By Huseyin Abut, John Hansen, Kazuya Takeda
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Extra resources for Advances for In-Vehicle and Mobile Systems: Challenges for International Standards (v. 2)
Features were extracted from the driving data (stop&go regions) of 30 drivers. For each driver, each set of features can be further classified into four sets corresponding to the signal under study, namely, the accelerator pedal pressure, brake pedal pressure, dynamics of accelerator pedal pressure 2. Driver Recognition System using FNN and Statistical Methods 17 and dynamics of brake pedal pressure. Each driver can be modeled by a single or up to four networks corresponding to the different signal types.
CONCLUSION In this study, we have presented an artificial-vision based environment perception system for autonomous navigation of vehicles in urban areas. The proposed system ensures obstacle detection using stereo vision, road line following by image, road line tracking and artificial landmarks recognition. The techniques under study have been integrated and tested using an experimental fully automated vehicle. Our current research work is focused on fusion of information from multi-sensor signals to improve the perception Chapter 4 46 techniques using passive and active technologies.
In general, there would be a total of ten driver templates corresponding to forty sets of GMM parameters for each driver recognition system. For identification, the input signal(s) are presented to the driver recognition system where the likelihood is measured for each driver template and the driver template that gives the maximum likelihood is identified as the driver. For verification, the input signal is presented to the driver template for which the claim is asserted and the likelihood is computed.
Advances for In-Vehicle and Mobile Systems: Challenges for International Standards (v. 2) by Huseyin Abut, John Hansen, Kazuya Takeda