In order to solve the problems of low accuracy and poor generalization of traffic sign recognition caused by factors such as fog, light, occlusion and large inclination, a lightweight traffic sign recognition method based on neural network was proposed. First, in order to improve image quality, the methods of image normalization, affine transformation and Contrast Limited Adaptive Histogram Equalization (CLAHE) were used for image preprocessing. Second, based on Convolutional Neural Network (CNN), spatial pyramid structure and Batch Normalization (BN) were fused to construct an improved CNN with Spatial Pyramid Pooling (SPP) and BN (SPPN-CNN), and Softmax classifier was used to perform the traffic sign recognition. Finally, the German Traffic Sign Recognition Benchmark (GTSRB) was used to compare the training effects of different image preprocessing methods, model parameters and model structures, and to verify and test the proposed model. Experimental results show that for SPPN-CNN model, the recognition accuracy reaches 98.04% and the loss is less than 0.1, and the recognition rate is greater than 3 000 frame/s under the condition of GPU with low configuration,verifying that the SPPN-CNN model has high accuracy, strong generalization and good real-time performance.

Aiming at positioning failure occured when positioning and navigation system of the intelligent connected vehicle fail to receive the signal of Global Navigation Satellite System (GNSS), a GNSS/Inertial Navigation System (INS) global high-precision positioning method based on Elman neural network was proposed. Firstly, a GNSS/INS high-precision positioning training model and a GNSS failure prediction model based on Elman neural network were established. Then, by using GNSS, INS and Real-Time Kinematic (RTK) and other positioning techniques, a data acquisition experiment system of GNSS/INS high-precision positioning was designed. Finally, the effective experimental data were collected to compare the performance of the training model of Back Propagation (BP) neural network, Cased-Forward BP (CFBP) neural network, Elman neural network, and the prediction model of GNSS signal outage based on Elman network was verified. The experimental results show that the training error of GNSS/INS prediction model based on Elman network is better than those based on BP and CFBP neural networks. When GNSS fails for 1 min, 2 min and 5 min, the prediction Mean Absolute Error (MAE), Variance (VAR) and Root Mean Square Error (RMSE) were 18.88 cm, 19.29 cm, 58.83 cm and 8.96, 8.45, 5.68 and 20.90, 21.06, 59.10 respectively, and with the increase of GNSS signal outage time, the positioning prediction accuracy is reduced.