Abstract
Economical driving not only saves fuel, but also reduces the carbon dioxide emissions from cars. Apart from environmental benefits, road safety is also increased when drivers avoid speeding and sudden changes of speeds. However, speed measurements usually do not reflect speed changes. In this paper, we address automatic detection of speed changes, based on audio on-road recordings, which can be taken at night and at low-vision conditions. In our approach, the extraction of information on speed changes is based on spectrogram data, converted to black-and-white representation. Next, the parameters of lines reflecting speed changes are extracted, and these parameters become a basis for distinguishing between three classes: accelerating, decelerating, and maintaining stable speed. Theoretical discussion of the thresholds for these classes are followed by experiments with automatic search for these thresholds. In this paper, we also discuss how the choice of the representation model parameters influences the correctness of classification of the audio data into one of three classes, i.e. acceleration, deceleration, and stable speed. Moreover, for 12-element feature vector we achieved accuracy comparable with the accuracy achieved for 575-element feature vector, applied in our previous work. The obtained results are presented in the paper.
Keywords
- Driver behavior
- Hough transform
- Intelligent transportation systems
Partially supported by research funds sponsored by the Ministry of Science and Higher Education in Poland.
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Elvik, R., Vaa, T. (eds.): The Handbook of Road Safety Measures. Emerald Group Pub Ltd., Bingley (2004)
Huvarinen, Y., Svatkova, E., Oleshchenko, E., Pushchina, S.: Road safety audit. Transp. Res. Procedia 20, 236–241 (2017)
Czechowski, P.O., Oniszczuk-Jastrza̧bek, A., Czuba, T.: Eco-driving: behavioural pattern change in Polish passenger vehicle drivers. E3S Web Conf. 28, 01009 (2018)
Fontaras, G., Zacharof, N.-G., Biagio, C.: Fuel consumption and CO\(_{2}\) emissions from passenger cars in Europe Laboratory versus real-world emissions. Progress Energy Combust. Sci. 60, 97–131 (2017)
Franco, V., Kousoulidou, M., Muntean, M., Ntziachristos, L., Hausberger, S., Dilara, P.: Road vehicle emission factors development: a review. Atmos. Environ. 70, 84–97 (2013)
Sarkan, B., Stopka, O., Gnap, J., Caban, J.: Investigation of exhaust emissions of vehicles with the spark ignition engine within emission control. Procedia Eng. 187, 775–782 (2017)
Bas, E., Tekalp, M., Salman, F.S.: Automatic vehicle counting from video for traffic flow analysis. In: 2007 IEEE Intelligent Vehicles Symposium, Istanbul, Turkey, pp. 392–397. IEEE (2007)
Liang, M., Huang, X., Chen, C.-H., Chen, X., Tokuta, A.: Counting and classification of highway vehicles by regression analysis. IEEE Trans. Intell. Transp. Syst. 16, 2878–2888 (2015)
Wang, H., Fu, L., Zhou, Y., Li, H.: Modelling of the fuel consumption for passenger cars regarding driving characteristics. Transp. Res. D 13(7), 479–482 (2008)
Cambridge Systematics: Effects of Travel Reduction and Efficient Driving on Transportation: Energy Use and Greenhouse Gas Emissions. https://www.nrel.gov/docs/fy13osti/55635.pdf. Accessed 20 Dec 2019
Adnan, M.A., Sulaiman, N., Zainuddin, N.I., Besar, T.B.H.T.: Vehicle speed measurement technique using various speed detection instrumentation. In: 2013 IEEE Business Engineering and Industrial Applications Colloquium BEIAC, Langkawi, Malaysia, pp. 668–672. IEEE (2013)
Balid, W., Tafish, H., Refai, H.H.: Intelligent vehicle counting and classification sensor for real-time traffic surveillance. IEEE Trans. Intell. Transp. Syst. 19(6), 1784–1794 (2018)
Meiring, G.A.M., Myburgh, H.C.: A review of intelligent driving style analysis systems and related artificial intelligence algorithms. Sensors 15(12), 30653–30682 (2015)
