PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

On statistical estimations of vehicle speed measurements

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The accuracy of vehicle speed measured by a speedometer is analysed. The stress on the application of skew normal distribution is laid. The accuracy of measured vehicle speed depends on many error sources: construction of speedometer, measurement method, model inadequacy to real physical process, transferring information signal, external conditions, production process technology etc. The errors of speedometer are analysed in a complex relation to errors of the speed control gauges, whose functionality is based on the Doppler effect. Parameters of the normal distribution and skew normal distribution were applied in the errors analysis. It is shown that the application of maximum permissible errors to control the measuring results of vehicle speed gives paradoxical results when, in the case of skew normal distribution, the standard deviations of higher vehicle speeds are smaller than the standard deviations of lower speeds. In the case of normal distribution a higher speed has a greater standard deviation. For the speed measurements by Doppler speed gauges it is suggested to calculate the vehicle weighted average speed instead of the arithmetic average speed, what will correspond to most real dynamic changes of the vehicle speed parameters.
Rocznik
Strony
551--559
Opis fizyczny
Bibliogr. 33 poz., tab., wykr., wzory
Twórcy
  • Vilnius Gediminas Technical University, Faculty of Environmental Engineering, Saulètekio 11, LT-10223 Vilnius, Lithuania
  • Vilnius Gediminas Technical University, Faculty of Environmental Engineering, Saulètekio 11, LT-10223 Vilnius, Lithuania
  • Vilnius Gediminas Technical University, Faculty of Environmental Engineering, Saulètekio 11, LT-10223 Vilnius, Lithuania
  • Vilnius Gediminas Technical University, Faculty of Environmental Engineering, Saulètekio 11, LT-10223 Vilnius, Lithuania
Bibliografia
  • [1] Bailo, G., Bariani, M., Ijas, P., Raggio, M. (2005). Background estimation with Gaussian distribution for image segmentation, a fast approach. Proc. of the IEEE International workshop on Measurement Systems for Homeland Security, Contraband Detection and Personal safety, 2-5.
  • [2] Bauer, D., Belbachir, A.N., Donath, N.; Gritsch, G., Kohn, B., Litzenberger, M., Posch, C., Schon, P., Schraml, S. (2007). Embedded Vehicle Speed Estimation System Using an Asynchronous Temporal Contrast Vision Sensor. EURASIP Journal on Embedded Systems, 1, 12.
  • [3] Bramberger, M., Rinner, B., Schwabach, H. (2004). An embedded smart camera on a scalable heterogeneous multi-DSP system. Proc. of the 1st European DSP Education and Research Sympium.
  • [4] Cathey, F.W., Dailey, D.J. (2005). A novel technique to dynamically measure vehicle speed using uncalibrated roadway cameras. Proc. of IEEE Intelligent Vehicles Symposium, 777-782.
  • [5] Coifman, B., Beymer, D., McLauchlan, P., Malik, J. (1998). A real-time computer vision system for vehicle tracking and traffic surveillance. Transportation Research Part C, 6(4), 777-782.
  • [6] Dailey, D.J., Cathey, F.W., Pumrin, S. (2000). An algorithm to estimate mean traffic speed using uncalibrated cameras. IEEE Transactions on Intelligent Transportation Systems, 1(2), 98-107.
  • [7] Litzenberger, M., Belbachir, A.N., Schon, P., Posch, C. (2007). Embedded smart camera for high speed vision. Proc. of First ACM/IEEE International Conference on Distributed Smart Cameras, 81-89.
  • [8] Rahim, H.A., Sheikh, U.U., Ahmad, R.B., Zain, A.S.M. (2010). Vehicle velocity estimation for traffic surveillance system. Engineering and technology. World academy of Science, 69, 772-775.
  • [9] Schoepflin, T.N., Dailey, D.J. (2003). Algorithms for Estimating Mean Vehicle Speed Using Uncalibrated Traffic Management Cameras. Washington State Transportation Center (TRAC), Research Report, WA-RD 575.1, 263.
  • [10] Schoepflin, T.N., Dailey, D.J. (2003). Dynamic camera calibration of roadside traffic management cameras for vehicle speed estimation. IEEE Transactions on Intelligent Transportation Systems, 4(2), 90-98.
  • [11] Tai, J.C., Tseng, S.T., Lin, C.P., Song, K.T. (2004). Real-time image tracking for automatic traffic monitoring and enforcement applications. Image Vision Computing, 22(6), 485-501.
