Bayesian Methods in Automated Vehicle's Car-following Uncertainties: Enabling Strategic Decision Making

TL;DR

This paper introduces a Bayesian inference-based methodology for real-time uncertainty estimation in automated vehicle dynamics, enabling continuous monitoring and strategic control adjustments to maintain desired car-following performance.

Contribution

It presents a novel real-time uncertainty estimation framework using SGLD, integrating dynamic monitoring and strategic control actions for AVs in uncertain conditions.

Findings

Real-time uncertainty estimation improves AV car-following stability.

The methodology detects anomalies and triggers control adjustments.

Enhanced safety and performance in automated driving scenarios.

Abstract

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.