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

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.