Online Out-of-Domain Detection for Automated Driving
Timo Sämann Horst-Michael Groß
Ensuring safety in automated driving is a major challenge for the automotive
industry. Special attention is paid to artificial intelligence, in particular
to Deep Neural Networks (DNNs), which is considered a key technology in the
realization of highly automated driving. DNNs learn from training data, which
means that they only achieve good accuracy within the underlying data
distribution of the training data. When leaving the training domain, a
distributional shift is caused, which can lead to a drastic reduction of
accuracy. In this work, we present a proof of concept for a safety mechanism
that can detect the leaving of the domain online, i.e. at runtime. In our
experiments with the Synthia data set we can show that a 100 % correct
detection of whether the input data is inside or outside the domain is
achieved. The ability to detect when the vehicle leaves the domain can be an
important requirement for certification.