Abstract
An approach which would allow us to better understand behavioral states inherent in observed behaviors is proposed, based on the development of a mathematical representation of driving behaviors signals using our large driving behavior signal corpus. In particular, the project is aimed at developing technologies for preventing excessive trust in users of automated systems. Misuse/disuse of automation is introduced as a cognitive model of excessive trust, and methods of quantitative measurement are devised. PWARX and GMM models are proposed to represent discrete and continuous information in the cognition/decision/action process. We also develop a method of modeling visual behavior aiming at understanding environmental awareness while driving. We showed the effectiveness of the model experimentally through risky lane change detection. Finally, we show the effectiveness of the method to quantify excessive trust based on developed technology.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
P. Angkititrakul, C. Miyajima, K. Takeda, Stochastic mixture modeling of driving behavior during car following. J. Inf. Commun. Convergence Eng. 11(2), 95–102 (2013)
H. Okuda, N. Ikami, T. Suzuki, Y. Tazaki, K. Takeda, Modeling and analysis of driving behavior based on a probability-weighted ARX model. IEEE Trans. Intell. Transport. Syst. 14(1), 98–112 (2013)
H. Okuda, X. Guo, Y. Tazaki, T. Suzuki, B. Levedahl, Model predictive driver assistance control for cooperative cruise based on hybrid system driver model, in Proceedings of American Control Conference (2014)
K. Maeda, E. Konaka, H. Okuda, T. Suzuki, Hierarchical modeling of obstacle avoidance and steering behavior, in World Congress on Intelligent Transport Systems and Services (2012)
H. Okuda, Y. Kondo, Y. Tazaki, T. Suzuki, S. Tanaka, T. Owada, Evaluation of decision-making ability in car driving while operating interior devices based on probability-weighted ARX models, in World Congress on Intelligent Transport Systems and Services, AP-00054, pp. 1–8 (2012)
P. Angkititrakul, C. Miyajima, K. Takeda, Modeling and adaptation of stochastic driver-behavior model with application to car following, in Proceedings of IEEE Intelligent Vehicles Symposium (IV2011) (2011)
M. Mori, C. Miyajima, T. Hirayama, N. Kitaoka, K. Takeda, Integrated modeling of driver gaze and vehicle operation behavior to estimate risk level of lane change maneuvers, in Proceedings of IEEE Conference on Intelligent Transportation Systems (ITSC2013), pp. 2020–2025 (2013)
M. Mori, C. Miyajima, T. Hirayama, N. Kitaoka, K. Takeda, Modeling driver gaze and vehicle operation patterns to estimate long-term risk levels of driving behavior, in Proceedings of Workshop on Digital Signal Processing for In-Vehicle Systems (DSP in Vehicles 2013) (2013)
M. Mori, C. Miyajima, T. Hirayama, N. Kitaoka, K. Takeda, Use of driver gaze information for detecting risky lane changes, in Proceedings of RISP International Workshop on Nonlinear Circuits, Communications and Signal Processing (NCSP14) (2014)
T. Hirayama, K. Mase, K. Takeda, Analysis of temporal relationships between eye gaze and peripheral vehicle behavior for detecting driver distraction. Int. J. Veh. Technol. 2013, 8 (2013)
M. Mori, C. Miyajima, P. Angkititrakul, T. Hirayama, Y. Li, N. Kitaoka, K. Takeda, Measuring driver awareness based on correlation between gaze behavior and risks of surrounding vehicles, in Proceedings of IEEE Conference on Intelligent Transportation Systems (ITSC2012), pp. 644–947 (2012)
T. Hirayama, S. Sato, K. Mase, C. Miyajima, K. Takeda, Analysis of peripheral vehicular behavior in drivers gaze transition: differences between drivers neutral and cognitive distraction states, in Proceedings of IEEE Intelligent Transportation Systems Conference (2014)
J. Gao , J.D. Lee, Extending the decision field theory to model operators’ reliance on automation in supervisory control situations, IEEE Trans. Syst. Man Cybern. B Cybern., 36(5), pp. 943–959 (2006)
A. Maehigashi, K. Miwa, H. Terai, K. Kojima, J. Morita, Experimental investigation of calibration and resolution in human-automation system interaction. IEICE Trans. Fund. E96-A, 1625–1636 (2013)
A. Maehigashi, K. Miwa, H. Terai, K. Kojima, J. Morita, Y. Hayashi, Experimental investigation of misuse and disuse in using automation system, in Proceedings of HCI International (2011)
A. Maehigashi, K. Miwa, H. Terai, K. Kojima, J. Morita, Selection strategy of effort control: allocation of function to manual operator or automation system, in Proceedings of Conference of the Cognitive Science Society (CogSci2011), pp. 1977–1982 (2011)
Y. Mizuno, K. Kondo, T. Nishino, N. Kitaoka, K. Takeda, Fast source separation based on selection of effective temporal frames, in Proceedings of European Signal Processing Conference (EUSIPCO2012) (2012)
J. Morita, K. Miwa, A. Maehigashi, H. Terai, K. Kojima, F. Ritter, Modeling human-automation interaction in a unified cognitive architecture, in 20th Behavior Representation in Modeling & Simulation (BRIMS) Conference, March 21–24, 2011, pp. 148–153
J. Morita, K. Miwa, A. Maehigashi, H. Terai, A. Kojima, F. Ritter, Modeling decision making on the use of automation, in Proceedings of Conference of the Cognitive Science Society (CogSci2011), pp. 1971–1976 (2011)
C. Miyajima, S. Yamazaki, T. Bando, K. Hitomi, H. Terai, T. Hirayama, M. Egawa, T. Suzuki, K. Takeda, Analyzing driver gaze behavior and consistency of decision making during automated driving, in IEEE Intelligent Vehicles Symposium, June 2015
S. Hayakawa, K. Takeda, F. Itakura, Speaker Identification Using Harmonic Structure of LP-residual Spectrum (Audio and Video-based Biometric Person Authentication) (Springer, Berlin Heidelberg, 1999), pp. 253–260
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer Japan
About this chapter
Cite this chapter
Takeda, K. (2016). Modeling and Detecting Excessive Trust from Behavior Signals: Overview of Research Project and Results. In: Nishida, T. (eds) Human-Harmonized Information Technology, Volume 1. Springer, Tokyo. https://doi.org/10.1007/978-4-431-55867-5_3
Download citation
DOI: https://doi.org/10.1007/978-4-431-55867-5_3
Published:
Publisher Name: Springer, Tokyo
Print ISBN: 978-4-431-55865-1
Online ISBN: 978-4-431-55867-5
eBook Packages: Computer ScienceComputer Science (R0)