Autonomous Vehicles - Relations to Human Intelligence Lehrstuhl fr - - PowerPoint PPT Presentation

▶
autonomous vehicles relations to human intelligence
SMART_READER_LITE
LIVE PREVIEW

Autonomous Vehicles - Relations to Human Intelligence Lehrstuhl fr - - PowerPoint PPT Presentation

Sep 06, 2022 210 likes •525 views

Lehrstuhl fr Ergonomie Technische Universitt Mnchen Autonomous Vehicles - Relations to Human Intelligence Lehrstuhl fr Ergonomie Technische Universitt Mnchen Prof. Klaus Bengler 13.11.2015 Lehrstuhl fr Ergonomie Technische

slide-1
SLIDE 1

Technische Universität München Lehrstuhl für Ergonomie

Autonomous Vehicles - Relations to Human Intelligence

Lehrstuhl für Ergonomie Technische Universität München

  • Prof. Klaus Bengler

13.11.2015

slide-2
SLIDE 2

Technische Universität München Lehrstuhl für Ergonomie

….

We need data for the HMI design for The support of mode awareness The anticipation To increase trust To prepare transitions and arbitration This is Prospective level of service Identification of objects Reproducable behavior

13.11.2015 2

slide-3
SLIDE 3

Technische Universität München Lehrstuhl für Ergonomie

slide-4
SLIDE 4

Technische Universität München Lehrstuhl für Ergonomie

Human Intelligence

.. increases and decreases traffic safety (getting ccoperative vs. bending the rules) For automation human intelligence is something

  • to be replaced („Take the human out of the loop!“)
  • to be supported („We still have to work on this“)
  • to learn from („How to understand a scene?“)
slide-5
SLIDE 5

Technische Universität München Lehrstuhl für Ergonomie

Automation in a Human-Machine-System

  • Should not loose performance in summary
  • There will still be humans all over the place
  • These humans will change their behavior
slide-6
SLIDE 6

Technische Universität München Lehrstuhl für Ergonomie

Models of Human Behavior and HMI

from: Winner, H., Hakuli, S. & Wolf, G. (2012). Handbuch Fahrerassistenzsysteme

slide-7
SLIDE 7

Technische Universität München Lehrstuhl für Ergonomie

Take-Over in Automated Vehicles

Take-Over (here): Machine initiated transition from automated to manual driving. Time Budget Take-Over Request (TOR) System Limit Highly Automated Driving Manual Driving Take-Over / Transition Reaction Time Non-Driving Related Task

slide-8
SLIDE 8

Technische Universität München Lehrstuhl für Ergonomie

Wickens, C. & Carswell, C. (2006). Information Processing. In G. Salvendy (Hrsg.), Handbook of Human Factors and Ergonomics (S. 111-149). Hoboken: John Wiley.

Information Processing

slide-9
SLIDE 9

Technische Universität München Lehrstuhl für Ergonomie

To Replace Human Intelligence

Benefits in the area of

  • very short reaction times, situative automation
  • permanently loading and monotonous tasks
  • reduced human skills and info-processing abilities
slide-10
SLIDE 10

Technische Universität München Lehrstuhl für Ergonomie

Scenario: „Relief on Motorways“ „Automation in congestion

Yerkes-Dodson-Law (1908): performance and earousal

Lack of Arousal or to Much Arousal

Scenario: „Support in urban traffic“ „Compensating driver distraction“

slide-11
SLIDE 11

UR:BAN • UR:BAN Plenum 2015 Impulsvortrag • Endlich Freiheit für die MMI! • 03.03.2015 • Dresden

Freedom – Drivers will „Learn“ the End of Distraction will they „Learn“ the levels of automation or develop an own model?

slide-12
SLIDE 12

Technische Universität München Lehrstuhl für Ergonomie

Controllability / Visual Information

Preview of system behavior leads to Quicker responses and better learnability by visualisation But still to total misses of traffic signs while automated driving

(Weißgerber 2012)

slide-13
SLIDE 13

Technische Universität München Lehrstuhl für Ergonomie

Visual Behavior During Tertiary Task (Damböck 2012)

