Abstract:

To detect and identify driving fatigue state, which is one of the causes of a traffic death accident; a two-dimensional cloud model was established based on the “percentage of eyelid closure over the pupil over time” (Per-clos) and blink time mean in view of the randomness and fuzziness of eye movement. Fourteen
pieces of qualitative rules were constructed based on the cloud model features of the two parameters. A generator on 2-D and multi-rules was constructed for uncertainty reasoning in fatigue detection based on a 2-D single rule generator to recognize and detect the fatigue state of an experimental data of 60 samples. The analysis results show that the average recognition rate is 73.98%. Therefore, the method has higher detection rate than classification algorithm KNN (k-nearest neighbor) and SVM (support vector machine) under the same experimental data. The recognition rate of the generator can be improved when the number of training samples increases.

Key words: automobile safety, fatigue driving, eye movement characteristics, generator for uncertainty reasoning, 2-D cloud model