Visual and Radar Data Fusion: Complementary or Alternative Sensors in Intelligent Driving Systems

This article explores the role and advantages and disadvantages of visual and radar data fusion in intelligent driving systems. With the rapid development of intelligent driving technology, the comprehensive perception of the environment by car perception systems has become crucial, usually requiring the combination of low, medium, and high-precision sensors. Visual sensors perform excellently in acquiring high-resolution images and recognizing detailed information, while radar sensors have higher stability and penetration in harsh weather conditions. Due to the limitations of a single sensor in complex driving environments, data fusion has become a key strategy for improving the performance of perception systems. By comparing different perception schemes and application scenarios, this article analyzes the actual effects and potential advantages of visual and radar data fusion, revealing its important role in intelligent driving technology. Fusion technology can compensate for the shortcomings of various sensors, and improve the accuracy and reliability of environmental perception, but still faces challenges in data synchronization, algorithm selection, and real-time performance. This article aims to provide valuable insights for the development of future intelligent driving technology.