Research Status and Hotspots Analysis for Object Detection of Driverless Vehicles Based on Knowledge Graph
With the continuous improvement of the intelligent level of driverless vehicles and the increasing demand for object detection accuracy,it is necessary to systematically and comprehensively review the research status and frontier directions in the field of object detection for driverless vehicles.This paper utilized the knowledge graph visualization software Citespace to conduct a literature search and analysis in the CNKI database using the keywords"object detection"and"vehicle".The result showes that the frontier directions of object detection for driverless vehicles mainly focus on:intelligent vehicle technology based on artificial intelligence assistance,multi-data feature fusion technology based on deep learning algorithms,image depth completion technology for object detection based on point cloud data,and small object detection technology based on deep learning algorithms.Based on the analysis of research hotspots,it is discovered through"burst terms"that enhancing the adaptability of deep learning algorithms to low-resolution objects in complex environments,as well as lightweighting data,accelerating computation,and improving target retrieval accuracy in autonomous driving technology based on deep learning,are currently the forefront issues in research.