0,0 0,5 1,0 1,5 2,0 2,5

manuell assistiert teilautom_mH teilautom_oH

N=24

Szene Tacho CID Mean Single Glance Time ± 95%-CI [s]

0,0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 manuell assistiert teilautom_mH teilautom_oH

N=24

Szene Tacho CID Mean Glance Frequency ± 95%-CI [1/s]

  • Higher LOA lead to more visual divertion
  • Haptic information is used to reducte glances to speedometer
  • Visual behavior can be used as an availability metric for cooperation [H-M-S]

(Damböck 2012)

slide-14
SLIDE 14

Technische Universität München Lehrstuhl für Ergonomie

To Replace Human Intelligence

High/Full automation systems

  • Have to be highly reliable and trustable
  • Should know about user state and intention
  • Have to be learnable to enable intelligent usage

patterns and cooperative behavior

  • The systems adapt to surrounding traffic or serve as

good example?

slide-15
SLIDE 15

Technische Universität München Lehrstuhl für Ergonomie

Automation to Support Human Intelligence

In areas of

  • Weaknesses on both sides
  • Interesting, experiencing activities
slide-16
SLIDE 16

Technische Universität München Lehrstuhl für Ergonomie

Automation to Complete and Support Human Intelligence

Shared control systems

  • Have to be cooperative
  • Have to be transparent for the user
  • Could be user adaptive
  • Will be an interesting mode between

automation and manual driving

slide-17
SLIDE 17

Technische Universität München Lehrstuhl für Ergonomie

Automation to Learn From Human Intelligence

Automation could benefit from knowledge about

  • Situation understanding of drivers
  • Generation and application of driving strategies
  • Cooperative problem solving
  • Learning of complex sceneries
slide-18
SLIDE 18

Technische Universität München Lehrstuhl für Ergonomie

To Drive Safely is a Learning Process

10 20 30 40 50 60 70 80 Proportion of Causation in %

Accicents and Age, 2008

gesamt männlich weiblich

all

Quelle: Statistisches Bundesamt, Verkehrsunfälle 2008

slide-19
SLIDE 19

Technische Universität München Lehrstuhl für Ergonomie

ExampleTailgate Inattentive driver, braking late

(Video: Inattentive driver, braking late)

Humans Situation Interpretation

slide-20
SLIDE 20

Technische Universität München Lehrstuhl für Ergonomie

SEEV-Modell as an Example

The probability of a glance is a function of Salience, Effort, Expectancy and Value

slide-21
SLIDE 21

Technische Universität München Lehrstuhl für Ergonomie

Environment and Perception

The traffic environment is and will be optimized for human perception This trained our perception:

  • Signs, lines
  • Indicators
  • Wheels
  • Trajectories
slide-22
SLIDE 22

Technische Universität München Lehrstuhl für Ergonomie

Automation Means

  • Users will change their behavior due to positive or

negative experience (i.e. learning)

  • Surrounding traffic will

change its behavior

slide-23
SLIDE 23

Technische Universität München Lehrstuhl für Ergonomie

Research on

  • Longterm usage in „normal“ situations
  • Behavorial styles in mixed traffic
  • Mental models of users about system modes
  • Relevance of driver status and intention
  • Cooperation (Car<-> user, Car2Car)
  • Evaluation of adaptive/learning automation
slide-24
SLIDE 24

Technische Universität München Lehrstuhl für Ergonomie

References

  • 2008. FISITA World automotive Congress.
  • Abbink, D. A., Mulder, M., & Boer, E. R. (2012). Haptic shared control: smoothly shifting control authority? Cognition, Technology &

Work, 14(1), 19–28. doi:10.1007/s10111-011-0192-5

  • Ardelt, M., Coester, C., & Kaempchen, N. (2012). Highly Automated Driving on Freeways in Real Traffic Using a Probabilistic
  • Framework. IEEE Transactions on Intelligent Transportation Systems, 13(4), 1576–1585. doi:10.1109/TITS.2012.2196273
  • Badke-Schaub, P., Hofinger, G., & Lauche, K. (2012). Human Factors: Psychologie sicheren Handelns in Risikobranchen (2.,

