Driver recognition system using FNN and statistical methods. (English) Zbl 1195.93109

Abut, Hüseyin (ed.) et al., Advances for in-vehicle and mobile systems. Challenges for international standards. New York, NY: Springer (ISBN 978-0-387-33503-2/hbk). 11-23 (2007).
Summary: Advancements in biometrics-based authentication have led to its increasing prominence and are being incorporated into everyday tasks. Existing vehicle security systems rely currently on electronic alarm or smart card systems. A biometrie driver recognition system utilizing driving behavior signals can be incorporated into existing vehicle security systems to form a multimodal identification system and offer a higher degree of protection. The system can be subsequently integrated into intelligent vehicle systems where it can be used for detection of any abnormal driver behavior with the purposes of improved safety or comfort level. In this chapter, we present features extracted using Gaussian Mixture Models (GMM) from accelerator and brake pedal pressure signals, which are then employed as input to the driver recognition module. A novel Evolving Fuzzy Neural Network (EFuNN) was used to illustrate the validity of the proposed system. Results obtained from the experiments are compared with those of statistical methods. They show potential of the proposed recognition system to be used in real-time scenarios. A high identification rate and the low verification error rate indicate a considerable difference in the way different drivers apply pressure to the pedals.
For the entire collection see [Zbl 1129.93037].


93C95 Application models in control theory
92C55 Biomedical imaging and signal processing
93A30 Mathematical modelling of systems (MSC2010)
92B20 Neural networks for/in biological studies, artificial life and related topics
93B30 System identification
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