Abstract
This paper introduces a vehicle safety system for moving backwards during night time. The system consists of two RGB-D cameras supported by two computers connected via Gigabit Ethernet. The cameras are mounted on the back side of the vehicle at different altitudes and tilt angles allowing detection of objects behind the vehicle and defects of the road surface. The safety system triggers an alarm if an object appears in dangerous proximity to the car. The dangerous proximity is decided according to the mass of the vehicle and its speed. Obstacles are tracked to predict if ones may become dangerous in the near future. Experiments have shown that the system is able to detect obstacles and holes on the road pavement when there is not enough light for common cameras.
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References
Treat, J.R., Tumbas, N.S., McDonald, S.T., Shinar, D., Hume, R.D., Mayer, R.E., Stansifer, R.L., Castellan, N.J.: Tri-level study of the causes of traffic accidents: final report. Executive summary, DOT-HS-034-3-535-79-TAC(S)
Edwards, C.: Car safety with a digital dashboard, Engineering & Technology, pp. 60–64 (2014)
Mandici, S., Nedevschi, S.: Aggregate road surface based environment representation using digital elevation maps. In: Intelligent Computer Communication and Processing (ICCP), pp. 149–156, Cluj Napoca (2014)
Lee, K.-Y., Park, J.-M., Lee, J.-W.: Estimation of longitudinal profile of road surface from stereo disparity using Dijkstra algorithm. Int. J. Control Autom. Syst. 12(4), 895–903 (2014)
Premachandra, C.H., Premachandra, W.H., Parape, C.D., Kawanaka, H.: Road crack detection using color variance distribution and discriminant analysis for approaching smooth vehicle movement on non-smooth roads, Int. J. Mach. Learn. Cybern. (2014)
Chen, Y-L., Wang, C-A.: Vehicle safety distance warning system: a novel algorithm for vehicle safety distance calculating between moving cars. In: Vehicular Technology Conference, pp. 2570–2574. Dublin (2007)
Raj, A., Krishna, D., Hari Priya, R., Shantanu, K., Niranjani Devi, S.: Vision based road surface detection for automotive systems, In: Applied Electronics (AE), pp. 223–228, Pilsen (2012)
Welch, G., Bishop, G.: An Introduction to the Kalman Filter. Tech. Rep. University. North Carolina, Chapel Hill, NC (1995)
Jo, K.-H., Vavilin, A.: HDR image generation based on intensity clustering and local feature analysis. Comput. Hum. Behav. 27(5), 150–1511 (2011)
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Filonenko, A., Hernández, D.C., Vavilin, A., Kim, T., Jo, KH. (2015). Distance Sensor Fusion for Obstacle Detection at Night Based on Kinect Sensors. In: Huang, DS., Jo, KH., Hussain, A. (eds) Intelligent Computing Theories and Methodologies. ICIC 2015. Lecture Notes in Computer Science(), vol 9226. Springer, Cham. https://doi.org/10.1007/978-3-319-22186-1_13
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DOI: https://doi.org/10.1007/978-3-319-22186-1_13
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