Abstract
This paper uses inductive logic programming (ILP) to identify a driver’s cognitive state in real driving situations to determine whether a driver will be ready to select a suitable operation and recommended service in the next generation car navigation systems. We measure the driver’s eye movement and collect various data such as braking, acceleration and steering angles that are qualitatively interpreted and represented as background knowledge. A set of data about the driver’s degree of tension or relaxation regarded as a training set is obtained from the driver’s mental load based on resource-limited cognitive process analysis. Given such information, our ILP system has successfully produced logic rules that are qualitatively understandable for rule verification and are actively employed for user-oriented interface design. Realistic experiments were conducted to demonstrate the learning performance of this approach. Reasonable accuracy was achieved for an appropriate service providing safe driving.
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References
Bain, M., Sammut, C.: A framework for behavioural cloning. Machine Intelligence 15, 103–129 (1998)
Bratko, I., Muggleton, S., Varsek, A.: Learning Qualitative Models of Dynamic Systems. In: Inductive Logic Programming, pp. 437–452 (1992)
Carpenter, R.H.S.: Movements of the Eyes. Pion Ltd. (1988)
Chapin, J.K., Moxon, K.A., Markowitz, R.S., Nicolelis, M.A.L.: Real-time control of a robot arm using simultaneously recorded neurons in the motor cortex. Nature Neuroscience 2(7), 664–670 (1999)
Clancey, W.J.: Situated Cognition: On Human Knowledge and Computer Representations. Cambridge University Press (1997)
Enderle, J., Blanchard, S.M., Bronzino, J.: Introduction to Biomedical Engineering. Academic Press (2005)
Findlay, J.M., Walker, R.: A model of saccade generation based on parallel processing and competitive inhibition. Behavioral and Brain Science 22, 661–721 (1999)
Findlay, J.M., Gilchrist, I.D.: Active Vision. Oxford University Press (2003)
Kuipers, B.: Qualitative Reasoning: Modeling and Simulation with Incomplete Knowledge. The MIT Press (1994)
Mizoguchi, F., Ohwada, H.: Constrained Relative Least General Generalization for Inducing Constraint Logic Programs. New Generation Computing 13, 335–368 (1995)
Nielsen, J., Pernice, K.: Eyetracking Web Usability. New Riders Press (2009)
Norman, D.A., Bobrow, D.G.: On data-limited and resource-limited processes. Cognitive Psychology 7(1), 44–64 (1975)
Sega, S., Iwasaki, H., Hiraishi, H., Mizoguchi, F.: Qualitative Reasoning Approach to a Driver’s Cognitive Mental Load. International Journal of Software Science and Computational Intelligence 3(4), 18–32 (2011)
Suc, D., Vladusic, D., Bratko, I.: Qualitatively faithful quantitative prediction. Artificial Intelligence 158(2), 189–214 (2004)
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Mizoguchi, F., Ohwada, H., Nishiyama, H., Iwasaki, H. (2013). Identifying Driver’s Cognitive Load Using Inductive Logic Programming. In: Riguzzi, F., Železný, F. (eds) Inductive Logic Programming. ILP 2012. Lecture Notes in Computer Science(), vol 7842. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38812-5_12
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DOI: https://doi.org/10.1007/978-3-642-38812-5_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38811-8
Online ISBN: 978-3-642-38812-5
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