Citation

Kontar W, Ahn S. Transp. Res. C Emerg. Technol. 2024; 160: e104499.

Copyright

(Copyright © 2024, Elsevier Publishing)

DOI

10.1016/j.trc.2024.104499

PMID

unavailable

Abstract

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.

Language: en

Keywords

Autonomous vehicle; Bayesian inference; Car following; Decision making; Langevin dynamics; Linear control; Stochastic gradient; Uncertainty quantification