überarbeitete Auflage). Heidelberg: Springer. Retrieved from http://dx.doi.org/10.1007/978-3-642-19886-1

  • Banks, V. A., Stanton, N. A., & Harvey, C. (2014). What the drivers do and do not tell you: using verbal protocol analysis to investigate

driver behaviour in emergency situations. Ergonomics, 57(3), 332–342. doi:10.1080/00140139.2014.884245

  • Beggiato, M., & Krems, J. F. (2013). The evolution of mental model, trust and acceptance of adaptive cruise control in relation to initial
  • information. Transportation Research Part F: Traffic Psychology and Behaviour, 18, 47–57. doi:10.1016/j.trf.2012.12.006
  • Beller, J., Heesen, M., & Vollrath, M. (2013). Improving the Driver-Automation Interaction: An Approach Using Automation Uncertainty.

Human Factors: The Journal of the Human Factors and Ergonomics Society, 55(6), 1130–1141. doi:10.1177/0018720813482327

  • Carsten, O., Lai, F. C. H., Barnard, Y., Jamson, A. H., & Merat, N. (2012). Control Task Substitution in Semiautomated Driving: Does It

Matter What Aspects Are Automated? Human Factors: The Journal of the Human Factors and Ergonomics Society, 54(5), 747–761. doi:10.1177/0018720812460246

  • Damböck, D., Farid, M., Tönert, l., & Bengler, K. (Eds.) 2012. Untersuchung von Einflüssen automatischer Bremsmanöver und

Verkehrssituationen auf die Übernahmezeit und -qualität in hochautomatisierten Fahrzeugen.

  • Damböck, D., Weissgerber, T., Kienle, M., & Bengler, K. (2013). Requirements for cooperative vehicle guidance. Proceedings of the

16th International IEEE Annual Conference on Intelligent Transportation Systems (ITSC 2013), 1656–1661. doi:10.1109/ITSC.2013.6728467

  • Damböck, D. (2013). Automationseffekte im Fahrzeug - von der Reaktion zur Übernahme (Dissertation). Technische Universität

München, München.

  • Damböck, D., Farid, M., Tönert, L., & Bengler, K. (2012). Übernahmezeiten beim hochautomatisierten Fahren. 5. Tagung
  • Fahrerassistenz. Retrieved from http://www.ftm.mw.tum.de/uploads/media/24\_Damboeck.pdf
slide-25
SLIDE 25

Technische Universität München Lehrstuhl für Ergonomie

References

  • Donges, E. (2012). Fahrerverhaltensmodelle. In H. Winner, S. Hakuli, & G. Wolf (Eds.), Handbuch Fahrerassistenzsysteme (pp. 15–23).

Wiesbaden: Vieweg+Teubner Verlag.

  • Endsley, M. R. (1995). Toward a Theory of Situation Awareness in Dynamic Systems. Human Factors: The Journal of the Human

Factors and Ergonomics Society, 37(1), 32–64. doi:10.1518/001872095779049543

  • Endsley, M. R. (1999). Level of automation effects on performance, situation awareness and workload in a dynamic control task.

Ergonomics, 42(3), 462–492.

  • Endsley, M. R., & Kiris, E. O. (1995). The Out-of-the-Loop Performance Problem and Level of Control in Automation. Human Factors:

The Journal of the Human Factors and Ergonomics Society, 37(2), 381–394. doi:10.1518/001872095779064555

  • Fitch, G. M., Bowman, D. S., & Llaneras, R. E. (2014). Distracted Driver Performance to Multiple Alerts in a Multiple-Conflict Scenario.

