.. .. Immersion,

.. .. Training-content,

. .. , Open questions and perspectives, p.130

. .. Epilogue, Dans ce tableau, sont représentés 9 personnages ressentant tous des humeurs différentes. Sélectionnez le personnage qui représente au mieux votre état actuel. * Mark only one oval, Neutre Calme, vol.130, p.13

. Relaxé, détendu(e) Content(e), joyeux(se)

G. Enthousiaste,

M. Triste,

, Ennuyé(e), fatigué(e)

, Nerveux(se), tendu(e)

, Irrité(e), énervé(e)

, À quel point la formation avant la conduite atelle aidée à: * Mark only one oval per row

K. Abdelgawad, J. Gausemeier, J. Stöcklein, M. Grafe, J. Berssenbrügge et al., A platform with multiple head-mounted displays for advanced training in modern driving schools, Designs, vol.1, issue.2, p.44, 2017.

H. Abraham, H. Mcanulty, B. Mehler, and B. Reimer, Case study of today's automotive dealerships: Introduction and delivery of advanced driver assistance systems, Transportation Research Record: Journal of the Transportation Research Board, p.24, 2017.

S. M. Alessi, Fidelity in the design of instructional simulations, Journal of computerbased instruction, pp.1-9

R. W. Allen, G. D. Park, M. L. Cook, and D. Fiorentino, The effect of driving simulator fidelity on training effectiveness, North America, 2007.

. Andasoft, Easyroads3d pro, p.61, 2016.

B. Arnaldi, P. Guitton, and G. Moreau, Virtual Reality and Augmented Reality: Myths and Realities, p.32, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01812771

A. , Audi a8 ai traffic jam pilot, 2019.

G. Avveduto, C. Tanca, C. Lorenzini, F. Tecchia, M. Carrozzino et al., Safety training using virtual reality: A comparative approach, International Conference on Augmented Reality, p.44, 2017.

B. Aykent, Z. Yang, F. Merienne, and A. Kemeny, Simulation sickness comparison between a limited field of view virtual reality head mounted display (oculus) and a medium range field of view static ecological driving simulator (eco2), Driving CHAPTER
URL : https://hal.archives-ouvertes.fr/hal-01097461

, Society for Modeling & Simulation International, Simulation Conference Europe 2014 Proceedings, p.91, 2014.

E. Azimi, A. Winkler, E. Tucker, L. Qian, J. Doswell et al., Can mixed-reality improve the training of medical procedures?, 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), p.44, 2018.

J. Bacca, S. Baldiris, R. Fabregat, and S. Graf, Augmented reality trends in education: a systematic review of research and applications, p.43, 2014.

J. N. Bailenson, N. Yee, J. Blascovich, A. C. Beall, N. Lundblad et al., The use of immersive virtual reality in the learning sciences: Digital transformations of teachers, students, and social context, The Journal of the Learning Sciences, issue.1, p.33, 2008.

L. Bainbridge, Design and Evaluation of ManMachine Systems, Analysis, p.14, 1982.

P. Bazilinskyy, A. Eriksson, B. Petermeijer, and J. Winter, Usefulness and satisfaction of take-over requests for highly automated driving, Proceedings of the Road Safety & Simulation International Conference

A. Boelhouwer, A. P. Van-den, M. C. Beukel, . Van-der, M. Voort et al., Should i take over? does system knowledge help drivers in making take-over decisions while driving a partially automated car?, Transportation Research Part F: Traffic Psychology and Behaviour, vol.60, p.124, 2019.

. Bonecrakergames, Realistic car controller, p.61, 2016.

S. S. Borojeni, A. Meschtscherjakov, A. G. Mirnig, S. Boll, F. Naujoks et al., Control transition workshop: Handover and takeover procedures in highly automated driving, Proceedings of the 9th International Conference on Automotive User Interfaces and Interactive Vehicular Applications Adjunct, vol.17, p.173, 2017.

C. Bossard, G. Kermarrec, C. Buche, and J. Tisseau, Transfer of learning in virtual environments: a new challenge, Virtual Reality, vol.12, issue.3, p.49, 2008.
URL : https://hal.archives-ouvertes.fr/halshs-00855740

A. C. Boud, D. J. Haniff, C. Baber, and S. Steiner, Virtual reality and augmented reality as a training tool for assembly tasks, iv, p.43, 1999.

