Cooperative perception, as a critical technology of intelligent connected vehicles, aims to use wireless communication technology to interact and fuse environmental information obtained by edge nodes with local perception information, which can improve vehicle perception accuracy, reduce latency, and eliminate perception blind spots. It has become a current research hotspot. Based on the analysis of the related literature on the Internet of vehicles (IoV), this paper summarizes the multi-sensor information fusion method, information sharing strategy, and communication technology of autonomous driving cooperative perception technology in the IoV environment. Firstly, cooperative perception information fusion methods, such as image fusion, point cloud fusion, and image–point cloud fusion, are summarized and compared according to the approaches of sensor information fusion. Secondly, recent research on communication technology and the sharing strategies of cooperative perception technology is summarized and analyzed in detail. Simultaneously, combined with the practical application of V2X, the influence of network communication performance on cooperative perception is analyzed, considering factors such as latency, packet loss rate, and channel congestion, and the existing research methods are discussed. Finally, based on the summary and analysis of the above studies, future research issues on cooperative perception are proposed, and the development trend of cooperative perception technology is forecast to help researchers in this field quickly understand the research status, hotspots, and prospects of cooperative perception technology.

This paper presents a solution region synthesis methodology to perform the dimensional synthesis of spatial 5-spherical–spherical (SS) linkages for six specified positions of the end-effector. Dimensional synthesis equations for an SS link are formulated. After solving the synthesis equations, the curves of moving and fixed joints can be obtained, and they are called moving and fixed solution curves, respectively. Each point on the curves represents an SS link. Considering the limited range of joints at the first position, we can obtain the feasible solution curves. The link length curves can be obtained based on the feasible solution curves. We determine three SS links by selecting three points meeting the requirements on link length curves. Then, the solution region is built by sorting and adding feasible solution curves and projecting the feasible solution curves on the line. The feasible solution region can be obtained by eliminating defective linkages and linkages that fail to meet the other requirements from the solution region. The validity of the formulas and applicability of the proposed approach is illustrated by example.

In order to solve the difficulty for intelligent vehicles in detecting near-field targets, this paper proposes a near-field object detection method based on multi-sensor information fusion. Firstly, the F-CenterFusion method is proposed to fuse the information from LiDAR, millimeter wave (mmWave) radar, and camera to fully obtain target state information in the near-field area. Secondly, multi-attention modules are constructed in the image and point cloud feature extraction networks, respectively, to locate the targets’ class-dependent features and suppress the expression of useless information. Then, the dynamic connection mechanism is used to fuse image and point cloud information to enhance feature expression capabilities. The fusion results are input into the predictive inference head network to obtain target attributes, locations, and other data. This method is verified by the nuScenes dataset. Compared with the CenterFusion method using mmWave radar and camera fusion information, the NDS and mAP values of our method are improved by 5.1% and 10.9%, respectively, and the average accuracy score of multi-class detection is improved by 22.7%. The experimental results show that the proposed method can enable intelligent vehicles to realize near-field target detection with high accuracy and strong robustness.

The solution region methodology for solving the problem of four-bar linkage synthesis with four specified positions was extended to solve the problem of eight-bar linkage synthesis. The processes to build solution regions for synthesizing different types of eight-bar linkages are described, and the methods of building solution regions are divided into five types. First, the synthesis equation is derived, and the curve expressed by the synthesis equation is called the solution curve. Second, the process to build the spatial solution regions from the solution curves is detailed, and a new defect identification method is developed for building the spatial feasible solution region, which is a set of linkage solutions meeting four positions and excluding defects. Finally, linkage solutions that do not meet practical engineering requirements are eliminated from the spatial feasible solution region to obtain the useful spatial solution region. The examples demonstrate the feasibility of the proposed method. The proposed synthesis methodology is simple and easy to program, and provides reference for four specified position synthesis of other multi-bar linkages.