ERI: Toward mmWave Vehicular Communication: A Multisensor Multimodal Deep Data Fusion Approach

ERI：迈向毫米波车辆通信：多传感器多模态深度数据融合方法

• 批准号: 2138680
• 负责人: Haijian Sun
• 金额: $ 20万
• 依托单位: University of Wisconsin-Whitewater
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-05-01 至 2024-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2138680&HistoricalAwards=false
• 关键词: ERI; Toward; mmWave; Vehicular; Communication

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).More than 35,000 people die each year on U.S. highways, 1.25 million worldwide. Efforts from all aspects are needed to reduce such a high fatality rate. Among them, connected and automated vehicles (CAV) is the only solution that can bring the number to nearly 0. As the name suggests, CAV involves two interconnected concepts: “connected” and “automated”. Recent years have witnessed significant advances in “automated” vehicles, creating self-driving capability up to Level 5 (fully autonomous). Current autonomous control is implemented in a more isolated way, made possible by sensors, pre-trained AI algorithms, and on-board computer processing within individual vehicle. With more tested and deployed CAVs in the near future, there is a pressing need to provide capability to exchange, transmit, and collect real-time data via vehicle-to-everything (V2X) communications, such as vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I), i.e., the need for “connected”. This research seeks to integrate high frequency signals in millimeter wave (mmWave) band to CAV, thereby providing reliable vehicular connectivity solution with high data rate and low latency. Outcomes from this project will bring following impacts: 1) a transformative signal-less approach utilizing sensory data from existing CAVs; 2) cutting-edge research experience to a primarily undergraduate institution (PUI); 3) integration of research and curriculum development, capstone projects to both undergraduate and graduate students; 4) an open-source platform with hardware, software, and datasets to the research community. mmWave for CAV faces many challenges such as high attenuation during mmWave signal propagation and mobility management. Existing solutions have to initiate pilot signals to measure channel information, then calculate the best narrow beam towards the receiver end to guarantee sufficient signal power. This process takes significant overhead and time, hence not suitable for vehicular applications. Recent works have investigated the possibility to integrate inputs from multisensor, such as LiDAR (Light Detection and Ranging) and camera, traditionally for enabling autonomous driving capability, to facilitate mmWave communications. However, prior work is built from the wireless simulator and 3D modelling software, lacks measurement data from field tests, in addition with assumption on idealized beam patterns that are not available from existing devices. Hence feasibility of such an approach on real-world scenario is largely unknown. To close the gap, our goal for this project is to: 1) develop a low-cost, real-time, cooperative, and synchronized data collection platform for both lab (indoor) and field (outdoor) mmWave CAV communication tests; 2) develop a signal-less codebook/beam selection algorithm with advanced deep data fusion, such that both base station and vehicle user can choose best beam pairs with extremely low overhead; 3) validate the effectiveness of both platform and algorithm with detailed test plans.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该奖项的全部或部分资金来源于《2021 年美国救援计划法案》（公法 117-2）。美国每年有超过 35,000 人死于高速公路，全球有 125 万人需要从各方面做出努力来减少此类事故。其中，联网自动驾驶汽车（CAV）是唯一可以使死亡率接近于0的解决方案。顾名思义，CAV涉及两个方面。互联概念：“互联”和“自动化”近年来，“自动化”车辆取得了显着进步，自动驾驶能力达到了 5 级（完全自主），目前的自主控制以更加孤立的方式实现。在不久的将来，随着越来越多的 CAV 经过测试和部署，迫切需要通过传感器、预先训练的人工智能算法和单车内的车载计算机处理来提供交换、传输和收集实时数据的能力。车辆到一切（V2X）通信，例如车辆对车辆（V2V）和车辆对基础设施（V2I），即“连接”的需求，本研究旨在将毫米波（mmWave）频段的高频信号集成到CAV，从而提供具有高数据速率和低延迟的可靠车辆连接解决方​​案，该项目的成果将带来以下影响：1）利用现有 CAV 的传感数据的变革性无信号方法； 2) 为本科院校 (PUI) 提供前沿研究经验； 3) 为本科生和研究生提供研究和课程开发、顶点项目； 4) 为学生提供硬件、软件和数据集的开源平台；研究界认为毫米波 CAV 面临着许多挑战，例如毫米波信号传播和移动性管理期间的高衰减。过程需要大量的开销和时间，因此不适合车辆应用，但最近的工作研究了集成来自多传感器（例如激光雷达（光检测和测距）和摄像头）的输入以促进毫米波通信的可能性。之前的工作是基于无线模拟器和 3D 建模软件构建的，缺乏现场测试的测量数据，此外还缺乏现有设备无法提供的理想波束方向图的假设，因此这种方法的可行性。为了缩小差距，我们这个项目的目标是：1）为实验室（室内）和现场（室外）开发一个低成本、实时、协作和同步的数据收集平台。 ) 毫米波 CAV 通信测试；2) 开发具有先进深度数据融合的无信号码本/波束选择算法，以便基站和车辆用户能够以极低的开销选择最佳波束对；3) 验证两个平台的有效性；以及经过详细测试的算法该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。