  This paper proposes a methodology to estimate uncertainty in automated
vehicle (AV) dynamics in real time via Bayesian inference. Based on the
estimated uncertainty, the method aims to continuously monitor the
car-following (CF) performance of the AV to support strategic actions to
maintain a desired performance. Our methodology consists of three sequential
components: (i) the Stochastic Gradient Langevin Dynamics (SGLD) is adopted to
estimate parameter uncertainty relative to vehicular dynamics in real time,
(ii) dynamic monitoring of car-following stability (local and string-wise), and
(iii) strategic actions for control adjustment if anomaly is detected. The
proposed methodology provides means to gauge AV car-following performance in
real time and preserve desired performance against real time uncertainty that
are unaccounted for in the vehicle control algorithm.
