ( 26 ), and by task completion ( 27 ). Interestingly, they also found that younger drivers showed greater improvements in secondary task performance than did older ones ( 26 ), and, despite improved task completion, there were no significant differences in task accuracy in ( 27 ).…”

Section: Discussionmentioning

confidence: 99%

“…Miller and Boyle conducted a series of simulator studies with LKS/LKA (25)(26)(27) and their results showed some improvement in secondary task performance as measured by miss rate (25), by response times and miss rates as participants gained experience over drives (26), and by task completion (27). Interestingly, they also found that younger drivers showed greater improvements in secondary task performance than did older ones (26), and, despite improved task completion, there were no significant differences in task accuracy in (27). Petersen et al (31) found in a driving simulator study that drivers performed better in ACC and LKA drives than they did in drives with only ACC or only LKA, an effect attributed to improved situation awareness (through an experimental manipulation), which in turn moderated the impact of trust in the system.…”

Section: Research Question 2: Adas Use and Changes In Secondary Task Performancementioning

confidence: 99%

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Systematic Review of Research on Driver Distraction in the Context of Advanced Driver Assistance Systems

Hungund

Pai

Pradhan

2021

Transportation Research Record

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Advanced driver assistance systems (ADAS) promise improved driving performance and safety. With ADAS taking on more vehicle control tasks, the driver’s role may be reduced to that of passive supervision. This in turn may increase drivers’ engagement in non-driving-related tasks, thereby potentially reducing any promised safety benefit. We conducted a systematic review, in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, to study the relationship between ADAS use and driver distraction. Four research questions were addressed—two questions examined the effect of ADAS on secondary task engagement, and the quality of secondary task performance, and two addressed the effects of ADAS on driver attention and on driver behavior changes caused by secondary task engagement. Twenty-nine papers were selected for full text synthesis. The majority of the papers indicate an association between ADAS and increased secondary task engagement, as well as improved secondary task performance. Ten papers reported that drivers tend to divert their attention to secondary tasks and away from driving tasks. These outcomes highlight the continued importance of the role of the human driver despite vehicle automation, especially in the context of driver distraction, and that user understanding of ADAS functionalities and limitations is essential to appropriate and effective use of these systems.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Research Question 2: Adas Use and Changes In Secondary Task Performancementioning

confidence: 99%

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Systematic Review of Research on Driver Distraction in the Context of Advanced Driver Assistance Systems

Hungund

Pai

Pradhan

2021

Transportation Research Record

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“…In these accidents, the underlying computer vision systems struggled to accurately capture a dynamic world model, and the driver monitoring systems also failed to detect driver disengagements, leading to several fatalities [4]. Because of such problems, the interface between the human and machine is of critical importance in such L2+ vehicles as changes in human attention and behavior with high levels of autonomy make the handover regime particularly dangerous [5]- [7].…”

Section: Introductionmentioning

confidence: 99%

Safety Implications of Variability in Autonomous Driving Assist Alerting

Cummings

Bauchwitz

2022

IEEE Trans. Intell. Transport. Syst.

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Advanced Driving Assist Systems (ADAS) are on the rise in new cars, including versions that embed artificial intelligence in computer vision systems that leverage deep learning algorithms. Because these systems, at the present time, cannot operate in all operational driving domains, they employ some type of driver monitoring system for assessing driver attention, so that drivers can effectively take control if and when an ADAS system can no longer control the car. To determine the reliability of a driver alerting system when linked to autonomy that leverages deep learning, a set of increasingly complex tests were conducted on three Tesla Model 3 vehicles. Tests were conducted on a highway and a closed test track to test road departure and construction zone detection capabilities. Results revealed significant between-and within-vehicle variation on a number of metrics related to driver monitoring, alerting, and safe operation of the underlying autonomy. In some cases, cars performed better than expected but all cars exhibited both inconsistent and unsafe behaviors as well as poor driver alerting. These results highlight that a post-deployment regulatory process is ill-equipped to flag significant issues in vehicles with embedded artificial intelligence.

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“…After decoupling from a platoon, drivers may drive with reduced TWH for a couple of minutes. Several previous studies also pointed to carryover effects of driving automation on drivers' attention allocation (e.g., Miller & Boyle, 2019;Vogelpohl, Kühn, Hummel, Gehlert, &Vollrath, 2018), such as reduced focus on road centre, longer eyes-off-road glances, and inadequate mirror checks. These findings suggest that drivers should be supported through this vulnerable period of performance recovery.…”

Section: Sleeping At the Wheel May Pose A Large Safety Threat As Longmentioning

confidence: 96%

Taking back the wheel: transition of control from automated cars and trucks to manual driving

Zhang¹

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for being my doctoral committee members. Thank you for your time and patience, and for your intellectual contributions to my development as a scientist. I would like to thank all my HFauto research fellows for sharing their knowledge and expertise in diverse research areas, and for the fantastic time at the consortium meetings. Many thanks to Silvia Varotto and Zhenji Lu at TU Delft for their invaluable contributions to two important chapters of my thesis, and for their encouragement and companion as friends. Thanks to Marjan Hagenzieker,

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Adaptations in attention allocation: Implications for takeover in an automated vehicle

Cited by 5 publications

References 17 publications

Systematic Review of Research on Driver Distraction in the Context of Advanced Driver Assistance Systems

Systematic Review of Research on Driver Distraction in the Context of Advanced Driver Assistance Systems

Safety Implications of Variability in Autonomous Driving Assist Alerting

Taking back the wheel: transition of control from automated cars and trucks to manual driving

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