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Study on Image Correction and Optimization of Mounting Positions of Dual Cameras for Vehicle Test

Author

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  • Si-Ho Lee

    (Department of Mechanical Engineering, Keimyung University, Daegu 42601, Korea)

  • Bong-Ju Kim

    (Department of Mechanical Engineering, Keimyung University, Daegu 42601, Korea)

  • Seon-Bong Lee

    (Division of Mechanical and Automotive Engineering, Keimyung University, Daegu 42601, Korea)

Abstract

Among surrounding information-gathering devices, cameras are the most accessible and widely used in autonomous vehicles. In particular, stereo cameras are employed in academic as well as practical applications. In this study, commonly used webcams are mounted on a vehicle in a dual-camera configuration and used to perform lane detection based on image correction. The height, baseline, and angle were considered as variables for optimizing the mounting positions of the cameras. Then, a theoretical equation was proposed for the measurement of the distance to the object, and it was validated via vehicle tests. The optimal height, baseline, and angle of the mounting position of the dual camera configuration were identified to be 40 cm, 30 cm, and 12°, respectively. These values were utilized to compare the performances of vehicles in stationary and driving states on straight and curved roads, as obtained by vehicle tests and theoretical calculations. The comparison revealed the maximum error rates in the stationary and driving states on a straight road to be 3.54% and 5.35%, respectively, and those on a curved road to be 9.13% and 9.40%, respectively. It was determined that the proposed method is reliable because the error rates were less than 10%.

Suggested Citation

  • Si-Ho Lee & Bong-Ju Kim & Seon-Bong Lee, 2021. "Study on Image Correction and Optimization of Mounting Positions of Dual Cameras for Vehicle Test," Energies, MDPI, vol. 14(16), pages 1-19, August.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:16:p:4857-:d:611152
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    References listed on IDEAS

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    1. Zengcai Wang & Xiaojin Wang & Lei Zhao & Guoxin Zhang, 2018. "Vision-Based Lane Departure Detection Using a Stacked Sparse Autoencoder," Mathematical Problems in Engineering, Hindawi, vol. 2018, pages 1-15, September.
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    Cited by:

    1. Mohammad Junaid & Zsolt Szalay & Árpád Török, 2021. "Evaluation of Non-Classical Decision-Making Methods in Self Driving Cars: Pedestrian Detection Testing on Cluster of Images with Different Luminance Conditions," Energies, MDPI, vol. 14(21), pages 1-16, November.
    2. Marek Guzek & Rafał S. Jurecki & Wojciech Wach, 2022. "Vehicle and Traffic Safety," Energies, MDPI, vol. 15(13), pages 1-4, June.

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