{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T07:33:10Z","timestamp":1772609590575,"version":"3.50.1"},"reference-count":54,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2020,1,22]],"date-time":"2020-01-22T00:00:00Z","timestamp":1579651200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Nowadays, vehicles have advanced driver-assistance systems which help to improve vehicle safety and save the lives of drivers, passengers and pedestrians. Identification of the road-surface type and condition in real time using a video image sensor, can increase the effectiveness of such systems significantly, especially when adapting it for braking and stability-related solutions. This paper contributes to the development of the new efficient engineering solution aimed at improving vehicle dynamics control via the anti-lock braking system (ABS) by estimating friction coefficient using video data. The experimental research on three different road surface types in dry and wet conditions has been carried out and braking performance was established with a car mathematical model (MM). Testing of a deep neural networks (DNN)-based road-surface and conditions classification algorithm revealed that this is the most promising approach for this task. The research has shown that the proposed solution increases the performance of ABS with a rule-based control strategy.<\/jats:p>","DOI":"10.3390\/s20030612","type":"journal-article","created":{"date-parts":[[2020,1,22]],"date-time":"2020-01-22T11:17:57Z","timestamp":1579691877000},"page":"612","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":126,"title":["Identification of Road-Surface Type Using Deep Neural Networks for Friction Coefficient Estimation"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4724-0787","authenticated-orcid":false,"given":"Eldar","family":"\u0160abanovi\u010d","sequence":"first","affiliation":[{"name":"Transport and Logistics Competence Centre; Vilnius Gediminas Technical University, Saul\u0117tekio al. 11, LT-10223 Vilnius, Lithuania"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6210-7642","authenticated-orcid":false,"given":"Vidas","family":"\u017duraulis","sequence":"additional","affiliation":[{"name":"Transport and Logistics Competence Centre; Vilnius Gediminas Technical University, Saul\u0117tekio al. 11, LT-10223 Vilnius, Lithuania"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0910-591X","authenticated-orcid":false,"given":"Olegas","family":"Prentkovskis","sequence":"additional","affiliation":[{"name":"Department of Mobile Machinery and Railway Transport, Vilnius Gediminas Technical University, Plytin\u0117s g. 27, LT-10105 Vilnius, Lithuania"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8080-875X","authenticated-orcid":false,"given":"Viktor","family":"Skrickij","sequence":"additional","affiliation":[{"name":"Transport and Logistics Competence Centre; Vilnius Gediminas Technical University, Saul\u0117tekio al. 11, LT-10223 Vilnius, Lithuania"}]}],"member":"1968","published-online":{"date-parts":[[2020,1,22]]},"reference":[{"key":"ref_1","unstructured":"Chowdhury, S.R., Zhao, M., Jonasson, M., and Ohlsson, N. (2019). Methods and Systems for Generating and Using a Road Friction Estimate Based on Camera Image Signal Processing. (Application No. US20190340445A1), U.S. Patent."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"363","DOI":"10.3846\/transport.2019.10372","article-title":"Technological measures of forefront road identification for vehicle comfort and safety improvement","volume":"34","author":"Surblys","year":"2019","journal-title":"Transport"},{"key":"ref_3","first-page":"1","article-title":"The effects of OFDM design parameters on the V2X communication performance","volume":"7","author":"Arslan","year":"2017","journal-title":"Surv. Veh. Commun."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"320","DOI":"10.1016\/j.procs.2017.06.101","article-title":"A New approach on communications architectures for intelligent transportation systems","volume":"110","author":"Sousa","year":"2017","journal-title":"Procedia Comput. Sci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1016\/j.apacoust.2014.03.021","article-title":"Identification of winter tires using vibration signals generated on the road surface","volume":"83","author":"Tanizaki","year":"2014","journal-title":"Appl. Acoust."