Experimenting with Routes of Different Geometric Complexity in the Context of Urban Road Environment Detection from Traffic Sign Data

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 844)


Traffic sign data is used as input for detecting change in the type of urban road environment in which an ego-car is driven. The automatic urban road environment type detection is seen as a useful advanced driver assistance systems (ADAS) function that could be implemented in a straightforward manner relying on an existing ADAS function. Concretely, the traffic signs encountered along the route could be detected and logged by an on-board camera-based traffic sign recognition (TSR) system; and in order to perform the required function, it could be augmented with the change detection method described in the paper. Based on the analysis of the traffic sign and car trajectory data gathered for this preliminary study, the empirical distributions of the traffic signs along routes taken depend also on the geometrical complexity of these routes. A convenient model for describing traffic sign data along a route is a marked Poisson process. A traffic sign log is seen as a realization of such a process, and the minimum description length principle is harnessed to detect change in the road environment type. To test the applicability of this approach for urban routes of different complexity, a number of road environment transitions are looked at, while taking the route complexity into consideration. Finally, the detected changes are compared to ground truth.


Urban environments Autonomous vehicles Change detection Statistical inference Poisson processes Random walk Fractals 



This work was supported by the National Research, Development and Innovation Fund through the project ‘SEPPAC: Safety and Economic Platform for Partially Automated Commercial Vehicles’ (VKSZ 14-1-2015-0125).


  1. 1.
    Bengler, K., Dietmayer, K., Farber, B., Maurer, M., Stiller, C., Winner, H.: Three decades of driver assistance systems: review and future perspectives. IEEE Intell. Transp. Syst. Mag. 6(4), 6–22 (2014)CrossRefGoogle Scholar
  2. 2.
    García-Garrido, M.A., Ocana, M., Llorca, D.F., Arroyo, E., Pozuelo, J., Gavilán, M.: Complete vision-based traffic sign recognition supported by an I2V communication system. Sensors 12(2), 1148–1169 (2012)CrossRefGoogle Scholar
  3. 3.
    Forczmański, P., Małecki, K.: Selected aspects of traffic signs recognition: visual versus RFID approach. In: International Conference on Transport Systems Telematics, pp. 268–274. Springer, Berlin (2013)Google Scholar
  4. 4.
    Belbachir, A.: An embedded testbed architecture to evaluate autonomous car driving. Intell. Serv. Robot. 10, 109–119 (2017)CrossRefGoogle Scholar
  5. 5.
    Jiménez, F., Naranjo, J.E., Anaya, J.J., García, F., Ponz, A., Armingol, J.M.: Advanced driver assistance system for road environments to improve safety and efficiency. Transp. Res. Procedia 14, 2245–2254 (2016)CrossRefGoogle Scholar
  6. 6.
    Fazekas, Z., Balázs, G., Gerencsér, L., Gáspár, P.: Inferring the actual urban road environment from traffic sign data using a minimum description length approach. Transp. Res. Procedia 27, 516–523 (2017)CrossRefGoogle Scholar
  7. 7.
    Katz, M.J., George, E.B.: Fractals and the analysis of growth paths. Bull. Math. Biol. 47, 273–286 (1985)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Page, E.S.: Continuous inspection schemes. Biometrika 41, 100–115 (1954)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Hinkley, D.: Inference about the change-point from cumulative sum tests. Biometrika 58, 509–523 (1971)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Lorden, G.: Procedures for reacting to a change in distribution. Ann. Math. Stat. 42, 1897–1908 (1971)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Rissanen, J.: Modeling by shortest data description. Automatica 14, 465–658 (1978)CrossRefGoogle Scholar
  12. 12.
    Baikovicius, J., Gerencsér, L.: Change point detection in a stochastic complexity frame-work. In: 29th IEEE Conference on Decision and Control, pp. 3554–3555 (1990)Google Scholar
  13. 13.
    Lakshmanan, L.V.S., Ng, R.T., Wang, C.X., Zhou, X., Johnson, T.J.: The generalized MDL approach for summarization. In: 28th International Conference on Very Large Data Bases, pp. 766–777 (2002)Google Scholar
  14. 14.
    Kiernan, J., Terzi, E.: Constructing comprehensive summaries of large event sequences. ACM Trans. Knowl. Discov. Data 3, Article-Number 21 (2009)Google Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Institute for Computer Science and Control (MTA SZTAKI)BudapestHungary

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