With the rapid development of Advanced Driver Assistance Systems (ADAS) and unmanned technology

the identification of traffic signs becomes an important research topic In order to assist the driver to achieve safe driving and reduce the possibility of traffic accidents

a Mixed Forecasting Model (MFM) based on AdaBoostSVM and CNN was proposed to identify traffic signs In MFM

the CNN is used as a trained feature extractor and AdaBoostSVM is used as a recognizer The two layers of hidden layer structure were constructed by convolution and subsampling The pretreatment images were used as the input of CNN model The CNN model was trained by Back propagation (BP) until convergence. Finally the test set of the dimensional features were extracted

and the AdaBoostSVM classifier was adopted to classify and identify the dimensional features The experimental results show that the MFM has high recognition rate and robustness to traffic signs

and the recognition rate and convergence time are superior to other traditional algorithms

which are of great significance to improve the driving and unmanned safety