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Roadside acoustic sensors to support vulnerable pedestrians via their smartphones | ||
| AUT Journal of Mathematics and Computing | ||
| مقاله 1، دوره 1، شماره 2، آذر 2020، صفحه 135-143 اصل مقاله (565.71 K) | ||
| نوع مقاله: Original Article | ||
| شناسه دیجیتال (DOI): 10.22060/ajmc.2019.15479.1017 | ||
| نویسندگان | ||
| Mehdi Ghatee* 1؛ Masoomeh Khalili1؛ Mehdi Teimouri2؛ Mohammad Mahdi Bejani1 | ||
| 1Department of Mathematics and Computer Science, Amirkabir University of Technology, Tehran, Iran | ||
| 2Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran | ||
| چکیده | ||
| This paper proposes a smartphone-based warning system to evaluate the risk of a motor vehicle for vulnerable pedestrians (VP). The acoustic sensors are embedded in the roadside to receive vehicle sounds and they are classified into heavy vehicles, light vehicles with low speed, light vehicles with high speed, and no vehicle classes. For this aim, we extract new features by Mel-frequency Cepstrum Coefficients (MFCC) and Linear Predictive Coefficients (LPC) algorithms. We use different classification algorithms and show that MLP neural network achieves at least $96.77$% accuracy criterion. To install this system, directional microphones are embedded on the roadside and the risk is classified. Then, for every microphone, a danger area is defined and the warning alarms have been sent to every VPs’ smartphones covered in this danger area. | ||
| کلیدواژه‌ها | ||
| Intelligent transportation systems؛ Acoustic signal analysis؛ Smartphone؛ Road traffic sensors؛ Road safety؛ Risk analysis؛ Vulnerable pedestrians | ||
| مراجع | ||
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[1] R. San-Segundo, J. M. Montero, R. Barra-Chicote, F. Fern´andez, J. M. Pardo, Feature extraction from smartphone inertial signals for human activity segmentation, Signal Processing 120 (2016) 359–372.
[2] H. R. Eftekhari, M. Ghatee, An inference engine for smartphones to preprocess data and detect stationary and transportation modes, Transportation Research Part C: Emerging Technologies 69 (2016) 313–327.
[3] R. B. Zadeh, M. Ghatee, H. R. Eftekhari, Three-phases smartphone-based warning system to protect vulnerable road users under fuzzy conditions, IEEE Transactions on Intelligent Transportation Systems 19 (7) (2017) 2086–2098.
[4] M. M. Bejani, M. Ghatee, A context aware system for driving style evaluation by an ensemble learning on smartphone sensors data, Transportation Research Part C: Emerging Technologies 89 (2018) 303–320.
[5] H. R. Eftekhari, M. Ghatee, Hybrid of discrete wavelet transform and adaptive neuro fuzzy inference system for overall driving behavior recognition, Transportation research part F: traffic psychology and behaviour 58 (2018) 782–796.
[6] H. R. Eftekhari, M. Ghatee, A similarity-based neuro-fuzzy modeling for driving behavior recognition applying fusion of smartphone sensors, Journal of Intelligent Transportation Systems 23 (1) (2019) 72–83.
[7] M. M. Bejani, M. Ghatee, Convolutional neural network with adaptive regularization to classify driving styles on smartphones, IEEE Transactions on Intelligent Transportation Systems 21 (2) (2019) 543–552.
[8] N. Desai, K. Dhameliya, V. Desai, Feature extraction and classification techniques for speech recognition: A review, International Journal of Emerging Technology and Advanced Engineering 3 (12) (2013) 367–371.
[9] J. A. G´omez-Tejedor, J. C. Castro-Palacio, J. A. Monsoriu, The acoustic doppler effect applied to the study of linear motions, European Journal of Physics 35 (2) (2014) 025006.
[10] J. Gajda, R. Sroka, M. Stencel, A. Wajda, T. Zeglen, A vehicle classification based on inductive loop detectors, in: IMTC 2001. Proceedings of the 18th IEEE Instrumentation and Measurement Technology Conference. Rediscovering Measurement in the Age of Informatics (Cat. No. 01CH 37188), Vol. 1, IEEE, 2001, pp. 460– 464.
[11] J. George, A. Cyril, B. I. Koshy, L. Mary, Exploring sound signature for vehicle detection and classification using ann, International Journal on Soft Computing 4 (2) (2013) 29.