  • [12] Grammatikopoulos, L., Karras, G., Petsa, E. (2005). Automatic Estimation of Vehicle Speed from uncalibrated Video Sequences. Modern Technologies, Education and Professional Practice in Geodesy and Related Fields, 332-338.
  • [13] Goodson, M.E. (1985). Technical Shortcomings of Doppler Traffic Radar. JFSCA, 30(4), 1186-1193.
  • [14] Pushkar, A., Hall, F.L., Acha-Daza, J.A. (1994). Estimation of Speeds from Single-loop Freeway Flow and Occupancy Data Using Cusp Catastrophe Theory Model. Transportation Research Record, 1457, 149-157.
  • [15] Dailey, D.J. (1999). A Statistical Algorithm for Estimating Speed from Single Loop Volume and Occupancy Measurements. Transportation Research: Part B, 33(5), 313-322.
  • [16] Wang, Y., Nihan, N.L. (2000). Freeway Traffic Speed Estimation Using Single Loop Outputs. Transportation Research Record, 1727, 120-126.
  • [17] Hellinga, B.R. (2002). Improving freeway speed estimates from single loop detectors. Journal of Transportation Engineering, 128(1), 58-67.
  • [18] Coifman, B., Dhoorjaty, S., Lee, Z. (2003). Estimating Median Velocity Instead of Mean Velocity at Single Loop Detectors. Transportation Research: Part C, 11(3-4), 211-222.
  • [19] Zhang, Y., Ye, Z., Xie, Y. (2006). Accurate speed estimation using single loop detector data, Research Report 167761-1, Texas Transportation Institute and Southwest Region University Transportation Center, College Station, Texas, 74.
  • [20] Speed-Measuring Device Performance Specifications: Across-the-Road Radar Module. 2007. NHTSA Technical Report, DOT HS 810 845 56.
  • [21] Guo, J., Xia, J., Smith, B.L. (2009). Kalman Filter Approach to Speed Estimation Using Single Loop Detector Measurements under Congested Conditions. Journal of Transportation Engineering, 927-934.
  • [22] Anagnostopoulos, C.N.E., Anagnostopoulos, I.E., Psoroulas, I.D., Loumos, V., Kayafas, E. (2008). License Plate Recognition From Still Images and Video Sequences. A Survey, Intelligent Transportation Systems, 9, 377-391.
  • [23] Shan, D., Ibrahim, M., Shehata, M., Badawy, W. (2013). Automatic License Plate Recognition (ALPR): A State-of the-Art Review. Circuits and Systems for Video Technology, 23, 311-325.
  • [24] Lee, K.H., Lee, Y.J., Hwang, J.N. (2013). Multiple-kernel based vehicle tracking using 3-D deformable model and license plate self-similarity. IEEE International Conference on Acoustics, Speech, and Signal Processing, 1793-1797.
  • [25] Luvizon, D.C., Nassu, B.T., Minetto, R. (2014). Vehicle speed estimation by license plate detection and tracking. The 39th International Conference on Acoustics, Speech and Signal Processing, 5.
  • [26] Ondrejka, A.R., Johnk, R.T. (1998). A Portable Calibrator for Across-the-Road Radar Systems. NIST Tech Note, 1398.
  • [27] Lewis, S.R. (2005). A Guide to Type Approval Procedures for Speedmeters Used for Road Traffic Law Enforcement in Great Britain. Home Office Scientific Development Branch, 15(5), 35.
  • [28] Jendzurski, J., Paulter, N.G. (2008). Calibration of Speed Enforcement Down-The-Road Radars. Journal of Research of the National Institute of Standards and Technology, 114(3), 137-148.
  • [29] Kaskonas, P., Meskuotiene, A. (2012). Vehicle Speed Meters Validation and Verification System. Electronics and electrical engineering, 119(3), 95-98.
  • [30] Azzalini, A. (2008). References on the skew-normal distribution and related ones. http://azzalini.stat.unipd.it/SN/list-publ.pdf.
  • [31] Chen, J.T., Gupta, A.K., Nguyen, T.T. (2004). The density of the skew normal sample mean and its applications. Journal of Statistical Computation and Simulation, 74(7), 487-494.
  • [32] Skeivalas, J. (1991). Mathematical treatment of results of geodetic measurements. Moskva: Nedra, 160.
  • [33] Skeivalas, J. (1995). Mathematical treatment of results of correlated geodetic measurements. Vilnius: Technika, 272.
Uwagi
PL
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-5c6f887f-c4d8-4f2c-9f6e-58615f2965bb
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.