Multisensor Information Fusion: Future of Environmental Perception in Intelligent Vehicles
Yongsheng Zhang, Chen Tu, Kun Gao, Liang Wang
Abstract
As urban transportation increasingly impacts daily life, efficiently utilizing traffic resources and developing public transportation have become crucial for addressing issues such as congestion, frequent accidents, and noise pollution. The rapid advancement of intelligent autonomous driving technologies, particularly environmental perception technologies, offers new directions for solving these problems. This review discusses the application of multisensor information fusion technology in environmental perception for intelligent vehicles, analyzing the components and performance of various sensors and their specific applications in autonomous driving. Through multisensor information fusion, the accuracy of environmental perception is enhanced, optimizing decision support for autonomous driving systems and thereby improving vehicle safety and driving efficiency. This study also discusses the challenges faced by information fusion technology and future development trends, providing references for further research and application in intelligent transportation systems.