D. A. Bowman and R. P. Mcmahan, Virtual reality: how much immersion is enough? Computer, vol.4, p.0

D. A. Bowman, E. Kruijff, J. J. Laviola, and I. Poupyrev, An introduction to 3-d user interface design, Presence: Teleoperators & Virtual Environments, vol.1, issue.1, p.35, 2001.

D. A. Bowman, C. Stinson, E. D. Ragan, S. Scerbo, T. Höllerer et al., Evaluating effectiveness in virtual environments with mr simulation, Interservice/Industry Training, Simulation, and Education Conference, vol.4, p.44, 2012.

C. Braunagel, W. Rosenstiel, and E. Kasneci, Ready for take-over? a new driver assistance system for an automated classification of driver take-over readiness, IEEE Intelligent Transportation Systems Magazine, vol.9, issue.4, pp.10-22, 2017.

M. Bueno, E. Dogan, F. H. Selem, E. Monacelli, S. Boverie et al., How different mental workload levels affect the take-over control after automated driving, Intelligent Transportation Systems (ITSC), vol.55, p.128, 2016.

J. Burkhardt, D. Lourdeaux, and D. Mellet-d'huart, La conception des environnements virtuels pour l'apprentissage, vol.38, p.47, 2003.
URL : https://hal.archives-ouvertes.fr/hal-01434545

J. M. Burkhardt, V. Corneloup, C. Garbay, Y. Bourrier, F. Jambon et al., Simulation and virtual reality-based learning of non-technical skills in driving: critical situations, diagnostic and adaptation, vol.49, p.44, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01502327

S. M. Casner, R. W. Geven, and K. T. Williams, The effectiveness of airline pilot training for abnormal events, Human factors, vol.55, issue.3, p.45, 2013.

R. Champney, J. N. Salcedo, S. J. Lackey, S. Serge, and M. Sinagra, Mixed reality training of military tasks: Comparison of two approaches through reactions from subject matter experts, International Conference on Virtual, Augmented and Mixed Reality, p.43, 2016.

T. Chapron and J. Colinot, The new psa peugeot-citroen advanced driving simulator overall design and motion cue algorithm, Proceedings of Driving Simulation Conference, p.173

H. Clark and J. Feng, Age differences in the takeover of vehicle control and engagement in non-driving-related activities in simulated driving with conditional automation, Accident Analysis & Prevention, vol.106, p.24, 2017.

M. Cunningham and M. A. Regan, Autonomous vehicles: human factors issues and future research, Proceedings of the 2015 Australasian Road Safety Conference, p.14, 2015.

B. Dalgarno and M. J. Lee, What are the learning affordances of 3-d virtual environments?, British Journal of Educational Technology, vol.41, issue.1, p.173, 2010.

D. A. Dickie and L. N. Boyle, Drivers' understanding of adaptive cruise control limitations, Proceedings of the Human Factors and Ergonomics Society Annual Meeting, vol.53, p.24, 2009.

C. Directive, Council directive 85/374/eec of 25 july 1985 on the approximation of the laws, regulations and administrative provisions of the member states concerning liability for defective products, Official Journal L, p.25, 1985.

J. E. Domeyer, N. D. Cassavaugh, and R. W. Backs, The use of adaptation to reduce simulator sickness in driving assessment and research, Accident Analysis & Prevention, vol.53, p.73, 2013.

A. Dosovitskiy, G. Ros, F. Codevilla, A. Lopez, and V. Koltun, CARLA: An open urban driving simulator, Proceedings of the 1st Annual Conference on Robot Learning, p.60, 2017.

F. M. Dwyer, The program of systematic evaluation (pse): Evaluating the effects of multimedia instruction, Educational Technology, p.37, 1965.

M. R. Endsley, Design and evaluation for situation awareness enhancement, Proceedings of the Human Factors Society annual meeting, vol.32, p.15, 1988.

M. R. Endsley, Situation awareness in future autonomous vehicles: Beware of the unexpected, Congress of the International Ergonomics Association, pp.303-309, 2018.

M. R. Endsley and E. O. Kiris, The out-of-the-loop performance problem and level of control in automation, Human factors, vol.37, issue.2, p.17, 1995.

A. Eriksson, V. Banks, and N. Stanton, Transition to manual: Comparing simulator with on-road control transitions, Accident Analysis & Prevention, vol.102, p.103, 2017.

