Robustifying the Deployment of tinyML Models for Autonomous mini-vehicles

Understand

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.