Abstract
Multiple infotainment sources can significantly overload the driver’s cognitive load and increase the collision probabilities. Current solutions provided have attempted to alleviate this issue with the centralization of infotainment devices to single touchscreen devices in the dashboard area. Yet this solution still requires the driver to take the eyes from the road and concentrate on operating the secondary tasks unrelated to the driving process. The paper presents a prototype Augmented Reality Head-Up Display (AR HUD) system that superimposes a selected number of infotainment data on the vehicle’s windshield only when this is safe for the driver. The selection of infotainment data and projection timing is calculated by a prototype Artificial Intelligence (AI) Co-Driver that aims to reduce the driver’s cognitive load. The proposed system was evaluated by 50 users against a typical touchscreen dashboard system. This work presents and discusses the subjective feedback related to the cognitive load that the users perceived during the trials. The paper concludes with a future plan for improving both the AI and AR HUD elements to perform in an urban environment.
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References
Young, K., Regan, M.: Driver distraction: a review of the literature. In: Faulks, I.J., Regan, M., Stevenson, M., Brown, J., Porter, A., Irwin, J.D. (eds.) Distracted Driving, pp. 379–405. Australasian College of Road Safety, Sydney, NSW (2007)
Lagoo, R., Charissis, V., Harrison, D.K.: Mitigating driver’s distraction: automotive head-up display and gesture recognition system. IEEE Consum. Electron. Mag. 8(5), 79–85 (2019). https://doi.org/10.1109/MCE.2019.2923896
Wang, S., Charissis, V., Lagoo, R., Campbell, J., Harrison, D.K.: Reducing driver distraction by utilising augmented reality head-up display system for rear passengers. In: IEEE International Conference on Consumer Electronics (ICCE), Las Vegas, USA (2019). https://doi.org/10.1109/ICCE.2019.8661927
Charissis, V., Papanastasiou, S.: Human-machine collaboration through vehicle head-up display interface. Int. J. Cogn. Technol. Work 12(1), 41–50 (2010). https://doi.org/10.1007/s10111-008-0117. Cacciabue, P.C., Hollangel, E. (eds.) Springer, London
Kim, S., Dey, A.K.: Augmenting human senses to improve the user experience in cars: applying augmented reality and haptics approaches to reduce cognitive distances. Multimed. Tools Appl. 75, 9587–9607 (2015). https://doi.org/10.1007/s11042-015-2712-4
Fernández, A., Usamentiaga, R., Carús, J.L., Casado, R.: Driver distraction using visual-based sensors and algorithms. Sensors 16, 1805 (2016). https://doi.org/10.3390/s16111805
Sweller, J.: Cognitive load during problem solving: effects on learning. Cogn. Sci. 12, 257–285 (1988)
Engström, J., Markkula, G., Victor, T., Merat, N.: Effects of cognitive load on driving performance: the cognitive control hypothesis. Hum. Factors 59(5), 734–764 (2017). https://doi.org/10.1177/0018720817690639
Victor, T.W., Engström, J., Harbluk, J.: Distraction assessment methods based on visual behavior and event detection. In: Regan, M., Lee, J., Young, K. (eds.) Driver Distraction: Theory, Effects and Mitigation, pp. 135–165. CRC Press, Boca Raton, FL (2008)
Engström, J., Johansson, E., Östlund, J.: Effects of visual and cognitive load in real and simulated motorway driving. Transp. Res. Part F: Traffic Psychol. Behav. 8(2), 97–120 (2005). https://doi.org/10.1016/j.trf.2005.04.012
Jahagirdar, T.: Modeling and measuring cognitive load to reduce driver distraction in smart cars. Master thesis, Arizona State University (2015)
Charissis, V., Papanastasiou, S., Chan, W., Peytchev, E.: Evolution of a full-windshield HUD designed for current VANET communication standards. In: IEEE Intelligent Transportation Systems International Conference (IEEE ITS), The Hague, Netherlands, pp. 1637–1643 (2013). https://doi.org/10.1109/ITSC.2013.6728464
Okumura, H., Hotta, A., Sasaki, T., Horiuchi, K., Okada, N.: Wide field of view optical combiner for augmented reality head-up displays. In: 2018 IEEE International Conference on Consumer Electronics (IEEE ICCE) (2018)
Charissis, V., Naef, M., Papanastasiou, S., Patera, M.: Designing a direct manipulation HUD interface for in-vehicle infotainment. In: Jacko, J.A. (ed.) HCI 2007. LNCS, vol. 4551, pp. 551–559. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-73107-8_62
Barker, J.: Driven to distraction: children's experiences of car travel. Brunel University, UK (2009). https://doi.org/10.1080/17450100802657962