G. Evans, J. Miller, M. I. Pena, A. Macallister, and E. Winer, Evaluating the microsoft hololens through an augmented reality assembly application, page 101970V. International Society for Optics and Photonics, vol.10197, p.44, 2017.

. F80saka, Uc-win road air driving, vol.35, p.173, 2011.

T. Falkmer and N. P. Gregersen, The TRAINER project: the evaluation of a new simulator-based driver training methodology, p.44, 2003.

M. J. Farr, The long-term retention of knowledge and skills: A cognitive and instructional perspective, p.48, 2012.

J. A. Ferwerda, Three varieties of realism in computer graphics, International Society for Optics and Photonics, vol.5007, p.173, 2003.

D. L. Fisher, N. E. Laurie, R. Glaser, K. Connerney, A. Pollatsek et al., Use of a fixed-base driving simulator to evaluate the effects of experience and pc-based risk awareness training on drivers' decisions, Human factors, vol.4, issue.2, p.44, 2002.

L. Freina and M. Ott, A literature review on immersive virtual reality in education: State of the art and perspectives. eLearning & Software for Education, vol.5, p.43

P. Fuchs, The challenges and risks of democratization of vr-ar, Virtual Reality and Augmented Reality: Myths and Realities, vol.35, p.36, 2018.

K. Gardelis, A. S. Lalos, and K. Moustakas, Development of an eco-driving simulation training system with natural and haptic interaction in virtual reality environments, VISIGRAPP (2: HUCAPP), p.44, 2018.

N. Gavish, T. Gutiérrez, S. Webel, J. Rodriguez, M. Peveri et al., Evaluating virtual reality and augmented reality training for industrial maintenance and assembly tasks, Interactive Learning Environments, vol.23, issue.6, p.44, 2015.

D. Goedicke, J. Li, V. Evers, and W. Ju, Vr-oom: Virtual reality on-road driving simulation, Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, p.45, 2018.

C. Gold, D. Damböck, L. Lorenz, and K. Bengler, 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, vol.57, p.92, 2013.

C. Gold, M. Körber, D. Lechner, and K. Bengler, Taking over control from highly automated vehicles in complex traffic situations: the role of traffic density, Human factors, vol.58, issue.4, p.22, 2016.

C. Gold, F. Naujoks, J. Radlmayr, H. Bellem, and O. Jarosch, Testing scenarios for human factors research in level 3 automated vehicles, International Conference on Applied Human Factors and Ergonomics, vol.109, p.177, 2017.

N. Goode, P. M. Salmon, and M. G. Lenné, Simulation-based driver and vehicle crew training: applications, efficacy and future directions, Applied ergonomics,4, vol.4, issue.3, p.44, 2013.

J. Greenberg, B. Artz, and L. Cathey, The effect of lateral motion cues during simulated driving, Proceedings of DSC North America, p.42, 2003.

R. Grossman and E. Salas, The transfer of training: what really matters, International Journal of Training and Development, vol.15, issue.2, pp.103-120, 2011.

A. Habibovic, J. Andersson, M. Nilsson, V. M. Lundgren, and J. Nilsson, Evaluating interactions with non-existing automated vehicles: three wizard of oz approaches, 2016 IEEE Intelligent Vehicles Symposium (IV), p.103, 2016.

E. Hamilton, D. Scott, J. Fleming, R. Rege, R. Laycock et al., Comparison of video trainer and virtual reality training systems on acquisition of laparoscopic skills, Surgical Endoscopy and Other Interventional Techniques, vol.16, issue.3, p.44, 2002.

R. Happee, C. Gold, J. Radlmayr, S. Hergeth, and K. Bengler, Take-over performance in evasive manoeuvres, Accident Analysis & Prevention, vol.106, issue.23, p.175, 2017.

J. C. Hayward, Near miss determination through use of a scale of danger, p.82, 1972.

S. Hergeth, L. Lorenz, J. F. Krems, and L. Toenert, Effects of take-over requests and cultural background on automation trust in highly automated driving, vol.25, p.26, 2015.

S. Hergeth, L. Lorenz, and J. F. Krems, Prior familiarization with takeover requests affects drivers' takeover performance and automation trust, Human factors, vol.5, issue.3, p.128, 2017.

