Quantification of Actual Road User Behavior on the Basis of Given Traffic Rules

TL;DR

This paper analyzes human driving behavior to quantify how strictly drivers follow traffic rules, using data-driven methods to understand deviations and their impact on traffic flow and autonomous driving.

Contribution

It introduces a method to derive the distribution of rule conformity degrees from real driving data, aiding autonomous systems in better understanding human behavior.

Findings

Derived rule conformity distributions from the Waymo dataset

Quantified deviations from safety distance and speed limit rules

Provided insights into human rule adherence patterns

Abstract

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.