Driver drowsiness and behavior detection in prolonged conditionally automated drives

Schmidt, Jürgen; Braunagel, Christian; Stolzmann, Wolfgang; Karrer-Gauß, Katja

doi:10.1109/ivs.2016.7535417

Cited by 14 publications

(12 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The general and significant decrease in the correct detection rate from manual to automated driving (drop in the detection rate of the EOG and video based algorithm with 9 to 29%; U = 1,832.500, p < .001, r = .49) and significant increase in the FDR BR (U = 2,940.500, p = .004, r = .22) cannot be explained by the experiments' constraints. Therefore, they imply a change in the behavior of the eyelid movements of the drivers, confirming the results of Schmidt, Braunagel, et al (2016). This further shows that detection rates obtained during manual driving should not be applied universally to CAD mode.…”

Section: Discussionsupporting

confidence: 80%

“…With respect to the detection of blinks in drowsy manual driving, several studies report a drop in the correct detection rate relative to the detection rate of the alert driving phases (Ebrahim, 2016;Jammes et al, 2008;Skotte et al, 2007). Following the higher rate of long eye closures observed for drowsy drivers during CAD by Schmidt, Braunagel, Stolzmann, and Karrer-Gauß (2016), a similar drop in the correct detection rate between the manual mode and the CAD mode can be expected and will be investigated in the following sections.…”

Section: Drowsiness Detection and Influence By The Performance Of Thementioning

confidence: 82%

See 1 more Smart Citation

Eye blink detection for different driver states in conditionally automated driving and manual driving using EOG and a driver camera

et al. 2017

Self Cite

View full text Add to dashboard Cite

In this article, we examine the performance of different eye blink detection algorithms under various constraints. The goal of the present study was to evaluate the performance of an electrooculogram- and camera-based blink detection process in both manually and conditionally automated driving phases. A further comparison between alert and drowsy drivers was performed in order to evaluate the impact of drowsiness on the performance of blink detection algorithms in both driving modes. Data snippets from 14 monotonous manually driven sessions (mean 2 h 46 min) and 16 monotonous conditionally automated driven sessions (mean 2 h 45 min) were used. In addition to comparing two data-sampling frequencies for the electrooculogram measures (50 vs. 25 Hz) and four different signal-processing algorithms for the camera videos, we compared the blink detection performance of 24 reference groups. The analysis of the videos was based on very detailed definitions of eyelid closure events. The correct detection rates for the alert and manual driving phases (maximum 94%) decreased significantly in the drowsy (minus 2% or more) and conditionally automated (minus 9% or more) phases. Blinking behavior is therefore significantly impacted by drowsiness as well as by automated driving, resulting in less accurate blink detection.

show abstract

Section: Discussionsupporting

confidence: 80%

Section: Drowsiness Detection and Influence By The Performance Of Thementioning

confidence: 82%

Eye blink detection for different driver states in conditionally automated driving and manual driving using EOG and a driver camera

et al. 2017

Self Cite

View full text Add to dashboard Cite

show abstract

“…In another study, Miller et al (2015) found significant differences in the behavior of drivers in 40 min drives using secondary devices compared to when drivers were supervising the system. These findings support the results of a study by Schmidt, Braunagel, Stolzmann, & Karrer-Gauß (2016), investigating the behavior in prolonged CAD, compared with manual drives, even though their results indicate that the behavior change is based rather on the system constraints of CAD itself than on fatigue. Looking specifically on the performance in take-over situations, found a safe and anticipatory reaction of participants in comparable long and monotonous CAD with drivers exposed to passive fatigue.…”

Section: Introductionsupporting

confidence: 87%

“…In addition, the driver was asked to confirm alertness requests throughout the drive with CAD. The results and analysis of this stage was part of a previous evaluation (see : Schmidt, Braunagel et al, 2016). Faced with an alertness request the driver had to push a button on the steering wheel to confirm his/her alertness and ability to react.…”

Section: Conditional Automated Drivingmentioning

confidence: 99%

The Influence of Prolonged Conditionally Automated Driving on the Take-Over Ability of the Driver

Schmidt

Mariella

Stolzmann

et al. 2017

Proceedings of the Human Factors and Ergonomics Society Annual

Self Cite

View full text Add to dashboard Cite

Other than during manual and partly automated driving, the driver will not need to constantly observe and correct the path of his/her car with future conditionally automated driving (CAD) systems. The driver will be only responsible to stay alert and be ready to take-over the driving in a preset handover time, if requested. The aim of this simulator study was to examine the reactions of drivers, when requested to take-over after a long, monotonous conditionally automated drive. The conditionally automated drives of 20 drivers (mean driving time of 2:51 ± 0:18 h) were evaluated. To test their reaction and take-over ability, the drivers experienced four easy take-over situations and were additionally challenged with a demanding situation in their worst state regarding fatigue. There was no significant influence found of their fatigue state on the take-over time and quality. The different take-over times in different situations point merely towards an adaption of the drivers’ reactions onto the specific necessities of the different driving situations.

show abstract

“…A similar device is introduced and tested in [96], using not only eye tracking but also heart rate and skin conductance measurements. Fatigue is also analyzed in [97], and how it leads to driver sleepiness during automated drives. In this paper a dedicated camera monitored head movement in addition to driver's gaze and the results showed that common features used for detecting drowsiness during manual driving might not be sufficient for performing the same detection during automated driving.…”

Section: A Driver Studiesmentioning

confidence: 99%

A Review of Driving Simulation Technology and Applications

Bruck

Haycock

Emadi

2021

IEEE Open J. Veh. Technol.

View full text Add to dashboard Cite

Driving simulation has become a very useful tool for vehicle design and research in industry and educational institutes. This paper provides a review of driving simulator components, including the vehicle dynamics model, the motion system, and the virtual environment, and how they interact with the human perceptual system in order to create the illusion of the driving. In addition, a sample of current state-of-the-art vehicle simulators and algorithms are described. Finally, current applications are discussed, such as driver-centered studies, chassis and powertrain design, and autonomous systems development.

show abstract

Driver drowsiness and behavior detection in prolonged conditionally automated drives

Cited by 14 publications

References 12 publications

Eye blink detection for different driver states in conditionally automated driving and manual driving using EOG and a driver camera

Eye blink detection for different driver states in conditionally automated driving and manual driving using EOG and a driver camera

The Influence of Prolonged Conditionally Automated Driving on the Take-Over Ability of the Driver

A Review of Driving Simulation Technology and Applications

Contact Info

Product

Resources

About