Managing Driving Modes in Automated Driving Systems

David Ríos Insua (), William N. Caballero () and Roi Naveiro ()
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David Ríos Insua: Institute of Mathematical Sciences, Madrid 28049, Spain
William N. Caballero: United States Air Force Academy, USAF Academy, Colorado 80840
Roi Naveiro: Institute of Mathematical Sciences, Madrid 28049, Spain

Transportation Science, 2022, vol. 56, issue 5, 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.

Keywords: automated driving systems; request to intervene; Bayesian decision analysis (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:inm:ortrsc:v:56:y:2022:i:5:p:1259-1278

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