Journal ArticleDOI
Driving Profile Modeling and Recognition Based on Soft Computing Approach
TLDR
Results show great potential in the use of the FNN for real-time driver identification and verification and the profiling of driver behaviors has numerous other potential applications for use by law enforcement and companies dealing with buses and truck drivers.Abstract:
Advancements in biometrics-based authentication have led to its increasing prominence and are being incorporated into everyday tasks. Existing vehicle security systems rely only on alarms or smart card as forms of protection. A biometric driver recognition system utilizing driving behaviors is a highly novel and personalized approach and could be incorporated into existing vehicle security system to form a multimodal identification system and offer a greater degree of multilevel protection. In this paper, detailed studies have been conducted to model individual driving behavior in order to identify features that may be efficiently and effectively used to profile each driver. Feature extraction techniques based on Gaussian mixture models (GMMs) are proposed and implemented. Features extracted from the accelerator and brake pedal pressure were then used as inputs to a fuzzy neural network (FNN) system to ascertain the identity of the driver. Two fuzzy neural networks, namely, the evolving fuzzy neural network (EFuNN) and the adaptive network-based fuzzy inference system (ANFIS), are used to demonstrate the viability of the two proposed feature extraction techniques. The performances were compared against an artificial neural network (NN) implementation using the multilayer perceptron (MLP) network and a statistical method based on the GMM. Extensive testing was conducted and the results show great potential in the use of the FNN for real-time driver identification and verification. In addition, the profiling of driver behaviors has numerous other potential applications for use by law enforcement and companies dealing with buses and truck drivers.read more
Citations
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Journal ArticleDOI
A Review of Research on Driving Styles and Road Safety
TL;DR: There is an acute need for a unifying conceptual framework in order to synthesize these results and make useful generalizations on driving styles, and there is a considerable potential for increasing road safety by means of behavior modification.
Journal ArticleDOI
Personalized Driver/Vehicle Lane Change Models for ADAS
Vadim Butakov,Petros Ioannou +1 more
TL;DR: A methodology that learns the characteristics of an individual driver/vehicle response before and during lane changes and under different driving environments is developed and can be used as a kernel component of ADAS to provide more personalized recommendations to the driver, increasing the potential for more widespread acceptance and use ofADAS.
Journal ArticleDOI
Cognitive Cars: A New Frontier for ADAS Research
TL;DR: This paper provides a survey of recent works on cognitive cars with a focus on driver-oriented intelligent vehicle motion control and discusses how to combine the two directions into a single integrated system to obtain safety and comfort while driving.
Patent
Auto-control of vehicle infotainment system based on extracted characteristics of car occupants
TL;DR: In this paper, an infotainment system for delivering content to multiple occupants of a vehicle is presented. But the system is limited to the delivery of content to one or more occupants of the vehicle.
Journal ArticleDOI
Driving Style Classification Using a Semisupervised Support Vector Machine
TL;DR: Experiments show that the S3VM method can improve the classification accuracy by about 10% and reduce the labeling effort by using only a few labeled data clusters among huge amounts of unlabeled data.
References
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Journal ArticleDOI
Maximum likelihood from incomplete data via the EM algorithm
Book
Neural networks for pattern recognition
TL;DR: This is the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition, and is designed as a text, with over 100 exercises, to benefit anyone involved in the fields of neural computation and pattern recognition.
Book
Ten lectures on wavelets
TL;DR: This paper presents a meta-analyses of the wavelet transforms of Coxeter’s inequality and its applications to multiresolutional analysis and orthonormal bases.
Book ChapterDOI
Neural Networks for Pattern Recognition
Suresh Kothari,Heekuck Oh +1 more
TL;DR: The chapter discusses two important directions of research to improve learning algorithms: the dynamic node generation, which is used by the cascade correlation algorithm; and designing learning algorithms where the choice of parameters is not an issue.