Scheme Analysis for Enhancing Autonomous Driving Based on Computer Vision - Chen Xinyi; Luo Binbin; Xu Ziyue (ITM Web of Conferences)
This article explores the application and challenges of computer vision technology in autonomous driving, a critical component for the advancement of this field. Thesis adopted both literature review and technical analysis, focusing on recent developments in key technologies such as image processing, hybrid convolutional neural network (CNN)-transformer models, object detection, and multi-sensor fusion. The principles, benefits, limitations, and practical challenges of each technology were examined in detail. Thesis findings indicate that CNNs and their variants excel in tasks like object detection and semantic segmentation, significantly enhancing system perception and accuracy. Additionally, multi-sensor fusion technology boosts the reliability and the robustness of autonomous driving systems. However, challenges remain, including high computational demands, environmental perception accuracy, multi-sensor data fusion efficiency, and the high costs associated with implementation. Future research will prioritize developing highly effective deep learning models and optimizing cognitive computing visual systems to ameliorate the efficiency and ensure the safety of autonomous driving. The insights from this study offer valuable references for advancing autonomous driving technology and guide future research directions.