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The automated detection of atypical and critical traffic situations is essentially important to help to understand driver behaviour, to find functional correlations between traffic conflicts and real accidents, and eventually, to prevent, particularly severe accidents. In this paper, a tool chain is introduced that enables fully automated traffic situation detection in wide-area traffic on the basis of a single camera. The tool chain takes into account novel powerful methods for object detection, classification and track- ing on the basis of robust regression with preconditioning. Moreover, the tool chain considers methods for traffic situation detection and classification on the basis of probabilistic approaches and eventually, traffic event recording. The approach was tested at an ungated level crossing in the small town Bien-rode, which is a district of Brunswick, Germany. It is shown that atypical situations, e.g. overtaking, braking, stopping, inadequate speeds, and accelerations, as well as critical situations, e.g. tailgating, can be detected within a range of up to 120 m distance of the camera automatically. The approach enables new ways of analysing traffic areas with regard to traffic safety and performance. The results shown in this paper were obtained in the project OptiSiLK, whose abbreviation means “Optimisation of the safety and the performance at intersections of different traffic modes”. OptiSiLK was funded by the Ministry for Science and Culture of the State of Lower Saxony (MWK).
atypical and critical traffic situations, surrogate safety measures, wide-area traffic detection
[1] Gettman, D., Pu, L., Sayed, T. & Shelby, S., Surrogate safety assessment model and validation: final report, report no. fhwa-hrt-08-051, turner-fairbanks highway research center, federal highway administration, McLean, VA, 2008.
[2] Souleyrette, R. & Hochstein, J., Development of a conflict analysis methodology using ssam. final report, center for transportation research and education, Iowa State University, 2012.
[3] Reulke, R., Meysel, F. & Bauer, S., Situation analysis and atypical event detection with multiple cameras and multi-object tracking. In RobVis, pp. 234–247, Springer-Verlag Berlin-Heidelberg, 2008.
[4] Owens, J. & Hunter, A., Application of the self-organizing map to trajectory classifcation. Proceedings of the Third IEEE International Workshop on Visual Surveillance (VS’2000), IEEE Computer Society: Washington, DC, USA, 2000.
[5] Ismail, K., Sayed, T. & Saunier, N., Automated analysis of pedestrian-vehicle conflicts: a context for before-and-after studies. in transportation research board (trb): Journal of the Transportation Research Board, 2009.
[6] Laureshyn, A., Application of automated video analysis to road user behavior. PhD Thesis, Lund University, Sweden, 2010.
[7] Saunier, N., Sayed, T. & Ismail, K., Large scale automated analysis of vehicle interactions and collisions. In Transportation Research Board (TRB): Journal of the Transportation Research Board, 2010.
[8] Knake-Langhorst, S., Frankiewicz, T., Gimm, K. & Köster, F., Test Site AIM – Toolbox and enabler for applied research and development in traffic and mobility. Accepted for publication at Traffic Research Arena (TRA) 2016, Warsaw, 2016.
[9] Dalal, N. & Triggs, B., Histograms of oriented gradients for human detection. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Vol. 1. IEEE, 2005.
[10] Leich, A., Junghans, M., Kozempel, K. & Saul, H., Road user tracker based on robust regression with gnc and preconditioning. In SPIE Conference on Electronic Imaging, San Francisco, USA, 2015.
[11] Punzo, V., Borzacchiello, M.T. & Ciuffo, B., On the assessment of vehicle trajectory data accuracy and application to the Next Generation SIMulation (NGSIM) program data. Transportation Research Part C: Emerging Technologies 19.6, 1243–1262, 2011. http://dx.doi.org/10.1016/j.trc.2010.12.007
[12] Hydén, C., Traffic conflicts technique: state-of-the-art. In Traffic Safety Work with video processing ed. H.H. Topp, University Kaiserslautern. Transportation Department, Green Series No. 43, 1998.
[13] van der Horst, A.R.A., A time-based analysis of road user behaviour in normal and critical encounters. PhD thesis, TU Delft, 1990.
[14] Shelby, S.G., Delta-V as a measure of traffic conflict severity. In Transportation Research Board 90th Annual Meeting, 2011.
[15] Detzer, S., Junghans, M., Kozempel, K. & Saul, H., Analysis of traffic safety for cyclists – an automatic detection of critical traffic situations of cyclists. 20th International Conference on Urban Transport and the Environment, The Algarve, Portugal, 2014.
[16] Biemann, R., Untersuchung von Zusammenhängen zwischen Konfliktkenngrößen und verkehrstechnischen Kenngrößen, Internal Report, DLR, Institute of Transportation Systems, Berlin, Germany, 2014.
[17] Saul, H., Kozempel, K. & Haberjahn, M., A comparison of methods for detecting atypical trajectories. 20th International Conference on Urban Transport and the Environment. The Algarve, Portugal, 2014. http://dx.doi.org/10.2495/UT140331