Multi-source Heterogeneous Data Fusion Empowers Safe Driving: Technology, Application and Challenge Analysis

In view of the severe global road traffic safety situation and the limitations of traditional safe driving technologies that rely on manual operation and are vulnerable to interference from single sensors, this paper systematically studies the application value and practical path of multi-source heterogeneous data fusion technology in the field of safe driving. Firstly, it defines the types and sources of multi-source heterogeneous data in the field of safe driving, and sorts out the three-level fusion hierarchy of data layer, feature layer and decision layer, as well as core fusion methods such as weighted average method, Kalman filtering method and neural network method. Secondly, it analyzes the correlation mechanism between the technology and safe driving, that is, supporting driving decisions, improving system environmental perception, fault diagnosis and risk warning performance through the integration of multi-source data. Then, it combines case studies to analyze the application practices of the technology in three scenarios: autonomous driving assistance systems, Internet of Vehicles and intelligent traffic management, and driver behavior monitoring. Finally, it deeply discusses four major challenges: data heterogeneity, data quality, algorithm resource limitations, and security and privacy, and proposes countermeasures such as standardization, quality control, algorithm optimization, and multi-layer protection. It also looks forward to the integration trend of the technology with artificial intelligence and quantum computing, as well as its application expansion in fields such as new energy vehicles and intelligent logistics. The research provides theoretical support and practical reference for the intelligent transformation of safe driving, and helps to reduce the accident rate and improve traffic efficiency.