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"633","DOI":"10.1016\/j.optlaseng.2013.01.003","article-title":"A near-infrared optoelectronic approach to detection of road conditions","volume":"51","author":"Colace","year":"2013","journal-title":"Opt. Lasers Eng."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/j.jterra.2014.09.001","article-title":"Road surface condition identification approach based on road characteristic value","volume":"56","author":"Wang","year":"2014","journal-title":"J. Terramech."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1016\/j.trc.2011.09.004","article-title":"Surface prediction and control algorithms for anti-lock brake system","volume":"21","author":"Bhandari","year":"2012","journal-title":"Transp. Res. Part C"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1016\/j.apacoust.2013.09.011","article-title":"On-board wet road surface identification using tyre\/road noise and support vector machines","volume":"76","author":"Alonso","year":"2014","journal-title":"Appl. Acoust."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Kalliris, M., Kanarachos, S., Kotsakis, R., Haas, O., and Blundell, M. (2019, January 18\u201320). Machine learning algorithms for wet road surface detection using acoustic measurements. Proceedings of 2019 IEEE International Conference on Mechatronics (ICM), Ilmenau, Germany.","DOI":"10.1109\/ICMECH.2019.8722834"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.jterra.2014.03.006","article-title":"Application of an ANN-based methodology for road surface condition identification on mining vehicles and roads","volume":"53","author":"Ngwangwa","year":"2014","journal-title":"J. Terramech."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Taniguchi, Y., Nishii, K., and Hisamatsu, H. (2015, January 3\u20135). Evaluation of a bicycle-mounted ultrasonic distance sensor for monitoring road surface condition. Proceedings of the 7th International Conference on Computational Intelligence, Communication Systems and Networks (CICSyN 2015), Riga, Latvia.","DOI":"10.1109\/CICSyN.2015.16"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"19251","DOI":"10.3390\/s150819251","article-title":"Three three-axis IEPE accelerometers on the inner liner of a tire for finding the tire-road friction potential indicators","volume":"15","author":"Niskanen","year":"2015","journal-title":"Sensors"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Yunta, J., Garcia-Pozuelo, D., Diaz, V., and Olatunbosun, O. (2018). A strain-based method to detect tires\u2019 loss of grip and estimate lateral friction coefficient from experimental data by fuzzy logic for intelligent tire development. Sensors, 18.","DOI":"10.3390\/s18020490"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Acosta, M., Kanarachos, S., and Blundell, M. (2017). Road friction virtual sensing: A review of estimation techniques with emphasis on low excitation approaches. Appl. Sci., 7.","DOI":"10.3390\/app7121230"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.conengprac.2015.07.003","article-title":"Vehicle state estimation for anti-lock control with nonlinear observer","volume":"43","author":"Sun","year":"2015","journal-title":"Control Eng."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Chen, Z., and Huang, X. (2017, January 11\u201314). End-to-end learning for lane keeping of self-driving cars. Proceedings of the IEEE Intelligent Vehicles Symposium (IV), Redondo Beach, CA, USA.","DOI":"10.1109\/IVS.2017.7995975"},{"key":"ref_18","unstructured":"Hane, C., Sattler, T., and Pollefey, M. (October, January 28). Obstacle detection for self-driving cars using only monocular cameras and wheel odometry. Proceedings of IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Hamburg, Germany."},{"key":"ref_19","unstructured":"Wang, Q., Wei, Z., Wang, J., Chen, W., and Wang, N. (2019). Curve recognition algorithm based on edge point curvature voting. Proc. Inst. Mech. Eng. D, 1\u201314."