Fatigue driving evaluation method based on artificial nerve network and evidence theory

Abstract

The invention discloses a fatigue driving evaluation method based on an artificial nerve network and an evidence theory, comprising the steps of: collecting a fatigue characteristic data sample; building an artificial nerve network, and utilizing sample data to determine artificial nerve network parameters; calculating fatigue characteristic parameters in real time; performing characteristic grade fusion based on the artificial nerve network; performing decision grade fusion based on a D-S evidence theory; and performing driving state identification according to decision rules. The fatigue driving evaluation method overcomes limitations of a fatigue driving evaluation method based on a single class characteristic or a single information source characteristic, reduces a false drop rate and a miss rate, improves fatigue driving evaluation reliability and accuracy, and is suitable for fatigue driving fatigue real-time evaluation occasions of high accuracy and strong robustness.

Description

technical field [0001] The invention relates to a fatigue driving evaluation method based on artificial neural network and evidence theory, and belongs to the technical field of driver fatigue driving evaluation. Background technique [0002] Fatigue driving detection and evaluation has become a research hotspot in the field of automotive active safety, and the non-contact fatigue driving detection method based on physical sensors has attracted extensive attention in theoretical research and application fields in recent years. However, most of the existing fatigue driving evaluation methods only focus on a single fatigue feature in a certain aspect of the driver, such as frequent blinking and yawning, abnormal head rotation, or abnormal driving status of the vehicle. With the development of information fusion technology, although many evaluation methods have begun to consider the fusion of multiple fatigue features, most of them only integrate a few fatigue features of the d...

AI Technical Summary

Problems solved by technology

However, most of the existing fatigue driving evaluation methods only focus on a single fatigue characteristic of a driver, such as frequent blinking and yawning, abnormal head rotation, or abnormal driving conditions, etc.

With the development of information fusion technology, although many evaluation methods have begun to consider the fusion of multiple fatigue features, most of them only integrate a few fatigue features of the driver's face, and for vehicle behavior features that indirectly reflect whether the driver is tired, such as The abnormal deviation of the vehicle due to fatigue driving, the abnormal steering wheel rotation and the abnormal change of vehicle speed are ignored, which leads to the unsatisfactory evaluation effect of these current methods, which may easily lead to misjudgments and missed evaluations.

At the same time, fatigue driving is a very complex physiological phenomenon. There are many causes, complex symptoms, and difficulties in detection and evaluation. These have brought great challenges to traditional fatigue driving evaluation methods.

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