Skip to main content
Springer Nature Link
Log in

Road Recognition for Autonomous Vehicles Based on Intelligent Tire and SE-CNN

  • Conference paper
  • First Online:
Intelligent Systems and Pattern Recognition (ISPR 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1589))

  • 799 Accesses

Abstract

High-level autonomous driving relies on the comprehensive perception of the environment. However, current perception research usually pays low attention to road recognition which is essential to the reliability and safety of autonomous driving. Even though existing vehicle sensors such as cameras, Lidars, and accelerometers can provide input for road recognition, recognition methods based on these sensors have challenges in balancing the needs of low cost, stability, and high accuracy. In this study, we proposed a low-cost piezoelectric sensor based intelligent tire system with a lightweight convolutional neural network (CNN) for accurate road surface recognition of autonomous vehicles. The time-frequency domain features of collected piezoelectric sensor signals are extracted by applying discrete wavelet transform (DWT). These features are input to the CNN embedded with the Squeezing-and-Excitation (SE) block. The SE block emphasizes valuable input information and improves road recognition accuracy. We perform experiments on the asphalt, marble, and painted roads using our test vehicle. The results show that the proposed SE-CNN achieves an accuracy of 99.14% in recognizing the road types, which enhances the environmental perception of autonomous driving.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Aron, M., Billot, R., Faouzi, N.-E.E., Seidowsky, R.: Traffic indicators, accidents and rain: some relationships calibrated on a french urban motorway network. Transp. Res. Procedia 10, 31–40 (2015)

    Article  Google Scholar 

  2. Bajic, M., Pour, S.M., Skar, A., Pettinari, M., Levenberg, E., Alstrøm, T.S.: Road roughness estimation using machine learning (2021). arXiv:210701199 

  3. Bystrov, A., Hoare, E., Tran, T.-Y., Clarke, N., Gashinova, M., Cherniakov, M.: Automotive surface identification system. In: 2017 IEEE International Conference on Vehicular Electronics and Safety (ICVES), pp. 115–120. IEEE, Vienna (2017)

    Google Scholar 

  4. Bystrov, A., Hoare, E., Tran, T.-Y., Clarke, N., Gashinova, M., Cherniakov, M.: Sensors for automotive remote road surface classification. In: 2018 IEEE International Conference on Vehicular Electronics and Safety (ICVES), pp. 1–6 (2018)

    Google Scholar 

  5. Dadashova, B., Ramírez, B.A., McWilliams, J.M., Izquierdo, F.A.: The identification of patterns of interurban road accident frequency and severity using road geometry and traffic indicators. Transp. Res. Procedia 14, 4122–4129 (2016)

    Article  Google Scholar 

  6. Dewangan, D.K., Sahu, S.P.: RCNet: road classification convolutional neural networks for intelligent vehicle system. Intel. Serv. Robot. 14(2), 199–214 (2021). https://doi.org/10.1007/s11370-020-00343-6

    Article  Google Scholar 

  7. Díaz-Vilariño, L., González-Jorge, H., Bueno, M., Arias, P., Puente, I.: Automatic classification of urban pavements using mobile LiDAR data and roughness descriptors. Constr. Build. Mater. 102, 208–215 (2016)

    Article  Google Scholar 

  8. Du, Y., Chen, J., Zhao, C., Liu, C., Liao, F., Chan, C.-Y.: Comfortable and energy-efficient speed control of autonomous vehicles on rough pavements using deep reinforcement learning. Transp. Res. Part C: Emerg. Technol. 134, 103489 (2022)

    Article  Google Scholar 

  9. Erdogan, G., Alexander, L., Rajamani, R.: Estimation of tire-road friction coefficient using a novel wireless piezoelectric tire sensor. IEEE Sens. J. 11, 267–279 (2011)

    Article  Google Scholar 

  10. Feng, J., Zhao, F., Ye, M., Sun, W.: The Auxiliary System of Cleaning Vehicle Based on Road Recognition Technology. SAE International, Warrendale (2021)

    Google Scholar 

  11. Guo, K., Liu, Q.: A Model of Tire Enveloping Properties and Its Application on Modelling of Automobile Vibration Systems, 980253 (1998)

    Google Scholar 

  12. Heinzler, R., Schindler, P., Seekircher, J., Ritter, W., Stork, W.: Weather influence and classification with automotive lidar sensors. In: 2019 IEEE Intelligent Vehicles Symposium (IV), pp. 1527–1534 (2019)

    Google Scholar 

  13. Hu, J., Shen, L., Sun, G.: Squeeze-and-Excitation Networks, pp. 7132–7141 (2018)

    Google Scholar 

  14. Hu, X., Chen, L., Tang, B., Cao, D., He, H.: Dynamic path planning for autonomous driving on various roads with avoidance of static and moving obstacles. Mech. Syst. Signal Process. 100, 482–500 (2018)

    Article  Google Scholar 

  15. Johnsson, R., Odelius, J.: Methods for road texture estimation using vehicle measurements, 10 (2012)

    Google Scholar 

  16. Kang, S.-W., Kim, J.-S., Kim, G.-W.: Road roughness estimation based on discrete Kalman filter with unknown input. Veh. Syst. Dyn. 1–15 (2018)

    Google Scholar 

  17. Khaleghian, S.: Terrain classification using intelligent tire. J. Terramech. 10 (2017)

    Google Scholar 

  18. Kim, H.-J., et al.: A road condition classification algorithm for a tire acceleration sensor using an artificial neural network. Electronics 9, 404 (2020)