[12] G. Padmavathi, D. Shanmugapriya, M. Kalaivani, et al., A study on vehicle detection and tracking using wireless sensor networks, Wireless Sensor Network 2 (02) (2010) 173.
[13] A. Y. Nooralahiyan, M. Dougherty, D. McKeown, H. R. Kirby, A field trial of acoustic signature analysis for vehicle classification, Transportation Research Part C: Emerging Technologies 5 (3-4) (1997) 165–177.
[14] A. Averbuch, E. Hulata, V. Zheludev, I. Kozlov, A wavelet packet algorithm for classification and detection of moving vehicles, Multidimensional Systems and Signal Processing 12 (1) (2001) 9–31.
[15] M. V. Ghiurcau, C. Rusu, Vehicle sound classification. application and low pass filtering influence, in: 2009 International Symposium on Signals, Circuits and Systems, IEEE, 2009, pp. 1–4.
[16] A. Dalir, A. A. Beheshti, M. H. Masoom, Classification of vehicles based on audio signals using quadratic discriminant analysis and high energy feature vectors, arXiv preprint arXiv:1804.01212.
[17] M. P. Paulraj, A. H. Adom, S. Sundararaj, N. B. A. Rahim, Moving vehicle recognition and classification based on time domain approach, Procedia Engineering 53 (2013) 405–410.
[18] J. Lee, A. Rakotonirainy, Acoustic hazard detection for pedestrians with obscured hearing, IEEE Transactions on Intelligent Transportation Systems 12 (4) (2011) 1640–1649.
[19] N. Lubbe, E. Ros´en, Pedestrian crossing situations: Quantification of comfort boundaries to guide intervention timing, Accident Analysis & Prevention 71 (2014) 261–266.
[20] X. Jiang, W. Wang, K. Bengler, Intercultural analyses of time-to-collision in vehicle–pedestrian conflict on an urban midblock crosswalk, Ieee transactions on intelligent transportation systems 16 (2) (2014) 1048–1053.
[21] E. L. Salomons, P. J. Havinga, A survey on the feasibility of sound classification on wireless sensor nodes, Sensors 15 (4) (2015) 7462–7498.
[22] J. Scholliers, D. Bell, A. Morris, A. B. Garc´ıa Mel´endez, O. M. Perez, Improving safety and mobility of vulnerable road users through its applications, Traffic Safety 4 (2016) 251–269.
[23] T. Williams, P. Alves, G. Lachapelle, C. Basnayake, Evaluation of gps-based methods of relative positioning for automotive safety applications, Transportation research part C: emerging technologies 23 (2012) 98–108.
[24] G. Korres, A. El Issawi, M. Eid, Tactile glasses (tag) for obstacle avoidance, in: International Conference on Universal Access in Human-Computer Interaction, Springer, 2014, pp. 741–749.
[25] T. Schwarze, M. Lauer, M. Schwaab, M. Romanovas, S. B¨ohm, T. J¨urgensohn, A camera-based mobility aid for visually impaired people, KI-K¨unstliche Intelligenz 30 (1) (2016) 29–36.
[26] D. Ni, A. Song, L. Tian, X. Xu, D. Chen, A walking assistant robotic system for the visually impaired based on computer vision and tactile perception, International Journal of Social Robotics 7 (5) (2015) 617–628.
[27] M. Bagheri, M. Siekkinen, J. K. Nurminen, Cellular-based vehicle to pedestrian (v2p) adaptive communication for collision avoidance, in: 2014 international conference on connected vehicles and expo (ICCVE), IEEE, 2014, pp. 450–456.
[28] L. Zhenyu, P. Lin, Z. Konglin, Z. Lin, Design and evaluation of v2x communication system for vehicle and pedestrian safety, The Journal of China Universities of Posts and Telecommunications 22 (6) (2015) 18–26.
[29] N. A. Rahim, M. Paulraj, A. Adom, Adaptive boosting with svm classifier for moving vehicle classification, Procedia Engineering 53 (2013) 411–419.
[30] J. Berdnikova, T. Ruuben, V. Kozevnikov, S. Astapov, Acoustic noise pattern detection and identification method in doppler system, Elektronika ir Elektrotechnika 18 (8) (2012) 65–68. | ||
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