The University of Southampton
University of Southampton Institutional Repository

Cloud2Edge elastic AI framework for prototyping and deployment of AI inference engines in autonomous vehicles

Cloud2Edge elastic AI framework for prototyping and deployment of AI inference engines in autonomous vehicles
Cloud2Edge elastic AI framework for prototyping and deployment of AI inference engines in autonomous vehicles

Self-driving cars and autonomous vehicles are revolutionizing the automotive sector, shaping the future of mobility altogether. Although the integration of novel technologies such as Artificial Intelligence (AI) and Cloud/Edge computing provides golden opportunities to improve autonomous driving applications, there is the need to modernize accordingly the whole prototyping and deployment cycle of AI components. This paper proposes a novel framework for developing so-called AI Inference Engines for autonomous driving applications based on deep learning modules, where training tasks are deployed elastically over both Cloud and Edge resources, with the purpose of reducing the required network bandwidth, as well as mitigating privacy issues. Based on our proposed data driven V-Model, we introduce a simple yet elegant solution for the AI components development cycle, where prototyping takes place in the cloud according to the Software-in-the-Loop (SiL) paradigm, while deployment and evaluation on the target ECUs (Electronic Control Units) is performed as Hardware-in-the-Loop (HiL) testing. The effectiveness of the proposed framework is demonstrated using two real-world use-cases of AI inference engines for autonomous vehicles, that is environment perception and most probable path prediction.

Artificial intelligence, Autonomous vehicles, Cloud computing, Deep learning, Edge computing, Self-driving cars
1424-8220
1-21
Grigorescu, Sorin
3b0ec4bd-7929-4b94-a5e9-c47632c37426
Cocias, Tiberiu
8959460b-f586-4b0f-84bb-f377609995ed
Trasnea, Bogdan
19182c70-1fb9-43e3-a4f3-ac3c730a434a
Margheri, Andrea
4b87c32d-3eaf-445e-8ac0-8207daace2e1
Lombardi, Federico
78e41297-64c9-4c1e-9515-8eb59334a795
Aniello, Leonardo
9846e2e4-1303-4b8b-9092-5d8e9bb514c3
Grigorescu, Sorin
3b0ec4bd-7929-4b94-a5e9-c47632c37426
Cocias, Tiberiu
8959460b-f586-4b0f-84bb-f377609995ed
Trasnea, Bogdan
19182c70-1fb9-43e3-a4f3-ac3c730a434a
Margheri, Andrea
4b87c32d-3eaf-445e-8ac0-8207daace2e1
Lombardi, Federico
78e41297-64c9-4c1e-9515-8eb59334a795
Aniello, Leonardo
9846e2e4-1303-4b8b-9092-5d8e9bb514c3

Grigorescu, Sorin, Cocias, Tiberiu, Trasnea, Bogdan, Margheri, Andrea, Lombardi, Federico and Aniello, Leonardo (2020) Cloud2Edge elastic AI framework for prototyping and deployment of AI inference engines in autonomous vehicles. Sensors (Switzerland), 20 (19), 1-21, [5450]. (doi:10.3390/s20195450).

Record type: Article

Abstract

Self-driving cars and autonomous vehicles are revolutionizing the automotive sector, shaping the future of mobility altogether. Although the integration of novel technologies such as Artificial Intelligence (AI) and Cloud/Edge computing provides golden opportunities to improve autonomous driving applications, there is the need to modernize accordingly the whole prototyping and deployment cycle of AI components. This paper proposes a novel framework for developing so-called AI Inference Engines for autonomous driving applications based on deep learning modules, where training tasks are deployed elastically over both Cloud and Edge resources, with the purpose of reducing the required network bandwidth, as well as mitigating privacy issues. Based on our proposed data driven V-Model, we introduce a simple yet elegant solution for the AI components development cycle, where prototyping takes place in the cloud according to the Software-in-the-Loop (SiL) paradigm, while deployment and evaluation on the target ECUs (Electronic Control Units) is performed as Hardware-in-the-Loop (HiL) testing. The effectiveness of the proposed framework is demonstrated using two real-world use-cases of AI inference engines for autonomous vehicles, that is environment perception and most probable path prediction.

Full text not available from this repository.

More information

Accepted/In Press date: 16 September 2020
Published date: 23 September 2020
Additional Information: Funding Information: Funding: This research was funded by the European Commission under the CyberKit4SMEs project, grant number 883188. Publisher Copyright: © 2020 by the authors. Licensee MDPI, Basel, Switzerland. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.
Keywords: Artificial intelligence, Autonomous vehicles, Cloud computing, Deep learning, Edge computing, Self-driving cars

Identifiers

Local EPrints ID: 450823
URI: http://eprints.soton.ac.uk/id/eprint/450823
ISSN: 1424-8220
PURE UUID: 19796ebf-7560-46a8-b07f-ae4264418db1
ORCID for Andrea Margheri: ORCID iD orcid.org/0000-0002-5048-8070
ORCID for Federico Lombardi: ORCID iD orcid.org/0000-0001-6463-8722
ORCID for Leonardo Aniello: ORCID iD orcid.org/0000-0003-2886-8445

Catalogue record

Date deposited: 12 Aug 2021 16:33
Last modified: 13 Aug 2021 01:54

Export record

Altmetrics

Contributors

Author: Sorin Grigorescu
Author: Tiberiu Cocias
Author: Bogdan Trasnea
Author: Andrea Margheri ORCID iD
Author: Federico Lombardi ORCID iD
Author: Leonardo Aniello ORCID iD

University divisions

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×