基于 MLP-SVM 的驾驶员换道行为预测

The uncertainty of human driver behavior brings challenges to the application of advanced driver assistance systems. In order to more accurately predict the lane-changing behavior of a driver, we studied the multi-layer perceptron (MLP) and the support vector machine (SVM), and designed the hybrid algorithm of MLP-SVM to predict the lane-changing behavior of the driver. Based on the vehicle information and the surrounding traffic environment information, the prediction model of driver's lane changing behavior is built. The real traffic dataset is used to verify the proposed model. The results show that compared with the prediction model of driver's lane changing behavior based on support vector machines or multi-layer perceptrons, the hybrid prediction model of driver's lane changing behavior achieves the highest prediction accuracy of 92. 6%, and can predict the lane changing behavior earlier with the advanced prediction time up to 4. 54 s.