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Developing an On-Road Object Detection System Using Monovision and Radar Fusion

Author

Listed:
  • Ya-Wen Hsu

    (Department of Mechanical and Electro-Mechanical Engineering, National Sun Yat-sen University, Kaohsiung 80424, Taiwan)

  • Yi-Horng Lai

    (School of Mechanical and Electrical Engineering, Xiamen University Tan Kah Kee College, Zhangzhou 363105, China)

  • Kai-Quan Zhong

    (Department of Mechanical and Electro-Mechanical Engineering, National Sun Yat-sen University, Kaohsiung 80424, Taiwan)

  • Tang-Kai Yin

    (Department of Computer Science and Information Engineering, National University of Kaohsiung, Kaohsiung 81148, Taiwan)

  • Jau-Woei Perng

    (Department of Mechanical and Electro-Mechanical Engineering, National Sun Yat-sen University, Kaohsiung 80424, Taiwan)

Abstract

In this study, a millimeter-wave (MMW) radar and an onboard camera are used to develop a sensor fusion algorithm for a forward collision warning system. This study proposed integrating an MMW radar and camera to compensate for the deficiencies caused by relying on a single sensor and to improve frontal object detection rates. Density-based spatial clustering of applications with noise and particle filter algorithms are used in the radar-based object detection system to remove non-object noise and track the target object. Meanwhile, the two-stage vision recognition system can detect and recognize the objects in front of a vehicle. The detected objects include pedestrians, motorcycles, and cars. The spatial alignment uses a radial basis function neural network to learn the conversion relationship between the distance information of the MMW radar and the coordinate information in the image. Then a neural network is utilized for object matching. The sensor with a higher confidence index is selected as the system output. Finally, three kinds of scenario conditions (daytime, nighttime, and rainy-day) were designed to test the performance of the proposed method. The detection rates and the false alarm rates of proposed system were approximately 90.5% and 0.6%, respectively.

Suggested Citation

  • Ya-Wen Hsu & Yi-Horng Lai & Kai-Quan Zhong & Tang-Kai Yin & Jau-Woei Perng, 2019. "Developing an On-Road Object Detection System Using Monovision and Radar Fusion," Energies, MDPI, vol. 13(1), pages 1-18, December.
    • Handle: RePEc:gam:jeners:v:13:y:2019:i:1:p:116-:d:301866
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    Cited by:

    1. Luis B. Elvas & Joao C Ferreira, 2021. "Intelligent Transportation Systems for Electric Vehicles," Energies, MDPI, vol. 14(17), pages 1-9, September.

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