    Article  Google Scholar 

  19. Lee, H., Taheri, S.: Intelligent Tires?a review of tire characterization literature. IEEE Intell. Trans. Syst. Mag 9, 114–135 (2017)

    Article  Google Scholar 

  20. Li, J., Zhang, Z., Wang, W.: New approach for estimating international roughness index based on the inverse pseudo excitation method. J. Transp. Eng. 13 (2018)

    Google Scholar 

  21. Ocak, H.: Automatic detection of epileptic seizures in EEG using discrete wavelet transform and approximate entropy. Expert Syst. Appl. 36, 2027–2036 (2009)

    Article  Google Scholar 

  22. Pereira, V., Tamura, S., Hayamizu, S., Fukai, H.: Classification of paved and unpaved road image using convolutional neural network for road condition inspection system. In: 2018 5th International Conference on Advanced Informatics: Concept Theory and Applications (ICAICTA), pp. 165–169 (2018)

    Google Scholar 

  23. Putra, T.E., Machmud, M.N.: Predicting the fatigue life of an automotive coil spring considering road surface roughness. Eng. Fail. Anal. 116, 104722 (2020)

    Google Scholar 

  24. Qin, Y., Langari, R., Wang, Z., Xiang, C., Dong, M.: Road excitation classification for semi-active suspension system with deep neural networks. IFS 33, 1907–1918 (2017)

    Article  Google Scholar 

  25. Qin, Y., Xiang, C., Wang, Z., Dong, M.: Road excitation classification for semi-active suspension system based on system response. J. Vib. Control 24, 2732–2748 (2018)

    Article  Google Scholar 

  26. Rateke, T., Justen, K.A., von Wangenheim, A.: Road surface classification with images captured from low-cost camera - road traversing knowledge (RTK) dataset. Rev. de Informática Teórica e Aplicada 26, 50–64 (2019)

    Article  Google Scholar 

  27. Rhif, M., Ben Abbes, A., Farah, I., Martínez, B., Sang, Y.: Wavelet transform application for/in non-stationary time-series analysis: a review. Appl. Sci. 9, 1345 (2019)

    Article  Google Scholar 

  28. Singh, K.B., Ali Arat, M., Taheri, S.: An intelligent tire based tire-road friction estimation technique and adaptive wheel slip controller for antilock brake system. J. Dyn. Syst. Meas. Contr. 135, 031002 (2013)

    Article  Google Scholar 

  29. Singh, K.B., Taheri, S.: Estimation of tire–road friction coefficient and its application in chassis control systems. Syst. Sci. Control Eng. 3, 39–61 (2015)

    Article  Google Scholar 

  30. Slavkovikj, V., Verstockt, S., De Neve, W., Van Hoecke, S., Van De Walle, R.: Image-based road type classification. In: 2014 22nd International Conference on Pattern Recognition, pp. 2359–2364 (2014)

    Google Scholar 

  31. Van der Maaten, L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9 (2008)

    Google Scholar 

  32. Vargas, J., Alsweiss, S., Toker, O., Razdan, R., Santos, J.: An overview of autonomous vehicles sensors and their vulnerability to weather conditions. Sensors 21, 5397 (2021)

    Article  Google Scholar 

  33. Wang, H., Xu, J., Yan, R., Gao, R.X.: A new intelligent bearing fault diagnosis method using SDP representation and SE-CNN. IEEE Trans. Instrum. Meas. 69, 2377–2389 (2020)

    Article  Google Scholar 

  34. Wang, S.: Road terrain type classification based on laser measurement system data, 13 (2012)

    Google Scholar 

  35. Ward, C.C., Iagnemma, K.: Speed-independent vibration-based terrain classification for passenger vehicles. Veh. Syst. Dyn. 47, 1095–1113 (2009)

    Article  Google Scholar 

  36. Yang, S., Lu, Y., Li, S.: An overview on vehicle dynamics. Int. J. Dyn. Control 1(4), 385–395 (2013). https://doi.org/10.1007/s40435-013-0032-y

    Article  Google Scholar 

  37. Yi, J., Tseng, E.H.: A “Smart Tire” system for tire/road friction estimation. In: ASME 2008 Dynamic Systems and Control Conference, Parts A and B, pp. 1293–1300. ASMEDC, Ann Arbor, Michigan (2008)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Beihang University, Beijing, China

    Runwu Shi, Shichun Yang, Yuyi Chen, Rui Wang, Jiayi Lu & Yaoguang Cao

  2. Hainan University, Hannan, China

    Zhaowen Pang

Authors
  1. Runwu Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shichun Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yuyi Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Rui Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jiayi Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Zhaowen Pang
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Yaoguang Cao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yaoguang Cao .

Editor information

Editors and Affiliations

  1. Larbi Tebessi University, Tebessa, Algeria

    Akram Bennour

  2. Arkansas Tech University, Russellville, AR, USA

    Tolga Ensari

  3. Digital Research Centre of Sfax, Sakiet Ezzit, Tunisia

    Yousri Kessentini

  4. Southeast Missouri State University, Cape Girardeau, MO, USA

    Sean Eom

Rights and permissions

Reprints and permissions

Copyright information

© 2022 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Shi, R. et al. (2022). Road Recognition for Autonomous Vehicles Based on Intelligent Tire and SE-CNN. In: Bennour, A., Ensari, T., Kessentini, Y., Eom, S. (eds) Intelligent Systems and Pattern Recognition. ISPR 2022. Communications in Computer and Information Science, vol 1589. Springer, Cham. https://doi.org/10.1007/978-3-031-08277-1_24

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-08277-1_24

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-08276-4

  • Online ISBN: 978-3-031-08277-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics