Type of communication between driver and car, based on the augmented reality: "new trend" in building intelligent transportation systems 1256
, post graduate student of the Department of psychology of Management, Moscow State University of Psychology and Education, Moscow, Russia, email@example.com
In order to increase safety while driving and to minimize the burden on the driver, the information should be transmitted to him/her in such a way that the driver needn’t spent time on its recognition and comprehension. Projecting and visualization of information on the windshield can help simplify the dialogue between a car and a driver ("operator") and expand the influence of intellectual transport system using projection information about traffic jams in the field of perception of the driver, so that it does not interfere with the driver on the road. This article discusses the possible advantages and disadvantages of using "hints", created within the framework of the "augmented reality" to increase driving safety by treating them as a new form of communication between a car and a driver. So, it seems to be a new approach to the utilization of the system, based on performances in the field of augmented reality to recognize road signs, which impose virtual objects on the field of perception in all types of traffic situations including the uncomfortable weather conditions. This approach can be used to increase accuracy of intellectual transport system with the augmented reality to support the driver in various driving situations, increasing comfort and reducing the number of accidents
- Abdi L., Abdallah F.B., Meddeb A. In-Vehicle Augmented Reality Traffic
Information System: A New Type of Communication Between Driver and Vehicle.
Procedia Computer Science, 2015. Vol. 73, pp. 242–249.
- Agarwal S., Awan A., Roth D. Learning to detect objects in images via a
sparse, part-based representation Pattern Analysis and Machine Intelligence.
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004.
Vol. 26, no. 11, pp. 1475–1490. doi:10.1109/TPAMI.2004.108
- Schall M.C. et al. Augmented Reality Cues and Elderly Driver Hazard
Perception. Human Factors: The Journal of the Human Factors and Ergonomics
Society, 2013. Vol. 55, no. 3, pp. 643–658.
- Sobel D. [et al.] Camera Calibration for Tracked Vehicles Augmented Reality
Applications. In Nawrat A.M. (ed.). Innovative Control Systems for Tracked
Vehicle Platforms. Cham: Springer, 2014, pp. 147–162.
- Rusch M.L. et al. Directing driver attention with augmented reality cues.
Transportation research part F: traffic psychology and behavior, 2013.
Vol. 16, pp. 127 –137. doi:10.1016/j.trf.2012.08.007
- Doshi A., Cheng S.Y., Trivedi M.M. A Novel Active Heads-Up Display for
Driver Assistance. IEEE Transactions on Systems, Man, and Cybernetics. Part B:
Cybernetics, 2009. Vol. 39, no. 1, pp. 85–93.
- Fu W.T., Gasper J., Kim S.W. Effects of an In-Car Augmented Reality System
on Improving of Younger and Older Drivers. In 2013 IEEE International Symposium
on Mixed and Augmented Reality (ISMAR 2013). Adelaide, SA: IEEE, 2013,
pp. 59–66. doi:10.1109/ISMAR.2013.6671764
- Jeffrey C. Continental’s augmented reality hud puts information on the road
[Elektronnyi resurs]. In New Atlas. Available at:
- Kim S., Dey A.K. Simulated augmented reality windshield display as a
cognitive mapping aid for elder driver navigation. In Proceedings of the SIGCHI
Conference on Human Factors in Computing Systems. New York: ACM, 2009,
pp. 133–142. doi: 10.1145/1518701.1518724
- Park H.S., Kim K.H. AR-Based Vehicular Safety Information System for
Forward Collision Warning. In Shumaker R., Lackey S. (eds.). In Virtual,
Augmented and Mixed Reality. Applications of Virtual and Augmented Reality: 6th
International Conference, VAMR 2014, Held as Part of HCI International 2014,
Heraklion, Crete, Greece, June 22-27, 2014, Proceedings. Part 2. Cham:
Springer, 2014, pp. 435–442.
- Pensyl W.R. Real-time stable markerless tracking for augmented reality
using image analysis/synthesis technique [Elektronnyi resurs]. In Pensyl.
Available at: http://www.pensyl.com/hue/markerless.html (Accessed:
- Livingston M.A. et al. Resolving Multiple Occluded Layers in Augmented
Reality. In Proceedings of the 2nd IEEE/ACM International Symposium on Mixed
and Augmented Reality (IEEE Computer Society, 2003). Washington, DC: IEEE,
2003, pp. 56–56.
- Stallkamp J. et al. The German Traffic Sign Recognition Benchmark: A
multi-class classification competition Detection. In Neural Networks (IJCNN):
The 2011 International Joint Conference on. San Jose, CA: IEEE, 2011),
pp. 1453–1460. doi:10.1109/IJCNN.2011.6033395
- Timofte R. Kul belgium traffic signs and classification benchmark datasets
[Elektronnyi resurs]. Available at: http://btsd.ethz.ch/shareddata (Accessed:
- Topór-Kamiński T., Krupanek B., Homa J. Delays Models of Measurement and
Control Data Transmission Network. In Nawrat A., Simek K., Świerniak A.
(eds.). Advanced Technologies for Intelligent Systems of National Border
Security. Berlin; New York: Springer, 2012, pp. 257–279.
- Viola P., Jones M.J. Robust Real-Time Face Detection. International journal
of computer vision, 2004. Vol. 57, no. 2, pp. 137–154.