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Cao, J., Song, C., Song, S., Xiao, F., and Peng, S. (2019). Lane detection algorithm for intelligent vehicles in complex road conditions and dynamic environments. Sensors, 19.","DOI":"10.3390\/s19143166"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Cafiso, S., D\u2019Agostino, C., Delfino, E., and Montella, A. (2017, January 26\u201328). From manual to automatic pavement distress detection and classification. Proceedings of the 5th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS), Napoli, Italy.","DOI":"10.1109\/MTITS.2017.8005711"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Shen, G. (2016, January 19\u201321). Road crack detection based on video image processing. Proceedings of the 3rd International Conference on Systems and Informatics ICSAI, Shanghai, China.","DOI":"10.1109\/ICSAI.2016.7811081"},{"key":"ref_23","unstructured":"Meignen, D., Bernadet, M., and Briand, H. (1997, January 1\u20135). One application of neural networks for detection of defects using video data bases: Identification of road distress. Proceedings of the 8th International Conference and Workshop on Database and Expert Systems Applications (DEXA \u201897), Toulouse, France."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Oliveira, H., and Lobato Correia, P. (2008, January 12\u201315). Identifying and retrieving distress images from road pavement surveys. Proceedings of the 15th IEEE International Conference on Image Processing (ICIP 2008), San Diego, CA, USA.","DOI":"10.1109\/ICIP.2008.4711690"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Smolyanskiy, N., Kamenev, A., and Birchfield, S. (2018, January 18\u201322). On the importance of stereo for accurate depth estimation: An efficient semi-supervised deep neural network approach. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW 2018), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPRW.2018.00147"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"67","DOI":"10.5755\/j01.eie.24.6.22293","article-title":"Recognition of road type and quality for advanced driver assistance systems with deep learning","volume":"24","author":"Tumen","year":"2018","journal-title":"Elektronika ir Elektrotechnika"},{"key":"ref_27","unstructured":"Gimonet, N., Cord, A., and Saint Pierre, G. (July, January 28). How to predict real road state from vehicle embedded camera?. Proceedings of the IEEE Intelligent Vehicles Symposium (IV), Seoul, Korea."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"522","DOI":"10.1109\/TIV.2018.2873920","article-title":"Modeling weather and illuminations in driving views based on big-video mining","volume":"3","author":"Cheng","year":"2018","journal-title":"IEEE Trans. Intell. Veh."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Roychowdhury, S., Zhao, M., Wallin, A., Ohlsson, N., and Jonasson, M. (2018, January 8\u201313). Machine learning models for road surface and friction estimation using front-camera images. Proceedings of the International Joint Conference on Neural Networks (IJCNN 2018), Rio de Janeiro, Brazil.","DOI":"10.1109\/IJCNN.2018.8489188"},{"key":"ref_30","unstructured":"Du, Y., Liu, C., Song, Y., Li, Y., and Shen, Y. (2019). Rapid estimation of road friction for anti-skid autonomous driving. IEEE Trans. Intell. Transp., 1\u201310."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1231","DOI":"10.1177\/0278364913491297","article-title":"Vision meets robotics: The KITTI dataset","volume":"32","author":"Geiger","year":"2013","journal-title":"Int. J. Robot. Res."},{"key":"ref_32","unstructured":"Udacity (2019, December 16). Dataset Wiki. Available online: https:\/\/github.com\/udacity\/self-driving-car\/tree\/master \/datasets."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1177\/0278364916679498","article-title":"1 year, 1000 km: The Oxford RobotCar dataset","volume":"36","author":"Maddern","year":"2016","journal-title":"Int. J. Robot. Res."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1177\/0278364913507326","article-title":"The M\u00e1laga urban dataset: High-rate stereo and LiDAR in a realistic urban scenario","volume":"33","year":"2014","journal-title":"Int. J. Robot. Res."},{"key":"ref_35","unstructured":"LG Electronics, Inc. (2016, December 16). LGSVL Simulator: An Autonomous Vehicle Simulator. Available online: https:\/\/github.com\/lgsvl\/simulator."},{"key":"ref_36","unstructured":"Burckhardt, M., and Reimpell, J. (1993). Fahrwerktechnik, Radschlupf-Regelsysteme, Vogel Verlag."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1016\/j.proeng.2017.04.383","article-title":"Magic formula tyre model application for a tyre-ice interaction","volume":"187","year":"2017","journal-title":"Procedia Eng."