N. D. Herzberger, G. M. Voß, F. K. Becker, F. Grazioli, E. Altendorf et al., Derivation of a model of safety critical transitions between driver and vehicle in automated driving, International Conference on Applied Human Factors and Ergonomics, vol.28, p.173, 2018.

Q. C. Ihemedu-steinke, R. Erbach, P. Halady, G. Meixner, and M. Weber, Virtual reality driving simulator based on head-mounted displays, Automotive User Interfaces, vol.42, p.43, 2017.

Q. C. Ihemedu-steinke, S. Rangelova, M. Weber, R. Erbach, G. Meixner et al., Marsden. Simulation sickness related to virtual reality driving simulation, International Conference on Virtual, Augmented and Mixed Reality, vol.45, p.92, 2017.

S. , Taxonomy and definitions for terms related to driving automation systems for on-road motor vehicles, p.12, 2016.

B. Kappe and P. Padmos, Headslaved displays in a driving simulator, Proceedings of the 1st Human-Centered Transportation Simulation Conference, p.43, 2001.

R. S. Kennedy, N. E. Lane, K. S. Berbaum, and M. G. , Simulator sickness questionnaire: An enhanced method for quantifying simulator sickness. The international journal of aviation psychology, vol.3, p.114, 1992.

R. S. Kennedy, K. M. Stanney, and W. P. Dunlap, Duration and exposure to virtual environments: sickness curves during and across sessions, Presence: Teleoperators & Virtual Environments, vol.9, issue.5, p.73, 2000.

R. S. Kennedy, J. M. Drexler, D. E. Compton, K. M. Stanney, D. S. Lanham et al., Configural scoring of simulator sickness, cybersickness and space adaptation syndrome: Similarities and differences. Virtual and adaptive environments: Applications, implications, and human performance issues, vol.84, p.247, 2003.

M. Klüver, C. Herrigel, C. Heinrich, H. Schöner, and H. Hecht, The behavioral validity of dual-task driving performance in fixed and moving base driving simulators, Transportation research part F: traffic psychology and behaviour, vol.37, p.42, 2016.

M. Körber, C. Gold, D. Lechner, and K. Bengler, The influence of age on the take-over of vehicle control in highly automated driving, Transportation research part F: traffic psychology and behaviour, vol.39, p.92, 2016.

J. Kozak, P. Hancock, E. Arthur, and S. Chrysler, Transfer of training from virtual reality, Ergonomics, vol.36, issue.7, p.48, 1993.

M. Kyriakidis, J. C. Winter, N. Stanton, T. Bellet, B. Van-arem et al., A human factors perspective on automated driving, Theoretical Issues in Ergonomics Science, vol.14, p.122, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01703353

S. Lavalle, Virtual reality, p.32, 2016.

W. Lee, J. Kim, and J. Cho, A driving simulator as a virtual reality tool, Proceedings. 1998 IEEE International Conference on, vol.1, p.42, 1998.

D. Liu, N. D. Macchiarella, and D. A. Vincenzi, Simulation fidelity. Human factors in simulation and training, p.38, 2008.

L. Lorenz, P. Kerschbaum, and J. Schumann, Designing take over scenarios for automated driving: How does augmented reality support the driver to get back into the loop?, Proceedings of the Human Factors and Ergonomics Society Annual Meeting, vol.58, p.26, 2014.

M. Matthews, D. Bryant, R. Webb, and J. Harbluk, Model for situation awareness and driving: Application to analysis and research for intelligent transportation systems, Transportation Research Record: Journal of the Transportation Research Board, vol.15, issue.1779, p.18, 2001.

R. Mccall, F. Mcgee, A. Mirnig, A. Meschtscherjakov, N. Louveton et al., A taxonomy of autonomous vehicle handover situations, Transportation Research Part A: Policy and Practice, vol.16, p.30, 2015.

N. Merat, A. H. Jamson, F. C. Lai, M. Daly, and O. M. Carsten, Transition to manual: Driver behaviour when resuming control from a highly automated vehicle, Transportation research part F: traffic psychology and behaviour, vol.27, p.25, 2014.

N. Merat, B. Seppelt, T. Louw, J. Engström, J. D. Lee et al., The "out-of-the-loop" concept in automated driving: Proposed definition, measures and implications, Technology & Work, p.16, 2018.

