ABSTRACT
Drivers’ role changes with increasing automation from the primary driver to a system supervisor. This study investigates how supervising an SAE L2 and L3 automated vehicle (AV) affects drivers’ mental workload and sleepiness compared to manual driving. Using an AV prototype on a test track, the oscillatory brain activity of 23 adult participants was recorded during L2, L3, and manual driving. Results showed decreased mental workload and increased sleepiness in L3 drives compared to L2 and manual drives, indicated by self-report scales and changes in the frontal alpha and theta power spectral density. These findings suggest that fatigue and mental underload are significant issues in L3 driving and should be considered when designing future AV interfaces.
- Brian P Bailey and Joseph A Konstan. 2006. On the need for attention-aware systems: Measuring effects of interruption on task performance, error rate, and affective state. Computers in human behavior 22, 4 (2006), 685–708.Google Scholar
- Klaus Bengler, Klaus Dietmayer, Berthold Farber, Markus Maurer, Christoph Stiller, and Hermann Winner. 2014. Three decades of driver assistance systems: Review and future perspectives. IEEE Intelligent transportation systems magazine 6, 4 (2014), 6–22.Google Scholar
- Hans-Joachim Bieg, Constantina Daniilidou, Britta Michel, and Anna Sprung. 2020. Task load of professional drivers during level 2 and 3 automated driving. Proceedings of the Human Factors and Ergonomics Society Europe (2020), 41–52.Google Scholar
- Maarten AS Boksem, Theo F Meijman, and Monicque M Lorist. 2005. Effects of mental fatigue on attention: an ERP study. Cognitive brain research 25, 1 (2005), 107–116.Google Scholar
- Miriam Bongo and Rosemary Seva. 2022. Effect of Fatigue in Air Traffic Controllers’ Workload, Situation Awareness, and Control Strategy. The International Journal of Aerospace Psychology 32, 1(2022), 1–23.Google Scholar
- Stefan Brandenburg and E-M Skottke. 2014. Switching from manual to automated driving and reverse: Are drivers behaving more risky after highly automated driving?. In 17th international IEEE conference on intelligent transportation systems (ITSC). IEEE, Institute of Electrical and Electronics Engineers (IEEE), 2978–2983.Google ScholarCross Ref
- Tang-Hsien Chang and Yi-Ru Chen. 2014. Driver fatigue surveillance via eye detection. In 17th international ieee conference on intelligent transportation systems (itsc). IEEE, 366–371.Google Scholar
- Joost CF De Winter, Riender Happee, Marieke H Martens, and Neville A Stanton. 2014. Effects of adaptive cruise control and highly automated driving on workload and situation awareness: A review of the empirical evidence. Transportation research part F: traffic psychology and behaviour 27 (2014), 196–217.Google Scholar
- Kartik Dwivedi, Kumar Biswaranjan, and Amit Sethi. 2014. Drowsy driver detection using representation learning. In 2014 IEEE international advance computing conference (IACC). IEEE, 995–999.Google Scholar
- Nikol Figalová, Lewis L Chuang, Jürgen Pichen, Martin Baumann, and Olga Pollatos. 2022. Ambient light conveying reliability improves drivers’ takeover performance without increasing mental workload. Multimodal Technologies and Interaction 6, 9 (2022), 73.Google ScholarCross Ref
- Anna-Katharina Frison, Philipp Wintersberger, Tianjia Liu, and Andreas Riener. 2019. Why do you like to drive automated? a context-dependent analysis of highly automated driving to elaborate requirements for intelligent user interfaces. In Proceedings of the 24th international conference on intelligent user interfaces. 528–537.Google Scholar
- Sandra G Hart. 2006. NASA-task load index (NASA-TLX); 20 years later. In Proceedings of the human factors and ergonomics society annual meeting, Vol. 50. Sage publications Sage CA: Los Angeles, CA, 904–908.Google ScholarCross Ref
- J Stephen Higgins, Jeff Michael, Rory Austin, Torbjörn Åkerstedt, Hans Van Dongen, Nathaniel Watson, Charles Czeisler, Allan I Pack, and Mark R Rosekind. 2017. Asleep at the wheel—the road to addressing drowsy driving. Sleep 40, 2 (2017).Google Scholar
- Philipp Hock, Johannes Kraus, Franziska Babel, Marcel Walch, Enrico Rukzio, and Martin Baumann. 2018. How to design valid simulator studies for investigating user experience in automated driving: review and hands-on considerations. In Proceedings of the 10th International Conference on Automotive User Interfaces and Interactive Vehicular Applications. 105–117.Google ScholarDigital Library
