Quantification of Actual Road User Behavior on the Basis of Given Traffic Rules
This addresses the issue of integrating autonomous vehicles into human-driven traffic by modeling rule deviations, but it is incremental as it applies existing methods to new data.
The paper tackles the problem of autonomous vehicles disrupting traffic flow by not accounting for human deviations from traffic rules, and presents a method to derive the distribution of rule conformity from human driving data, demonstrated using the Waymo Open Motion dataset for safety distance and speed limit rules.
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.