| People | Locations | Statistics |
|---|---|---|
| Ziakopoulos, Apostolos | Athens |
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| Vigliani, Alessandro | Turin |
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| Catani, Jacopo | Rome |
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| Statheros, Thomas | Stevenage |
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| Utriainen, Roni | Tampere |
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| Guglieri, Giorgio | Turin |
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| Martínez Sánchez, Joaquín |
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| Tobolar, Jakub |
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| Volodarets, M. |
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| Piwowar, Piotr |
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| Tennoy, Aud | Oslo |
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| Matos, Ana Rita |
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| Cicevic, Svetlana |
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| Sommer, Carsten | Kassel |
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| Liu, Meiqi |
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| Pirdavani, Ali | Hasselt |
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| Niklaß, Malte |
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| Lima, Pedro | Braga |
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| Turunen, Anu W. |
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| Antunes, Carlos Henggeler |
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| Krasnov, Oleg A. |
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| Lopes, Joao P. |
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| Turan, Osman |
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| Lučanin, Vojkan | Belgrade |
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| Tanaskovic, Jovan |
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Russwinkel, Nele
in Cooperation with on an Cooperation-Score of 37%
Topics
- driving
- attention
- autonomous driving
- driver
- driving simulator
- modeling
- variable
- mediation
- driving behavior
- automobile
- passenger
- passenger car
- vehicle occupant
- design
- truck driver
- truck
- highway
- steering
- reaction time
- expected value
- test track
- lateral control
- professional driver
- level 3 driving automation
- air traffic
- air traffic control
- computer programming
- computer science
- flight
- behavior
- machinery
- human being
- weather
- foundation
- aeronautics
- aviation
- cognition
- cockpit
- flight deck
- assessment
- perception
- beltway
- data
- crash
- poison
- interface
- brain
- alertness
- automation
- flight crew
- psychiatry
- emergency response time
- aircraft
- eye
- guideline
- eye movement
- control device
- estimating
- employed
- validation
- airport
- traffic load
- engineering
- supporting
- airport traffic
- tower
- workload
- dispatcher
- goodness of fit
- show 39 more
Publications (11/11 displayed)
- 2022A Cognitive Model to Anticipate Variations of Situation Awareness and Attention for the Takeover in Highly Automated Driving
- 2021Familiarity and Complexity during a Takeover in Highly Automated Drivingcitations
- 2020Take-over expectation and criticality in Level 3 automated driving: a test track study on take-over behavior in semi-truckscitations
- 2020The Impact of Situational Complexity and Familiarity on Takeover Quality in Uncritical Highly Automated Driving Scenarioscitations
- 2020Towards Cognitive Assistance and Teaming in Aviation by Inferring Pilot’s Mental Statecitations
- 2020Towards a cognitive model of the takeover in highly automated driving for the improvement of human machine interaction.
- 2020ACT-R model for cognitive assistance in handling flight deck alerts
- 2020Tracing Pilots’ Situation Assessment by Neuroadaptive Cognitive Modelingcitations
- 2019Response times and gaze behavior of truck drivers in time critical conditional automated driving take-overscitations
- 2017A guideline for integrating dynamic areas of interests in existing set-up for capturing eye movement: Looking at moving aircraftcitations
- 2015Workload of Airport Tower Controllers: Empirical Validation of a Macro-cognitive Model
Places of action
article
Familiarity and Complexity during a Takeover in Highly Automated Driving
Abstract
This paper shows, how objective complexity and familiarity impact the subjective complexity and the time to make an action decision during the takeover task in a highly automated driving scenario. In the next generation of highly automated driving the driver remains as fallback and has to take over the driving task whenever the system reaches a limit. It is thus highly important to develop an assistance system that supports the individual driver based on information about the drivers’ current cognitive state. The impact of familiarity and complexity (objective and subjective) on the time to make an action decision during a takeover is investigated. To produce replicable driving scenarios and manipulate the independent variables situation familiarity and objective complexity, a driving simulator is used. Results show that the familiarity with a traffic situation as well as the objective complexity of the environment significantly influence the subjective complexity and the time to make an action decision. Furthermore, it is shown that the subjective complexity is a mediator variable between objective complexity/familiarity and the time to make an action decision. Complexity and familiarity are thus important parameters that have to be considered in the development of highly automated driving systems. Based on the presented mediation effect, the opportunity of gathering the drivers’ subjective complexity and adapting cognitive assistance systems accordingly is opened up. The results of this study provide a solid basis that enables an individualization of the takeover by implementing useful cognitive modeling to individualize cognitive assistance systems for highly automated driving.
Topics
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