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Fatigue driving fusion detection method based on soft computing

A technology for fatigue driving and detection methods, applied in neural learning methods, safety devices of power plant control mechanisms, biological neural network models, etc.

Inactive Publication Date: 2010-06-23
SOUTHEAST UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0003] The purpose of the present invention is to overcome the shortcomings of the existing fatigue driving detection method based on a single feature, and provide a highly reliable fatigue driving fusion detection method based on soft computing

Method used

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  • Fatigue driving fusion detection method based on soft computing
  • Fatigue driving fusion detection method based on soft computing
  • Fatigue driving fusion detection method based on soft computing

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Embodiment Construction

[0053] Below with reference to accompanying drawing of description, the specific embodiment of the present invention is described in more detail:

[0054] The fatigue driving fusion detection method based on soft computing of the present invention can be realized through two stages, one is an offline training stage, and the other is an online detection stage. The detection flow chart of this method is as follows: figure 1 shown.

[0055] 1. Determine the optimal network structure and network parameters by offline training of data samples

[0056] The optimal network structure and network parameters can be completed in six steps:

[0057] (1) Data collection

[0058] Two CCD cameras C1 and C2 are installed on the experimental vehicle, C1 is responsible for the collection of the driver’s facial image feature signal, C2 is responsible for the collection of the signal of the lane marking line in front of the vehicle, and a photoelectric corner sensor is installed on the steerin...

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Abstract

The invention discloses a fatigue driving fusion detection method based on soft computing, which can detect the fatigue driving of the driver and is characterized in that: the fatigue driving is detected in fusion mode through two aspects which include two facial characteristics for directly indicating the fatigue state of the driver and two vehicle behavior characteristics for indirectly indicating the fatigue state of the driver, wherein the two facial characteristics respectively are frequent blinking and yawning, and the two vehicle behavior characteristics respectively are abnormal vehicle lane deviation and abnormal steering wheel rotation; the invention utilizes the TS fuzzy neural network to recognize fatigue driving, adopts abstraction clustering for the optimized recognition of the network structure, and determines the number of fuzzy rules of the fuzzy neural network and the initial values of the relevant network parameters; genetic algorithm is utilized to train and optimize the network parameters, and determine the optimum network parameters; the TS fuzzy neural network is utilized to detect the fatigue driving of the driver in real-time according to the optimum network parameters and the four fatigue characteristic parameters.

Description

technical field [0001] The present invention relates to a driver fatigue driving fusion detection method, in particular to a fatigue driving fusion detection method based on soft computing, which uses TS fuzzy neural network to fuse two facial fatigue features and two vehicle behavior features, and uses subtraction The method for optimizing and training a network by clustering and a genetic algorithm, and then performing fusion detection on driver fatigue driving belongs to the technical field of driver fatigue driving detection. Background technique [0002] Fatigue driving detection and early warning has become a research hotspot in the field of automotive active safety. Among them, the non-contact fatigue driving detection method based on physical sensors has attracted extensive attention in the field of theoretical research and application in recent years. However, most of the existing fatigue driving detection methods only focus on a single fatigue feature in a certain...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): B60K28/06B60W40/08G06N3/08G08B21/06B60W40/09
Inventor 张为公孙伟张小瑞林国余王雨辰于家河
Owner SOUTHEAST UNIV
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