Print Email Facebook Twitter Takeover Quality Title Takeover Quality: Assessing the Effects of Time Budget and Traffic Density with the Help of a Trajectory-Planning Method Author Doubek, F.H. (TU Delft Intelligent Vehicles; Dr. Ing. h.c. F. Porsche AG) Loosveld, Erik (Student TU Delft; Dr. Ing. h.c. F. Porsche AG) Happee, R. (TU Delft Intelligent Vehicles) de Winter, J.C.F. (TU Delft Human-Robot Interaction) Date 2020 Abstract In highly automated driving, the driver can engage in a nondriving task but sometimes has to take over control. We argue that current takeover quality measures, such as the maximum longitudinal acceleration, are insufficient because they ignore the criticality of the scenario. This paper proposes a novel method of quantifying how well the driver executed an automation-to-manual takeover by comparing human behaviour to optimised behaviour as computed using a trajectory planner. A human-in-the-loop study was carried out in a high-fidelity 6-DOF driving simulator with 25 participants. The takeover required a lane change to avoid roadworks on the ego-lane while taking other traffic into consideration. Each participant encountered six different takeover scenarios, with a different time budget (5 s, 7 s, or 20 s) and traffic density level (low or medium). Results showed that drivers exhibited a considerably higher longitudinal and lateral acceleration than the optimised behaviour, especially in the short time budget scenarios. In scenarios of medium traffic density, the trajectory planner showed a moderate deceleration to let a vehicle in the left lane pass; many participants, on the other hand, did not decelerate before making a lane change, resulting in a dangerous emergency brake of the left-lane vehicle. In conclusion, our results illustrate the value of assessing human takeover behaviour relative to optimised behaviour. Using the trajectory planner, we showed that human drivers are unable to behave optimally in urgent scenarios and that, in some conditions, a medium deceleration, as opposed to a maximal or minimal deceleration, is optimal. Subject OA-Fund TU Delft To reference this document use: http://resolver.tudelft.nl/uuid:1b72b5e1-aa9e-4167-85f2-d88cb367cecc DOI https://doi.org/10.1155/2020/6173150 ISSN 0197-6729 Source Journal of Advanced Transportation, 2020 Part of collection Institutional Repository Document type journal article Rights © 2020 F.H. Doubek, Erik Loosveld, R. Happee, J.C.F. de Winter Files PDF 6173150.pdf 4.07 MB Close viewer /islandora/object/uuid:1b72b5e1-aa9e-4167-85f2-d88cb367cecc/datastream/OBJ/view