Beamforming and Scalable Image Processing in Vehicle-to-Vehicle Networks
Beamforming and Scalable Image Processing in Vehicle-to-Vehicle Networks
复制标题
车对车网络中的波束成形和可扩展图像处理
DOI：
10.1007/s11265-021-01696-6
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Wang, Honggang
中科院分区：
文献类型：
--
作者：
Ngo, Hieu;Fang, Hua;Wang, Honggang
车对车（V2V）通信使车辆能够无线交换周围环境的信息，并实现协同感知。它有助于预防事故，增加乘客的安全性，提高交通流效率。然而，只有当车辆能够以快速可靠的方式相互通信时，这些好处才能实现。为此，我们从两个方面研究了提高V2V通信质量的方法：一是利用波束形成技术，通过在道路车辆之间建立精确、稳定的协同波束连接，提高V2V通信带宽；第二，确保可扩展传输，减少需要传输的数据量，从而降低自动驾驶车辆协同感知所需的带宽需求。V2V通信中的波束形成可以通过利用基于图像和激光雷达的基于3D数据的车辆检测和跟踪来实现。在车辆检测和跟踪仿真中，我们测试了基于Single Shot Multibox Detector深度学习的目标检测方法，该方法的平均精度为0.837，并使用卡尔曼滤波进行跟踪。对于可扩展传输，我们模拟了不同像素分辨率以及不同图像压缩技术对数据文件大小的影响。结果表明，在不压缩的情况下，仅传输包含检测对象的边界框的文件大小比原始文件大小小10倍。对文件进行不同程度的无损压缩和有损压缩时，也可以观察到类似的结果。基于这些发现，利用现有数据库，这些压缩方法和有效结合特征映射的方法对目标检测和跟踪模型性能的影响将在现实世界的自动驾驶系统中进一步测试。
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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采用双波束成形的 V2V 网络定位误差评估
DOI：
10.1109/vtcfall.2018.8690921
发表时间：
2018
期刊：
2018 IEEE 88th Vehicular Technology Conference (VTC-Fall
影响因子：
--
作者：
Kanthasamy, Nivetha;Du, Ruixiang;Gill, Kuldeep S.;Wyglinski, Alexander M.;Cowlagi, Raghvendra
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Cowlagi, Raghvendra
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DOI：
--
发表时间：
2019
期刊：
International Conference on Advanced Intelligent System and Informatics
影响因子：
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作者：
Abdelhameed S. Eltanany;M. Elwan;A. Amein
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A. Amein
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DOI：
--
发表时间：
2018
期刊：
J. Robotics Mechatronics
影响因子：
--
作者：
Jacob Lambert;Leslie Liang;Luis Yoichi Morales;Naoki Akai;Alexander Carballo;E. Takeuchi;Patiphon Narksri;Shunya Seiya;K. Takeda
通讯作者：
K. Takeda
影响因子：
9.2
作者：
Geiger, A.;Lenz, P.;Urtasun, R.
通讯作者：
Urtasun, R.