Transition probability estimation and its application in evaluation of automated driving

Evaluating driving performance of autonomous vehicles is as important as developing automated driving algorithms. In order to ensure passenger safety, evaluation of driving behavior is required before delivering autonomous vehicles to customers. An Interacting Multiple Model (IMM)-based driver evaluation algorithm was developed and it provides various information associated with multiple driving aggressiveness modes. This paper estimates transition probabilities by utilizing those information from the IMM-based evaluation algorithm, which are expected to unveil hidden driving performance of autonomous vehicles. Three estimation approaches are presented and they are tested with experimental drives.