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Robustifying the Deployment of tinyML Models for Autonomous mini-vehicles
[article]
2021
arXiv
pre-print
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To address them, we propose a closed-loop learning flow for autonomous driving mini-vehicles that includes the target environment in-the-loop. ...
Standard-size autonomous navigation vehicles have rapidly improved thanks to the breakthroughs of deep learning. ...
ROBUST NAVIGATION WITH TINYML We aim to take a tinyML approach and replace the initial CVA solution by a tinyCNN. ...
arXiv:2007.00302v2
fatcat:kxylimhhqrcutehe2dfqpa2nhu
Robustifying the Deployment of tinyML Models for Autonomous Mini-Vehicles
2021
Sensors
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To address these challenges, we propose a closed-loop learning flow for autonomous driving mini-vehicles that includes the target deployment environment in-the-loop. ...
However, scaling autonomous driving to mini-vehicles poses several challenges due to their limited on-board storage and computing capabilities. ...
Figure 2 . 2 Challenges and methodology for robust and efficient deployment with tinyML for autonomous low-power driving vehicles.
Figure 3 . 3 Closed-loop learning pipeline. ...
doi:10.3390/s21041339
pmid:33668645
pmcid:PMC7918899
fatcat:krfqzoi5pnbnfnyn3gndzj7j3q
Robustifying the Deployment of tinyML Models for Autonomous mini-vehicles
2021
2021 IEEE International Symposium on Circuits and Systems (ISCAS)
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A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Robustifying the Deployment of tinyML Models for Autonomous Mini-Vehicles. Sensors ...
Figure 2 . 2 Challenges and methodology for robust and efficient deployment with tinyML for autonomous low-power driving vehicles.
Figure 3 . 3 Closed-loop learning pipeline. ...
Table 2 shows the different network configurations -networks and datasets have been opensourced from https://github.com/praesc/Robust-navigation-with-TinyML. ...
doi:10.1109/iscas51556.2021.9401154
fatcat:cqieoaxwm5cwxhgbellngethze
Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications
[article]
2021
arXiv
pre-print
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In this paper, we shall provide our vision for scalable and trustworthy edge AI systems with integrated design of wireless communication strategies and decentralized machine learning models. ...
By embedding model training and inference capabilities into the network edge, edge AI stands out as a disruptive technology for 6G to seamlessly integrate sensing, communication, computation, and intelligence ...
Edge learning over a vehicle-to-everything network is critical to enable autonomous driving with delay-sensitive applications [145] . ...
arXiv:2111.12444v1
fatcat:crrbtfylvjeihogumggdnxcbpq
D1.1 - State of the Art Analysis
2021
Zenodo
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The deliverable starts with an overview of AI applications and edge computing market trends. ...
., devices with intelligence and data processing capabilities), providing resource efficiency, performance, data privacy, and security guarantees. ...
as voice and image recognition, machine translation, control of assisted driving and autonomous vehicle navigation. ...
doi:10.5281/zenodo.6372377
fatcat:f6ldfuwivbcltew4smiiwphfty