- Jianfeng Hu and Jianliang Min. 2018. Automated detection of driver fatigue based on EEG signals using gradient boosting decision tree model. Cognitive neurodynamics 12, 4 (2018), 431–440.Google Scholar
- Sang-Joong Jung, Heung-Sub Shin, and Wan-Young Chung. 2014. Driver fatigue and drowsiness monitoring system with embedded electrocardiogram sensor on steering wheel. IET Intelligent Transport Systems 8, 1 (2014), 43–50.Google ScholarCross Ref
- Waldemar Karwowski, Przemysław Reszke, and Marian Rusek. 2020. Artificial Intelligence System for Drivers Fatigue Detection. In International Conference on Computer Information Systems and Industrial Management. Springer, 39–50.Google Scholar
- Moritz Klischat and Matthias Althoff. 2019. Generating critical test scenarios for automated vehicles with evolutionary algorithms. In 2019 IEEE Intelligent Vehicles Symposium (IV). IEEE, 2352–2358.Google ScholarDigital Library
- Marius Klug, Sein Jeung, Anna Wunderlich, Lukas Gehrke, Janna Protzak, Zakaria Djebbara, Andreas Argubi-Wollesen, Bettina Wollesen, and Klaus Gramann. 2022. The BeMoBIL Pipeline for automated analyses of multimodal mobile brain and body imaging data. bioRxiv (2022).Google Scholar
- Thomas Köhn, Matthias Gottlieb, Michael Schermann, and Helmut Krcmar. 2019. Improving take-over quality in automated driving by interrupting non-driving tasks. In Proceedings of the 24th international conference on intelligent user interfaces. 510–517.Google ScholarDigital Library
- BM Kusuma Kumari and P Ramakanth Kumar. 2017. A survey on drowsy driver detection system. In 2017 International Conference on Big Data Analytics and Computational Intelligence (ICBDAC). IEEE, 272–279.Google ScholarCross Ref
- Thomas Kundinger, Andreas Riener, Nikoletta Sofra, and Klemens Weigl. 2020. Driver drowsiness in automated and manual driving: insights from a test track study. In Proceedings of the 25th International Conference on Intelligent User Interfaces. 369–379.Google ScholarDigital Library
- Monika Lohani, Brennan R Payne, and David L Strayer. 2019. A review of psychophysiological measures to assess cognitive states in real-world driving. Frontiers in human neuroscience 13 (2019), 57.Google Scholar
- Alistair W MacLean, David RT Davies, and Kris Thiele. 2003. The hazards and prevention of driving while sleepy. Sleep medicine reviews 7, 6 (2003), 507–521.Google Scholar
- Gustav Markkula and J Engström. 2017. Simulating effects of arousal on lane keeping: Are drowsiness and cognitive load opposite ends of a single spectrum?. In Tenth International Conference on Managing Fatigue, San Diego, CA.Google Scholar
- Thomas McWilliams and Nathan Ward. 2021. Underload on the road: measuring vigilance decrements during partially automated driving. Frontiers in psychology 12 (2021), 631364.Google Scholar
- Natasha Merat and A Hamish Jamson. 2009. How do drivers behave in a highly automated car?. In Driving Assesment Conference, Vol. 5. University of Iowa.Google ScholarCross Ref
- Rebecca Michael and Renata Meuter. 2006. Sustained attention and hypovigilance: The effect of environmental monotony on continuous task performance and implications for road safety. In Proceedings of the Australasian road safety research, policing and education conference, Vol. 10. Monash University.Google Scholar
- José M Morales, Carolina Díaz-Piedra, Héctor Rieiro, Joaquín Roca-González, Samuel Romero, Andrés Catena, Luis J Fuentes, and Leandro L Di Stasi. 2017. Monitoring driver fatigue using a single-channel electroencephalographic device: A validation study by gaze-based, driving performance, and subjective data. Accident Analysis & Prevention 109 (2017), 62–69.Google ScholarCross Ref
- Raja Parasuraman and Victor Riley. 1997. Humans and automation: Use, misuse, disuse, abuse. Human factors 39, 2 (1997), 230–253.Google Scholar
- James Robinson. 2023. Police in Germany chase tesla for 15 minutes after driver turns on autopilot and ’goes to sleep’. https://news.sky.com/story/police-in-germany-chase-tesla-for-15-minutes-after-driver-turns-on-autopilot-and-goes-to-sleep-12778306Google Scholar
- SAE International. 2021. Taxonomy and Definitions for Terms Related to Driving Automation Systems for On-Road Motor Vehicles(SAE Standard J3016, Report No. J3016-202104). Technical Report. SAE International, Warrendale, PA.Google Scholar
