Back to Search
Start Over
Takeover Quality
- Source :
- Journal of Advanced Transportation, 2020, Journal of Advanced Transportation, Vol 2020 (2020)
- Publication Year :
- 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.
- Subjects :
- Economics and Econometrics
Article Subject
Operations research
Computer science
Strategy and Management
media_common.quotation_subject
Control (management)
Task (project management)
Acceleration
0502 economics and business
0501 psychology and cognitive sciences
Quality (business)
050107 human factors
HE1-9990
media_common
computer.programming_language
050210 logistics & transportation
TA1001-1280
Mechanical Engineering
05 social sciences
Driving simulator
Planner
Computer Science Applications
Transportation engineering
OA-Fund TU Delft
Automotive Engineering
Trajectory
Transportation and communications
computer
Emergency brake
Subjects
Details
- Language :
- English
- ISSN :
- 01976729
- Volume :
- 2020
- Database :
- OpenAIRE
- Journal :
- Journal of Advanced Transportation
- Accession number :
- edsair.doi.dedup.....02391a4a8b7fe7a24a444d5b299a79ad