Mobility Compass

Discover mobility and transportation research. Find experts, partners, networks.

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The Mobility Compass is an open tool for improving networking and interdisciplinary exchange within mobility and transport research. It enables cross-database search for cooperation and network partners and discovering of the research landscape.

The dashboard provides detailed information about the selected scientist, e.g. publications. The dashboard can be filtered and shows the relationship to co-authors in different diagrams. In addition, a link is provided to find contact information.

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Ziakopoulos, ApostolosAthens
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Russwinkel, Nele

  • Google
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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
  • show less

Publications (11/11 displayed)

  • 2022A Cognitive Model to Anticipate Variations of Situation Awareness and Attention for the Takeover in Highly Automated Drivingcitations
  • 2021Familiarity and Complexity during a Takeover in Highly Automated Driving3citations
  • 2020Take-over expectation and criticality in Level 3 automated driving: a test track study on take-over behavior in semi-trucks4citations
  • 2020The Impact of Situational Complexity and Familiarity on Takeover Quality in Uncritical Highly Automated Driving Scenarios12citations
  • 2020Towards Cognitive Assistance and Teaming in Aviation by Inferring Pilot’s Mental State2citations
  • 2020Towards a cognitive model of the takeover in highly automated driving for the improvement of human machine interaction.citations
  • 2020ACT-R model for cognitive assistance in handling flight deck alertscitations
  • 2020Tracing Pilots’ Situation Assessment by Neuroadaptive Cognitive Modeling7citations
  • 2019Response times and gaze behavior of truck drivers in time critical conditional automated driving take-overs17citations
  • 2017A guideline for integrating dynamic areas of interests in existing set-up for capturing eye movement: Looking at moving aircraft17citations
  • 2015Workload of Airport Tower Controllers: Empirical Validation of a Macro-cognitive Modelcitations

Places of action

Chart of shared publication
Wiese, Sebastian
1 / 1 shared
Scharfe, Marlene Susanne Lisa
3 / 3 shared
Scharfe-Scherf, Marlene Susanne Lisa
2 / 2 shared
Wohlfarth, Enrico
1 / 1 shared
Lotz, Alexander
1 / 1 shared
Zeeb, Kathrin
1 / 4 shared
Klaproth, Oliver W.
2 / 2 shared
Vernaleken, Christoph
2 / 4 shared
Scharfe, M.
1 / 1 shared
Halbrügge, M.
1 / 1 shared
Klaproth, O. W.
1 / 1 shared
Krol, Laurens R.
1 / 2 shared
Zander, Thorsten O.
1 / 2 shared
Halbruegge, Marc
1 / 1 shared
Wohlfarth, E.
1 / 1 shared
Lotz, A.
1 / 1 shared
Rußwinkel, N.
1 / 1 shared
Friedrich, M.
1 / 10 shared
Möhlenbrink, C.
1 / 1 shared
Joeres, Fabian
1 / 1 shared
Smieszek, Hardy
1 / 4 shared
Chart of publication period
2022
2021
2020
2019
2017
2015

Co-Authors (by relevance)

  • Wiese, Sebastian
  • Scharfe, Marlene Susanne Lisa
  • Scharfe-Scherf, Marlene Susanne Lisa
  • Wohlfarth, Enrico
  • Lotz, Alexander
  • Zeeb, Kathrin
  • Klaproth, Oliver W.
  • Vernaleken, Christoph
  • Scharfe, M.
  • Halbrügge, M.
  • Klaproth, O. W.
  • Krol, Laurens R.
  • Zander, Thorsten O.
  • Halbruegge, Marc
  • Wohlfarth, E.
  • Lotz, A.
  • Rußwinkel, N.
  • Friedrich, M.
  • Möhlenbrink, C.
  • Joeres, Fabian
  • Smieszek, Hardy

article

Familiarity and Complexity during a Takeover in Highly Automated Driving

  • Russwinkel, Nele
  • Scharfe, Marlene Susanne Lisa
  • Scharfe-Scherf, Marlene Susanne Lisa
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
  • driver
  • driving
  • driving simulator
  • modeling
  • variable
  • autonomous driving
  • mediation

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