A strategic approach to handle performance uncertainties in autonomous vehicle's car-following behavior

This paper proposes a methodology to estimate uncertainties in automated vehicle (AV) dynamics in real time via Bayesian inference. Based on the estimated uncertainty, the method aims to track the car -following (CF) performance of the AV to support strategic actions to maintain 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.