Rain classification for autonomous vehicle navigation : A support vector machine approach

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)

Rain classification for autonomous vehicle navigation.pdf
Available under License Creative Commons Attribution Non-commercial.

Download (514kB) | Preview


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.

Item Type: Article
Uncontrolled Keywords: LIDAR; Autonomous vehicle; Support vector machine
Subjects: T Technology > TJ Mechanical engineering and machinery
T Technology > TS Manufactures
Faculty/Division: Institute of Postgraduate Studies
Faculty of Manufacturing and Mechatronic Engineering Technology
Depositing User: Mrs Norsaini Abdul Samat
Date Deposited: 07 Apr 2022 02:33
Last Modified: 07 Apr 2022 02:33
URI: http://umpir.ump.edu.my/id/eprint/33644
Download Statistic: View Download Statistics

Actions (login required)

View Item View Item