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Cabrera, J.A., Castillo, J.J., P\u00e9rez, J., Velasco, J.M., Guerra, A.J., and Hern\u00e1ndez, P. (2018). A Procedure for determining tire-road friction characteristics using a modification of the magic formula based on experimental results. Sensors, 18.","DOI":"10.3390\/s18030896"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1016\/j.mechatronics.2014.11.004","article-title":"Vehicle motion control with subsystem prioritization","volume":"30","author":"Shyrokau","year":"2015","journal-title":"Mechatronics"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.jterra.2018.10.003","article-title":"ABS braking on rough terrain","volume":"80","author":"Els","year":"2018","journal-title":"J. Terramech."},{"key":"ref_41","unstructured":"Ngwangwa, H.M., Heyns, P.S., Labuschagne, K.F.J.J., and Kululanga, G.K. (2008, January 7\u201311). Overview of the neural network based technique for monitoring of road condition via reconstructed road profiles. Proceedings of the 27th Southern African Transport Conference (SATC 2008), Pretoria, South Africa."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1504\/IJPT.2013.054158","article-title":"Vehicle dynamics control with energy recuperation based on control allocation for independent wheel motors and brake system","volume":"2","author":"Shyrokau","year":"2013","journal-title":"Int. J. Powertrains"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"349","DOI":"10.1109\/TITS.2006.880620","article-title":"Modeling and identification of passenger car dynamics using robotics formalism","volume":"7","author":"Venture","year":"2006","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_44","unstructured":"Blaupunkt (2015). BP 3.0 FHD GPS, Blaupunkt. Available online: https:\/\/www.blaupunkt.com\/uploads\/tx_ddfproductsbp\/BP%203.0%20User%20%20manual_English.pdf."},{"key":"ref_45","unstructured":"OmniVision (2015). OV2710-1E Full HD (1080p) Product Brief, OmniVision. Available online: https:\/\/www.ovt.com\/download\/sensorpdf\/33\/OmniVision_OV2710-1E.pdf."},{"key":"ref_46","unstructured":"NVIDIA (2019, December 16). NVIDIA DIGITS. Available online: https:\/\/developer.nvidia.com\/digits."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., and Darrell, T. (2014). Caffe: Convolutional architecture for fast feature embedding. arXiv.","DOI":"10.1145\/2647868.2654889"},{"key":"ref_48","unstructured":"Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., and Devin, M. (2016). TensorFlow: Large-scale machine learning on heterogeneous systems. arXiv."},{"key":"ref_49","unstructured":"NVIDIA (2019, December 16). NVIDIA TensorRT. Available online: https:\/\/developer.nvidia.com\/tensorrt."},{"key":"ref_50","unstructured":"NVIDIA (2019, December 16). NVIDIA Jetson TX2. Available online: https:\/\/www.nvidia.com\/en-us\/autonomous-machines \/embedded-systems\/jetson-tx2\/."},{"key":"ref_51","unstructured":"NVIDIA (2019, December 16). Two Days to a Demo. Available online: https:\/\/developer.nvidia.com\/embedded\/ twodaystoademo."},{"key":"ref_52","unstructured":"Gnadler, R., Unrau, H.J., Hartmut, F., and Frey, M. (1995). FAT-Schriftenreihe 119, Forschungsvereinigung Automobiltechnik e.V."},{"key":"ref_53","unstructured":"H\u00f6pping, K., Augsburg, K., and B\u00fcchner, F. (2017, January 11\u201315). Extending the HSRI tyre model for large inflation pressure changes. Proceedings of the Engineering for a Changing World: 59th IWK, Ilmenau Scientific Colloquium, Ilmenau, Germany."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"4212","DOI":"10.1109\/TII.2018.2817588","article-title":"Robust continuous wheel slip control with reference adaptation: Application to the brake system with decoupled architecture","volume":"14","author":"Savitski","year":"2018","journal-title":"IEEE Trans. Ind. Inform."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/3\/612\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T14:08:21Z","timestamp":1760364501000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/3\/612"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,1,22]]},"references-count":54,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2020,2]]}},"alternative-id":["s20030612"],"URL":"https:\/\/doi.org\/10.3390\/s20030612","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,1,22]]}}}