Yunxiang Liu, Haili Ma, Jianlin Zhu^*, Qiangbo Zhang

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2963-2978, 2024, DOI:10.32604/cmc.2024.053710 - 15 August 2024

Abstract To enhance the efficiency and accuracy of environmental perception for autonomous vehicles, we propose GDMNet, a unified multi-task perception network for autonomous driving, capable of performing drivable area segmentation, lane detection, and traffic object detection. Firstly, in the encoding stage, features are extracted, and Generalized Efficient Layer Aggregation Network (GELAN) is utilized to enhance feature extraction and gradient flow. Secondly, in the decoding stage, specialized detection heads are designed; the drivable area segmentation head employs DySample to expand feature maps, the lane detection head merges early-stage features and processes the output through the Focal Modulation More >

Bilal Bataineh^*

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1941-1961, 2023, DOI:10.32604/iasc.2023.039868 - 21 June 2023

Abstract Autonomous vehicles are currently regarded as an interesting topic in the AI field. For such vehicles, the lane where they are traveling should be detected. Most lane detection methods identify the whole road area with all the lanes built on it. In addition to having a low accuracy rate and slow processing time, these methods require costly hardware and training datasets, and they fail under critical conditions. In this study, a novel detection algorithm for a lane where a car is currently traveling is proposed by combining simple traditional image processing with lightweight machine learning… More >

Ling Ding^{1, 2}, Huyin Zhang^{1, *}, Jinsheng Xiao^{3, *}, Cheng Shu³, Shejie Lu²

CMES-Computer Modeling in Engineering & Sciences, Vol.122, No.3, pp. 1039-1053, 2020, DOI:10.32604/cmes.2020.08268 - 01 March 2020

Abstract This paper proposes a novel method of lane detection, which adopts VGG16 as the basis of convolutional neural network to extract lane line features by cavity convolution, wherein the lane lines are divided into dotted lines and solid lines. Expanding the field of experience through hollow convolution, the full connection layer of the network is discarded, the last largest pooling layer of the VGG16 network is removed, and the processing of the last three convolution layers is replaced by hole convolution. At the same time, CNN adopts the encoder and decoder structure mode, and uses… More >