Driver mental load identification model Adapting to Urban Road Traffic Scenarios

Objective: At present, most research on driver mental load identification is based on a single driving scene. However, the driver mental load model established in a road traffic scene is difficult to adapt to the changes of the surrounding road environment during the actual driving process. We proposed a driver mental load identification model which adapts to urban road traffic scenarios. Methods: The model includes a driving scene discrimination sub-model and driver load identification sub-model, in which the driving scene discrimination sub-model can quickly and accurately determine the road traffic scene. The driver load identification sub-model selects the best feature subset and the best model algorithm in the scene based on the judgement of the driving scene classification sub-model. Results: The results show that the driving scene discrimination sub-model using five vehicle features as feature subsets has the best performance. The driver load identification sub-model based on the best feature subset reduces the feature noise, and the recognition effect is better than the feature set using a single source signal and all data. The best recognition algorithm in different scenarios tends to be consistent, and the support vector machine (SVM) algorithm is better than the K-nearest neighbors (KNN) algorithm. Conclusion: The proposed driver mental load identification model can discriminate the driving scene quickly and accurately, and then identify the driver mental load. In this way, our model can be more suitable for actual driving and improve the effect of driver mental load identification.

Automated summary

This study proposes a driver mental load identification model that adapts to urban road traffic scenarios. The model can quickly and accurately discriminate driving scenes and identify driver mental load, thereby improving the effectiveness of driver mental load identification.