Next Article in Journal
Quantitative Analysis and Discrimination of Partially Fermented Teas from Different Origins Using Visible/Near-Infrared Spectroscopy Coupled with Chemometrics
Previous Article in Journal
RELIABLE: Resource Allocation Mechanism for 5G Network using Mobile Edge Computing
Previous Article in Special Issue
A Novel Time Delay Estimation Algorithm for 5G Vehicle Positioning in Urban Canyon Environments
Open AccessArticle

Cloud2Edge Elastic AI Framework for Prototyping and Deployment of AI Inference Engines in Autonomous Vehicles

1
Robotics, Vision and Control Laboratory (ROVIS), Transilvania University of Brasov and Elektrobit Automotive, 500036 Brasov, Romania
2
School of Electronics and Computer Science, University of Southampton, Southampton SO16 7NS, UK
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(19), 5450; https://doi.org/10.3390/s20195450
Received: 30 August 2020 / Revised: 16 September 2020 / Accepted: 16 September 2020 / Published: 23 September 2020
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. View Full-Text
Keywords: autonomous vehicles; self-driving cars; artificial intelligence; deep learning; cloud computing; edge computing autonomous vehicles; self-driving cars; artificial intelligence; deep learning; cloud computing; edge computing
Show Figures

Figure 1

MDPI and ACS Style

Grigorescu, S.; Cocias, T.; Trasnea, B.; Margheri, A.; Lombardi, F.; Aniello, L. Cloud2Edge Elastic AI Framework for Prototyping and Deployment of AI Inference Engines in Autonomous Vehicles. Sensors 2020, 20, 5450.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop