Beyond WYSIWYG: Sharing contextual sensing data through mmWave V2V communications

Abstract

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.

A survey on road safety and traffic efficiency vehicular applications based on C-V2X technologies

“…An early work about the use of mmWave to share LIDAR data between vehicles was [108]. This paper proposed a distributed algorithm for communication link allocation between vehicles, and concluded that it is important to consider not only link information (distance for example) but also the value of the information exchanged (receiving information from two nearby vehicles may cause resources to be expended in obtaining redundant information).…”

“…Distributed direct communication link allocation [108] Network-based resource allocation in PC5 interface [135] [136] V2V communications through the Uu interface [110] Demonstration/analysis of specific applications [108] [110] [103] Performance evaluation tested with real-life equipment [110] [103]…”

Taming the Latency in Multi-User VR 360°: A QoE-Aware Deep Learning-Aided Multicast Framework

“…The use of Matching Theory [27] -a mathematical framework from labor economics that attempts to describe the formation of mutually beneficial relationships over time-, has recently garnered a considerable interest in the context of resource allocation for wireless networks [28] with applications as varied as V2V communications [29], [30], FD-NOMA/OMA [31] or Fog computing [32]. However, for the sake of completeness we will first provide several definitions to properly address the fundamentals of this framework adapted to the problem at hand.…”

Real-Time Inter-Vehicle Data Fusion Based on a New Metric for Evidence Distance in Autonomous Vehicle Systems

“…However, autonomous vehicle systems relying solely on ego-vehicle sensors have limitations in dealing with the collisions due to reasons such as bad weather, sensor malfunctioning, or blind spots, etc. [7][8][9]. The risk can be reduced by utilizing sensed information from the neighboring vehicles through Vehicle-to-Vehicle (V2V) communication [10,11] such as Dedicated Short Range Communications (DSRC) [12] which can support safety applications in high data rates [13,14].…”