@article{ref1,
title="Managing driving modes in automated driving systems",
journal="Transportation science",
year="2022",
author="Rios Insua, David and Caballero, William N. and Naveiro, Roi",
volume="56",
number="5",
pages="1259-1278",
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.<p /> <p>Language: en</p>",
language="en",
issn="0041-1655",
doi="10.1287/trsc.2021.1110",
url="http://dx.doi.org/10.1287/trsc.2021.1110"
}