Supervised Learning via Optimal Control Labeling for Criticality Classification in Vehicle Active Safety

A core component of vehicle active safety algo-rithms is the estimation of criticality, which is a measure of the threat or danger of a traffic situation. Based on the criticality esti-mate, an active safety system can significantly increase passenger safety by triggering collision avoidance or mitigation maneuvers like emergency braking or steering. Interpreting criticality as the intensity of an evasion maneuver, we formulate a MinMax optimal control problem which incorporates moving obstacles and clothoidal lane constraints. We show how the solution of this optimal control problem can be used as a criticality labeling function to generate reference data sets for collision scenes. In order to achieve fast execution speeds, we present a supervised classification approach to criticality estimation. Using the Random Forest classifier with feature selection, we show that the criticality of combined braking and steering maneuvers can be predicted with high precision.