3 research outputs found
Support Vector Machine for Behavior-Based Driver Identification System
We present an intelligent driver
identification system to handle vehicle theft based on modeling
dynamic human behaviors. We propose to recognize illegitimate
drivers through their driving behaviors. Since human driving
behaviors belong to a dynamic biometrical feature which is
complex and difficult to imitate compared with static features
such as passwords and fingerprints, we find that this novel
idea of utilizing human dynamic features for enhanced security
application is more effective. In this paper, we first describe
our experimental platform for collecting and modeling human
driving behaviors. Then we compare fast Fourier transform
(FFT), principal component analysis (PCA), and independent
component analysis (ICA) for data preprocessing. Using machine
learning method of support vector machine (SVM), we derive the individual
driving behavior model and we then demonstrate
the procedure for recognizing different drivers by analyzing
the corresponding models. The experimental results of learning
algorithms and evaluation are described
Actuators for Intelligent Electric Vehicles
This book details the advanced actuators for IEVs and the control algorithm design. In the actuator design, the configuration four-wheel independent drive/steering electric vehicles is reviewed. An in-wheel two-speed AMT with selectable one-way clutch is designed for IEV. Considering uncertainties, the optimization design for the planetary gear train of IEV is conducted. An electric power steering system is designed for IEV. In addition, advanced control algorithms are proposed in favour of active safety improvement. A supervision mechanism is applied to the segment drift control of autonomous driving. Double super-resolution network is used to design the intelligent driving algorithm. Torque distribution control technology and four-wheel steering technology are utilized for path tracking and adaptive cruise control. To advance the control accuracy, advanced estimation algorithms are studied in this book. The tyre-road peak friction coefficient under full slip rate range is identified based on the normalized tyre model. The pressure of the electro-hydraulic brake system is estimated based on signal fusion. Besides, a multi-semantic driver behaviour recognition model of autonomous vehicles is designed using confidence fusion mechanism. Moreover, a mono-vision based lateral localization system of low-cost autonomous vehicles is proposed with deep learning curb detection. To sum up, the discussed advanced actuators, control and estimation algorithms are beneficial to the active safety improvement of IEVs