With the development and progress of information technology, especially V2X technology, the research focus of intelligent vehicles gradually shifted from single-vehicle control to multi-vehicle control, and the cooperative control system of intelligent connected vehicles became an important topic of development. In order to track the research progress of intelligent connected vehicle cooperative driving systems in recent years, this paper discusses the current research of intelligent connected vehicle cooperative driving systems with vehicles, infrastructure, and test sites, and analyzes the current development status, development trend, and development limitations of each object. Based on the analysis results of relevant references of the cooperative control algorithm, this paper expounds on vehicle collaborative queue control, vehicle collaborative decision making, and vehicle collaborative positioning. In the case of taking the infrastructure as the object, this paper expounds the communication security, communication delay, and communication optimization algorithm of the vehicle terminal and the road terminal of intelligent connected vehicles. In the case of taking the test site as the object, this paper expounds the development process and research status of the real vehicle road test platform, virtual test platform, test method, and evaluation mechanism, and analyzes the problems existing in the intelligent connected vehicle test environment. Finally, the future development trend and limitations of intelligent networked vehicle collaborative control system are discussed. This paper summarizes the intelligent connected car collaborative control system, and puts forward the next problems to be solved and the direction of further exploration. The research results can provide a reference for the cooperative driving of intelligent vehicles.

Environment perception is the premise for intelligent vehicles to drive safely and stably. Despite the rapid development of road detection technology based on visual images, it is still challenging to robustly identify road areas in visual images due to the influence of illumination changes and noise. In order to solve this problem, we introduce a new optimized lidar and camera sensor fusion method for road environment sensing of intelligent vehicles. In road boundary detection based on laser data, a median point filtering method of ordered pole cloud is proposed. A method of boundary search, boundary seed point growth and obstacle clustering is proposed to identify road boundary. In the lane line classification based on visual image, a lane line search classification method is proposed, which can effectively classify lane lines and extract single lane lines. On the basis of the optimization of sensors, several constraint conditions are proposed based on the fusion of the two data, and the location of missing lane lines is predicted by using the road information identified by lidar and image, and the lane lines are identified again. Finally, a large number of experiments are carried out on kitti-Road benchmark data set, and a test platform is built to verify the results of the identification method proposed in this paper in rainy day, cloudy day, night and other special scenarios. Experimental results show that this method is superior to existing methods.

Computer vision has four basic tasks: image classification,^{1 – 4} object detection,^{5 – 9} semantic segmentation,^{10 – 13} and instance segmentation.^14,15 These technologies form the basis for addressing many practical problems. The main purpose of image classification is to find a classification label from the classification label set and then to assign the label to the input image. The task of object detection is not only to assign classification labels to images but also to mark the locations of specific objects. Both image classification and object detection are the foundation for solving complex problems in computer vision, such as object tracking,^{16 – 19} image segmentation, and scene interpretation. The task of semantic segmentation refers to assigning a category label to each pixel in an image, so as to segment and detect specific categories and finely label the boundaries. While distinguishing each category, instance segmentation also provides different labels for individual instances in the same type of objects. It can be regarded as delivering the tasks of object detection and semantic segmentation at the same time, which usually need to deal with multiple overlapping objects and complex backgrounds. This is the most challenging problem of the four basic tasks of computer vision and has important application value in the fields of intelligent driving,^{20 – 22} remote sensing detection,^{23 – 26} and medical health.^{27 – 30} The four basic tasks of computer vision are shown in Fig. 1. With breakthroughs in computer vision technology, as well as progress in wireless sensor network^{31 – 33} and multiple communications,^{34 – 39} especially in cloud computing^{40 – 42} and other techniques to lower the hardware requirements, it is possible to solve these practical problems.^{43 – 47} Currently, neural network technology is widely deployed.^{48 – 51} Instance segmentation can provide richer information than other computer vision tasks can, but it is also a more difficult job. Although the development of deep learning has greatly promoted the progress of instance segmentation tasks over recent years, there are still few related reviews, without a comprehensive summary of its latest development and a deep discussion of its current difficulties. This study will make an in-depth discussion of instance segmentation on the basis of image segmentation.

Pedestrian detection is a specific application of object detection. Compared with general object detection, it shows similarities and unique characteristics. In addition, it has important application value in the fields of intelligent driving and security monitoring. In recent years, with the rapid development of deep learning, pedestrian detection technology has also made great progress. However, there still exists a huge gap between it and human perception. Meanwhile, there are still a lot of problems, and there remains a lot of room for research. Regarding the application of pedestrian detection in intelligent driving technology, it is of necessity to ensure its real‐time performance. Additionally, it is necessary to lighten the model while ensuring detection accuracy. This paper first briefly describes the development process of pedestrian detection and then concentrates on summarizing the research results of pedestrian detection technology in the deep learning stage. Subsequently, by summarizing the pedestrian detection dataset and evaluation criteria, the core issues of the current development of pedestrian detection are analyzed. Finally, the next possible development direction of pedestrian detection technology is explained at the end of the paper.