eHMI on the Vehicle or on the Infrastructure? A Driving Simulator Study


Shiva Nischal Lingam Department of Transport and Planning, Faculty of Civil Engineering and Geosciences, Delft University of Technology | Royal HaskoningDHV, The NetherlandsJoost De Winter Department of Cognitive Robotics, Faculty of Mechanical Engineering, Delft University of Technology, The Netherlands;Yongqi Dong Department of Transport and Planning, Faculty of Civil Engineering and Geosciences, Delft University of Technology, The NetherlandsAnastasia Tsapi Royal HaskoningDHV, The NetherlandsBart Van Arem Department of Transport and Planning, Faculty of Civil Engineering and Geosciences, Delft University of Technology, The NetherlandsHaneen Farah Department of Transport and Planning, Faculty of Civil Engineering and Geosciences, Delft University of Technology, The Netherlands



Automated vehicles (AVs) may require the implementation of an external human-machine interface (eHMI) to communicate their intentions to human-driven vehicles. The optimal placement of the eHMI, either on the AV itself or as part of the road infrastructure, remains undetermined. The current driving simulator study investigated the effect of eHMI positioning on human driving behavior, during the approach and execution of right turns at T-intersections. Forty-three participants drove under three conditions: absence of eHMI, eHMI on the AV (eHMIv), and eHMI integrated into the infrastructure (eHMIi). Participants encountered AVs that either yielded or did not yield to their vehicles. The results regarding the placement of the eHMI showed that both concepts are advantageous, but for different reasons. eHMIv was appreciated because implicit and explicit communication are congruent, although the AV must first be visually identified to respond to it. eHMIi was appreciated because a familiar cue is always at a known location in the environment; as a result, participants braked earlier for the intersection and came less close to the AV (which can be interpreted as a safety advantage or efficiency disadvantage). Although there are limitations to a driving simulator study like this, this research provides important insights into the fundamental question of how information placement affects drivers’ visual attention demands and driving behavior, topics that are important in view of the development of future cities.


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Avsar, H., Utesch, F., Wilbrink, M., Oehl, M. and Schießl, C. (2021). Efficient communication of automated vehicles and manually driven vehicles through an external human-machine interface (eHMI): Evaluation at t-junctions. In Stephanidis, C., Antona, M. and Ntoa, S. (eds) HCI International 2021 - Posters. HCII 2021 (pp. 224–232), Springer, Cham.

Bazilinskyy, P., Dodou, D. and De Winter, J. (2019). Survey on eHMI concepts: The effect of text, color, and perspective. Transportation Research Part F: Traffic Psychology and Behaviour, 67, 175–194.

Bazilinskyy, P., Dodou, D. and De Winter, J.C.F. (2020). External Human-Machine Interfaces: Which of 729 colors is best for signaling ‘Please (do not) cross’? Proceedings of the IEEE International Conference on Systems, Man and Cybernetics (SMC) (pp. 3721–3728). Toronto, Canada.

Bimberg, P., Weissker, T. and Kulik, A. (2020). On the usage of the Simulator Sickness Questionnaire for virtual reality research. 2020 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW) (pp. 464–467). Atlanta, GA.

Bindschädel, J., Krems, I. and Kiesel, A. (2021). Interaction between pedestrians and automated vehicles: Exploring a motion-based approach for virtual reality experiments. Transportation Research Part F: Traffic Psychology and Behaviour, 82, 316–332.

BMW. (2021). Owner’s handbook. The BMW X7.

Bradley, M.M. and Lang, P.J. (1994). Measuring emotion: The self-assessment manikin and the semantic differential. Journal of Behavior Therapy and Experimental Psychiatry, 25(1), 49–59.

Braun, V. and Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101.

Bulumulle, G. and Bölöni, L. (2016). Reducing side-sweep accidents with vehicle-to-vehicle communication. Journal of Sensor and Actuator Networks, 5(4), 19.

Colley, M., Bajrovic, E. and Rukzio, E. (2022a). Effects of pedestrian behavior, time pressure, and repeated exposure on crossing decisions in front of automated vehicles equipped with external communication. Proceedings of the CHI Conference on Human Factors in Computing Systems. New Orleans, LA.

