Abstract
More and more often, there is talk of autonomous cars or “intelligent guidance” that can detect and navigate without human intervention. The proposed system is a vehicle equipped for the Mobile Mapping System (MMS) with sensors that fathom the environment with radar techniques, LIDAR, GPS and cameras, capable of tracking paths, identify obstacles, and recognize road signs for the detailed knowledge of road network. This system supports both the user in the automatic driving systems via the creation/update of maps, both the public administrations with the help of GIS platforms and/or more additional sensors, in order to have databases and references regarding road maintenance, mitigating the risk of accidents, maintaining higher levels of safety.
We are trying an experimentation on a “rudimental” equipped vehicle; it is therefore only a rudimentary vehicle for testing and experimentations, which are still at an early stage.
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References
Wolf, P., DeWitt, B.A.: Elements of Photogrammetry with Applications in GIS. McGraw-Hill, New York (2000)
Barrile, V., Postorino, M.N.: Un approccio GPS and GIS per la ricostruzione delle traiettorie veicolari in ambito urbano. LaborEst 13, 38–43 (2016)
Quddus, M.A., Ochieng, W.Y., Noland, R.B.: Integrity of map matching algorithms. Transp. Res. C-Emer. 14(4), 283–302 (2006)
Chen, R., Toran-Marti, F., Ventura-Traveset, J.: Access to the EGNOS signal in space over mobile-IP. GPS Solut. 7(1), 16–22 (2003)
Al-Shaery, A., Zhang, S., Rizos, C.: An enhanced calibration method of GLONASS inter-channel bias for GNSS RTK. GPS Solut. 17(2), 165–173 (2013)
Wikipedia: Hough transform (2007). http://en.wikipedia.org/wiki/Houghtransform
Barrile, V., Cacciola, M., Meduri, G.M., Morabito, F.C.: Automatic recognition of road signs by hough transform. In: 5th Symposium on Mobile Mapping Technology ISPRS Archives, vol. XXXVI-5/C55, pp. 62–67 (2008)
Gonzalez, R.C., Woods, R.E.: Digital Image Processing. Pearson Education, Inc., Englewood Cliffs (2002)
Sonka, M., Hlavac, V., Boyle, R.: Image Processing, Analysis, and Machine Vision. Springer, New York (1998)
Ballard, D.: Computer Vision. Prentice-Hall, Englewood Cliffs (1982)
Fu, L.M.: Neural Networks in Computer Intelligence, pp. 101–130. McGraw-Hill, New York (1994)
Simpson, P.K: Artificial Neural Systems-Foundations, Paradigms, Applications, and Implementations, pp. 80–103. Pergamon Press, New York (1990)
Barrile, V., Postorino, M.N.: GPS and GIS methods to reproduce vehicle trajectories in urban areas. Proc. Soc. Behav. Sci. 223, 890–895 (2016)
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Barrile, V., Meduri, G.M., Critelli, M., Bilotta, G. (2017). MMS and GIS for Self-driving Car and Road Management. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2017. ICCSA 2017. Lecture Notes in Computer Science(), vol 10407. Springer, Cham. https://doi.org/10.1007/978-3-319-62401-3_6
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DOI: https://doi.org/10.1007/978-3-319-62401-3_6
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