Estimation of the pavement skid resistance by automated vehicle based on one-dimensional convolutional neural network

References

• Albinsson, A., et al., 2017. Design of tyre force excitation for tyre–road friction estimation. Vehicle System Dynamics, 55 (2), 208–230.
   Web of Science ®Google Scholar
• Alonso, J., et al., 2014. On-board wet road surface identification using tyre/road noise and support vector machines. Applied Acoustics, 76, 407–415.
   Web of Science ®Google Scholar
• Baidu, 2017, Apollo auto. Available from: https://github.com/ApolloAuto/apollo.
   Google Scholar
• Bakker, E., Nyborg, L., and Pacejka, H.B., 1987. Tyre modelling for use in vehicle dynamics studies. SAE Transactions, 96, 190–204.
   Google Scholar
• Canudas-de Wit, C., et al., 2003. Dynamic friction models for road/tire longitudinal interaction. Vehicle System Dynamics, 39 (3), 189–226.
   Web of Science ®Google Scholar
• Chen, L., et al., 2016. Tire–road friction coefficient estimation based on the resonance frequency of in-wheel motor drive system. Vehicle System Dynamics, 54 (1), 1–19.
   Web of Science ®Google Scholar
• Cui, Z., Chen, W., and Chen, Y., 2016. Multi-scale convolutional neural networks for time series classification. Preprint arXiv:1603.06995.
   Google Scholar
• De Simone, M.C., Rivera, Z.B., and Guida, D., 2018. Obstacle avoidance system for unmanned ground vehicles by using ultrasonic sensors. Machines, 6 (2), 18.
   Web of Science ®Google Scholar
• De Wit, C.C., and Tsiotras, P., 1999. Dynamic tire friction models for vehicle traction control. In: Proceedings of the 38th IEEE conference on decision and control (Cat. no. 99CH36304), Vol. 4. Phoenix, AZ: IEEE, 3746–3751.
   Google Scholar
• Du, Y., et al., 2019. Rapid estimation of road friction for anti-skid autonomous driving. IEEE Transactions on Intelligent Transportation Systems, 21 (6), 2461–2470.
   Web of Science ®Google Scholar
• Enisz, K., et al., 2015. Tyre–road friction coefficient estimation based on the discrete-time extended kalman filter. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 229 (9), 1158–1168.
   Web of Science ®Google Scholar
• Fan, H., et al., 2018. Baidu apollo em motion planner. Preprint arXiv:1807.08048.
   Google Scholar
• Farroni, F., et al., 2017. Comparison of modelling tools for the assessment of the parameters of driving assistance solutions. In: International conference on robotics in Alpe-Adria Danube region. Torino: Springer, 433–443.
   Google Scholar
• Furlan, M., and Mavros, G., 2022. A neural network approach for roughness-dependent update of tyre friction. Simulation Modelling Practice and Theory, 116, 102484.
   Web of Science ®Google Scholar
• Gharieb, M., et al., 2022. Modeling of pavement roughness utilizing artificial neural network approach for laos national road network. Journal of Civil Engineering and Management, 28 (4), 261–277.
   Web of Science ®Google Scholar
• Hall, J., et al., 2009. Guide for Pavement Friction. Final Report for NCHRP Project, 43.
   Google Scholar
• He, K., et al., 2016. Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition. Las Vegas, Nevad, 770–778.
   Google Scholar
• Hsu, C.H., Ni, S.P., and Hsiao, T., 2021. Look-up table-based tire-road friction coefficient estimation of each driving wheel. IEEE Control Systems Letters, 6, 2168–2173.
   Google Scholar
• Ince, T., et al., 2016. Real-time motor fault detection by 1-d convolutional neural networks. IEEE Transactions on Industrial Electronics, 63 (11), 7067–7075.
   Web of Science ®Google Scholar
• International, S., 2018. Taxonomy and definitions for terms related to driving automation systems for on-road motor vehicles. SAE International, 4970 (724), 1–5.
   Google Scholar
• Jiang, D., et al., 2021. A fully convolutional neural network-based regression approach for effective chemical composition analysis using near-infrared spectroscopy in cloud. Journal of Artificial Intelligence and Technology, 1 (1), 74–82.
   Google Scholar
• Kirkpatrick, K., 2022. Still waiting for self-driving cars. Communications of the ACM, 65 (4), 12–14.
   Web of Science ®Google Scholar
• Major, R.C., 1997. L2 acquisition, L1 loss, and the critical period hypothesis¹. Second-Language Speech: Structure and Process, 13, 147.
