TY  - JOUR
PY  - 2022//
TI  - Managing driving modes in automated driving systems
JO  - Transportation science
A1  - Rios Insua, David
A1  - Caballero, William N.
A1  - Naveiro, Roi
SP  - 1259
EP  - 1278
VL  - 56
IS  - 5
N2  - 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>
LA  - en
SN  - 0041-1655
UR  - http://dx.doi.org/10.1287/trsc.2021.1110
ID  - ref1
ER  -