D. Mestre, P. Fuchs, A. Berthoz, and J. Vercher, Immersion et présence. Le traité de la réalité virtuelle, Ecole des Mines de Paris, p.33, 2006.

J. Micallef, J. Dupouey, E. Jouve, R. Truillet, B. Lacarelle et al., Cannabis smoking impairs driving performance on simulator and real driving: A randomized, double blind, placebo-controlled
URL : https://hal.archives-ouvertes.fr/hal-01880788

T. A. Mikropoulos and A. Natsis, Educational virtual environments: A ten-year review of empirical research, Computers & Education, vol.56, issue.3, p.45, 1999.

P. Milgram and F. Kishino, A taxonomy of mixed reality visual displays, IEICE TRANSACTIONS on Information and Systems, vol.77, issue.12, p.34, 1994.

I. Milleville-pennel and C. Charron, Driving for real or on a fixed-base simulator: is it so different? An explorative study, Presence, vol.24, issue.1, p.103, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01162091

J. Morgan, S. Tidwell, A. Medina, and M. Blanco, On the training and testing of entry-level commercial motor vehicle drivers, Accident Analysis & Prevention,4, vol.3, issue.4, p.44, 2011.

F. Naujoks, D. Befelein, K. Wiedemann, and A. Neukum, A review of non-drivingrelated tasks used in studies on automated driving, International Conference on Applied Human Factors and Ergonomics, pp.525-537, 2017.

J. Navarro, M. François, and F. Mars, Obstacle avoidance under automated steering: impact on driving and gaze behaviours. Transportation research part F: traffic psychology and behaviour, vol.43, p.22, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01396206

W. Payre, J. Cestac, N. Dang, F. Vienne, and P. Delhomme, Impact of training and in-vehicle task performance on manual control recovery in an automated car, Transportation Research Part F: Traffic Psychology and Behaviour, vol.46, p.26, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01491150

W. Payre, J. Cestac, N. Dang, F. Vienne, and P. Delhomme, Impact of training and in-vehicle task performance on manual control recovery in an automated car, Transportation research part F: traffic psychology and behaviour, vol.46, p.124, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01491150

N. Pennington, R. Nicolich, and J. Rahm, Transfer of training between cognitive subskills: Is knowledge use specific?, Cognitive Psychology, vol.28, issue.2, p.48, 1995.

. Peperkamp, Mais comment font-ils pour apprendre une langue?, 2016.

S. Petermeijer, F. Doubek, and J. Winter, Driver response times to auditory, visual, and tactile take-over requests: A simulator study with 101 participants, 2017 IEEE International Conference on, pp.1505-1510, 1921.

N. Petersen and D. Stricker, Cognitive augmented reality, Computers & Graphics, vol.5, p.47, 2015.

G. F. Piccinini, C. M. Rodrigues, M. Leitão, and A. Simões, Reaction to a critical situation during driving with adaptive cruise control for users and non-users of the system, Safety science, vol.72, p.24, 2015.

P. Piedimonte and S. L. Ullo, Applicability of the mixed reality to maintenance and training processes of c4i systems in italian air force, 5th IEEE International Workshop on Metrology for AeroSpace (MetroAeroSpace), p.44, 2018.

. Psa, Psa highway chauffeur, p.14, 2019.

. Psa, Psa traffic jam chauffeur, 2019.

J. Radlmayr, C. Gold, L. Lorenz, M. Farid, and K. Bengler, How traffic situations and non-driving related tasks affect the take-over quality in highly automated driving, Proceedings of the Human Factors and Ergonomics Society Annual Meeting, vol.58, p.23, 2014.

J. Radlmayr, M. Ratter, A. Feldhütter, M. Körber, L. Prasch et al., Take-overs in level 3 automated driving-proposal of the take-over performance score (tops), Congress of the International Ergonomics Association, vol.20, p.21, 2018.

E. D. Ragan, A. Sowndararajan, R. Kopper, and D. A. Bowman, The effects of higher levels of immersion on procedure memorization performance and implications for educational virtual environments, Presence: Teleoperators and Virtual Environments, vol.19, issue.6, p.33, 2010.

J. Rasmussen, Skills, rules, and knowledge; signals, signs, and symbols, and other distinctions in human performance models, IEEE transactions on systems, man, and cybernetics, vol.56, issue.3, p.57, 1983.

