Data fusion method in multi-sensors autonomous tracking

Hai-jiao, 单海蛟 Shan; Wei-ning, 刘伟宁 Liu; Jia-cheng, 王嘉成 Wang; Pei-xun, 刘培勋 Liu; Zhi-gen, 李治根 Li; Yao-hua, 邓耀华 Deng

doi:10.3788/yjyxs20163108.0801

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“…Its advantage is that the learning speed is fast. For the algorithm applied to the product, it can greatly reduce the calculation amount, increase the calculation speed, and greatly improve the calculation speed [20,21]. Its characteristic is that the weights of hidden layer nodes are randomly or artificially given and do not need to be updated.…”

Section: Introduction To Over-limit Learning Machinementioning

confidence: 99%

Heterogeneous Signal Fusion Method in Driving Fatigue Detection Signals

Wang

Zhang

2021

Journal of Advanced Transportation

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Driving fatigue is a physiological phenomenon that often occurs during driving. When the driver enters a fatigue state, they will become distracted and unresponsive, which can easily lead to traffic accidents. The driving fatigue detection method based on a single information source has poor stability in a specific driving environment and has great limitations. This work helps with being able to judge the fatigue state of the driver more comprehensively and achieving a higher accuracy rate of driving fatigue detection. This work mainly introduces research into different signal fusion methods to detect fatigue drive. This work will take the normal driver’s breathing signal, eye signals, and steering wheel signal as research objects and collect and isolate the characteristics of the fatigue detection signal. Research was then conducted on different signal fusion methods for the detected depth of breath. Change of steering angle, eyelid closure, and blinking marks and the fatigue driving experiment was designed to evaluate the results of different data fusion methods. Experimental results show that the detection accuracy of the heterogeneous signal fusion method in fatigue detection is as high as 80%.

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Section: Introduction To Over-limit Learning Machinementioning

confidence: 99%