Human Factors: The Journal of the Human Factors and Ergonomics Society. doi:10.1177/0018720814531785

  • Flemisch, F. O., Bengler, K., Bubb, H., Winner, H., & Bruder, R. (2014). Towards cooperative guidance and control of highly automated

vehicles: H-Mode and Conduct-by-Wire. Ergonomics, 57(3), 343–360. doi:10.1080/00140139.2013.869355

  • Flemisch, F., Heesen, M., Hesse, T., Kelsch, J., Schieben, A., & Beller, J. (2011). Towards a dynamic balance between humans and

automation: authority, ability, responsibility and control in shared and cooperative control situations. Cognition, Technology & Work, 14(1), 3–18. doi:10.1007/s10111-011-0191-6

  • Flemisch, F., Kelsch, J., Löper, C., Schieben, A., Schindler, J., & Heesen, M. (2008). Cooperative control and active interfaces for

vehicle assistance and automation. In FISITA World automotive Congress .

  • Flemisch, F., Schieben, A., Schoemig, N., Strauss, M., Lueke, S., & Heyden, A. (2011). Design of Human Computer Interfaces for

Highly Automated Vehicles in the EU-Project HAVEit. Berlin Heidelberg: Springer.

  • Gasser, T. M. (Ed.) 2012. Ergebnisse der Projektgruppe Automatisierung: Rechtsfolgen zunehmender Fahrzeugautomatisierung.

Bergisch Gladbach: Wirtschaftsverlag NW.

  • Gasser, T. M. & Westhoff, D. (2012). BASt-study: Definitions of Automation and Legal Issues in Germany.
  • Geyer, S., Hakuli, S., Winner, H., Franz, B., & Kauer, M. (2011). Development of a cooperative system behavior for a highly automated

vehicle guidance concept based on the Conduct-by-Wire principle, 411–416. doi:10.1109/IVS.2011.5940437

slide-26
SLIDE 26

Technische Universität München Lehrstuhl für Ergonomie

References

  • Gold, C., Dambock, D., Lorenz, L., & Bengler, K. (2013). "Take over!" How long does it take to get the driver back into the loop?

Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 57(1), 1938–1942. doi:10.1177/1541931213571433

  • Gold, C., Lorenz, L., Damböck, D., & Bengler, K. (Eds.) 2013. Partially Automated Driving as a Fallback Level of High Automation:

TÜV SÜD Akademie GmbH.

  • Gold, Christian; Lorenz, Lutz; Bengler, Klaus (2014): Influence of Automated Brake Application on Take-Over Situations in Highly

Automated Driving Scenarios. FISITA 2014 World Automotive Congress, Maastrich, Netherlands, 2-6 June (in print)

  • Hancock, P. A. (2014). Automation: how much is too much? Ergonomics, 57(3), 449–454. doi:10.1080/00140139.2013.816375
  • Helldin, T., Falkman, G., Riveiro, M., & Davidsson, S. (2013). Presenting system uncertainty in automotive UIs for supporting trust

calibration in autonomous driving. In J. Terken (Ed.), Proceedings of the 5th International Conference on Automotive User Interfaces and Interactive Vehicular Applications (pp. 210–217). New York, NY: ACM.

  • Hoedemaeker, M., & Brookhuis, K. A. (1998). Behavioural adaptation to driving with an adaptive cruise control (ACC). Transportation

Research Part F: Traffic Psychology and Behaviour, 1(2), 95–106. doi:10.1016/S1369-8478(98)00008-4

  • Hoff, K. A., & Bashir, M. (2014). Trust in Automation: Integrating Empirical Evidence on Factors That Influence Trust. Human Factors:

The Journal of the Human Factors and Ergonomics Society. doi:10.1177/0018720814547570

  • Kienle, M., Damböck, D., Bubb, H., & Bengler, K. (2013). The ergonomic value of a bidirectional haptic interface when driving a highly

automated vehicle. Cognition, Technology & Work, 15(4), 475–482. doi:10.1007/s10111-012-0243-6

  • Kienle, M., Franz, B., Winner, H., Bengler, K., Baltzer, M., Flemisch, F., . . . . (2013). Concept and development of a unified ontology for

generating test and use-case catalogues for assisted and automated vehicle guidance. IET Intelligent Transport Systems. doi:10.1049/iet-its.2012.0188

  • Lank, C., Haberstroh, M., & Wille, M. (2011). Interaction of Human, Machine, and Evironment in Automated Driving Systems.

Transportation Research Record: Journal of the Transportation Research Board, 2243, 138–145.

