TY - GEN
T1 - Rain Classification for Autonomous Vehicle Navigation Using Machine Learning
AU - Habeeb Mohamed, Abdul Haleem
AU - Zakaria, Muhammad Aizzat
AU - Razman, Mohd Azraai Mohd
AU - Abdul Majeed, Anwar P.P.
AU - Peeie, Mohamed Heerwan Bin
AU - Sern, Choong Chun
AU - Kunjunni, Baarath
N1 - Funding Information:
Acknowledgements The author would like to thank Ministry of Higher Education Malaysia (KPT) and Universiti Malaysia Pahang (www.ump.edu.my) for financial supports given under FRGS/1/2018/TK08/UMP/02/1 and RDU1903139. The authors also thank the research team from Autonomous Vehicle Laboratory AEC, Innovative Manufacturing, Mechatronics and Sport Laboratory (iMAMS); who provided insight and expertise that greatly assisted in the present research work.
Funding Information:
The author would like to thank Ministry of Higher Education Malaysia (KPT) and Universiti Malaysia Pahang (www.ump.edu.my) for financial supports given under FRGS/1/2018/TK08/UMP/02/1 and RDU1903139. The authors also thank the research team from Autonomous Vehicle Laboratory AEC, Innovative Manufacturing, Mechatronics and Sport Laboratory (iMAMS); who provided insight and expertise that greatly assisted in the present research work.
Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
PY - 2022
Y1 - 2022
N2 - Autonomous vehicles (AV) has gained popularity in research and development in many countries due to the advancement of sensor technology that is used in the AV system. Despite that, sensing and perceiving in harsh weather conditions has been an issue in this modern sensor technology as it needs the ability to adapt to human behaviour in various situations. This paper aims to classify clear and rainy weather using a physical-based simulator to imitate the real-world environment which consists of roads, vehicles, and buildings. The real-world environment was constructed in a physical-based simulator to publish the data logging and testing using the ROS network. Point cloud data generated from LiDAR with a different frame of different weather are to be coupled with three machine learning models namely Naïve Bayes (NB), Random Forest (RF), and k-Nearest Neighbour (kNN) as classifiers. The preliminary analysis demonstrated that with the proposed methodology, the RF machine learning model attained a test classification accuracy (CA) of 99.9% on the test dataset, followed by kNN with a test CA of 99.4% and NB at 92.4%. Therefore, the proposed strategy has the potential to classify clear and rainy weather that provides objective-based judgement.
AB - Autonomous vehicles (AV) has gained popularity in research and development in many countries due to the advancement of sensor technology that is used in the AV system. Despite that, sensing and perceiving in harsh weather conditions has been an issue in this modern sensor technology as it needs the ability to adapt to human behaviour in various situations. This paper aims to classify clear and rainy weather using a physical-based simulator to imitate the real-world environment which consists of roads, vehicles, and buildings. The real-world environment was constructed in a physical-based simulator to publish the data logging and testing using the ROS network. Point cloud data generated from LiDAR with a different frame of different weather are to be coupled with three machine learning models namely Naïve Bayes (NB), Random Forest (RF), and k-Nearest Neighbour (kNN) as classifiers. The preliminary analysis demonstrated that with the proposed methodology, the RF machine learning model attained a test classification accuracy (CA) of 99.9% on the test dataset, followed by kNN with a test CA of 99.4% and NB at 92.4%. Therefore, the proposed strategy has the potential to classify clear and rainy weather that provides objective-based judgement.
KW - Autonomous vehicle
KW - K-nearest neighbour
KW - Machine learning
KW - Naïve Bayes
KW - Rain modelling
KW - Random forest
UR - http://www.scopus.com/inward/record.url?scp=85112559795&partnerID=8YFLogxK
U2 - 10.1007/978-981-33-4597-3_80
DO - 10.1007/978-981-33-4597-3_80
M3 - Conference Proceeding
AN - SCOPUS:85112559795
SN - 9789813345966
T3 - Lecture Notes in Electrical Engineering
SP - 895
EP - 903
BT - Recent Trends in Mechatronics Towards Industry 4.0 - Selected Articles from iM3F 2020
A2 - Ab. Nasir, Ahmad Fakhri
A2 - Ibrahim, Ahmad Najmuddin
A2 - Ishak, Ismayuzri
A2 - Mat Yahya, Nafrizuan
A2 - Zakaria, Muhammad Aizzat
A2 - P. P. Abdul Majeed, Anwar
PB - Springer Science and Business Media Deutschland GmbH
T2 - Innovative Manufacturing, Mechatronics and Materials Forum, iM3F 2020
Y2 - 6 August 2020 through 6 August 2020
ER -