Abstract
Range of recognition of experienced drivers considerably decreases in low light conditions (i.e., night, fog, heavy rain, etc.) and this further impacts their judgement in anticipating road activities. Despite the large body of work on driving in nighttime conditions, like providing night vision cameras to the drivers using white hot configuration or vulnerable road users (VRU) detection systems using white hot configuration, the former carries a risk of drivers driving by the camera whereas the later heavily focuses on VRUs. Moreover, these studies consider only white-hot configuration of the Far Infrared (FIR) technology. Our solution focuses on considering white hot as well as black hot FIR configuration combined with Augmented Reality (AR) technology. Multiple studies suggest that the Far Infrared produces better recognition performance at night than near-infrared (NIR) technology. This paper attempts to take the FIR research a step further by studying it in combination with Augmented Reality (AR) systems. More specifically, we study the effects of FIR configuration (black and white-hot) on drivers’ preference and recognition rate in an AR environment. To gather data, interviews with 11 volunteers were conducted. The results indicated that the white-hot configuration had a higher recognition performance in comparison to the black-hot configuration. Nonetheless, participants preferred black hot over white-hot configuration. These results can guide future development of in-vehicle augmented reality and night vision systems.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
AI Trends: Nighttime Driving and AI Autonomous Cars (2019). https://www.aitrends.com/ai-insider/nighttime-driving-and-ai-autonomous-cars/
ERSO: Annual Accident Report 2018. Erso, 86 (2018). https://ec.europa.eu/transport/road_safety/sites/roadsafety/files/pdf/statistics/dacota/asr2018.pdf
Ferreira, L.M., Coimbra, A.P., de Almeida, A.T.: Autonomous system for wildfire and forest fire early detection and control. Inventions 5(3), 1–14 (2020). https://doi.org/10.3390/inventions5030041
FLIR: Teledyne FLIR Thermal Security Cameras Help Firefighters Monitor Wildfires in California (2021)
Lewis, C., Rieman, J.: Task-Centered User Interface Design: A Practical Introduction. Text, 190 (1993). http://hcibib.org/tcuid/tcuid.pdf
Li, X., Yi, W., Chi, H. L., Wang, X., Chan, A.P.C.: A critical review of virtual and augmented reality (VR/AR) applications in construction safety. Autom. Constr. 86(July 2016), 150–162 (2018). https://doi.org/10.1016/j.autcon.2017.11.003
Medenica, Z., Kun, A., Paek, T.: Comparing augmented reality and street view navigation. In: Adjunct Proceedings of the Second International Conference on Automotive User Interfaces and Interactive Vehicular Applications, Automotive UI (2010)
National Safety Council. (n.d.-a). National Safety Council Injury Facts. https://injuryfacts.nsc.org/motor-vehicle/overview/crashes-by-time-of-day-and-day-of-week/#_ga=2.181839975.1700599729.1646689738-1591115977.1646689738
National Safety Council. (n.d.-b). The most dangerous time to drive. National Safety Council. https://www.nsc.org/road-safety/safety-topics/night-driving#:~:text=While. We do only one quarter of our, car crashes in 2016%2C according to Injury Facts
Owens, D.A., Tyrrell, R.A.: Effects of luminance, blur, and age on nighttime visual guidance: a test of the selective degradation hypothesis. J. Exp. Psychol. Appl. 5(2), 115–128 (1999). https://doi.org/10.1037/1076-898X.5.2.115
Owens, D.A., Wood, J.M., Owens, J.M.: Effects of age and illumination on night driving: a road test. Human Factors J. Human Factors Ergon. Soc. 49(6), 1115–1131 (2007). https://doi.org/10.1518/001872007X249974
Pratt, J., Hommel, B.: Symbolic control of visual attention: the role of working memory and attentional control settings. J. Exp. Psychol. Hum. Percept. Perform. 29(5), 835–845 (2003). https://doi.org/10.1037/0096-1523.29.5.835
Simon, H.A., Newell, A.: Human problem solving: the state of the theory in 1970. Am. Psychol. 26(2), 145–159 (1971). https://doi.org/10.1037/h0030806
Singh, S.K.: Road traffic accidents in India: issues and challenges. Transp. Res. Proc. 25, 4708–4719 (2017). https://doi.org/10.1016/j.trpro.2017.05.484
Sukovoy, O., Kuo, C.: An iterative design methodology for user-friendly natural language information applications. Proc. Inst. Mech. Eng.Part M: J. Eng. Maritime Environ. 2(1), 26–41 (2003)
Swanson, E.D., Yanagisawa, M., Najm, W., Foderaro, F., Azeredo, P.: Crash avoidance needs and countermeasure profiles for safety applications based on light-vehicle-to-pedestrian communications. August, 155 (2016)
Taillard, J., Capelli, A., Sagaspe, P., Anund, A., Akerstedt, T., Philip, P.: In-car nocturnal blue light exposure improves motorway driving: a randomized controlled trial. PLoS ONE 7(10), e46750 (2012). https://doi.org/10.1371/journal.pone.0046750
Tsimhoni, O., Bärgman, J., Flannagan, M.J.: Pedestrian detection with near and far infrared night vision enhancement. Leukos 4(2), 113–128 (2007)
US News: Blinded By the Night: Coping With Impaired Night Vision While Driving (2018). https://health.usnews.com/health-care/for-better/articles/2018-05-14/blinded-by-the-night-coping-with-impaired-night-vision-while-driving
Vandewalle, G., et al.: Brain responses to violet, blue, and green monochromatic light exposures in humans: prominent role of blue light and the brainstem. PLoS ONE 2(11), 1 (2007). https://doi.org/10.1371/journal.pone.0001247
Fisher, D.L., Tan, K.C.: Visual displays: the highlighting paradox. Human Factors J. Human Factors Ergon. Soc. 31(1), 17–30 (1989). https://doi.org/10.1177/001872088903100102
Wood, J.M.: Nighttime driving: visual, lighting and visibility challenges. Ophthalmic Physiol. Opt. 40(2), 187–201 (2020). https://doi.org/10.1111/opo.12659
Yeh, M., Wickens, C.D.: Display signaling in augmented reality: effects of cue reliability and image realism on attention allocation and trust calibration. Hum. Factors 43(3), 355–365 (2001). https://doi.org/10.1518/001872001775898269
Yi, L., Li, K.F., Chen, X., Tung, K.K.: Arctic fog detection using infrared spectral measurements. J. Atmos. Oceanic Tech. 36(8), 1643–1656 (2019). https://doi.org/10.1175/JTECH-D-18-0100.1
Tsimhoni, O., Flannagan, M.: Pedestrian detection with night vision systems enhanced by automatic warnings. Proc. Human Factors Ergon. Soc. Ann. Meet. 50(22), 2443–2447 (2006). https://doi.org/10.1177/154193120605002220
Dr. Knoll, Reppich, P., Andreas: A novel approach for a night vision system”, Robert Bosch GmbH, Germany
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Singhal, N., Alsaid, A., Talamonti, W., Mayer, K. (2023). Study of Night Vision Configuration with Augmented Reality in Automotive Context. In: Stephanidis, C., Antona, M., Ntoa, S., Salvendy, G. (eds) HCI International 2023 Posters. HCII 2023. Communications in Computer and Information Science, vol 1836. Springer, Cham. https://doi.org/10.1007/978-3-031-36004-6_13
Download citation
DOI: https://doi.org/10.1007/978-3-031-36004-6_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-36003-9
Online ISBN: 978-3-031-36004-6
eBook Packages: Computer ScienceComputer Science (R0)