  • Llaneras, R. E., Salinger, J., & Green, P. A. (2013). Human Factors Issues Associated with Limited Ability Autonomous Driving

Systems: Drivers’ Allocation of Visual Attention to the Forward Roadway. PROCEEDINGS of the Seventh International Driving Symposium on Human Factors in Driver Assessment, Training, and Vehicle Design, 92–98. Retrieved from http://drivingassessment.uiowa.edu/sites/default/files/DA2013/Papers/015\_Llaneras\_0.pdf

slide-27
SLIDE 27

Technische Universität München Lehrstuhl für Ergonomie

References

  • Lorenz, L., Kerschbaum, P., & Schumann, J. (2014). Designing take over scenarios for automated driving: How does augmented reality

support the driver to get back into the loop?

  • Martens, M. H., & Jenssen, G. D. (2012). Handbook of Intelligent Vehicles. London: Springer.
  • Matthews, G. Towards a transactional ergonomics for driver stress and fatigue. Theoretical Issues in Ergonomics Science, pp. 195–211.
  • Mayhorn, C. B., Wogalter, M. S., & Laughery, K. R. (2014). Special issue on warnings: advances in delivery, application, and methods.

Applied ergonomics, 45(5), 1267–1269. doi:10.1016/j.apergo.2014.02.012

  • Merat, N., & Jamson, A. H. (Eds.) 2009. How do drivers behave in a highly automated car?
  • Merat, N., Jamson, A. H., Lai, F. C. H., & Carsten, O. (2012). Highly Automated Driving, Secondary Task Performance, and Driver
  • State. Human Factors: The Journal of the Human Factors and Ergonomics Society, 54(5), 762–771. doi:10.1177/0018720812442087
  • Merat, N., Jamson, A. H., Lai, F. C. H., Daly, M., & Carsten, O. (2014). Publisher’s Note. Transportation Research Part F: Traffic

Psychology and Behaviour, 26, 1–9. doi:10.1016/j.trf.2014.05.006

  • Merat, N., & Lee, J. D. (2012). Preface to the Special Section on Human Factors and Automation in Vehicles: Designing Highly

Automated Vehicles With the Driver in Mind. Human Factors, 54(5), 681–686. doi:10.1177/0018720812461374

  • Michael, T. Ergebnisse der Projektgruppe Automatisierung: Rechtsfolgen zunehmender Fahrzeugautomatisierung.
  • MUIR, B. M. (1994). Trust in automation: Part I. Theoretical issues in the study of trust and human intervention in automated systems.

Ergonomics, 37(11), 1905–1922. doi:10.1080/00140139408964957

  • Naujoks, F., Mai, C., & Neukum, A. (Eds.) 2014. The effect of urgency of take-over requests during highly automated driving under

distraction conditions.

  • Neubauer, C., Matthews, G., Langheim, L., & Saxby, D. (2012). Fatigue and Voluntary Utilization of Automation in Simulated Driving.

The Journal of the Human Factors and Ergonomics Society, 54(54 // 5), 734–746. doi:10.1177/0018720811423261

  • Neubauer, C., Matthews, G., & Saxby, D. (2012). The Effects of Cell Phone Use and Automation on Driver Performance and Subjective

State in Simulated Driving. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 56(1), 1987–1991. doi:10.1177/1071181312561415

  • NHTSA. (2013). Preliminary Statement of Policy Concerning Automated Vehicles.
slide-28
SLIDE 28

Technische Universität München Lehrstuhl für Ergonomie

References

  • Parasuraman, R., & Manzey, D. H. (2010). Complacency and Bias in Human Use of Automation: An Attentional Integration. Human

Factors: The Journal of the Human Factors and Ergonomics Society, 52(3), 381–410. doi:10.1177/0018720810376055

  • Parasuraman, R., & Riley, V. (1997). Humans and Automation: Use, Misuse, Disuse, Abuse. Human Factors: The Journal of the Human

Factors and Ergonomics Society, 39(2), 230–253. doi:10.1518/001872097778543886

  • Payre, W., Cestac, J., & Delhomme, P. (2014). Intention to use a fully automated car: Attitudes and a priori acceptability. Transportation

