Rain classification for autonomous vehicle navigation : A support vector machine approach
The advancement of LIDAR technology used in the autonomous vehicle (AV) system has made it increasingly popular. Despite that, the ability of the sensor to adjust to human behaviour in sensing and perceiving different environments is still unsolved as it significantly impacting the performance of LI...
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| Format: | Article |
| Language: | English |
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Penerbit UMP
2020
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/33644/ http://umpir.ump.edu.my/id/eprint/33644/1/Rain%20classification%20for%20autonomous%20vehicle%20navigation.pdf |
| _version_ | 1848824307638075392 |
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| author | Abdul Haleem, Habeeb Mohamed Muhammad Aizzat, Zakaria Mohd Azraai, Mohd Razman Anwar P. P., Abdul Majeed Mohamad Heerwan, Peeie |
| author_facet | Abdul Haleem, Habeeb Mohamed Muhammad Aizzat, Zakaria Mohd Azraai, Mohd Razman Anwar P. P., Abdul Majeed Mohamad Heerwan, Peeie |
| author_sort | Abdul Haleem, Habeeb Mohamed |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | The advancement of LIDAR technology used in the autonomous vehicle (AV) system has made it increasingly popular. Despite that, the ability of the sensor to adjust to human behaviour in sensing and perceiving different environments is still unsolved as it significantly impacting the performance of LIDAR, causing the effect of missing points and false positives detection. The immerging of machine learning algorithms that have greatly impacted solving uncertainties and LIDAR's reliability in making judgments has proven a great success. This paper aims to classify different rain rates conditions in a controlled environment with real rain using a LIDAR. Then, the feature extraction using the time-domain method was employed to generate more features with a variation of SVM models in developing classification models. The preliminary observation shows that the Poly-SVM model can achieve a test classification accuracy of 97%. Noting that, the proposed method has the potential to evaluate weather classification. |
| first_indexed | 2025-11-15T03:10:57Z |
| format | Article |
| id | ump-33644 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:10:57Z |
| publishDate | 2020 |
| publisher | Penerbit UMP |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-336442022-04-07T02:33:54Z http://umpir.ump.edu.my/id/eprint/33644/ Rain classification for autonomous vehicle navigation : A support vector machine approach Abdul Haleem, Habeeb Mohamed Muhammad Aizzat, Zakaria Mohd Azraai, Mohd Razman Anwar P. P., Abdul Majeed Mohamad Heerwan, Peeie TJ Mechanical engineering and machinery TS Manufactures The advancement of LIDAR technology used in the autonomous vehicle (AV) system has made it increasingly popular. Despite that, the ability of the sensor to adjust to human behaviour in sensing and perceiving different environments is still unsolved as it significantly impacting the performance of LIDAR, causing the effect of missing points and false positives detection. The immerging of machine learning algorithms that have greatly impacted solving uncertainties and LIDAR's reliability in making judgments has proven a great success. This paper aims to classify different rain rates conditions in a controlled environment with real rain using a LIDAR. Then, the feature extraction using the time-domain method was employed to generate more features with a variation of SVM models in developing classification models. The preliminary observation shows that the Poly-SVM model can achieve a test classification accuracy of 97%. Noting that, the proposed method has the potential to evaluate weather classification. Penerbit UMP 2020 Article PeerReviewed pdf en cc_by_nc_4 http://umpir.ump.edu.my/id/eprint/33644/1/Rain%20classification%20for%20autonomous%20vehicle%20navigation.pdf Abdul Haleem, Habeeb Mohamed and Muhammad Aizzat, Zakaria and Mohd Azraai, Mohd Razman and Anwar P. P., Abdul Majeed and Mohamad Heerwan, Peeie (2020) Rain classification for autonomous vehicle navigation : A support vector machine approach. Mekatronika - Journal of Intelligent Manufacturing & Mechatronics, 2 (2). pp. 74-80. ISSN 2637-0883. (Published) https://doi.org/10.15282/mekatronika.v2i2.7022 https://doi.org/10.15282/mekatronika.v2i2.7022 |
| spellingShingle | TJ Mechanical engineering and machinery TS Manufactures Abdul Haleem, Habeeb Mohamed Muhammad Aizzat, Zakaria Mohd Azraai, Mohd Razman Anwar P. P., Abdul Majeed Mohamad Heerwan, Peeie Rain classification for autonomous vehicle navigation : A support vector machine approach |
| title | Rain classification for autonomous vehicle navigation : A support vector machine approach |
| title_full | Rain classification for autonomous vehicle navigation : A support vector machine approach |
| title_fullStr | Rain classification for autonomous vehicle navigation : A support vector machine approach |
| title_full_unstemmed | Rain classification for autonomous vehicle navigation : A support vector machine approach |
| title_short | Rain classification for autonomous vehicle navigation : A support vector machine approach |
| title_sort | rain classification for autonomous vehicle navigation : a support vector machine approach |
| topic | TJ Mechanical engineering and machinery TS Manufactures |
| url | http://umpir.ump.edu.my/id/eprint/33644/ http://umpir.ump.edu.my/id/eprint/33644/ http://umpir.ump.edu.my/id/eprint/33644/ http://umpir.ump.edu.my/id/eprint/33644/1/Rain%20classification%20for%20autonomous%20vehicle%20navigation.pdf |