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Managing Driving Modes in Automated Driving Systems

Author

Listed:
  • 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)

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.

Suggested Citation

  • David Ríos Insua & William N. Caballero & Roi Naveiro, 2022. "Managing Driving Modes in Automated Driving Systems," Transportation Science, INFORMS, vol. 56(5), pages 1259-1278, September.
    • Handle: RePEc:inm:ortrsc:v:56:y:2022:i:5:p:1259-1278
    DOI: 10.1287/trsc.2021.1110
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