Driver Behavior Analysis based on The Multi-Sensor Fuzzy Decision

• Main Authors: Kuo-Chu Hu, 胡國柱
• Other Authors: Din-Chang Tseng
• Format: Others
• Language: zh-TW
• Published: 2015
• Online Access: http://ndltd.ncl.edu.tw/handle/06182750147161891883

碩士 === 國立中央大學 === 資訊工程學系在職專班 === 103 === In this thesis, we have developed an inertial sensor-based automobile driver behavior analysis system. This system can help us to detect if a car is in a normal or extreme driving condition during vehicle acceleration, deceleration, and left or right turning. We used an Arduino open hardware and software platform core, and a three-axis accelerometer and three-axis gyroscope inertial sensing element analysis as a source of the signal. In the pre-processing of the sensed signals we used a digital low pass filter to filter out some of the vehicle engine or road surface interference caused by vibration. This was done in addition to previous measurement error correction. To be able to more reliably detect a variety of driving behavior events, we used the fuzzy logic theory as the basis of our analytic judgment. Fuzzy logic includes fuzzy membership function, the main step synthesis, and the maximum and minimum gravity defuzzification. After the above steps, we finally got a proper driving event classification based on the results of each logic judgment. Finally, we conduct experiments on a vehicle. Two passengers in a running vehicle record the vehicle status sequences. The status sequences were compared with those generated by the proposed behavior analysis system based on the fuzzy logic theory. The experiments results validate that indeed the system can successfully detect various driving behavior events; the results generated by the proposed system are consistent with the determination of cognitive passengers.