Bayesian Methods in Automated Vehicle's Car-following Uncertainties: Enabling Strategic Decision Making
This addresses uncertainty management for automated vehicles, but it is incremental as it builds on existing Bayesian and monitoring techniques.
The paper tackles the problem of real-time uncertainty in automated vehicle car-following dynamics by proposing a Bayesian inference method to estimate uncertainty and monitor performance, enabling strategic control adjustments to maintain desired performance.
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.