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1. Information design for Vehicle-to-Vehicle communication

   https://doi.org/10.1016/j.trc.2023.104084  

   Gould, Brendan T. ; Brown, Philip N. ( May 2023 , Transportation Research Part C: Emerging Technologies)
2. Vehicle Priority Scheduling Using Vehicle-to-Infrastructure Communications

   Alexander, William E. ; Hainen, Alexander M ; Hong, Xiaoyan ( January 2019 , Transportation Research Board Annual Meeting, 2019)
3. Vehicle-to-Vehicle Charging Coordination over Information Centric Networking

   https://doi.org/10.1109/LCN53696.2022.9843207  

   Thompson, Robert ; Ismail, Muhammad ; Shannigrahi, Susmit ( September 2022 , IEEE LCN)

   Cities around the world are increasingly promoting electric vehicles (EV) to reduce and ultimately eliminate greenhouse gas emissions. A huge number of EVs will put unprecedented stress on the power grid. To efficiently serve the increased charging load, these EVs need to be charged in a coordinated fashion. One promising coordination strategy is vehicle-to-vehicle (V2V) charging coordination, enabling EVs to sell their surplus energy in an ad-hoc, peer to peer manner. This paper introduces an Information Centric Networking (ICN)-based protocol to support ad-hoc V2V charging coordination (V2V-CC). Our evaluations demonstrate that V2V-CC can provide added flexibility, fault tolerance, and reduced communication latency than a conventional centralized cloud based approach. We show that V2V-CC can achieve a 93% reduction in protocol completion time compared to a conventional approach. We also show that V2V-CC also works well under extreme packet loss, making it ideal for V2V charging coordination. 

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4. Information Design Under Uncertainty for Vehicle-to-Vehicle Communication

   https://doi.org/10.1109/LCSYS.2023.3341572  

   Gould, Brendan T ; Brown, Philip N ( December 2023 , IEEE Control Systems Letters)
5. 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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