load model established in a road traffic scene is difficult to adapt to the changes of the surrounding road environment during the actual Objective:At present,most research on driver mental load identification is base...load model established in a road traffic scene is difficult to adapt to the changes of the surrounding road environment during the actual Objective:At present,most research on driver mental load identification is based on a single driving scene.However,the driver mental driving process.We proposed a driver mental load identification model which adapts to urban road traffie scenarios.scene discrimination sub-model can quickly and accurately determine the road traffic scene.The driver load identification sub-model Methods:The model includes a driving scene discrimination sub-model and driver load identification sub-model,in which the driving sub-model.selects the best feature subset and the best model algorithm in the scene based on the judgement of the driving scene classification 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 tends to be consistent,and the support vector machine(5VM)algorithm is better than the K-nearest neighbors(KNN)algorithm.effect is better than the feature set using a single source signal and all data.The best recognition algorithm in different scenarios Conclusion:The proposed driver mental load identificution 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.展开更多
基金supported by the National Natural Science Foundation of China(Grants No.52175088 and 52172399)the National Outstanding Youth Science Fund(NOYSF)in China(Grant No.52325211)the Zhejiang Provincial Natural Science Foundation of China(Grant No.LY19E050012).
文摘load model established in a road traffic scene is difficult to adapt to the changes of the surrounding road environment during the actual Objective:At present,most research on driver mental load identification is based on a single driving scene.However,the driver mental driving process.We proposed a driver mental load identification model which adapts to urban road traffie scenarios.scene discrimination sub-model can quickly and accurately determine the road traffic scene.The driver load identification sub-model Methods:The model includes a driving scene discrimination sub-model and driver load identification sub-model,in which the driving sub-model.selects the best feature subset and the best model algorithm in the scene based on the judgement of the driving scene classification 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 tends to be consistent,and the support vector machine(5VM)algorithm is better than the K-nearest neighbors(KNN)algorithm.effect is better than the feature set using a single source signal and all data.The best recognition algorithm in different scenarios Conclusion:The proposed driver mental load identificution 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.
