A Method for the Estimation of Coexisting Risk-Inducing Factors in Traffic Scenarios

The purpose of this paper is to analyze naturalistic driving data and crash data in the United States of America concerning the multiple risk-inducing factors which exist in real traffic. The derived method allows to identify neutral characteristics occurring in many situations and extract risk-inducing attributes from real data by conducting the Successive Odds Ratio Analysis (SORA). The SORA algorithm uses two different types of data, e.g., baseline and crash data, calculates the criticality of each attribute, and evaluates combinations whereby the total criticality is affected positively or negatively. This paper focuses on the exemplary environment-related variables which are provided by the considered databases. Based on identified risk-inducing attributes, their associated characteristics will be investigated by using three measures, i.e., Support, Confidence, and Lift. The method has the potential to generate a scenario catalog consisting of critical test cases for the development of advanced driver assistance systems.