# Robustifying the Deployment of tinyML Models for Autonomous mini-vehicles

arXiv: https://arxiv.org/abs/2007.00302 · HTML: https://mlsanity.com/paper/2007.00302/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jul 2020.

## Built on by (most cited first, 10 of 10)

- [Tiny Machine Learning: Progress and Futures](https://mlsanity.com/paper/2403.19076/) (2024)
- [A TinyML Platform for On-Device Continual Learning with Quantized Latent Replays](https://mlsanity.com/paper/2110.10486/) (2021)
- [Machine Learning for Microcontroller-Class Hardware: A Review](https://mlsanity.com/paper/2205.14550/) (2022)
- [A Machine Learning-oriented Survey on Tiny Machine Learning](https://mlsanity.com/paper/2309.11932/) (2023)
- [TinyML: Tools, Applications, Challenges, and Future Research Directions](https://mlsanity.com/paper/2303.13569/) (2023)
- [Intelligence at the Extreme Edge: A Survey on Reformable TinyML](https://mlsanity.com/paper/2204.00827/) (2022)
- [Datasheets for Machine Learning Sensors](https://mlsanity.com/paper/2306.08848/) (2023)
- [Attention in Motion: Secure Platooning via Transformer-based Misbehavior Detection](https://mlsanity.com/paper/2512.15503/) (2025)
- [Efficient IoT Inference via Context-Awareness](https://mlsanity.com/paper/2310.19112/) (2023)
- [Green-ELM: Efficient Analytic Learning via High-Dimensional Random Projections](https://mlsanity.com/paper/2604.15613/) (2026)

## Abstract (arXiv)

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 learn in the target environment by imitating a computer vision algorithm, i.e., the expert. Thus, the tinyCNNs, having only access to an on-board fast-rate linear camera, gain robustness to lighting conditions and improve over time. Further, we leverage GAP8, a parallel ultra-low-power RISC-V SoC, to meet the inference requirements. When running the family of CNNs, our GAP8's solution outperforms any other implementation on the STM32L4 and NXP k64f (Cortex-M4), reducing the latency by over 13x and the energy consummation by 92%.