  An online evolving framework is proposed to support modeling the safe
Automated Vehicle (AV) control system by making the controller able to
recognize unexpected situations and react appropriately by choosing a better
action. Within the framework, the evolving Finite State Machine (e-FSM), which
is an online model able to (1) determine states uniquely as needed, (2)
recognize states, and (3) identify state-transitions, is introduced.
  In this study, the e-FSM's capabilities are explained and illustrated by
simulating a simple car-following scenario. As a vehicle controller, the
Intelligent Driver Model (IDM) is implemented, and different sets of IDM
parameters are assigned to the following vehicle for simulating various
situations (including the collision). While simulating the car-following
scenario, e-FSM recognizes and determines the states and identifies the
transition matrices by suggested methods.
  To verify if e-FSM can recognize and determine states uniquely, we analyze
whether the same state is recognized under the identical situation. The
difference between probability distributions of predicted and recognized states
is measured by the Jensen-Shannon divergence (JSD) method to validate the
accuracy of identified transition-matrices. As shown in the results, the
Dead-End state which has latent-risk of the collision is uniquely determined
and consistently recognized. Also, the probability distributions of the
predicted state are significantly similar to the recognized state, declaring
that the state-transitions are precisely identified.
