Download Computational Intelligence in Automotive Applications by Danil Prokhorov PDF

By Danil Prokhorov

What's computational intelligence (CI)? regularly, CI is known as a set of equipment from the fields of neural networks (NN), fuzzy good judgment and evolutionary computation. This edited quantity is the 1st of its variety, compatible to car researchers, engineers and scholars. It offers a consultant pattern of up to date CI actions within the zone of car know-how. the quantity involves thirteen chapters, together with yet now not constrained to those themes: automobile diagnostics and automobile procedure security, keep watch over of vehicular structures, qc of car procedures, motive force kingdom estimation, defense of pedestrians, clever automobiles. All chapters comprise overviews of cutting-edge, and several other chapters illustrate their methodologies on examples of real-world structures. in regards to the Editor: Danil Prokhorov begun his technical profession in St. Petersburg, Russia, after graduating with Honors from Saint Petersburg country college of Aerospace Instrumentation in 1992 (MS in Robotics). He labored as a learn engineer in St. Petersburg Institute for Informatics and Automation, one of many institutes of the Russian Academy of Sciences. He got here to the USA in past due 1993 for Ph.D. experiences. He grew to become concerned about car examine in 1995 while he used to be a summer time intern at Ford clinical learn Lab in Dearborn, MI. Upon his commencement from the EE division of Texas Tech college, Lubbock, in 1997, he joined Ford to pursue application-driven examine on neural networks and different desktop studying algorithms. whereas at Ford, he took half in numerous production-bound tasks together with neural community dependent engine misfire detection. seeing that 2005 he's with Toyota Technical heart, Ann Arbor, MI, overseeing very important mid- and long term study tasks in computational intelligence.

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There are other proposals that use only a camera. In [6] we can find a 2D pupil monocular tracking system based on the differences in color and reflectivity between the pupil and iris. The system monitors driving vigilance by studying the eyelid movement. Another successful system of head/eye monitoring and tracking for drowsiness detection using one camera, which is based on color predicates, is presented in [37]. This system is based on passive vision techniques and its functioning can be problematical in poor or very bright lighting conditions.

3 Feature Combination To understand the contribution of different subsets of the features to the estimation performance, we trained See5 decision trees using various combinations of the features with the segment-level training. The time window size was set to be 30 s. The performance for various groupings of features is shown in Table 9. The best performance we were able to obtain was 81% using all of features. The eyegaze-related features were more predictive than the driving-performance features since removing all other features (the driving-performance features) from the training set reduced performance by only 1 percentage point from 81 to 80%.

Second, the near-IR illumination is not detected by the driver, and then, this does not suppose an interference with the user’s driving. Third, it produces the bright pupil effect, which constitutes the foundation of our detection and tracking system. A bright pupil is obtained if the eyes are illuminated with an IR illuminator beaming light along the camera optical axis. At the IR wavelength, the retina reflects almost all the IR light received along the path back to the camera, and a bright pupil effect will be produced in the image.

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