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Modeling and Detecting Excessive Trust from Behavior Signals: Overview of Research Project and Results

  • Kazuya TakedaEmail author
Chapter

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.

Keywords

Behavior signal processing Driving behavior Misuse/disuse of automation systems GMM PWARX model Visual behavior Excessive trust in automation systems Telephone fraud 

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Copyright information

© Springer Japan 2016

Authors and Affiliations

  1. 1.Nagoya UniversityNagoyaJapan

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