In-Vehicle Corpus and Signal Processing for Driver Behavior is comprised of expanded papers from the third biennial DSPin CARS held in Istanbul in June 2007. The goal is to bring together scholars working on the latest techniques, standards, and emerging deployment on this central field of living at the age of wireless communications, smart vehicles, and human-machine-assisted safer and comfortable driving. Topics covered in this book include: improved vehicle safety; safe driver assistance systems; smart vehicles; wireless LAN-based vehicular location information processing; EEG emotion recognition systems; and new methods for predicting driving actions using driving signals.
In-Vehicle Corpus and Signal Processing for Driver Behavior is appropriate for researchers, engineers, and professionals working in signal processing technologies, next generation vehicle design, and networks for mobile platforms.
สารบัญ
Improved Vehicle Safety and How Technology Will Get Us There, Hopefully.- New Concepts on Safe Driver-Assistance Systems.- Real-World Data Collection with “UYANIK”.- On-Going Data Collection of Driving Behavior Signals.- UTDrive: The Smart Vehicle Project.- Wireless Lan-Based Vehicular Location Information Processing.- Perceptually Optimized Packet Scheduling for Robust Real-Time Intervehicle Video Communications.- Machine Learning Systems for Detecting Driver Drowsiness.- Extraction of Pedestrian Regions Using Histogram and Locally Estimated Feature Distribution.- EEG Emotion Recognition System.- Three-Dimensional Ultrasound Imaging in Air for Parking and Pedestrian Protection.- A New Method for Evaluating Mental Work Load In n-Back Tasks.- Estimation of Acoustic Microphone Vocal Tract Parameters from Throat Microphone Recordings.- Cross-Probability Model Based on Gmm for Feature Vector Normalization.- Robust Feature Combination for Speech Recognition Using Linear Microphone Array in a Car.- Prediction of Driving Actions from Driving Signals.- Design of Audio-Visual Interface for Aiding Driver’s Voice Commands in Automotive Environment.- Estimation of High-Variance Vehicular Noise.- Feature Compensation Employing Model Combination for Robust In-Vehicle Speech Recognition.
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