Brackstone, M., Khastgir, Siddartha, Martin, J., Charr, A., Navin, G., Jackman, G., Tomlinson, P., Clay, N. and Jennings, Paul. A. (2020) OmniCAV : a simulation and modelling system that enables “CAVs for All”. In: The 23rd IEEE International Conference on Intelligent Transportation Systems (IEEE ITSC 2020), Virtual Conference, 20-23 Sep 2020. Published in: 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) ISBN 9781728141503. doi:10.1109/ITSC45102.2020.9294544

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Abstract

OmniCAV is laying the foundations for the development of a comprehensive, robust and secure simulator, aimed at providing a certification tool for Connected Autonomous Vehicles (CAVs) that can be used by regulatory and accreditation bodies, insurers and manufacturers to accelerate the safe development of CAVs. To achieve this, OmniCAV is using highly detailed road maps, together with a powerful combination of traffic management, accident and CCTV data, to create a high-fidelity traffic and driving simulation environment to interact with the AV under test. Scenarios for testing are developed and randomised in a holistic way to avoid CAVs training to specific conditions. Critically, the simulator offers coverage of a representative element of the U.K. road network, through encompassing rural roads, peri-urban and urban roads to enable autonomy for all. The validity of the synthetic test environment compared to the real-world is of particular importance, and OmniCAV will be tested and refined through an iterative approach involving real-world comparisons and working in conjunction with a CAV testbed.

Item Type:	Conference Item (Paper)
Subjects:	T Technology > TE Highway engineering. Roads and pavements T Technology > TL Motor vehicles. Aeronautics. Astronautics
Divisions:	Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group)
Library of Congress Subject Headings (LCSH):	Vehicular ad hoc networks (Computer networks) , Automated vehicles, Intelligent transportation systems, Intelligent transportation systems -- Data processing
Journal or Publication Title:	2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)
Publisher:	IEEE
ISBN:	9781728141503
Official Date:	24 December 2020
Dates:	Date Event 24 December 2020 Published 7 May 2020 Accepted
DOI:	10.1109/ITSC45102.2020.9294544
Status:	Peer Reviewed
Publication Status:	Published
Reuse Statement (publisher, data, author rights):	© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Access rights to Published version:	Restricted or Subscription Access
Date of first compliant deposit:	7 July 2020
Date of first compliant Open Access:	9 July 2020
RIOXX Funder/Project Grant:	Project/Grant ID RIOXX Funder Name Funder ID CCAV 104529 Department for Transport http://dx.doi.org/10.13039/100012356 CCAV 104529 Department for Business, Energy and Industrial Strategy http://dx.doi.org/10.13039/100011693 UNSPECIFIED Innovate UK http://dx.doi.org/10.13039/501100006041
Conference Paper Type:	Paper
Title of Event:	The 23rd IEEE International Conference on Intelligent Transportation Systems (IEEE ITSC 2020)
Type of Event:	Conference
Location of Event:	Virtual Conference
Date(s) of Event:	20-23 Sep 2020
Related URLs:	Organisation

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