One critical issue of autonomous vehicles is how to communicate the intention to pedestrians. In this study, the impact of two factors (vehicle motion and LED lighting signal type) on participants’ interpretation of LED signals were investigated. A total of 80 participants completed the study online. We found that motion affects participants’ interpretation, but depending on the LED pattern; participants were also able to understand the vehicle’s intention with the attention capturing pattern (flashing squares). In addition, some creative interpretations were reported suggesting an opportunity to redefine the meaning of light patterns. Implications of these findings and next steps were discussed.澳
BazilinskyyP.KooijmanL.DodouD.De WinterJ. C. F. (2021). How should external Human-Machine Interfaces behave? Examining the effects of colour, position, message, activation distance, vehicle yielding, and visual distraction among 1,434 participants. Applied Ergonomics, 95, 103450.
3.
ChangC.-M.TodaK.SakamotoD.IgarashiT. (2017). Eyes on a car: An interface design for communication between an autonomous car and a pedestrian. Proceedings of the 9th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, 65–73. https://doi.org/10.1145/3122986.3122989
4.
ClamannM.AubertM.CummingsM. (2017). Evaluation of vehicle-topedestrian communication displays for autonomous vehicles. Proceedings of the 96th Annual Transportation Research Board.
5.
DeyD.MartensM.WangC.RosF.TerkenJ. (2018). Interface concepts for intent communication from autonomous vehicles to vulnerable road users. Adjunct Proceedings of the 10th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, 82–86. https://doi.org/10.1145/3239092.3265946
6.
DeyD.MatviienkoA.BergerM.PflegingB.MartensM.TerkenJ. (2021). Communicating the intention of an automated vehicle to pedestrians: The contributions of eHMI and vehicle behavior. It Information Technology, 63(2), 123–141. https://doi.org/10.1515/itit-2020-0025
7.
DeyD.TerkenJ. (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, 109–113. https://doi.org/10.1145/3122986.3123009
8.
FarraherB. A. B.WeinholzerR.KowskiM. P. (1999). The effect of advanced warning flashers on red light running—A study using motion imaging recording system technology at Trunk Highway 169 and Pioneer Trail in Bloomington, Minnesota. Transportation Frontiers for the Next Millennium: 69th Annual Meeting of the Institute of Transportation EngineersInstitute of Transportation Engineers (ITE), CD-006.
9.
FridmanL.MehlerB.XiaL.YangY.FacusseL. Y.ReimerB. (2017). To walk or not to walk: Crowdsourced assessment of external vehicle-to-pedestrian displays. ArXiv Preprint ArXiv:1707.02698.
10.
HabibovicA.LundgrenV. M.AnderssonJ.KlingegårdM.LagströmT.SirkkaA.FagerlönnJ.EdgrenC.FredrikssonR.KrupeniaS.SaluäärD.LarssonP. (2018). Communicating intent of automated vehicles to pedestrians. Frontiers in Psychology, 9. https://doi.org/10.3389/fpsyg.2018.01336
11.
KlugmanA.BojeB.BelroseM. (1992). A study of the use and operation of advance warning flashers at signalized intersections. Final report (No. MN/RC-93/01).
12.
LagströmT.LundgrenV. M. (2016). AVIP -Autonomous vehicles’interaction with pedestrians—An investigation of pedestrian-driver communication and development of a vehicle external interface (Chalmers Open Digital Repository) [Master’s thesis, Chalmers University of Technology]. https://hdl.handle.net/20.500.12380/238401
13.
MahadevanK.SomanathS.SharlinE. (2018). Communicating awareness and intent in autonomous vehicle-pedestrian interaction. Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, 1–12. https://doi.org/10.1145/3173574.3174003
RasouliA.TsotsosJ. K. (2020). Autonomous vehicles that interact with pedestrians: A survey of theory and practice. IEEE Transactions on Intelligent Transportation Systems, 21(3), 900–918. https://doi.org/10.1109/TITS.2019.2901817
18.
RettenmaierM.AlbersD.BenglerK. (2020). After you?! – Use of external human-machine interfaces in road bottleneck scenarios. Transportation Research Part F: Traffic Psychology and Behaviour, 70, 175–190. https://doi.org/10.1016/j.trf.2020.03.004
19.
RistoM.EmmeneggerC.VinkhuyzenE.CefkinM.HollanJ. (2017). Human-vehicle interfaces: The power of vehicle movement gestures in human road user coordination. Proceedings of the 9th International Driving Symposium on Human Factors in Driver Assessment, Training, and Vehicle Design, 186–192. https://doi.org/10.17077/drivingassessment.1633
20.
SunR.ZhuangX.WuC.ZhaoG.ZhangK. (2015). The estimation of vehicle speed and stopping distance by pedestrians crossing streets in a naturalistic traffic environment. Transportation Research Part F: Traffic Psychology and Behaviour, 30, 97–106. https://doi.org/10.1016/j.trf.2015.02.002
21.
WangP.MotamediS.QiS.ZhouX.ZhangT.ChanC.-Y. (2021). Pedestrian interaction with automated vehicles at uncontrolled intersections. Transportation Research Part F: Traffic Psychology and Behaviour, 77, 10–25. https://doi.org/10.1016/j.trf.2020.12.005
22.
YangS. (2017). Driver behavior impact on pedestrians’ crossing experience in the conditionally autonomous driving context: Vol. Independent thesis Advanced level [Master’s thesis, KTH Royal Institute of Technology in Stockholm]. DiVA. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-220545
23.
ZimmermannR.WettachR. (2017). First step into visceral interaction with autonomous vehicles. Proceedings of the 9th International Conference on Automotive User Interfaces and Interactive Vehicular Applications. https://doi.org/10.1145/3122986.3122988
