Road Environment Recognition for Automotive FMCW RADAR Systems Through Convolutional Neural Network
In this study, we propose a method to recognize road environments with automotive frequency-modulated continuous wave (FMCW) radar systems. For automotive radar systems on the road, diverse road environments are observed. Each road environment generates unnecessary echoes known as clutter, and the m...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9153555/ |
| _version_ | 1831678831778332672 |
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| author | Heonkyo Sim The-Duong Do Seongwook Lee Yong-Hwa Kim Seong-Cheol Kim |
| author_facet | Heonkyo Sim The-Duong Do Seongwook Lee Yong-Hwa Kim Seong-Cheol Kim |
| author_sort | Heonkyo Sim |
| collection | DOAJ |
| description | In this study, we propose a method to recognize road environments with automotive frequency-modulated continuous wave (FMCW) radar systems. For automotive radar systems on the road, diverse road environments are observed. Each road environment generates unnecessary echoes known as clutter, and the magnitude distribution of received radar signal varies depending on road structures. Therefore, it is necessary to classify the road environment and adopt a target detection algorithm suitable for each road environment characteristic. To recognize the road environment in advance, it is necessary to identify the section where the road environment changes. In this paper, we define a changed road area as a transition region, and we classify the road environment and transition regions to improve the road environment recognition performance. Road environments are recognized by applying convolutional neural networks to the frequency-domain received signals of 77 GHz FMCW automotive radar systems. Experimental results in real-road environments demonstrate that the proposed method achieves 100% recognition performance, which is better if compared with that of the conventional methods. |
| first_indexed | 2024-12-20T05:07:01Z |
| format | Article |
| id | doaj.art-8a7a9fbb003a4b4382e2dd82095db82e |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-20T05:07:01Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-8a7a9fbb003a4b4382e2dd82095db82e2022-12-21T19:52:22ZengIEEEIEEE Access2169-35362020-01-01814164814165610.1109/ACCESS.2020.30132639153555Road Environment Recognition for Automotive FMCW RADAR Systems Through Convolutional Neural NetworkHeonkyo Sim0https://orcid.org/0000-0002-3319-4425The-Duong Do1https://orcid.org/0000-0002-0271-5646Seongwook Lee2https://orcid.org/0000-0001-9115-4897Yong-Hwa Kim3https://orcid.org/0000-0003-2183-5085Seong-Cheol Kim4https://orcid.org/0000-0002-7896-5625Department of Electrical and Computer Engineering, Seoul National University (SNU), Seoul, South KoreaDepartment of Electronic Engineering, Myongji University, Yongin, South KoreaSchool of Electronics and Information Engineering, Korea Aerospace University, Goyang, South KoreaDepartment of Electronic Engineering, Myongji University, Yongin, South KoreaDepartment of Electrical and Computer Engineering, Seoul National University (SNU), Seoul, South KoreaIn this study, we propose a method to recognize road environments with automotive frequency-modulated continuous wave (FMCW) radar systems. For automotive radar systems on the road, diverse road environments are observed. Each road environment generates unnecessary echoes known as clutter, and the magnitude distribution of received radar signal varies depending on road structures. Therefore, it is necessary to classify the road environment and adopt a target detection algorithm suitable for each road environment characteristic. To recognize the road environment in advance, it is necessary to identify the section where the road environment changes. In this paper, we define a changed road area as a transition region, and we classify the road environment and transition regions to improve the road environment recognition performance. Road environments are recognized by applying convolutional neural networks to the frequency-domain received signals of 77 GHz FMCW automotive radar systems. Experimental results in real-road environments demonstrate that the proposed method achieves 100% recognition performance, which is better if compared with that of the conventional methods.https://ieeexplore.ieee.org/document/9153555/Automotive frequency-modulated continuous wave~(FMCW) radarroad environment recognitionconvolutional neural network |
| spellingShingle | Heonkyo Sim The-Duong Do Seongwook Lee Yong-Hwa Kim Seong-Cheol Kim Road Environment Recognition for Automotive FMCW RADAR Systems Through Convolutional Neural Network IEEE Access Automotive frequency-modulated continuous wave~(FMCW) radar road environment recognition convolutional neural network |
| title | Road Environment Recognition for Automotive FMCW RADAR Systems Through Convolutional Neural Network |
| title_full | Road Environment Recognition for Automotive FMCW RADAR Systems Through Convolutional Neural Network |
| title_fullStr | Road Environment Recognition for Automotive FMCW RADAR Systems Through Convolutional Neural Network |
| title_full_unstemmed | Road Environment Recognition for Automotive FMCW RADAR Systems Through Convolutional Neural Network |
| title_short | Road Environment Recognition for Automotive FMCW RADAR Systems Through Convolutional Neural Network |
| title_sort | road environment recognition for automotive fmcw radar systems through convolutional neural network |
| topic | Automotive frequency-modulated continuous wave~(FMCW) radar road environment recognition convolutional neural network |
| url | https://ieeexplore.ieee.org/document/9153555/ |
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