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A Survey of Vision-Based Road Parameter Estimating Methods

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Intelligent Computing Methodologies (ICIC 2020)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12465))

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Abstract

Intelligent vehicles need to acquire real-time information on the road through sensors, calculate the limit of car speed and angular speed, so as to provide safety for the control decision. We argue that the road conditions such as snow, ice or humidity pose a major threat to driving safety. We divide the current methods of estimating the road parameters based on the visual sensor, as friction coefficient estimation method, road curvature estimation method and the road slope estimation method. The significance of various methods to intelligent driving, the current research status, as well as scientific difficulties are discussed in detail. Finally we discuss the possible research directions, including establish large-scale open data set, road status prediction methods under multi-task constraints and online learning mechanisms.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China (No. U19A2069).

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Authors and Affiliations

  1. College of Electronics and Information Engineering, Tongji University, Shanghai, 201804, China

    Yan Wu, Feilin Liu, Linting Guan & Xinneng Yang

Authors
  1. Yan Wu
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  2. Feilin Liu
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  3. Linting Guan
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  4. Xinneng Yang
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Corresponding author

Correspondence to Yan Wu .

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Editors and Affiliations

  1. Machine Learning and Systems Biology, Tongji University, Shanghai, China

    De-Shuang Huang

  2. School of Electrical, Computer and Telecommunications Engineering, University of Wollongong, North Wollongong, NSW, Australia

    Prashan Premaratne

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Wu, Y., Liu, F., Guan, L., Yang, X. (2020). A Survey of Vision-Based Road Parameter Estimating Methods. In: Huang, DS., Premaratne, P. (eds) Intelligent Computing Methodologies. ICIC 2020. Lecture Notes in Computer Science(), vol 12465. Springer, Cham. https://doi.org/10.1007/978-3-030-60796-8_27

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  • DOI: https://doi.org/10.1007/978-3-030-60796-8_27

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-60795-1

  • Online ISBN: 978-3-030-60796-8

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