  Autonomous driving has made great progress and been introduced in practical
use step by step. On the other hand, the concept of personal mobility is also
getting popular, and its autonomous driving specialized for individual drivers
is expected for a new step. However, it is difficult to collect a large driving
dataset, which is basically required for the learning of autonomous driving,
from the individual driver of the personal mobility. In addition, when the
driver is not familiar with the operation of the personal mobility, the dataset
will contain non-optimal data. This study therefore focuses on an autonomous
driving method for the personal mobility with such a small and noisy, so-called
personal, dataset. Specifically, we introduce a new loss function based on
Tsallis statistics that weights gradients depending on the original loss
function and allows us to exclude noisy data in the optimization phase. In
addition, we improve the visualization technique to verify whether the driver
and the controller have the same region of interest. From the experimental
results, we found that the conventional autonomous driving failed to drive
properly due to the wrong operations in the personal dataset, and the region of
interest was different from that of the driver. In contrast, the proposed
method learned robustly against the errors and successfully drove automatically
while paying attention to the similar region to the driver. Attached video is
also uploaded on youtube: https://youtu.be/KEq8-bOxYQA
