2023
doi: 10.12720/jait.14.6.1159-1168
Improving Autonomous Vehicle Performance through Integration of an Image Deraining and a Deep Learning-Based Network for Lane Following
Hoang Tran Ngoc*
,
Phuc Phan Hong ,
Anh Nguyen Quoc ,
Luyl-Da Quach
- Software Engineering Department, FPT University, Can Tho, VietNam
- Manuscript receivedApril 4, 2023
- revisedMay 25, 2023
- acceptedJune 27, 2023
- publishedNovember 3, 2023
Abstract
Lane-keeping is a vital component of autonomous driving that requires multiple artificial intelligence technologies and vision systems. However, maintaining a vehicle’s position within the lane is challenging when there is low visibility due to rain. In this research, a combination of image deraining and a deep learning-based network is proposed to improve the performance of the autonomous vehicle. First, a robust progressive Residual Network (ResNet) is used for rain removal. Second, a deep learning-based network architecture of the Convolutional Neural Networks (CNNs) is applied for lane-following on roads. To assess its accuracy and rain-removal capabilities, the network was evaluated on both synthetic and natural Rainy Datasets (RainSP), and its performance was compared to that of earlier research networks. Furthermore, the effectiveness of using both deraining and non-deraining networks in CNNs is evaluated by analyzing the predicted steering angle output. The experimental results show that the proposed model generates safe and accurate motion planning for lane-keeping in autonomous vehicles.
Keywords
- autonomous vehicle
- image processing
- rain removal
- convolutional neural networks
- autoencoder
- residual network
How to Cite
Hoang Tran Ngoc, Phuc Phan Hong, Anh Nguyen Quoc, and Luyl-Da Quach, "Improving Autonomous Vehicle Performance through Integration of an Image Deraining and a Deep Learning-Based Network for Lane Following," Journal of Advances in Information Technology, vol. 14, no. 6, pp. 1159-1168, 2023. https://doi.org/10.12720/jait.14.6.1159-1168
Copyright & License
Copyright © 2023 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
