@article{ref1,
title="A strategic approach to handle performance uncertainties in autonomous vehicle's car-following behavior",
journal="Transportation research part C: emerging technologies",
year="2024",
author="Kontar, Wissam and Ahn, Soyoung",
volume="160",
number="",
pages="e104499-e104499",
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.<p /> <p>Language: en</p>",
language="en",
issn="0968-090X",
doi="10.1016/j.trc.2024.104499",
url="http://dx.doi.org/10.1016/j.trc.2024.104499"
}