Research Part F: Traffic Psychology and Behaviour. doi:10.1016/j.trf.2014.04.009

  • Petermann, I., & Kiss, M. Die Rolle des Fahrers im Spektrum von Automation und Transition tionsstufen.
  • Petermann, I., & Kiss, M. (2010). Die Rolle des Fahrers im Spektrum der Automation und Transition. ATZextra, 15(2), 90–95.
  • Petermann, I., & Schlag, B. (2010). Auswirkung der Synthese von Assistenz und Automation auf das Fahrer-Fahrzeug-System. AAET

2010 - Automatisierungssysteme, Assistenzsysteme und eingebettete Systeme für Transportmittel, 383–403.

  • Petermann-Stock, I., Hackenberg, L., Muhr, T., & Mergl, C. (2013). Wie lange braucht der Fahrer? Eine Analyse zu Übernahmezeiten

aus verschiedenen Nebentätigkeiten während einer hochautomatisierten Staufahrt. 6. Tagung Fahrerassistenzsysteme. Der Weg zum automatischen Fahren.,

  • Radlmayr, J., Gold, C., Lorenz, L., Farid, M., & Bengler, K. (2014). How traffic situations and non-driving related tasks affect the take-
  • ver quality in highly automated driving. Proceedings of the Human Factors and Ergonomics Society Annual Meeting,
  • Rasmussen, J. (1983). Skills, Rules, and Knowledge; Signals, Signs, and Symbols, and Other Distinctions in Human Performance
  • Models. IEEE Transactions on Systems, 13(3), 257–266.
  • Rolfe, J. (1972). Ergonomics and air safety. Applied Ergonomics, 3(2), 75–81. doi:10.1016/0003-6870(72)90056-7
  • Röttger, S., Bali, K., & Manzey, D. (2009). Impact of automated decision aids on performance, operator behaviour and workload in a

simulated supervisory control task. Ergonomics, 52(5), 512–523. doi:10.1080/00140130802379129

  • Rusch, M. L., Schall, M. C., Gavin, P., Lee, J. D., Dawson, J. D., Vecera, S., & Rizzo, M. (2013). Directing driver attention with

augmented reality cues. Transportation research. Part F, Traffic psychology and behaviour, 16, 127–137. doi:10.1016/j.trf.2012.08.007

  • Saffarian, M., de Winter, J. C. F., & Happee, R. (2012). Automated Driving: Human-Factors Issues and Design Solutions. Proceedings
  • f the Human Factors and Ergonomics Society Annual Meeting, 56(1), 2296–2300. doi:10.1177/1071181312561483
slide-29
SLIDE 29

Technische Universität München Lehrstuhl für Ergonomie

References

  • Schieben, A., Temme, G., Köster, F., & Flemisch, F. (2011). How to interact with a highly automated vehiclegeneric interaction design

schemes and test results of a usability assessment. Human centred automation. Shaker Publishing, Maastricht, 251–266.

  • Schmidt, A., Dey, A., Seder, T., Juhlin, O., & Kern, D. (Eds.) 2009. User Interfaces and Interactive Vehicular Applications.
  • Schubert, R., Schulze, K., & Wanielik, G. (2010). Situation Assessment for Automatic Lane-Change Maneuvers. IEEE Transactions on

Intelligent Transportation Systems, 11(3), 607–616.

  • Shackel, B. (1967). Ergonomics research needs in automation. Ergonomics, 10(5), 627–632. doi:10.1080/00140136708930916
  • Shinar, D., & Oppenheim, I. (2011). Review of models of Driver Behaviour and development of a Unified Driver Behaviour model for

driving in safety critical situations. Human Modelling in Assisted Transportation, 215-223.

