Vehicle driving area detection and sensor data preprocessing based on deep learning - Jun Zhou; Nuo Xu; Xuexuan Wu (PLoS ONE, 2025)
With the rapid development of intelligent vehicles, it has become particularly important to effectively detect the environment of the vehicle’s driving area. A vehicle driving road recognition algorithm on the basis of an improved bilateral segmentation network is built to address the poor real-time performance and low accuracy in current intelligent vehicle driving area detection methods. Combined with the algorithm, a vehicle driving area detection model based on bilateral segmentation network and data dimensionality reduction is designed. The performance comparison analysis between the recognition algorithm and other algorithms showed that the average frame processed per second and the average recognition time were 68.78FPS and 4.45ms, outperforming the comparison algorithms. The average precision and accuracy were 98.97% and 97.66%, both higher than the comparison models. Finally, the application effect analysis showed that it had good detection performance. The proposed recognition algorithm and detection model have effectiveness and practical value, which can help improve the real-time and accuracy of intelligent vehicle driving area detection, and provide theoretical basis for related research.