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
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
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
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