Drowsiness in Conditional Automation: Proneness, diagnosis and driving performance effects

Jm, Gonçalves; Happee, Riender; Bengler, Klaus

doi:10.1109/itsc.2016.7795658

Cited by 31 publications

(27 citation statements)

References 24 publications

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“…They further noted that both fatigued and drowsy drivers with automation were biased toward choosing to brake rather than steer in response to a takeover request due to a rear-end emergency. Finally, Gonçalves et al (2016) found that subjectively drowsy drivers had higher maximum posttakeover lateral acceleration, although they observed no impacts on longitudinal control or takeover time. The preliminary findings suggest that driver task-related fatigue and drowsiness are relevant modeling components for steering and braking decisions and visual reaction time; however, findings are inconclusive, and significant future work is needed.…”

Section: Review Of Automated Vehicle Takeoversmentioning

confidence: 89%

“…Takeover time budgets also significantly impact the driver's choice of posttakeover response (i.e., braking, steering, or a combination), with braking becoming more common at lower time budgets Gold et al, 2017). This trend in decision making is also aligned Feldhütter, Gold, Schneider, and Bengler (2017); Gold, Berisha, and Bengler (2015); ; ; Gold, Körber, Lechner, and Bengler (2016); Gonçalves, Happee, and Bengler (2016); Kerschbaum, Lorenz, and Bengler (2015); Körber, Baseler, and Bengler (2018); Körber, Gold, Lechner, Bengler, and Koerber (2016); Kreuzmair, Gold, and Meyer (2017) with manual driving (Lee, Llaneras, Klauer, & Sudweeks, 2007). Summary of takeover time budget effects.…”

Section: Takeover Time Budgetmentioning

confidence: 99%

“…The effects of physical fatigue on automated takeovers have not been extensively explored; however, several studies have investigated the effects of drowsiness and task-related fatigue on takeovers. One persistent observation in these studies is that drivers are more prone to fatigue in automated vehicles compared with manually driving (Gonçalves et al, 2016; Jamson, Merat, Carsten, & Lai, 2013; Körber, Cingel, et al, 2015; Neubauer, Matthews, Langheim, & Saxby, 2012; Vogelpohl, Kühn, Hummel, & Vollrath, 2018). The impacts of drowsiness and task-related fatigue on takeover performance are inconclusive.…”

Section: Review Of Automated Vehicle Takeoversmentioning

confidence: 99%

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Toward Computational Simulations of Behavior During Automated Driving Takeovers: A Review of the Empirical and Modeling Literatures

McDonald¹,

et al. 2019

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Objective: This article provides a review of empirical studies of automated vehicle takeovers and driver modeling to identify influential factors and their impacts on takeover performance and suggest driver models that can capture them. Background: Significant safety issues remain in automated-to-manual transitions of vehicle control. Developing models and computer simulations of automated vehicle control transitions may help designers mitigate these issues, but only if accurate models are used. Selecting accurate models requires estimating the impact of factors that influence takeovers. Method: Articles describing automated vehicle takeovers or driver modeling research were identified through a systematic approach. Inclusion criteria were used to identify relevant studies and models of braking, steering, and the complete takeover process for further review. Results: The reviewed studies on automated vehicle takeovers identified several factors that significantly influence takeover time and post-takeover control. Drivers were found to respond similarly between manual emergencies and automated takeovers, albeit with a delay. The findings suggest that existing braking and steering models for manual driving may be applicable to modeling automated vehicle takeovers. Conclusion: Time budget, repeated exposure to takeovers, silent failures, and handheld secondary tasks significantly influence takeover time. These factors in addition to takeover request modality, driving environment, non-handheld secondary tasks, level of automation, trust, fatigue, and alcohol significantly impact post-takeover control. Models that capture these effects through evidence accumulation were identified as promising directions for future work. Application: Stakeholders interested in driver behavior during automated vehicle takeovers may use this article to identify starting points for their work.

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Section: Review Of Automated Vehicle Takeoversmentioning

confidence: 89%

Section: Takeover Time Budgetmentioning

confidence: 99%

Section: Review Of Automated Vehicle Takeoversmentioning

confidence: 99%

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Toward Computational Simulations of Behavior During Automated Driving Takeovers: A Review of the Empirical and Modeling Literatures

McDonald¹,

et al. 2019

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“…The negative impact of automated driving, mainly linked to fatigue appears to increase with time spent under automation [4, 15, 18–20]. A previous study found that drivers rated themselves as subjectively tired even after only 15 min of automated driving [21]. Objective measures of driver fatigue also confirmed the impairment of cognitive state during 20 [22] or 42 min of automated driving [23].…”

Section: Introductionmentioning

confidence: 95%

Long automated driving phase affects take‐over performance

Bourrelly

Naurois

Zran

et al. 2019

IET Intelligent Transport Systems

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We investigated the impact of automated driving phases of different durations (long vs. short) on take-over performance and driver state. Thirty participants drove on a dynamic simulator under autonomous mode for three successive periods of automated driving: a short (10 min), a long (1 hour) and another short (10 min) period, each ending with a take-over request. They performed a non-driving task, watching a film of their choice, throughout the autonomous phases. Driving performance -reaction time and quality -and driver drowsiness were assessed at each take-over. One hour of automated driving affected the driver's behaviour, leading to poorer take-over performance (longer reaction time and sharper avoidance manoeuvre) and increased drowsiness compared to a shorter autonomous period. Results also suggest that sequencing the autonomous phase in several short periods should improve the driver's take-over performance and help prevent drowsiness. Our findings sound a warning on the risks associated with long phases of automated driving. In particular, we provide evidence that series of short periods of automated driving are preferable to a long, continuous automated driving phase.

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“…Specifically, two studies used a combination of visual and auditory signals, and the other 2 studies used single auditory alerts. The lead times (i.e., time budget) between takeover events and takeover requests ranged from 5 to 7 seconds (Feldhutter et al, 2018(Feldhutter et al, , 2019Gonçalves et al, 2016;Jarosch et al, 2019).…”

Section: Takeover Measuresmentioning

confidence: 99%

The Impact of Mental States on Semi-autonomous Driving Takeover Performance: A Systematic Review

Huang

Petersen

Pitts

2020

Proceedings of the Human Factors and Ergonomics Society Annual

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Semi-autonomous vehicles still require drivers to occasionally resume manual control. However, drivers of these vehicles may have different mental states. For example, drivers may be engaged in non-driving related tasks or may exhibit mind wandering behavior. Also, monitoring monotonous driving environments can result in passive fatigue. Given the potential for different types of mental states to negatively affect takeover performance, it will be critical to highlight how mental states affect semi-autonomous takeover. A systematic review was conducted to synthesize the literature on mental states (such as distraction, fatigue, emotion) and takeover performance. This review focuses specifically on five fatigue studies. Overall, studies were too few to observe consistent findings, but some suggest that response times to takeover alerts and post-takeover performance may be affected by fatigue. Ultimately, this review may help researchers improve and develop real-time mental states monitoring systems for a wide range of application domains.

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Drowsiness in Conditional Automation: Proneness, diagnosis and driving performance effects

Cited by 31 publications

References 24 publications

Toward Computational Simulations of Behavior During Automated Driving Takeovers: A Review of the Empirical and Modeling Literatures

Toward Computational Simulations of Behavior During Automated Driving Takeovers: A Review of the Empirical and Modeling Literatures

Long automated driving phase affects take‐over performance

The Impact of Mental States on Semi-autonomous Driving Takeover Performance: A Systematic Review

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