Year: 2025
Type: article
Abstract: Road surface conditions significantly impact driving safety and maintenance costs. Especially in connected and automated vehicles (CAVs), the road surface type recognition is critical for environmental perception. Traditional road surface recognition methods face limitations in feature extraction, so an improved one-dimensional convolutional neural network (1D-CNN) algorithm was proposed based on the VGG16 architecture. A vibration signal acquisition system was developed to efficiently acquire high-quality vehicle vibration signals. The optimized 1D-CNN algorithm model contains only 101.6 k parameters, significantly reducing computational cost and training time while maintaining high accuracy. Data augmentation, Adam optimization algorithm and L2 regularization were integrated to enhance generalization capabilities and suppress overfitting. On public datasets and actual vehicles tests, recognition accuracy rate reached 99.3% and 99.4%, respectively, substantially outperforming conventional methods. The algorithm also exhibited strong adaptability to different data sources. The research findings have implications for the accurate and efficient identification of road surfaces.
Source: Sensors
DOI: 10.3390/s25185642
Authors: Jianfeng Cui, Hengxu Zhang, Xiao Wang, Yu Jing, Xiujian Chou
Institution: North University of China
Language: English
Subfield: Automotive Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,137
OpenAlex ID: W4414094507