  Driving on roads is restricted by various traffic rules, aiming to ensure
safety for all traffic participants. However, human road users usually do not
adhere to these rules strictly, resulting in varying degrees of rule
conformity. Such deviations from given rules are key components of today's road
traffic. In autonomous driving, robotic agents can disturb traffic flow, when
rule deviations are not taken into account. In this paper, we present an
approach to derive the distribution of degrees of rule conformity from human
driving data. We demonstrate our method with the Waymo Open Motion dataset and
Safety Distance and Speed Limit rules.
