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The wireless inter-vehicle communication provide a manner to achieve multi-vehicle cooperative driving, and the platoon of automotive vehicle can significantly improve traffic efficiency and ensure traffic safety. Previous researches mostly focus on the state of the proceeding vehicle, and transmit information from self to the succeeding vehicle. Nevertheless, this structure possesses high requirements for controller design and shows poor effect in system stability. In this paper, the state of vehicles is not only related to the information of neighbor vehicles, while V2V communication transmit information over a wide range of area. To begin with, the node dynamic model of vehicle is described by linear integrator with inertia delay and the space control strategy is proposed with different topological communication structures as BF, LBF, PBF, etc.

Nowadays, SC is recognized as a key element of hybrid energy storage system in modern energy supply chain for electric vehicles (EVs). Co9S8 as a promising electrode material attracts much attention for supercapacitor owing to its superior electrochemical capacity. However, its poor stability and electronic conductivity, which result in inferior cycling performance and rate capability, have seriously limited the practical application of Co9O8 in supercapacitors. In this article, Co9S8 nanoparticles were embedded in reduced graphene oxide (rGO) via a simple anneal approach as high efficient and stable electrodes for SCs. The Co9S8/rGO composites were characterized by scanning electron microscopy (SEM), transmission electron microscopy (TEM), and X-ray diffraction (XRD). The Co9S8 nanoparticles were inserted tightly between the rGO layers due to strong intermolecular forces, preventing the cluster in reduction process of rGO from graphene oxide (GO).

With the continuous improvement of positioning technology and industry demand, the shortcomings of each sensor are constantly amplified. Only relying on a single sensor, the demand of high-precision positioning and mapping for intelligent vehicles is difficult to be satisfied. The accuracy of system positioning and mapping is reduced due to the loss of feature points in pure visual SLAM as the environmental characteristics are not obvious or the texture is not abundant. IMU is a sensor with low cost and high update frequency, which can correct the running trajectory in real time and reduce the error of environmental factors on visual sensor data. Therefore, a method based on ORB_SLAM2 algorithm and VINS-Fusion algorithm, the stereo camera information and inertial measurement unit information are extracted and fused in robot operating system is proposed.

To resolve the issue of poor generality of current parking methods based on idle space, a panoramic parking path planning method based on DMPR-PS and Hybrid A-Star is proposed. In this paper, a pure vision approach is adopted, and a fisheye camera is selected as an image acquisition device to build panoramic parking assistance system. Subsequently, the parking slot directional marking points are obtained through DMPR-PS model, and the valid entrance line can be also acquired to recognize parking slot. The accuracy of parking slots recognition can reach 96.86%.Eventually, the planned path can be achieved based on the Hybrid A-Star algorithm where vehicle kinematics and obstacle avoidance constraints are considered. The effectiveness of the planned path can be verified with the adoption of MATLAB and real-vehicle test.