  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%.
