  In this paper, an online evolving framework is proposed to detect and revise
a controller's imperfect decision-making in advance. The framework consists of
three modules: the evolving Finite State Machine (e-FSM), action-reviser, and
controller modules. The e-FSM module evolves a stochastic model (e.g.,
Discrete-Time Markov Chain) from scratch by determining new states and
identifying transition probabilities repeatedly. With the latest stochastic
model and given criteria, the action-reviser module checks validity of the
controller's chosen action by predicting future states. Then, if the chosen
action is not appropriate, another action is inspected and selected. In order
to show the advantage of the proposed framework, the Deep Deterministic Policy
Gradient (DDPG) w/ and w/o the online evolving framework are applied to control
an ego-vehicle in the car-following scenario where control criteria are set by
speed and safety. Experimental results show that inappropriate actions chosen
by the DDPG controller are detected and revised appropriately through our
proposed framework, resulting in no control failures after a few iterations.