Colley, M., Fabian, T. and Rukzio, E. (2022b). Investigating the effects of external communication and automation behavior on manual drivers at intersections. Proceedings of the ACM on Human-Computer Interaction, 6(MHCI), 176.

Crosato, L., Tian, K., Shum, H.P.H., Ho, E.S.L., Wang, Y. and Wei, C. (2023). Social interaction‐aware dynamical models and decision‐making for autonomous vehicles. Advanced Intelligent Systems, 6(3), 2300575.

CROW. (2012). ASVV 2012 : Aanbevelingen voor verkeersvoorzieningen binnen de bebouwde kom [ASVV 2012: Recommendations for traffic facilities in built-up areas]. CROW Kenniscentrum voor Verkeer, Vervoer en Infrastructuur, Ede.

Deb, S., Strawderman, L.J. and Carruth, D.W. (2018). Investigating pedestrian suggestions for external features on fully autonomous vehicles: A virtual reality experiment. Transportation Research Part F: Traffic Psychology and Behaviour, 59, 135–149.

De Clercq, G.K., Dietrich, A., Núñez Velasco, P., De Winter, J.C.F. and Happee, R. (2019). External human-machine interfaces on automated vehicles: Effects on pedestrian crossing decisions. Human Factors, 61(8), 1353–1370.

De Winter, J.C.F. and Dodou, D. (2010). Five-point Likert items: t test versus Mann-Whitney-Wilcoxon (Addendum added October 2012). Practical Assessment, Research, and Evaluation, 15(1), 11.

De Winter, J.C.F. and Dodou, D. (2022). External human–machine interfaces: Gimmick or necessity? Transportation Research Interdisciplinary Perspectives, 15, 100643.

Dey, D., Ackermans, S., Martens, M., Pfleging, B. and Terken, J. (2022). Interactions of automated vehicles with road users. In Riener, A., Jeon, M. and Alvarez, I. (eds) User Experience Design in the Era of Automated Driving (pp. 533–581), Springer, Cham.

Dey, D., Habibovic, A., Löcken, A., Wintersberger, P., Pfleging, B., Riener, A., Martens, M. and Terken, J. (2020). Taming the eHMI jungle: A classification taxonomy to guide, compare, and assess the design principles of automated vehicles’ external human-machine interfaces. Transportation Research Interdisciplinary Perspectives, 7, 100174.

Dey, D. and Terken, J. (2017). Pedestrian interaction with vehicles: Roles of explicit and implicit communication. Proceedings of the 9th International Conference on Automotive User Interfaces and Interactive Vehicular Applications (pp. 109–113). Oldenburg, Germany.

Eisele, D. and Petzoldt, T. (2022). Effects of traffic context on eHMI icon comprehension. Transportation Research Part F: Traffic Psychology and Behaviour, 85, 1–12.

Eisma, Y.B., Cabrall, C.D.D. and De Winter, J.C.F. (2018). Visual sampling processes revisited: Replicating and extending Senders (1983) using modern eye-tracking equipment. IEEE Transactions on Human Machine Systems, 48(5), 526–540.

Eisma, Y.B., Van Bergen, S., Ter Brake, S.M., Hensen, M.T.T., Tempelaar, W.J. and De Winter, J.C.F. (2020). External human-machine interfaces: The effect of display location on crossing intentions and eye movements. Information, 11(1), 13.

Faas, S.M., Kao, A.C. and Baumann, M. (2020). A longitudinal video study on communicating status and intent for self-driving vehicle–pedestrian interaction. Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. Honolulu, HI.

Faas, S.M., Kraus, J., Schoenhals, A. and Baumann, M. (2021). Calibrating pedestrians’ trust in automated vehicles: Does an intent display in an external HMI support trust calibration and safe crossing behavior? Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems. Yokohama, Japan.

Ferenchak, N.N. and Shafique, S. (2022). Pedestrians’ perceptions of autonomous vehicle external human-machine interfaces. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering, 8(3), 034501.

