A scenario-based assessment approach for automated driving by using time series classification of human-driving behaviour

Automated driving functions are under intensified development by industry and academia since the last decade. Due to the large operation space and various complex scenarios automated driving functions have to cope with, assessment efforts are expected to rise dramatically. In order to quantify benefits and risks of these functions in an efficient way, this paper describes a holistic approach for the assessment of automated driving by using real world driving data. Based on a scenario definition a suitable method for identifying relevant scenarios from real world driving data is described which is able to handle scenario specific characteristics such as the temporal and spatial dependencies of all traffic participants. For quantifying the effect of automated driving within the considered driving scenarios, the statistical indicator `effect size' is applied. The basic requirement that automated driving needs to operate within mixed traffic implies that the reference for assessment needs to be human manual driving behaviour.