- Misbah Kazi Salimuddin, Shraddha Panbude, 2018. Driver drowsiness monitoring system using fusion of facial features & EEG. In 2018 Second International Conference on Intelligent Computing and Control Systems (ICICCS). IEEE, 1506–1510.Google ScholarCross Ref
- Azmeh Shahid, Kate Wilkinson, Shai Marcu, and Colin M Shapiro. 2011. Karolinska sleepiness scale (KSS). In STOP, THAT and one hundred other sleep scales. Springer, 209–210.Google Scholar
- Jork Stapel, Freddy Antony Mullakkal-Babu, and Riender Happee. 2019. Automated driving reduces perceived workload, but monitoring causes higher cognitive load than manual driving. Transportation research part F: traffic psychology and behaviour 60 (2019), 590–605.Google Scholar
- Burkay Sucu and Eelke Folmer. 2013. Haptic interface for non-visual steering. In Proceedings of the 2013 international conference on Intelligent user interfaces. 427–434.Google ScholarDigital Library
- Tobias Vogelpohl, Matthias Kühn, Thomas Hummel, and Mark Vollrath. 2019. Asleep at the automated wheel—Sleepiness and fatigue during highly automated driving. Accident Analysis & Prevention 126 (2019), 70–84.Google ScholarCross Ref
- Esra Vural, Mujdat Cetin, Aytul Ercil, Gwen Littlewort, Marian Bartlett, and Javier Movellan. 2007. Drowsy driver detection through facial movement analysis. In international workshop on human-computer interaction. Springer, 6–18.Google ScholarCross Ref
- Esra Vural, Müjdat Çetin, Aytül Erçil, Gwen Littlewort, Marian Bartlett, and Javier Movellan. 2008. Automated drowsiness detection for improved driving safety. (2008).Google Scholar
- Marc Wilbrink, Anna Schieben, and Michael Oehl. 2020. Reflecting the automated vehicle’s perception and intention: Light-based interaction approaches for on-board HMI in highly automated vehicles. In Proceedings of the 25th International Conference on Intelligent User Interfaces Companion. 105–107.Google ScholarDigital Library
- Philipp Wintersberger, Andreas Riener, Clemens Schartmüller, Anna-Katharina Frison, and Klemens Weigl. 2018. Let me finish before I take over: Towards attention aware device integration in highly automated vehicles. In Proceedings of the 10th international conference on automotive user interfaces and interactive vehicular applications. 53–65.Google ScholarDigital Library
- Philipp Wintersberger, Clemens Schartmüller, and Andreas Riener. 2019. Attentive user interfaces to improve multitasking and take-over performance in automated driving: the auto-net of things. International Journal of Mobile Human Computer Interaction (IJMHCI) 11, 3(2019), 40–58.Google ScholarCross Ref
- Zhitao Xiao, Zhiqiang Hu, Lei Geng, Fang Zhang, Jun Wu, and Yuelong Li. 2019. Fatigue driving recognition network: fatigue driving recognition via convolutional neural network and long short-term memory units. IET Intelligent Transport Systems 13, 9 (2019), 1410–1416.Google ScholarCross Ref
- Feng Zhou, Areen Alsaid, Mike Blommer, Reates Curry, Radhakrishnan Swaminathan, Dev Kochhar, Walter Talamonti, Louis Tijerina, and Baiying Lei. 2020. Driver fatigue transition prediction in highly automated driving using physiological features. Expert Systems with Applications 147 (2020), 113204.Google ScholarDigital Library
Index Terms
- Fatigue and mental underload further pronounced in L3 conditionally automated driving: Results from an EEG experiment on a test track
Recommendations
Estimating driving performance based on EEG spectrum analysis
The growing number of traffic accidents in recent years has become a serious concern to society. Accidents caused by driver's drowsiness behind the steering wheel have a high fatality rate because of the marked decline in the driver's abilities of ...
The impact of drowsiness on in-vehicle human-machine interaction with head-up and head-down displays
Various studies show that drowsiness reduces driver alertness and can significantly affect driver performance. In this paper, we investigate the effect of drowsiness on the interaction with the in-vehicle infotainment system (IVIS) while driving. The ...
An Augmented Reality Display for Conditionally Automated Driving
AutomotiveUI '18: Adjunct Proceedings of the 10th International Conference on Automotive User Interfaces and Interactive Vehicular ApplicationsThis paper investigates whether an Augmented Reality Head-up Display (AR-HUD) supports usability and reduces visual demand during conditionally automated driving. In a driving simulator study, 24 drivers experienced several driving scenarios while ...
Comments