TY  - JOUR
PY  - 2024//
TI  - A strategic approach to handle performance uncertainties in autonomous vehicle's car-following behavior
JO  - Transportation research part C: emerging technologies
A1  - Kontar, Wissam
A1  - Ahn, Soyoung
SP  - e104499
EP  - e104499
VL  - 160
IS  - 
N2  - 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>
LA  - en
SN  - 0968-090X
UR  - http://dx.doi.org/10.1016/j.trc.2024.104499
ID  - ref1
ER  -