Fatigue driving prediction model construction method and device and storage medium

Abstract

The invention discloses a fatigue driving prediction model construction method and device, and a storage medium. The construction method comprises the steps of obtaining fatigue features of a driver;establishing a basic fatigue driving prediction model; selecting an optimal feature subset through a boruta feature selection algorithm and a feature recursion elimination algorithm; training a plurality of base classifiers by using the optimal feature subset, and training a secondary classifier by using outputs of the plurality of base classifiers to obtain a classification result; and optimizingthe model through parameter optimization. The training process is optimized, and the problems of repeated calculation and data leakage of a training set and a test set are avoided; after the classifiers are trained, a plurality of different classifier prediction results are aggregated, so that the final model has better generalization and robustness; the Bayesian optimization algorithm is used for model hyper-parameter tuning, hyper-parameters are adjusted through fewer iterations, and the model performance is further improved.

Description

technical field [0001] The invention relates to the field of automobile driving, in particular to a method, device and storage medium for constructing a fatigue driving prediction model. Background technique [0002] From the statistical investigation of various traffic accident data, it is found that the traffic accidents caused by fatigue driving are increasing year by year. With the development of neural network technology and image processing technology, combining these two technologies to predict and monitor the driver's fatigue state in real time can effectively prevent fatigue driving. There are two technical difficulties in the existing fatigue driving prediction models that need to be overcome: (1) the characteristics and criteria of driving fatigue. Because the formation of fatigue is a process that is gradually generated with the driving environment and time prolongation, and not all characteristics can cause traffic accidents during this process. When the avail...

AI Technical Summary

Problems solved by technology

For example, high correlation between feature variables, missing values ​​in the sample, overfitting and poor generalization performance of the model, etc.

The feature information for fatigue driving has problems such as ambiguity and uncertainty, which will easily lead to a decrease in the robustness of the model

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