Titlebook: In-Vehicle Corpus and Signal Processing for Driver Behavior; Kazuya Takeda,John H. L. Hansen,Hüseyin Abut Book 2009 Springer-Verlag US 200

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,Real-World Data Collection with “UYANIK”,nger car instrumented with several sensors, CAN-Bus data logger, cameras, microphones, data acquisitions systems, computers, and support systems. Within the shared frameworks of Drive-Safe Consortium (Turkey) and the NEDO (Japan) International Collaborative Research on Driving Behavior Signal Proces

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On-Going Data Collection of Driving Behavior Signals, the synchronous recording of multimedia data including speech, video, driving behavior, and physiological signals. Driver’s speech and videos are captured with multi-channel microphones and cameras. Gas and brake pedal pressures, steering angles, vehicle velocities, and following distances are meas

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Machine Learning Systems for Detecting Driver Drowsiness,e pre-assumptions about the relevant behavior, focusing on blink rate, eye closure, and yawning. Here we employ machine learning to datamine actual human behavior during drowsiness episodes. Automatic classifiers for 30 facial actions from the facial action coding system were developed using machine

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Extraction of Pedestrian Regions Using Histogram and Locally Estimated Feature Distribution,s. The detection of pedestrians from cars is imperative for predicting their subsequent movements in safe driving assistance. However, the automatic extraction of pedestrian regions with various clothing styles and postures is difficult due to the complexities of textural/brightness patterns in clot

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Three-Dimensional Ultrasound Imaging in Air for Parking and Pedestrian Protection,tion. We address the problem of obstacle detection in a scene using ultrasound imaging with a 2D array under the constraint of a fixed platform. Applications are in the area of autonomous navigation such as a parking car as well as pedestrian detection for pre-crash measures of crash avoidance. The