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   https://doi.org/10.1109/GreenTech52845.2022.9772041  

   Balal, Afshin; Giesselmann, Michael (March 2022, 2022 IEEE Green Technologies Conference (GreenTech))

   This paper makes use of electric vehicles (EVs) that are simultaneously connected to the Photovoltaic Cells (PV) and the power grid. In micro-grids, batteries of the electric vehicles (EVs) used as a source of power to feed the power grid in the peak demands of electricity. EVs can help regulation of the power grid by storing excess solar energy and returning it to the grid during high demand hours. This paper proposes a new architecture of micro-grids by using a rooftop solar system, Battery Electric Vehicles (BEVs), grid connected inverters, a boost converter, a bidirectional half-bridge converter, output filter, including L, LC, or LCL, and transformers. The main parts of this micro-grid are illustrated and modeled, as well as a simulation of their operation. In addition, simulation results explore the charging and discharging scenarios of the BEVs. 

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2. Beamforming and Scalable Image Processing in Vehicle-to-Vehicle Networks

   https://doi.org/10.1007/s11265-021-01696-6  

   Ngo, Hieu; Fang, Hua; Wang, Honggang (January 2022, Journal of Signal Processing Systems)

   Vehicle to Vehicle (V2V) communication allows vehicles to wirelessly exchange information on the surrounding environment and enables cooperative perception. It helps prevent accidents, increase the safety of the passengers, and improve the traffic flow efficiency. However, these benefits can only come when the vehicles can communicate with each other in a fast and reliable manner. Therefore, we investigated two areas to improve the communication quality of V2V: First, using beamforming to increase the bandwidth of V2V communication by establishing accurate and stable collaborative beam connection between vehicles on the road; second, ensuring scalable transmission to decrease the amount of data to be transmitted, thus reduce the bandwidth requirements needed for collaborative perception of autonomous driving vehicles. Beamforming in V2V communication can be achieved by utilizing image-based and LIDAR’s 3D data-based vehicle detection and tracking. For vehicle detection and tracking simulation, we tested the Single Shot Multibox Detector deep learning-based object detection method that can achieve a mean Average Precision of 0.837 and the Kalman filter for tracking. For scalable transmission, we simulate the effect of varying pixel resolutions as well as different image compression techniques on the file size of data. Results show that without compression, the file size for only transmitting the bounding boxes containing detected object is up to 10 times less than the original file size. Similar results are also observed when the file is compressed by lossless and lossy compression to varying degrees. Based on these findings using existing databases, the impact of these compression methods and methods of effectively combining feature maps on the performance of object detection and tracking models will be further tested in the real-world autonomous driving system. 

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3. Information design for Vehicle-to-Vehicle communication

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   Gould, Brendan T.; Brown, Philip N. (May 2023, Transportation Research Part C: Emerging Technologies)
4. Vehicle-to-Vehicle Communication for Autonomous Vehicles: Safety and Maneuver Planning

   https://doi.org/10.1109/VTCFall.2018.8690946  

   Ali, Anum; Jiang, Libin; Patil, Shailesh; Li, Junyi; Heath, Robert W. (August 2018, Proceedings of the Vehicle-to-Vehicle Communication for Autonomous Vehicles: Safety and Maneuver Planning)
5. Vehicle-to-Vehicle Collaborative Graph-Based Proprioceptive Localization

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   Cheng, Hsin-Min; Chou, Chieh; Song, Dezhen (April 2021, IEEE Robotics and Automation Letters)