Abstract
Road surface identification is attracting more attention in recent years as part of the development of autonomous vehicle technologies. Most works consider multiple sensors and many features in order to produce a more reliable and robust result. However, on-board limitations and generalization concerns dictate the need for dimensionality reduction methods. This work considers four dimensionality reduction methods: principal component analysis, sequential feature selection, ReliefF, and a novel feature ranking method. These methods are used on data obtained from a modified passenger car with four types of sensors. Results were obtained using three classifiers (linear discriminant analysis, support vector machines, and random forests) and a late fusion method based on alpha integration, reaching up to 96.10% accuracy. The considered dimensionality reduction methods were able to reduce the number of features required for classification greatly and increased classification performance. Furthermore, the proposed method was faster than ReliefF and sequential feature selection and yielded similar improvements.
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References
Tudon-Martinez, J.C., Fergani, S., Sename, O., Martinez, J.J., Morales-Menendez, R., Dugard, L.: Adaptive road profile estimation in semiactive car suspensions. IEEE Trans. Control Syst. Technol. 23(6), 2293–2305 (2015)
Park, J., Min, K., Kim, H., Lee, W., Cho, G., Huh, K.: Road surface classification using a deep ensemble network with sensor feature selection. Sensors 18, (2018). Article no. 4342
Alonso, J., López, J.M., Pavón, I., Recuero, M., Asensio, C., Arcas, G., Bravo, A.: On-board wet road surface identification using tyre/road noise and support vector machines. Appl. Acoust. 76, 407–415 (2014)
Zhao, J., Wu, H., and Chen, L.: Road surface state recognition based on SVM optimization and image segmentation processing. J. Adv. Transp. 2017 (2017). Article no. 6458495
Masino, J., Pinay, J., Reischl, M., Gauterin, F.: Road surface prediction from acoustical measurements in the tire cavity using support vector machine. Appl. Acoust. 125, 41–48 (2017)
Bystrov, A., Hoare, E., Tran, T.Y., Clarke, N., Gashinova, M., Cherniakov, M.: Automotive surface identification system. In: IEEE International Conference on Vehicular Electronics and Safety (ICVES), Vienna, Austria, pp. 115–120 (2017)
Bystrov, A., Hoare, E., Tran, T.Y., Clarke, N., Gashinova, M., Cherniakov, M.: Road surface classification using automotive ultrasonic sensor. Procedia Eng. 168, 19–22 (2016)
Bystrov, A., Abbas, M., Hoare, E., Tran, T.Y., Clarke, N., Gashinova, M., Cherniakov, M.: Analysis of classification algorithms applied to road surface recognition. In: 2015 IEEE Radar Conference (RadarCon), Piscataway, NJ, USA, pp. 907–911 (2015)
Igual, J., Salazar, A., Safont, G., Vergara, L.: Semi-supervised Bayesian classification of materials with impact-echo signals. Sensors 15(5), 11528–11550 (2015)
Salazar, A., Igual, J., Vergara, L., Serrano, A.: Learning hierarchies from ICA mixtures. In: IEEE International Joint Conference on Artificial Neural Networks, Orlando, FL, USA, pp. 2271–2276 (2007)
Salazar, A., Gosalbez, J., Bosch, I., Miralles, R., Vergara, L.: A case study of knowledge discovery on academic achievement, student desertion and student retention. In: IEEE ITRE 2004 - 2nd International Conference on Information Technology: Research and Education, London, United Kingdom, pp. 150–154 (2004)
Salazar, A., Igual, J., Safont, G., Vergara, L., Vidal, A.: Image applications of agglomerative clustering using mixtures of non-Gaussian distributions. In: International Conference on Computational Science and Computational Intelligence (CSCI), Las Vegas, NV, USA, pp. 459–463 (2015)
Jolliffe, I.T.: Principal Component Analysis. Springer, New York (2002)
Shamir, O.: A stochastic PCA and SVD algorithm with an exponential convergence rate. In: International Conference on Machine Learning, Lille, France, pp. 144–155 (2015)
Llinares, R., Igual, J., Salazar, A., Camacho, A.: Semi-blind source extraction of atrial activity by combining statistical and spectral features. Digit. Signal Process. Rev. J. 21(2), 391–403 (2011)
Safont, G., Salazar, A., Rodriguez, A., Vergara, L.: On Recovering missing ground penetrating radar traces by statistical interpolation methods. Remote Sens. 6(8), 7546–7565 (2014)
Safont, G., Salazar, A., Vergara, L., Gomez, E., Villanueva, V.: Probabilistic distance for mixtures of independent component analyzers. IEEE Trans. Neural Netw. Learn. Syst. 29(4), 1161–1173 (2018)
Safont, G., Salazar, A., Vergara, L., Gómez, E., Villanueva, V.: Multichannel dynamic modeling of non-Gaussian mixtures. Pattern Recognit. 93, 312–323 (2019)
Lui, H., Motoda, H. (eds.): Computational Methods of Feature Selection. CRC Press, Boca Ratón (2007)
Kononenko, I., Šimec, E., Robnik-Šikonja, M.: Overcoming the myopia of inductive learning algorithms with RELIEFF. Appl. Intell. 7(1), 39–55 (1997)
Amari, S.: Information Geometry and its Applications. Springer, Berlin (2016)
Soriano, A., Vergara, L., Bouziane, A., Salazar, A.: Fusion of scores in a detection context based on alpha-integration. Neural Comput. 27, 1983–2010 (2015)
Safont, G., Salazar, A., Vergara, L.: Multiclass alpha integration of scores from multiple classifiers. Neural Comput. 31(4), 806–825 (2019)
Powers, D.M.W.: Evaluation: from precision, recall and F-measure to ROC, informedness, markedness & correlation. J. Mach. Learn. Technol. 2(1), 37–63 (2011)
Peeters, G.: A large set of audio features for sound description (similarity and classification) in the CUIDADO project. CUIDADO IST Project Report 54 (2004)
Acknowledgment
This work was supported by Spanish Administration and European Union grant TEC2017-84743-P, and Generalitat Valenciana under grant PROMETEO/2019/109.
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Safont, G., Salazar, A., Rodríguez, A., Vergara, L. (2020). Comparison of Dimensionality Reduction Methods for Road Surface Identification System. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Computing. SAI 2020. Advances in Intelligent Systems and Computing, vol 1229. Springer, Cham. https://doi.org/10.1007/978-3-030-52246-9_40
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