Gruenefeld, U., Weiß, S., Löcken, A., Virgilio, I., Kun, A.L. and Boll, S. (2019). VRoad: Gesture-based interaction between pedestrians and automated vehicles in virtual reality. 11th International Conference on Automotive User Interfaces and Interactive Vehicular Applications: Adjunct Proceedings (pp. 399–404). Utrecht, The Netherlands.

Habibovic, A., Malmsten Lundgren, V., Andersson, J., Klingegård, M., Lagström, T., Sirkka, A., Fagerlönn, J., Edgren, C., Fredriksson, R., Krupenia, S., Saluäär, D. and Larsson, P. (2018). Communicating intent of automated vehicles to pedestrians. Frontiers in Psychology, 9, 1336.

Hart, S.G. and Staveland, L.E. (1988). Development of NASA-TLX (Task Load Index): Results of empirical and theoretical research. In Hancock, P.A. and Meshkati, N. (eds) Human Mental Workload (pp. 139–183), North Holland Press, Amsterdam.

Hensch, A.-C., Neumann, I., Beggiato, M., Halama, J. and Krems, J.F. (2019). Effects of a light-based communication approach as an external HMI for automated vehicles – A Wizard-of-Oz Study. Transactions on Transport Sciences, 10(2), 18–32.

Kaleefathullah, A.A., Merat, N., Lee, Y.M., Eisma, Y.B., Madigan, R., Garcia, J. and De Winter, J.C.F. (2022). External human–machine interfaces can be misleading: An examination of trust development and misuse in a CAVE-based pedestrian simulation environment. Human Factors, 64(6), 1070–1085.

Kennedy, R.S., Lane, N.E., Berbaum, K.S. and Lilienthal, M.G. (1993). Simulator Sickness Questionnaire: An enhanced method for quantifying simulator sickness. The International Journal of Aviation Psychology, 3(3), 203–220.

Kettwich, C., Haus, R., Temme, G. and Schieben, A. (2016). Validation of a HMI concept indicating the status of the traffic light signal in the context of automated driving in urban environment. Proceedings of the 2016 IEEE Intelligent Vehicles Symposium (IV) (pp. 1374–1379). Gothenburg, Sweden.

Lau, M., Le, D.H. and Oehl, M. (2021). Design of external human-machine interfaces for different automated vehicle types for the interaction with pedestrians on a shared space. In Black, N.L., Neumann, W.P. and Noy, I. (eds) Proceedings of the 21st Congress of the International Ergonomics Association (pp. 710–717), Springer, Cham.

Lee, Y.M., Madigan, R., Giles, O., Garach-Morcillo, L., Markkula, G., Fox, C., Camara, F., Rothmueller, M., Vendelbo-Larsen, S.A., Holm Rasmussen, P., Dietrich, A., Nathanael, D., Portouli, V., Schieben, A. and Merat, N. (2021). Road users rarely use explicit communication when interacting in today’s traffic: implications for automated vehicles. Cognition, Technology & Work, 23, 367–380.

Löcken, A., Golling, C. and Riener, A. (2019). How should automated vehicles interact with pedestrians? A comparative analysis of interaction concepts in virtual reality. Proceedings of the 11th International Conference on Automotive User Interfaces and Interactive Vehicular Applications (pp. 262–274). Utrecht, The Netherlands.

Madigan, R., Lee, Y.M., Lyu, W., Horn, S., De Pedro, J.G. and Merat, N. (2023). Pedestrian interactions with automated vehicles: Does the presence of a zebra crossing affect how eHMIs and movement patterns are interpreted? Transportation Research Part F: Traffic Psychology and Behaviour, 98, 170–185.

Mahadevan, K., Somanath, S. and Sharlin, E. (2018). Communicating awareness and intent in autonomous vehicle-pedestrian interaction. Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. Montréal, Canada.

Martins, V., Rufino, J., Silva, L., Almeida, J., Fernandes Silva, B.M., Ferreira, J. and Fonseca, J. (2019). Towards personal virtual traffic lights. Information, 10(1), 32.

