Robustifying the Deployment of tinyML Models for Autonomous Mini-Vehicles

@article{Prado2021RobustifyingTD,
  title={Robustifying the Deployment of tinyML Models for Autonomous Mini-Vehicles},
  author={Miguel de Prado and Manuele Rusci and Romain Donze and Alessandro Capotondi and Serge Monnerat and Luca Benini and Nuria Pazos},
  journal={2021 IEEE International Symposium on Circuits and Systems (ISCAS)},
  year={2021},
  pages={1-5}
}
Standard-size autonomous navigation vehicles have rapidly improved thanks to the breakthroughs of deep learning. However, scaling autonomous driving to low-power systems deployed on dynamic environments poses several challenges that prevent their adoption. To address them, we propose a closed- loop learning flow for autonomous driving mini-vehicles that includes the target environment in-the-loop. We leverage a family of compact and high-throughput tinyCNNs to control the mini- vehicle, which…