  Abrupt maneuvers by surrounding vehicles (SVs) can typically lead to safety
concerns and affect the task efficiency of the ego vehicle (EV), especially
with model uncertainties stemming from environmental disturbances. This paper
presents a real-time fail-operational controller that ensures the asymptotic
convergence of an uncertain EV to a safe state, while preserving task
efficiency in dynamic environments. An incremental Bayesian learning approach
is developed to facilitate online learning and inference of changing
environmental disturbances. Leveraging disturbance quantification and
constraint transformation, we develop a stochastic fail-operational barrier
based on the control barrier function (CBF). With this development, the
uncertain EV is able to converge asymptotically from an unsafe state to a
defined safe state with probabilistic stability. Subsequently, the stochastic
fail-operational barrier is integrated into an efficient fail-operational
controller based on quadratic programming (QP). This controller is tailored for
the EV operating under control constraints in the presence of environmental
disturbances, with both safety and efficiency objectives taken into
consideration. We validate the proposed framework in connected cruise control
(CCC) tasks, where SVs perform aggressive driving maneuvers. The simulation
results demonstrate that our method empowers the EV to swiftly return to a safe
state while upholding task efficiency in real time, even under time-varying
environmental disturbances.