Métayer, N. and Coeugnet, S. (2021). Improving the experience in the pedestrian’s interaction with an autonomous vehicle: An ergonomic comparison of external HMI. Applied Ergonomics, 96, 103478.

Mirnig, A.G., Gärtner, M., Wallner, V., Gafert, M., Braun, H., Fröhlich, P., Suette, S., Sypniewski, J., Meschtscherjakov, A. and Tscheligi, M. (2021). Stop or go? Let me know! A field study on visual external communication for automated shuttles. Proceedings of the 13th International Conference on Automotive User Interfaces and Interactive Vehicular Applications (pp. 287–295). Leeds, UK.

Mok, C.S., Bazilinskyy, P. and De Winter, J.C.F. (2022). Stopping by looking: A driver-pedestrian interaction study in a coupled simulator using head-mounted displays with eye-tracking. Applied Ergonomics, 105, 103825.

Neukum, A., Lübbecke, T., Krüger, H.-P., Mayser, C. and Steinle, J. (2008). ACC-Stop&Go: Fahrverhalten an funktionalen Systemgrenzen. In Maurer, M. and Stiller, C. (eds) 5. Workshop Fahrerassistenzsysteme (FAS) (2008) (pp. 141–150).

Norman, D. (2014). Turn Signals Are the Facial Expressions of Automobiles. Diversion Books, New York.

Ohn-Bar, E. and Trivedi, M.M. (2016). Looking at humans in the age of self-driving and highly automated vehicles. IEEE Transactions on Intelligent Vehicles, 1(1), 90–104.

Othersen, I., Conti-Kufner, A.S., Dietrich, A., Maruhn, P. and Bengler, K. (2018). Designing for automated vehicle and pedestrian communication: Perspectives on eHMIs from older and younger persons. In De Waard, D., Brookhuis, K., Coelho, D., Fairclough, S., Manzey, D., Naumann, A., Onnasch, L., Röttger, S., Toffetti, A. and Wiczorek, R. (eds) Proceedings of the Human Factors and Ergonomics Society Europe Chapter 2018 Annual Conference (pp. 135–148).

Papakostopoulos, V., Nathanael, D., Portouli, E. and Amditis, A. (2021). Effect of external HMI for automated vehicles (AVs) on drivers’ ability to infer the AV motion intention: A field experiment. Transportation Research Part F: Traffic Psychology and Behaviour, 82, 32–42.

Rettenmaier, M., Albers, D. and Bengler, K. (2020). After you?! – Use of external human-machine interfaces in road bottleneck scenarios. Transportation Research Part F: Traffic Psychology and Behaviour, 70, 175–190.

Rettenmaier, M., Pietsch, M., Schmidtler, J. and Bengler, K. (2019). Passing through the bottleneck – The potential of external human-machine interfaces. Proceedings of the IEEE Intelligent Vehicles Symposium (pp. 1687–1692). Paris, France.

Rudenko, A., Palmieri, L., Herman, M., Kitani, K. M., Gavrila, D.M. and Arras, K.O. (2020). Human motion trajectory prediction: A survey. The International Journal of Robotics Research, 39(8), 895–935.

Sadeghian, S., Hassenzahl, M. and Eckoldt, K. (2020). An exploration of prosocial aspects of communication cues between automated vehicles and pedestrians. Proceedings of the 12th International Conference on Automotive User Interfaces and Interactive Vehicular Applications (pp. 1687–1692). Virtual Event.

Schwarting, W., Pierson, A., Alonso-Mora, J., Karaman, S. and Rus, D. (2019). Social behavior for autonomous vehicles. Proceedings of the National Academy of Sciences, 116(50), 24972–24978.

Siebert, F.W., Oehl, M. and Pfister, H.-R. (2014). The influence of time headway on subjective driver states in adaptive cruise control. Transportation Research Part F: Traffic Psychology and Behaviour, 25, 65–73.

