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

该奖项的全部或部分资金来自《2021年美国救援计划法案》(公法117-2)。每年有超过3.5万人死于美国高速公路，全球有125万人死亡。要降低如此高的死亡率，需要各方面的努力。其中，互联自动化车辆(CAV)是唯一可以使这一数字接近0的解决方案。顾名思义，CAV涉及两个相互关联的概念：互联和自动化。近年来，自动驾驶汽车取得了重大进展，自动驾驶能力达到了5级(完全自动驾驶)。目前的自主控制是以一种更孤立的方式实现的，这可以通过传感器、预先训练的人工智能算法和单个车辆内的车载计算机处理来实现。随着在不久的将来有更多经过测试和部署的CAV，迫切需要提供通过车辆到一切(V2X)通信(例如车辆到车辆(V2V)和车辆到基础设施(V2I))交换、传输和收集实时数据的能力，即需要“连接”。本研究旨在将毫米波(MmWave)波段的高频信号整合到CAV中，从而提供高数据速率和低延迟的可靠车载连接解决方案。该项目的成果将带来以下影响：1)利用现有CAV的感官数据的变革性无信号方法；2)为主要的本科生机构(PUI)提供尖端研究经验；3)研究与课程开发的整合，为本科生和研究生提供顶峰项目；4)为研究社区提供包含硬件、软件和数据集的开源平台。用于CAV的毫米波面临着许多挑战，如毫米波信号传播过程中的高衰减和移动性管理。现有的解决方案需要先发出导频信号来测量信道信息，然后计算出朝向接收端的最佳窄波束，以保证有足够的信号功率。这一过程耗费了大量的开销和时间，因此不适合车载应用。最近的工作已经研究了集成来自多传感器的输入的可能性，例如LiDAR(光检测和测距)和相机，传统上是为了实现自动驾驶能力，以促进毫米波通信。然而，以前的工作是基于无线模拟器和3D建模软件，缺乏来自现场测试的测量数据，以及现有设备无法获得的关于理想化波束方向图的假设。因此，这种方法在现实世界场景中的可行性在很大程度上是未知的。为了缩小差距，我们这个项目的目标是：1)开发一个低成本、实时、协作和同步的数据收集平台，用于实验室(室内)和野外(室外)毫米波CAV通信测试；2)开发具有先进的深度数据融合的无信号码本/波束选择算法，以便基站和车载用户都可以以极低的开销选择最佳波束对；3)通过详细的测试计划验证平台和算法的有效性。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.