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

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Intelligent Systems and Pattern Recognition (ISPR 2022)

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

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.

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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
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  2. Shichun Yang
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  3. Yuyi Chen
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  4. Rui Wang
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  5. Jiayi Lu
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  6. Zhaowen Pang
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  7. Yaoguang Cao
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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

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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

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

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-08276-4

  • Online ISBN: 978-3-031-08277-1

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