Workshop on Evolution of Connected Vehicles: The Role of AI/Machine Learning, Emerging Standards, and 5G and Beyond

Abstract

The field of connected vehicles stands at the confluence of three evolving disciplines – the Internet of Things (IoT), emerging standards for connectivity of vehicles, and AI/machine learning. Fueling the growth in the evolution of vehicles towards total automation is the development of novel sensors, 3D cameras, lidars and radars and their ability to connect to the Internet, upload the data to a cloud. The sensors of an autonomous vehicle collect anywhere from 1.4 TB to 19 TB of data per hour. Whether or not the vehicles are autonomous, one of the key features of connected vehicles is that they are able to share data between themselves in real-time. For example, the scene of an accident or road work encountered by a vehicle can be immediately shared with vehicles it is connected to. Thus vehicles may learn about accidents or road work well in advance so as to enable them to make smart decisions and establish alternate routes to their destinations. Facilitating the connectivity of vehicles is the development of standards in various standards organizations. Standards can target both long range and short range communications. Long range communication usually may have relaxed latency constraints whereas short range communication may impose stringent latency constraints. Short range communication standards have been variously called as ITS-G5 (in Europe), WAVE, Dedicated Short Range Communication (DSRC in North America) or IEEE 802.11p. 3GPP has been developing standards for communication between vehicles. Release 14 has prescribed LTE V2X and Release 15 5G V2X. Obviously, the intent is to facilitate the vision of connected vehicles with the help of widely deployed cellular technology and meet the bandwidth and latency constraints. The vast amount of raw data collected must be mined for it to become useful in ensuring traffic safety by means such as intelligent rerouting of traffic or distribution of information on roadwork activities or accidents. Machine learning is a mechanism that has become extremely powerful in extracting meaningful data. A number of machine learning algorithms exist and can be broadly classified under unsupervised, supervised, and reinforcement learning algorithms. A number of algorithms exist under each category.

The workshop will address the impact of machine learning and their applications to connected vehicles with use cases. The workshop will gather researchers and practitioners from industry and academia and help in understanding the role of emerging standards, 5G and beyond and machine learning with use cases in the context of connected vehicles.

Papers will be fully peer reviewed. IEEE takes the protection of intellectual property very seriously. All submissions will be screened for plagiarism using Cross Check. By submitting your work you agree to allow IEEE to screen your work for plagiarism: http://www.crossref.org/crosscheck/index.html

How to submit
Technical paper submissions can be up to 6 pages long and should be written either in an MS Word format or in a Latex format using the templates available at https://www.ieee.org/conferences/publishing/templates.html. All papers must be submitted electronically.

Submissions accepted through this https://edas.info/newPaper.php?c=26958&track=101411

Important Dates

Paper Submission Due: May 1, 2020  May 15, 2020
Acceptance Notification: June 30, 2020
Camera-Ready Submission: July 31, 2020
Workshop date: September 11, 2020

 

General Workshop Chair


Seshadri Mohan
University of Arkansas, Germany
sxmohan@ualr.edu

Nigel Jefferies
Wireless World Research Forum Chairman and Huawei Technologies
chairman@wwrf.ch