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Robustifying the Deployment of tinyML Models for Autonomous mini-vehicles
[article]
2021
arXiv
pre-print
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. ...
Contributions We present an end-to-end flow of data, algorithms, and deployment tools that facilitates the deployment and enhance the robustness of a family of tinyCNNs for an autonomous lowpower mini-vehicle ...
arXiv:2007.00302v2
fatcat:kxylimhhqrcutehe2dfqpa2nhu
Robustifying the Deployment of tinyML Models for Autonomous Mini-Vehicles
2021
Sensors
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. ...
Standard-sized autonomous vehicles have rapidly improved thanks to the breakthroughs of deep learning. ...
Goal Specification: Robust Low-Power Autonomous Driving We aim to shed light on the robustification of tinyML models for autonomous systems deployed in dynamic environments. ...
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)
Robustifying the Deployment of tinyML Models for Autonomous Mini-Vehicles. Sensors ...
The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results. ...
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.1109/iscas51556.2021.9401154
fatcat:cqieoaxwm5cwxhgbellngethze