Wang, S., Charissis, V., Harrison, D.K.: Augmented reality prototype HUD for passenger infotainment in a vehicular environment. Adv. Sci. Technol. Eng. Syst. J. 2(3), 634–641 (2017)
Grahn, H., Kujala, T.: Impacts of touch screen size, user interface design, and subtask boundaries on in-car task’s visual demand and driver distraction. Int. J. Hum.-Comput. Stud. 142, 102467 (2020)
Khan I., Khusro S.: Towards the design of context-aware adaptive user interfaces to minimize drivers’ distractions. Mob. Inf. Syst., Article ID 8858886, 23 (2020). Special Issue: Personal Communication Technologies for Smart Spaces, Hindawi
Shabeera, H.A., Wahidabanub, R.S.D.: Averting mobile phone use while driving and technique to locate the mobile phone used vehicle. In: International Conference on Communication Technology and System Design 2011 (2011). Proc. Eng. 30, 623–630 (2012). Elsevier Ltd. https://doi.org/10.1016/j.proeng.2012.01.907
Khandakar, A., et al.: Portable system for monitoring and controlling driver behavior and the use of a mobile phone while driving. Sensors 19(7), 1563 (2019). https://doi.org/10.3390/s19071563
Charissis, V., et al.: Employing emerging technologies to develop and evaluate in-vehicle intelligent systems for driver support: infotainment AR HUD case study. Appl. Sci. 11(4), 1397 (2021). https://doi.org/10.3390/app11041397
Bram-Larbi, K.F., Charissis, V., Khan, S., Lagoo, R., Harrison, D.K., Drikakis, D.: Intelligent collision avoidance and manoeuvring system with the use of augmented reality and artificial intelligence. In: Arai, K. (ed.) FICC 2021. AISC, vol. 1363, pp. 457–469. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-73100-7_32
Labský, M., Macek, T., Kleindienst, J., Quast, H., Couvreur, C.: In-car dictation and driver’s distraction: a case study. In: Jacko, J.A. (ed.) HCI 2011. LNCS, vol. 6763, pp. 418–425. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-21616-9_47
Wang, J., Wang, W., Hansen, P., Li, Y., You, F.: The situation awareness and usability research of different HUD HMI design in driving while using adaptive cruise control. In: Stephanidis, C., Duffy, V.G., Streitz, N., Konomi, S., Krömker, H. (eds.) HCII 2020. LNCS, vol. 12429, pp. 236–248. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59987-4_17
Tchankue, P., Wesson, J., Vogts, D.: The impact of an adaptive user interface on reducing driver distraction. In: Proceedings of the 3rd International Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutomotiveUI 2011), pp. 87–94. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2381416.2381430
Bram-Larbi, K.F., Charissis, V., Khan, S., Harrison, D.K., Drikakis, D.: Improving emergency vehicles’ response times with the use of augmented reality and artificial intelligence. In: Stephanidis, C., Duffy, V.G., Streitz, N., Konomi, S., Krömker, H. (eds.) HCII 2020. LNCS, vol. 12429, pp. 24–39. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59987-4_3
Bram-Larbi, K.F., Charissis, V., Khan, S., Lagoo, R., Harrison, D.K., Drikakis, D.: Collision avoidance head-up display: design considerations for emergency services’ vehicles. In: 2020 IEEE International Conference on Consumer Electronics (ICCE), pp. 1–7, Las Vegas, NV, USA (2020). https://doi.org/10.1109/ICCE46568.2020.9043068
Rothkrantz, L., Toma, M., Popa, M.: An intelligent co-driver surveillance system. In: Acta Polytechnica CTU Proceedings, vol. 12, p. 83 (2017). https://doi.org/10.14311/APP.2017.12.0083
Frank, M., Drikakis, D., Charissis, V.: Machine-learning methods for computational science and engineering. Computation 8, 15 (2020)
Charissis, V., Papanastasiou, S.: Artificial intelligence rationale for autonomous vehicle agents behaviour in driving simulation environment. In: Aramburo, J., Trevino, A.R. (eds.) Robotics, Automation and Control, pp. 314–332. I-Tech Education and Publishing KG, Vienna, Austria, EU (2008). ISBN 953761916-8I
Galy, E., Paxion, J., Berthelon, C.: Measuring mental workload with the NASA-TLX needs to examine each dimension rather than relying on the global score: an example with driving. Ergonomics 61, 517–527 (2017)
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Bram-Larbi, K.F. et al. (2021). Reducing Driver’s Cognitive Load with the Use of Artificial Intelligence and Augmented Reality. In: Stephanidis, C., et al. HCI International 2021 - Late Breaking Papers: HCI Applications in Health, Transport, and Industry. HCII 2021. Lecture Notes in Computer Science(), vol 13097. Springer, Cham. https://doi.org/10.1007/978-3-030-90966-6_17
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