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Simultaneous Recording of EEGs and Eye-Tracking for Investigating Situation Awareness and Working Memory Load in Distracted Driving: A Prospective Analysis Toward the Neuro-Driving Framework

Mayu Ichiki1*, Guangyi Ai1 and Hiroaki Wagatsuma1, 2
  • 1 Kyushu Institute of Technology, Graduate School of Life Science and Systems Engineering, Japan
  • 2 RIKEN Brain Science Institute, Japan

Automatic driving gains prominence not only in the automobile engineering field but also neuro-engineering research field [1, 2] to detect an abnormal brain activity for preventing serious accidents. Neuro-driving simulation frameworks were studied for the detection of emergency situation based on ERP-related neural activity associated with emergency situations while driving [3]. In the first place, to progress such research activities, the common criterion to describe the level of driving risks is necessary for the risk assessment such as accident predictions and managements of driver's comfort levels, which is discussed as a safety zone boundary in the state space spanned by Driver-Vehicle-Environment (DVE) axes [4, 5]. The DVE state space represents three areas: the comfort zone to allow the driver the adaptive control, discomfort zone but still in the safely margin and the inevitable accident zone called lose the control [6]. In this study, we attempt to describe the DVE state space related to driver conditions (skill levels, attention controls, feelings, health status and so on) and environmental factors around the vehicle (the complexity of situations including risk levels, road and weather conditions, and so on) in a possible form of the database and car ontology [7]. In the second place, experimental framework is necessary to investigate in the dynamic environment while driving, by investigating the attention change and working memory capacity. We preliminary established the eye-tracking experiment during an interactive communication with the robot to instruct multiple sources of information, which aims to extend a test for working memory load in distracted driving (Figure 1). American Automobile Association (AAA) reports [8] that factors of distraction leading to a teen driver crash are interaction with other passengers (15%), cell phone usage (12%), looking at something in the vehicle (10%) and outside the vehicle (9%), singing/dancing to music (8%), personal grooming (6%) and reaching for an object (6%), and other distractions included eating/drinking, smoking, reading something such as a map and talking to oneself. A systematic approach to measure the attentional change and working memory capacity can be extended to analyses with a simultaneous recording of the eye-tracker and EEGs and it contributes to the establishment of the common evaluation method of social and driver competencies.

Figure 1
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Acknowledgements

This work was partly supported by JSPS KAKENHI 26240032 and the collaborative research project with FUJITSU TEN LIMITED.

References

[1] Kim, J-W., Kim, Il-H.,Lee, S-W. (2013) Neuro-driving: Automatic perception technique for upcoming emergency situations, The IEEE International Winter Workshop on Brain-Computer Interface (BCI), 8-9.
[2] Haufe, S., Kim, J-W., Kim, Il-H., Sonnleitner, A., Schrauf, M., Curio, G., Blankertz, B. (2014) Electrophysiology-based detection of emergency braking intention in real-world driving, Journal of Neural Engineering 11(5):056011.
[3] Haufe, S., Treder, M. S., Gugler, M. F., Sagebaum, M., Curio, G., Blankertz, B. (2011) EEG potentials predict upcoming emergency brakings during simulated driving, Journal of Neural Engineering 8(5): 056001.
[4] AIDE - Adaptive Integrated Driver-vehicle InterfacE, http://www.aide-eu.org/res_sp1.html
[5] Amditis A., Pagle K., Joshi S., Bekiaris E. (2010) Driver–Vehicle–Environment monitoring for on-board driver support systems: Lessons learned from design and implementation, Applied Ergonomics 41(2): 225–235.
[6] Engström, J. (2011) Understanding attention selection in driving: From limited capacity to adaptive behaviour. Thesis for the Degree of Doctor of Philosophy, Vehicle Safety Division, Department of Applied Mechanics, Chalmers University of Technology, Goteborg, Sweden.
[7] Zhao, L, Ichise, R, Mita, S., Sasaki, Y. (2015) An Ontology-Based Intelligent Speed Adaptation System for Autonomous Cars, Lecture Notes in Computer Science 8943: 397-413.
[8] Distraction and Teen Crashes: Even Worse than We Thought, http://newsroom.aaa.com/tag/teen-driver/.

Keywords: Neuro-engineering, Driver-Vehicle-Environment (DVE), Distracted driving, EEG, Eye-Tracking Systems

Conference: Neuroinformatics 2015, Cairns, Australia, 20 Aug - 22 Aug, 2015.

Presentation Type: Poster, not to be considered for oral presentation

Topic: Brain-machine interface

Citation: Ichiki M, Ai G and Wagatsuma H (2015). Simultaneous Recording of EEGs and Eye-Tracking for Investigating Situation Awareness and Working Memory Load in Distracted Driving: A Prospective Analysis Toward the Neuro-Driving Framework. Front. Neurosci. Conference Abstract: Neuroinformatics 2015. doi: 10.3389/conf.fnins.2015.91.00010

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Received: 31 May 2015; Published Online: 05 Aug 2015.

* Correspondence: Ms. Mayu Ichiki, Kyushu Institute of Technology, Graduate School of Life Science and Systems Engineering, Kitakyushu, Fukuoka, 808-0196, Japan, ichiki-mayu@edu.brain.kyutech.ac.jp