  Adverse weather conditions are very challenging for autonomous driving
because most of the state-of-the-art sensors stop working reliably under these
conditions. In order to develop robust sensors and algorithms, tests with
current sensors in defined weather conditions are crucial for determining the
impact of bad weather for each sensor. This work describes a testing and
evaluation methodology that helps to benchmark novel sensor technologies and
compare them to state-of-the-art sensors. As an example, gated imaging is
compared to standard imaging under foggy conditions. It is shown that gated
imaging outperforms state-of-the-art standard passive imaging due to
time-synchronized active illumination.