Tabone, W., De Winter, J.C.F., Ackermann, C., Bärgman, J., Baumann, M., Deb, S., Emmenegger, C., Habibovic, A., Hagenzieker, M., Hancock, P.A., Happee, R., Krems, J., Lee, J. D., Martens, M., Merat, N., Norman, D.A., Sheridan, T.B. and Stanton, N.A. (2021). Vulnerable road users and the coming wave of automated vehicles: Expert perspectives. Transportation Research Interdisciplinary Perspectives, 9, 100293.

Taghavifar, H., Wei, C. and Taghavifar, L. (2024). Socially intelligent reinforcement learning for optimal automated vehicle control in traffic scenarios. IEEE Transactions on Automation Science and Engineering.

Tang, K. and Kuwahara, M. (2011). Implementing the concept of critical post-encroachment time for all-red clearance interval design at signalized intersections. Proceedings of the Eastern Asia Society for Transportation Studies, 8, 299–299.

Tran, T.T.M., Parker, C., Wang, Y. and Tomitsch, M. (2022). Designing wearable augmented reality concepts to support scalability in autonomous vehicle-pedestrian interaction. Frontiers in Computer Science, 4, 866516.

Tscharn, R., Naujoks, F. and Neukum, A. (2018). The perceived criticality of different time headways is depending on velocity. Transportation Research Part F: Traffic Psychology and Behaviour, 58, 1043–1052.

Umbrellium. (2017). Make roads safer, more responsive & dynamic.

Van der Laan, J.D., Heino, A. and De Waard, D. (1997). A simple procedure for the assessment of acceptance of advanced transport telematics. Transportation Research Part C: Emerging Technologies, 5(1), 1–10.

Von Sawitzky, T., Wintersberger, P., Löcken, A., Frison, A.-K. and Riener, A. (2020). Augmentation concepts with HUDs for cyclists to improve road safety in shared spaces. Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems. Honolulu, HI.

Von Sawitzky, T., Wintersberger, P., Riener, A. and Gabbard, J.L. (2019). Increasing trust in fully automated driving: Route indication on an augmented reality head-up display. Proceedings of the 8th ACM International Symposium on Pervasive Displays. Palermo, Italy.

Wickens, C.D., McCarley, J.S., Alexander, A.L., Thomas, L.C., Ambinder, M. and Zheng, S. (2008). Attention-situation awareness (A-SA) model of pilot error. In Foyle, D.C. and Hooey, B.L. (eds) Human Performance Modeling in Aviation (pp. 213–239), CRC Press, Boca Raton, FL.

Wilbrink, M., Lau, M., Illgner, J., Schieben, A. and Oehl, M. (2021). Impact of external human–machine interface communication strategies of automated vehicles on pedestrians’ crossing decisions and behaviors in an urban environment. Sustainability, 13(15), 8396.

Winzer, O.M., Conti-Kufner, A.S. and Bengler, K. (2018). Intersection Traffic Light Assistant – An evaluation of the suitability of two human machine interfaces. Proceedings of the 2018 21st International Conference on Intelligent Transportation Systems (ITSC) (pp. 1687–1692). Maui, HI.

Witmer, B.G., Jerome, C.J. and Singer, M.J. (2005). The factor structure of the Presence Questionnaire. Presence: Teleoperators and Virtual Environments, 14(3), 298–312.

Yang, B., Zheng, R., Yin, Y., Yamabe, S. and Nakano, K. (2016). Analysis of influence on driver behaviour while using in‐vehicle traffic lights with application of head‐up display. IET Intelligent Transport Systems, 10(5), 347–353.

Zimmermann, M., Schopf, D., Lütteken, N., Liu, Z., Storost, K., Baumann, M., Happee, R. and Bengler, K.J. (2018). Carrot and stick: A game-theoretic approach to motivate cooperative driving through social interaction. Transportation Research Part C: Emerging Technologies, 88, 159–175.




How to Cite

Lingam, S. N., De Winter, J., Dong, Y., Tsapi, A., Van Arem, B., & Farah, H. (2024). eHMI on the Vehicle or on the Infrastructure? A Driving Simulator Study. European Journal of Transport and Infrastructure Research, 24(2), 1–24.