Sensitivity Analysis of a Planning Algorithm Considering Uncertainties

Recent trajectory or maneuver planning approaches in automated driving show the tendency to get more complex or even become a black box. Thus, an algorithm's decisions become less transparent, especially when uncertain input parameters have a large influence. To identify those input parameters whose uncertainty is more relevant than others', Morris' method of elementary effects is used here [1]. It is a quantitative sensitivity analysis that classifies the inputs into relevant and irrelevant, depending on how sensitive the algorithm reacts to changes in the input. The method is adapted to analyze the behavior of a car-following and lane-changing model during an overtaking maneuver with two vehicles. The results show that Morris' method is capable of determining important parameters for each situation. It is even possible to identify boundaries for the necessary accuracy of each input. With this, we are able to determine input parameter ranges for which the planning algorithm is able to produce reliable output.