80 MPH and out-of-the-loop: Effects of real-world semi-automated driving on driver workload and arousal

Biondi, Francesco; Lohani, Monika; Hopman, Rachel J.; Mills, Sydney; Cooper, Joel M.; Strayer, David L.

doi:10.1177/1541931218621427

Cited by 40 publications

(34 citation statements)

References 21 publications

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“…Additionally, it is noted that physiological measures and the NASA Task Load Index capture only certain facets of stress and workload. Recent on-road research indicated that using the Tesla Autopilot is associated with higher response times on a secondary task as compared to manual driving, possibly due to monitoring demands (e.g., monitoring of the roadway or automation status) or difficulty with multitasking (Biondi, Lohani, Hopman, Mills, Cooper, & Strayer, 2018;Stapel et al, 2019). Also, it remains to be studied how our findings generalize to other driving scenarios, such as driving during rush hours.…”

Section: Conclusion and Recommendationsmentioning

confidence: 80%

Acclimatizing to automation: Driver workload and stress during partially automated car following in real traffic

Heikoop

Winter

Arem

et al. 2019

Transportation Research Part F: Traffic Psychology and Behaviou

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Automated driving systems are increasingly prevalent on public roads, but there is currently little knowledge on the level of workload and stress of drivers operating an automated vehicle in a real environment. The present study aimed to measure driver workload and stress during partially automated driving in real traffic. We recorded heart rate, heart rate variability, respiratory rate, and subjective responses of nine test drivers in the Tesla Model S with Autopilot. The participants, who were experienced with driver assistance systems but naïve to the Tesla, drove a 32 min motorway route back and forth while following a lead car in regular traffic. In one of the two drives, participants performed a heads-up detection task of bridges they went underneath. Averaged across the two drives, the participants' mean self-reported overall workload score on the NASA Task Load Index was 19%. Moreover, the participants showed a reduction in heart rate and self-reported workload over time, suggesting that the participants became accustomed to the experiment and technology. The mean hit (i.e., pressing the button near a bridge) rate in the detection task was 88%. In conclusion, driving with the Tesla Autopilot on a motorway involved a low level of workload that decreased with time on task.

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Section: Conclusion and Recommendationsmentioning

confidence: 80%

Acclimatizing to automation: Driver workload and stress during partially automated car following in real traffic

Heikoop

Winter

Arem

et al. 2019

Transportation Research Part F: Traffic Psychology and Behaviou

View full text Add to dashboard Cite

show abstract

“…Variations in drowsiness levels can also impact HRV (Noda et al, 2015; Piotrowski and Szypulska, 2017). Another recent study found that variations in HRV (TINN and RMSSD) was higher when participants drove a vehicle in automated mode relative to the manual mode (Biondi et al, 2018). Perhaps, drowsiness and a lack of engagement in the driving task during automated mode may have led to a higher HRV.…”

Section: Psychophysiological Measures To Assess Cognitive Statesmentioning

confidence: 99%

“…Drowsiness experienced in car drivers and aircraft pilots can also be associated with decreases in HR (Borghini et al, 2014 ). A recent on-road study (Biondi et al, 2018 ) found that driving a Tesla in semi-automated mode (e.g., autopilot) led to a lower heart rate relative to manual driving on a freeway. Another study found heart rate was sensitive to activity of the Adaptive Cruise Control (ACC) technology (Brouwer et al, 2017 ).…”

Section: Psychophysiological Measures To Assess Cognitive Statesmentioning

confidence: 99%

A Review of Psychophysiological Measures to Assess Cognitive States in Real-World Driving

Lohani

Payne

Strayer

2019

Front. Hum. Neurosci.

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241

181

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As driving functions become increasingly automated, motorists run the risk of becoming cognitively removed from the driving process. Psychophysiological measures may provide added value not captured through behavioral or self-report measures alone. This paper provides a selective review of the psychophysiological measures that can be utilized to assess cognitive states in real-world driving environments. First, the importance of psychophysiological measures within the context of traffic safety is discussed. Next, the most commonly used physiology-based indices of cognitive states are considered as potential candidates relevant for driving research. These include: electroencephalography and event-related potentials, optical imaging, heart rate and heart rate variability, blood pressure, skin conductance, electromyography, thermal imaging, and pupillometry. For each of these measures, an overview is provided, followed by a discussion of the methods for measuring it in a driving context. Drawing from recent empirical driving and psychophysiology research, the relative strengths and limitations of each measure are discussed to highlight each measures' unique value. Challenges and recommendations for valid and reliable quantification from lab to (less predictable) real-world driving settings are considered. Finally, we discuss measures that may be better candidates for a near real-time assessment of motorists' cognitive states that can be utilized in applied settings outside the lab. This review synthesizes the literature on in-vehicle psychophysiological measures to advance the development of effective human-machine driving interfaces and driver support systems.

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“…To identify and exclude moments with excessive movements, accelerometer data should be collected together with the HRV. It is worth noting that it is possible to perform other measurements in conjunction with ECG patch devices [ 25 , 26 ]. However, it is necessary to consider the electromagnetic effect on increased background noise upon the ECG signal [ 27 , 28 ].…”

Section: Methods and Challenges For Cardiac Monitoring In The Antamentioning

confidence: 99%

Smart Wearables for Cardiac Autonomic Monitoring in Isolated, Confined and Extreme Environments: A Perspective from Field Research in Antarctica

Moraes

Mendes

Arantes

2021

Sensors

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Antarctica is a space-analog ICE (isolated, cold, and extreme) environment. Cardiovascular and heart autonomic adjustments are key-adaptive physiological responses to Antarctica, both in summer camps and in research stations winter-over. Research fieldwork in ICE environments imposes limitations such as energy restriction, the need for portable and easy-to-handle resources, and resistance of materials to cold and snow/water. Herein, we present the methods we use for cardiac monitoring in the Antarctic field, the limitations of the equipment currently available, and the specific demands for smart wearables to physiological and health tracking in ICE environments, including the increased remote monitoring demand due to COVID-19 restrictions.

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80 MPH and out-of-the-loop: Effects of real-world semi-automated driving on driver workload and arousal

Cited by 40 publications

References 21 publications

Acclimatizing to automation: Driver workload and stress during partially automated car following in real traffic

Acclimatizing to automation: Driver workload and stress during partially automated car following in real traffic

A Review of Psychophysiological Measures to Assess Cognitive States in Real-World Driving

Smart Wearables for Cardiac Autonomic Monitoring in Isolated, Confined and Extreme Environments: A Perspective from Field Research in Antarctica

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