# Beyond WYSIWYG: Sharing Contextual Sensing Data Through mmWave V2V Communications

## TL;DR

This paper introduces a novel distributed V2V association scheme leveraging mmWave communications to enhance contextual sensing sharing among vehicles, significantly increasing the volume of shared information in vehicular networks.

## Contribution

It proposes a new utility-based distributed matching scheme for V2V pairing that considers both communication quality and sensing content value, improving information sharing.

## Key findings

- Up to 71% more sensed information shared compared to baseline methods.
- Utilizes a distributed many-to-one matching game for vehicle pairing.
- Enhances context awareness in vehicular scenarios through mmWave V2V.

## Abstract

In vehicular scenarios context awareness is a key enabler for road safety. However, the amount of contextual information that can be collected by a vehicle is stringently limited by the sensor technology itself (e.g., line-of-sight, coverage, weather robustness) and by the low bandwidths offered by current wireless vehicular technologies such as DSRC/802.11p. Motivated by the upsurge of research around millimeter-wave vehicle-to-anything (V2X) communications, in this work we propose a distributed vehicle-to-vehicle (V2V) association scheme that considers a quantitative measure of the potential value of the shared contextual information in the pairing process. First, we properly define the utility function of every vehicle balancing classical channel state and queuing state information (CSI/QSI) with context information i.e., sensing content resolution, timeliness and enhanced range of the sensing. Next we solve the problem via a distributed many-to-one matching game in a junction scenario with realistic vehicular mobility traces. It is shown that when receivers are able to leverage information from different sources, the average volume of collected extended sensed information under our proposed scheme is up to 71% more than that of distance and minimum delay-based matching baselines.