J. Reason, A. Manstead, S. Stradling, J. Baxter, and K. Campbell, Errors and violations on the roads: a real distinction?, Ergonomics, vol.33, p.56, 1990.

J. T. Reason and J. J. Brand, Motion sickness, p.37, 1975.

G. E. Riccio and T. A. Stoffregen, An ecological theory of motion sickness and postural instability, Ecological psychology, vol.3, issue.3, p.37, 1991.

L. D. Riek, Wizard of oz studies in hri: a systematic review and new reporting guidelines, Journal of Human-Robot Interaction, vol.1, issue.1, p.103, 2012.

D. Rothenbücher, J. Li, D. Sirkin, B. Mok, and W. Ju, Ghost driver: A field study investigating the interaction between pedestrians and driverless vehicles, Robot and Human Interactive Communication (RO-MAN), p.103, 2016.

W. B. Rouse, J. A. Cannon-bowers, and E. Salas, The role of mental models in team performance in complex systems, IEEE transactions on systems, man, and cybernetics, vol.22, issue.6, p.27, 1992.

M. M. Roy and M. J. Liersch, I am a better driver than you think: examining selfenhancement for driving ability, Journal of applied social psychology, vol.43, issue.8, p.20, 2013.

. Sae, Sae international, vol.10, p.173, 2019.

I. Sae, Taxonomy and definitions for terms related to driving automation systems for on-road motor vehicles, vol.105, p.173, 2018.

G. Schmidt, M. Kiss, E. Babbel, and A. Galla, The wizard on wheels: Rapid prototyping and user testing of future driver assistance using wizard of oz technique in a vehicle, Proceedings of the FISITA 2008 World Automotive Congress, p.103, 2008.

S. Shah, D. Dey, C. Lovett, and A. Kapoor, Airsim: High-fidelity visual and physical simulation for autonomous vehicles, Field and service robotics, p.60, 2018.

M. Slater, A note on presence terminology, Presence connect, vol.3, issue.3, p.35, 2003.

J. W. Son and M. O. Park, Situation awareness and transitions in highly automated driving a framework and mini-review, vol.21, p.173, 2017.

K. Spolander, Drivers'assessment of their own driving ability, p.20, 1983.

N. A. Stanton and P. Marsden, From fly-by-wire to drive-by-wire: safety implications of automation in vehicles, Safety Science, vol.24, issue.1, p.45, 1996.

P. J. Stappers, W. Gaver, and K. Overbeeke, Beyond the limits of real-time realism: Moving from stimulation correspondence to information correspondence. Virtual and adaptive environments, vol.37, p.45, 2003.

J. Stevens, P. Kincaid, and R. Sottilare, Visual modality research in virtual and mixed reality simulation, The Journal of Defense Modeling and Simulation, vol.12, issue.4, p.48, 2015.

H. A. Stoner, D. L. Fisher, and M. Mollenhauer, Simulator and scenario factors influencing simulator sickness, vol.37, p.92, 2011.

S. M. Taheri, K. Matsushita, and M. Sasaki, Development of a Driving Simulator with Analyzing Driver's Characteristics Based on a Virtual Reality Head Mounted Display, Journal of Transportation Technologies, vol.7, issue.03, p.43, 2017.

M. Treisman, Motion sickness: an evolutionary hypothesis, Science, vol.197, issue.4302, p.37, 1977.

S. Trösterer, M. Gärtner, A. Mirnig, A. Meschtscherjakov, R. Mccall et al., You Never Forget How to Drive: Driver Skilling and Deskilling in the Advent of Autonomous Vehicles, Proceedings of the 8th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, p.20, 2016.

J. Vince, 10 virtual reality techniques in flight simulation, Virtual Reality Systems, p.45, 1993.

J. Vince, Virtual reality techniques in flight simulation, Virtual Reality Systems, p.43, 1993.

T. Vogelpohl, M. Kühn, T. Hummel, T. Gehlert, and M. Vollrath, Transitioning to manual driving requires additional time after automation deactivation, Transportation research part F: traffic psychology and behaviour, vol.55, p.20, 2018.

J. Vora, S. Nair, A. K. Gramopadhye, A. T. Duchowski, B. J. Melloy et al., Using virtual reality technology for aircraft visual inspection training: presence and comparison studies, Applied ergonomics, vol.33, issue.6, p.44, 2002.

