This study focuses on multi-sensor fusion technology applied in environmental perception for intelligent driving systems and explores its role in enhancing the accuracy and robustness of environmental perception. Analysis shows that multi-sensor fusion can overcome the limitations of single sensors through complementary advantages. Among mainstream sensors, cameras provide 2D image details for target recognition and road condition judgment; LiDAR generates 3D point clouds for high-precision modeling, suitable for L3 and above levels; millimeter-wave radar has strong environmental adaptability but limited detail recognition. Multi-sensor fusion operates at three levels: data-level (retaining original information but requiring high computing power), feature-level (efficient but with data loss), and decision-level (fusing based on independent detection results). Combining multi-sensor fusion with deep learning (e.g., end-to-end fusion) can improve accuracy and real-time performance. Currently, intelligent driving faces challenges of low cost, high real-time performance, and high accuracy. Multi-sensor fusion is an important direction. In the future, it is necessary to optimize sensor performance (such as 4D millimeter-wave radar) and algorithms to meet the needs of complex environments.
Jingyan Lu (Wed,) studied this question.