Wang, W., Xi, J., Chong, A., Li, L.: Driving style classification using a semisupervised support vector machine. IEEE Trans. Hum.-Mach. Syst. 47, 650–660 (2017)
Schroeder, B.J., Cunningham, C.M., Findley, D.J., Hummer, J.E., Foyle, R.S.: Manual of Transportation Engineering Studies, 2nd edn. Institute of Transportation Engineers, Washington, DC (2010)
Banovic, N., Buzali, T., Chevalier, F., Mankoff, J., Dey, A.: Modeling and understanding human routine behavior. In: ACM CHI Conference on Human Factors in Computing Systems 2016, Santa Clara, CA, pp. 248–260. ACM (2016)
Bonsall, P.W., Liu, R., Young, W.: Modelling safety-related driving behaviour - the impact of parameter values. Transp. Res. A 39(5), 425–444 (2005)
Mehar, A., Chandra, S., Velmurugan, S.: Speed and acceleration characteristics of different types of vehicles on multi-lane highways. Eur. Transp. 55(1), 1–12 (2013)
Ahn, K., Rakha, H., Trani, A., Van Aerde, M.: Estimating vehicle fuel consumption and emissions based on instantaneous speed and acceleration levels. J. Transp. Eng. 128(2), 182–190 (2002)
Kousoulidou, M., Ntziachristos, L., Mellios, G., Samaras, Z.: Road-transport emission projections to 2020 in European urban environments. Atmos. Environ. 42(32), 7465–7475 (2008)
Health Effects Institute: Diesel exhaust: critical analysis of emissions, exposure, and health effects. Health Effects Institute, Boston, MA (1995)
Kim, K.H., Kabir, E., Kabir, S.: A review on the human health impact of airborne particulate matter. Environ. Int. 74, 136–143 (2015)
Ntziachristos, L., Samaras, Z. (Lead authors): EMEP/EEA air pollutant emission inventory guidebook 2019. 1.A.3.b.i-iv Road transport 2019. European Environment Agency, Copenhagen, Denmark (2019)
Silva, C.M., Farias, T.L., Frey, H.C., Rouphail, N.M.: Evaluation of numerical models for simulation of real-world hot-stabilized fuel consumption and emissions of gasoline light-duty vehicles. Transp. Res. D-Trans. Environ. 11(5), 377–385 (2006)
Kotus, J.: Determination of the vehicles speed using acoustic vector sensor. In: 2018 SPA, Signal Processing: Algorithms, Architectures, Arrangements, and Applications, Poznan, Poland, pp. 64–69 (2018)
Kubera, E., Wieczorkowska, A., Słowik, T., Kuranc, A., Skrzypiec, K.: Audio-based speed change classification for vehicles. In: NFMCP 2016. LNAI, vol. 10312, pp. 54–68. Springer, Cham (2017)
Wieczorkowska, A., Kubera, E., Koržinek, D., Słowik, T., Kuranc, A.: Time-frequency representations for speed change classification: a pilot study. In: ISMIS 2017. LNAI, vol. 10352, pp. 404–413. Springer, Cham (2017)
Kubera, E., Wieczorkowska, A., Kuranc, A.: Hough transform as a tool for the classification of vehicle speed changes in on-road audio recordings. In: Ceci, M., Loglisci, C., Manco, G., Masciari, E., Ras, Z. (eds.) NFMCP 2019, Revised Selected Papers, LNAI, vol. 11948, pp. 137–154. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-48861-1_9
Kubera, E., Wieczorkowska, A., Kuranc, A., Słowik, T.: Discovering speed changes of vehicles from audio data. Sensors 19(14), 3067 (2019)
Fisher, R., Perkins, S., Walker, A., Wolfart, E.: Hypermedia Image Processing Reference. Wiley, West Sussex (2000)
Canny, J.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 8(6), 679–98 (1986)
Frank, E., Hall, M.A, Witten, I.H.: The WEKA Workbench. Online Appendix for “Data Mining: Practical Machine Learning Tools and Techniques”, 4th edn. Morgan Kaufmann (2016)
R Core Team: R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria (2017). https://www.R-project.org/
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Kubera, E., Wieczorkowska, A., Kuranc, A. (2021). Parameter Tuning for Speed Changes Detection in On-Road Audio Recordings of Single Drives. In: Stettinger, M., Leitner, G., Felfernig, A., Ras, Z.W. (eds) Intelligent Systems in Industrial Applications. ISMIS 2020. Studies in Computational Intelligence, vol 949. Springer, Cham. https://doi.org/10.1007/978-3-030-67148-8_1
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