  • Stanton, N. A., & Young, M. S. (2005). Driver behaviour with adaptive cruise control. Ergonomics, 48(10), 1294–1313.

doi:10.1080/00140130500252990

  • STANTON, N. A., & YOUNG, M. S. (1998). Vehicle automation and driving performance. Ergonomics, 41(7), 1014–1028.

doi:10.1080/001401398186568

  • Strand, N., Nilsson, J., Karlsson, I. M., & Nilsson, L. (2014). Semi-automated versus highly automated driving in critical situations

caused by automation failures. Transportation Research Part F: Traffic Psychology and Behaviour. doi:10.1016/j.trf.2014.04.005

  • Terken, J. (Ed.) 2013. Proceedings of the 5th International Conference on Automotive User Interfaces and Interactive Vehicular
  • Applications. New York, NY: ACM.
  • Toffetti, A., Wilschut, E. S., Martens, M. H., Schieben, A., Rambaldini, A., Merat, N., & Flemisch, F. (2009). CityMobil. Transportation

Research Record: Journal of the Transportation Research Board, 2110(-1), 1–8. doi:10.3141/2110-01

  • Trimble, R. T., Bishop, R., Morgan, F. J., & Blanco, M. (2014). Human Factors Evaluation of Level 2 And Level 3 Automated Driving

Concepts: Past Research, State of Automation Technology, and Emerging System Concepts.

  • van den Beukel, Arie P., & van der Voort, Mascha C. (Eds.) 2013. The influence of time-criticality on Situation Awareness when

retrieving human control after automated driving.

  • Vanholme, B., Gruyer, D., Lusetti, B., Glaser, S., & Mammar, S. (2013). Highly automated driving on highways based on legal safety.

Intelligent Transportation Systems, IEEE Transactions on, 14(1), 333–347.

slide-30
SLIDE 30

Technische Universität München Lehrstuhl für Ergonomie

References

  • Vehicle Automation: TRB @ Stanford. (2013). HF Breakout Notes. Retrieved from

http://2013.vehicleautomation.org/program/breakouts/human-factors-and-human-machine-interaction/hf-breakout-notes

  • Viti, F., Hoogendoorn, S. P., Alkim, T. P., & Bootsma, G. Driving behavior interaction with ACC: results from a Field Operational Test in

the Netherlands, pp. 745–750.

  • Vollrath, M., Schleicher, S., & Gelau, C. (2011). The influence of cruise control and adaptive cruise control on driving behaviour--a

driving simulator study. Accident; analysis and prevention, 43(3), 1134–1139. doi:10.1016/j.aap.2010.12.023

  • Wickens, C. D. (2008). Multiple Resources and Mental Workload. Human Factors: The Journal of the Human Factors and Ergonomics

Society, 50(3), 449–455. doi:10.1518/001872008X288394

  • Wiener, Earl, L., & Curry, Renwick, E. (1980). Flight-deck automation: promises and problems. Ergonomics, 23(10), 995–1011.
  • Willemsen, D., Stuiver, A., & Hogema, J. (Eds.) 2014. Transition of Control: Automation Giving Back Control to the Driver.
  • Winner, H., Hakuli, S., & Wolf, G. (Eds.). (2012). Handbuch Fahrerassistenzsysteme. Wiesbaden: Vieweg+Teubner Verlag.
  • Winter, J. de, & Dodou, D. (2011). Preparing drivers for dangerous situations: A critical reflection on continuous shared control: A critical

reflection on continuos shared control, 1050–1056. doi:10.1109/ICSMC.2011.6083813

  • Winter, J. de, Happee, R., Martens, M. H., & Stanton, N. A. (2014). Effects of adaptive cruise control and highly automated driving on

workload and situation awareness: A review of the empirical evidence. Transportation Research Part F: Traffic Psychology and Behaviour,

  • Young, M. S., & Stanton, N. A. (2007). Back to the future: brake reaction times for manual and automated vehicles. Ergonomics, 50(1),

46–58. doi:10.1080/00140130600980789

  • YOUNG, M. S., & STANTON, N. A. (2007). What's skill got to do with it? Vehicle automation and driver mental workload. Ergonomics,

50(8), 1324–1339. doi:10.1080/00140130701318855

  • Zimmermann, M., & Bengler, K. (Eds.) 23-26. June. 2013. A multimodal interaction concept for cooperative driving.
  • Zlocki, A., & Eckstein, L. (Eds.) 2014. Automated Driving – Concept and Evaluation.