. Vuforia, Extended tracking, 2019.

I. Wagner, W. Broll, G. Jacucci, K. Kuutii, R. Mccall et al., On the role of presence in mixed reality, Presence: Teleoperators and Virtual Environments, vol.18, issue.4, p.32, 2009.

F. Walker, A. Boelhouwer, T. Alkim, W. B. Verwey, and M. H. Martens, Changes in trust after driving level 2 automated cars, Journal of advanced transportation, vol.102, p.103, 2018.

G. H. Walker and N. A. Stanton, Human factors in automotive engineering and technology.C R CP r e s s, vol.2, pp.0-1

P. Wang, S. Sibi, B. Mok, and W. Ju, Marionette: Enabling on-road wizard-of-oz autonomous driving studies, Proceedings of the 2017 ACM/IEEE International Conference on Human-Robot Interaction, p.103, 2017.

J. S. Warm, R. Parasuraman, and G. Matthews, Vigilance requires hard mental work and is stressful, Human factors, vol.50, issue.3, p.15, 2008.

. Waymo, Why we're aiming for fully self-driving vehicles, 2015.

F. Weidner, A. Hoesch, S. Poeschl, and W. Broll, Comparing VR and non-VR driving simulations: An experimental user study, Virtual Reality (VR), 2017 IEEE, vol.43, p.92, 2017.

C. D. Wickens and X. Xu, How does automation reliability influence workload ?, p.25, 2002.

K. L. Wilson, J. T. Doswell, O. S. Fashola, W. Debeatham, N. Darko et al., Using augmented reality as a clinical support tool to assist combat medics in the treatment of tension pneumothoraces. Military medicine, vol.178, p.44, 2013.

K. Zeeb, A. Buchner, and M. Schrauf, What determines the take-over time? an integrated model approach of driver take-over after automated driving, Accident Analysis & Prevention, vol.78, p.24, 2015.

K. Zeeb, A. Buchner, and M. Schrauf, Is take-over time all that matters? The impact of visual-cognitive load on driver take-over quality after conditionally automated driving, Accident Analysis & Prevention, vol.92, p.25, 2016.

B. Zhang, J. Winter, S. Varotto, R. Happee, and M. Martens, Determinants of take-over time from automated driving: A meta-analysis of 93 studies, vol.67, p.119, 2018.

H. Zhang, Head-mounted display-based intuitive virtual reality training system for the mining industry, International Journal of Mining Science and Technology

]. .. , 10 2.2 Taxonomy of Level of Automation proposed by SAE, Sensors on automated vehicles. Image from SAE, p.11, 2018.

T. Handover and . Taxonomy, Adapted from Borojeni et al, p.17, 2017.

T. Herzberger, ] model with training effect on take-over performance, 2018.

, Adapted from Son et al, p.21, 2017.

. Herzberger, Model of safety critical transition between driver and vehicle in automated driving, p.28, 2018.

, Venn diagram of Mixed Reality. Image from microsoft, p.31

, The Mixed Reality spectrum, Image from microsoft.com, p.31

, Examples of visualization devices : (a) a monitor display, (b) a panoramic projection-based display, (c) a head-mounted display, p.34

, Examples of manipulation interfaces for the driving task: (a) a gaming racing wheel and pedals; (b) a RC car remote; (c) a mid-air interaction without hardware, p.35

]. .. , 39 3.6 Comparison between a CAVE (a [image from visbox.com], b) and an HMD (c [image from vive.com],d [image from hp.com]) setups and visual rendering, Functional realism vs photo-realism. From Ferwerda, 2003.

, b) a high-end fixed-base simulator with panoramic display, (c) a compact fixed-base simulator with single screen, (d) a HMD-based simulator, Examples of driving simulator systems: (a) a moving-base simulator [Chapron and Colinot, 2007.

]. .. , Model of learning in 3-D VLEs, incorporating unique characteristics and learning affordances [Dalgarno and Lee, vol.46, 2010.

