The types of road surfaces on which intelligent connected cars operate are complicated and varied, and current research lacks the achievement of real-time and reasonably high accuracy for road surface categorization. In this research, we provide a deep learning-based technique for classifying and identifying road surfaces that makes use of an improved (VGGNet-16) model, in conjunction with a transfer learning strategy, to gather data from the road surface in front of the car using an on-board camera. To accurately classify data based on obtained road surface photos, the dataset is first preprocessed, then pretrained weights are frozen, and the network is initialized using transfer learning parameters. In order to explore the accuracy analysis of the various models regarding the identification of six types of road surfaces, comparisons were made via the VGG16, AlexNet, InceptionV3, and ResNet50 models, using the same parameter values. The experimental findings demonstrate that the improved VGGNet-16 model, combined with the transfer learning approach, achieves 96.87% accuracy for the classification and recognition of pavements, demonstrating the improved network model’s superior accuracy for these tasks. Additionally, the driving recorder of the vehicle may be used as the sensor to complete pavement detection, which has significant financial advantages.

To improve the identification accuracy of target detection for intelligent vehicles, a real-time target detection system based on the multi-source fusion method is proposed. Based on the ROS melodic software development environment and the NVIDIA Xavier hardware development platform, this system integrates sensing devices such as millimeter-wave radar and camera, and it can realize functions such as real-time target detection and tracking. At first, the image data can be processed by the You Only Look Once v5 network, which can increase the speed and accuracy of identification; secondly, the millimeter-wave radar data are processed to provide a more accurate distance and velocity of the targets. Meanwhile, in order to improve the accuracy of the system, the sensor fusion method is used. The radar point cloud is projected onto the image, then through space-time synchronization, region of interest (ROI) identification, and data association, the target-tracking information is presented. At last, field tests of the system are conducted, the results of which indicate that the system has a more accurate recognition effect and scene adaptation ability in complex scenes.

Based on modular design, the micro-automobile body acceleration waveform reproduction technology has been studied. The design of equivalent structures trolley was to replace the mule cart during the development process of platform derivative models, and frontal crash test results showed that test methods can achieve reproducibility body acceleration, which can effectively improve the efficiency of research and development.