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

arXiv: https://arxiv.org/abs/2210.13683 · HTML: https://mlsanity.com/paper/2210.13683/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 27 references that are in the map.
- First posted on arXiv 25 Oct 2022.

## Builds on (most cited first, 4 of 4)

- [Are commercially implemented adaptive cruise control systems string stable?](https://mlsanity.com/paper/1905.02108/) (2019)
- [openACC. An open database of car-following experiments to study the properties of commercial ACC systems](https://mlsanity.com/paper/2004.06342/) (2020)
- [Model based string stability of adaptive cruise control systems using field data](https://mlsanity.com/paper/1902.04983/) (2019)
- [Parsimonious shooting heuristic for trajectory control of connected automated traffic part I: Theoretical analysis with generalized time geography](https://mlsanity.com/paper/1511.04810/) (2015)

## Built on by (most cited first, 1 of 1)

- [Learning Driver Models for Automated Vehicles via Knowledge Sharing and Personalization](https://mlsanity.com/paper/2308.16870/) (2023)

## Abstract (arXiv)

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.