, The description of each vehicle state and the associated icon, p.55

, The Rasmussen's SRK model

, The Driving and Onboard Activities Simulator: (a) the 3D model of the car and (b) the interior view

. .. , The driving scenarios implemented in the DOAS, p.61

.. .. Simon,

, Self-reported measures

, Obstacle (red line) avoidance trajectories. In light red the lane in the opposite direction

, The Light Virtual Reality System

, The Acclimatization Environment: The car is displayed with a transparent effect and the panel on the front wall shows the indicators for the accelerator and brake pedal and for the steering wheel, p.69

, The Virtual Learning Environment: (a) a post-production illustration of a participant in the VLE; (b) a view of the interior of the car with the training message (on the transparent panel placed over the steering wheel) and the virtual tablet used for the secondary activity, (c) the reduction of the field of view with the visual tunnel effect, p.70

, Experimental platform diagram including the Light VR system and the VLE

, 2 Characteristics of immersion of the fixed-base simulator and the Light VR system

, The three training systems: (a) the user manual displayed on the laptop computer, (b) the fixed-base driving simulator

, The test drive simulator: (a) the real cabin with the 170 degree panoramic display and (b) a view of the cabin interior

, The test drive scenario with the three situations that provoked the TORs: (A) stationary car on the lane, (B) loss of ground marking, (C) sensor failure

, Likert responses to the questionnaire of training appreciation, p.84

.. .. Fb, 84 5.8 Results of SSQ scores (Nausea, Oculomotor, Disorientation subscales and Total) for the LVR group. In orange, the percentile graph. The vertical blue lines represent the value of SSQ if all the symptoms were reported as "slight" on that subscale, Likert responses to the realism survey for

, Mean of the answer to the pre-post questionnaires. A light-green bar indicates an increase in the post questionnaire for the given question. The values indicate the percentage of change in the questions, p.86

, Take-over reaction times for each systems and for each TOR. The first two were 10-second TORs; the last one was a 5-second TOR, vol.87

, The two lanes are plotted. The value 0 in the y-axis represents the center of the right lane in the driving scenario

T. To-collision and . Happee, the red block represents the stationary obstacle; the red line is the trajectory of the vehicle, 2017.

, Means and 95% Confidence Interval of the stress and confidence score attributed to the participants during the 3 autonomous driving phases

, 1 (a) The evolution of immersion from the first to the second user study; (b) The plan of the study, vol.6

, 2 Characteristics of Immersion of the fixed-base simulator the the VR-HMD system

, The design of the AR Learning Environment. The model of the cockpit and the virtual elements

. .. Environment, , vol.101

, The effect of adding the display of the user's hands in the virtual environment

, The VR training: (a) the learning environment; (b) the practice driving scenario

, Two frame from the test drives: (a) the high-end driving simulator and (b) the real driving

, The three training systems: (a) the on-board video tutorial, (b) the light VR system, (c) the AR system

, The protocol of the user study

, The vehicle used for the test drive with the participant's control interface on the left and the pilot wizard's controls n the right. The two seats were separated by a panel

, The test drive: a participant makes a phone call while the car is in autonomous mode, p.108

. .. , The default test drive itinerary on the public freeway, vol.109

, The three categories of TOR

, Correct answers to the Knowledge Test for each training system

, The reaction time according to the TOR type and the training system

, Red dots are the RTs of the first TOR for each group, Take Over reaction time (RT) according to group age and training system

, The results of the questionnaire for the training phase evaluation, p.115

, post and final questionnaire about perceived trust and usefulness of autonomous driving and willingness to perform a secondary activity during autonomous driving

, Disorientation subscales and Total) for the VR group with the percentile graph. The vertical dotted lines represent the value of SSQ if all the symptoms were reported as "slight" on that subscale, Results of SSQ scores (Nausea, Oculomotor

, 66 4.3 The tasks in the Virtual Learning Environment. Each action was notified to the driver with visual and auditory messages

. Take-over, Requests according to the taxonomy proposed by Gold et al, 2017.

, Demographic features distributed across the different systems

, Means (and standard deviation) of Time To Collision (TTC) and Distance To Collision (DTC) used to evaluate the evasive maneuver, p.89

, Number of gaze switch between the secondary activity and the road environment for each autonomous driving phase; Eyes-on-Road is the ratio between the total amount of time spent looking at the road and the duration of the autonomous driving phase

T. Requests and . Gold, , vol.109, 2017.

, Demographic features distributed across the different systems, p.110

, Results of the Simulator Sickness Questionnaire. The maximum possible score for the subscales is 200

. .. , Measures comparison between the two user studies, p.119