Citation

Rios Insua D, Caballero WN, Naveiro R. Transp. Sci. 2022; 56(5): 1259-1278.

Copyright

(Copyright © 2022, Institute for Operations Research and the Management Sciences)

DOI

10.1287/trsc.2021.1110

PMID

unavailable

Abstract

Current technology is unable to produce massively deployable, fully automated vehicles that do not require human intervention. Given that such limitations are projected to persist for decades, scenarios requiring a driver to assume control of a semiautomated vehicle, and vice versa, will remain a feature of modern roadways for the foreseeable future. Herein, we adopt a comprehensive perspective of this problem by simultaneously considering operational design domain supervision, driver and environment monitoring, trajectory planning, and driver-intervention performance assessment. More specifically, we develop a modeling framework for each of the aforementioned functions by leveraging decision analysis and Bayesian forecasting. Utilizing this framework, a suite of algorithms is subsequently proposed for driving-mode management and early warning emission, according to a management by exception principle. The efficacy of the developed methods is illustrated and examined via a simulated case study.

Language: en

Keywords

automated driving systems; Bayesian decision analysis; request to intervene