   Google Scholar
• Mataei, B., et al., 2016. Pavement friction and skid resistance measurement methods: A literature review. Open Journal of Civil Engineering, 6 (4), 537.
   Google Scholar
• Mayora, J.M.P., and Piña, R.J., 2009. An assessment of the skid resistance effect on traffic safety under wet-pavement conditions. Accident Analysis & Prevention, 41 (4), 881–886.
   PubMed Web of Science ®Google Scholar
• Midgley, J.W., Fleming, J., and Otoofi, M., 2023. Model-free road friction estimation using machine learning. In: 2023 IEEE international conference on mechatronics (ICM). Loughborough: IEEE, 1–7.
   Google Scholar
• Muïler, S., Uchanski, M., and Hedrick, K., 2003. Estimation of the maximum tire-road friction coefficient. Journal of Dynamic Systems, Measurement, and Control, 125 (4), 607–617.
   Web of Science ®Google Scholar
• Murdoch, W.J., et al., 2019. Definitions, methods, and applications in interpretable machine learning. Proceedings of the National Academy of Sciences, 116 (44), 22071–22080.
   PubMed Web of Science ®Google Scholar
• Murtagh, F., 1991. Multilayer perceptrons for classification and regression. Neurocomputing, 2 (5-6), 183–197.
   Google Scholar
• Niskanen, A., and Tuononen, A.J., 2015. Three three-axis iepe accelerometers on the inner liner of a tire for finding the tire-road friction potential indicators. Sensors, 15 (8), 19251–19263.
   PubMed Web of Science ®Google Scholar
• Pérez-Acebo, H., et al., 2023. A simplified skid resistance predicting model for a freeway network to be used in a pavement management system. International Journal of Pavement Engineering, 24 (2), 2020266.
   Web of Science ®Google Scholar
• Pu, Z., et al., 2021. Road surface friction prediction using long short-term memory neural network based on historical data. Journal of Intelligent Transportation Systems, 26 (1), 34–45.
   Web of Science ®Google Scholar
• Ramachandran, P., Zoph, B., and Le, Q.V., 2017. Searching for activation functions. Preprint arXiv:1710.05941.
   Google Scholar
• Rizvi, S.M.H., 2022. Time series deep learning for robust steady-state load parameter estimation using 1d-cnn. Arabian Journal for Science and Engineering, 47 (3), 2731–2744.
   Web of Science ®Google Scholar
• Rodríguez-Pérez, R., and Bajorath, J., 2020. Interpretation of machine learning models using shapley values: application to compound potency and multi-target activity predictions. Journal of Computer-Aided Molecular Design, 34, 1013–1026.
   PubMed Web of Science ®Google Scholar
• Sharifzadeh, M., et al., 2018. A real-time approach to robust identification of tyre–road friction characteristics on mixed-μ roads. Vehicle System Dynamics, 57, 1338–1362.
   Web of Science ®Google Scholar
• Song, S., et al., 2018. Estimating the maximum road friction coefficient with uncertainty using deep learning. In: 2018 21st International conference on intelligent transportation systems (ITSC). Maui, Hawaii: IEEE, 3156–3161.
   Google Scholar
• Tan, C., et al., 2018. A survey on deep transfer learning. In: International conference on artificial neural networks. Island of Rhodes: Springer, 270–279.
   Google Scholar
• Tang, W., et al., 2021. Omni-scale cnns: a simple and effective kernel size configuration for time series classification. In: International conference on learning representations. Vienna.
   Google Scholar
• Wang, Y., et al., 2022. Tire road friction coefficient estimation: review and research perspectives. Chinese Journal of Mechanical Engineering, 35 (1), 6.
   Web of Science ®Google Scholar
• Weng, Z., et al., 2022. Pavement texture depth estimation using image-based multiscale features. Automation in Construction, 141, 104404.
   Web of Science ®Google Scholar
• Xiao, F., et al., 2022. A novel estimation scheme of tyre–road friction characteristics based on parameter constraints on varied-μ roads. Measurement, 194, 111077.
   Web of Science ®Google Scholar
• Yu, Z., et al., 2000. Automobile theory. Beijing: Mechanical industry press.
   Google Scholar
• Zhu, F., et al., 2018. Baidu apollo auto-calibration system-an industry-level data-driven and learning based vehicle longitude dynamic calibrating algorithm. Preprint arXiv:1808.10134.
   Google Scholar