Human-Smartphone Interaction for Dangerous Situation Detection and Recommendation Generation While Driving
The paper presents a human-smartphone interaction system that is aimed at dangerous situation detection in a vehicle while driving. The system implements the driver head position and face tracking to detect if the driver is fine or he/she drowsed or distracted. For the image recognition, the OpenCV computer vision library is used that allows to determine the main head and face parameters that are analyzed to detect dangerous situations. Taking into account detected dangerous situation and current situation in the road (e.g., city or countryside driving; hotels, gas stations, cafes, restaurants around; Internet availability) the system generates recommendations for the driver to prevent accidents caused by dangerous driver behavior.
KeywordsHuman-computer interaction Image recognition Head tracking Face tracking Context-aware recommendations Dangerous situation detection
The presented results are part of the research carried out within the project funded by grants# 16-07-00462, 16-29-04349 of the Russian Foundation for Basic Research, programs # I.5, III.3, and # I.31 of the Russian Academy of Sciences. The work has been partially financially supported by Government of Russian Federation, Grant 074-U01.
- 4.Smirnov, A., Lashkov, I.: State-of-the-art analysis of available advanced driver assistance systems. In: 17th Conference of the Open Innovations Association FRUCT, pp. 345–349 (2015)Google Scholar
- 5.Smirnov, A., Kashevnik, A., Lashkov, I., Hashimoto, N., Boyali, A.: Smartphone-based two-wheeled self-balancing vehicles rider assistant. In: 17th Conference of the Open Innovations Association FRUCT, pp. 201–209 (2015)Google Scholar
- 6.Lashkov, I., Smirnov, A., Kashevnik, A., Parfenov, V.: Ontology-based approach and implementation of ADAS system for mobile device use while driving. In: Klinov, P., et al. (eds.) KESW 2015. CCIS, vol. 518, pp. 117–131. Springer, Heidelberg (2015). doi: 10.1007/978-3-319-24543-0_9 CrossRefGoogle Scholar
- 7.Rigas, G., Goletsis, Y., Bougia, P., Fotiadis, D.: Towards driver’s state recognition on real driving conditions. Int. J. Veh. Technol. 2011, 1687–5702 (2011)Google Scholar
- 8.Knipling, R.: PERCLOS, A Valid Psychophysiological Measure of Alertness as Assessed by Psychomotor Vigilance. Technical Report FHWA-MCRT-98-006, Federal Highway Administration, Office of Motor Carrier Research and Standards (1998)Google Scholar
- 9.Dinges, D.F., Grace, R.: PERCLOS: a valid psychophysiological measure of alertness as assessed by psychomotor vigilance. In: 6th International Driving Symposium on Human Factors in Driver Assessment, Training and Vehicle Design (1998)Google Scholar
- 10.Wierwille, W.W., Wreggit, S.S., Kirn, C.L., Ellsworth, L.A., Fairbanks, R.J.: Research on Vehicle-Based Driver Status/Performance Monitoring: Development, Validation, and Refinement of Algorithms for Detection of Driver Drowsiness, Technical Report DOT-HS-808-247, National Highway Traffic Safety Administration (1994)Google Scholar