A review of automatic driving target detection based on camera and millimeter wave radar fusion technology - Tao, Zhenhua; Ngui, Wai Keng (Universiti Malaysia Pahang, 2025)
Autonomous driving relies heavily on precise target detection to ensure safety and efficiency in navigating complex environments. It typically utilizes multiple sensors to achieve comprehensive environmental perception. This review explores advancements in integrating these complementary sensors, focusing on state-of-the-art fusion methods, challenges, and applications. The combination of these sensors addresses the limitations of individual modalities: cameras excel in capturing detailed textures and colors, while millimeter wave radar provides reliable distance, velocity, and motion information under adverse weather conditions. Key findings reveal that the sparse radar data, lack of comprehensive multimodal datasets, and difficulties in correlating radar with image data pose significant hurdles. Future research should focus on developing comprehensive multimodal datasets, 4D millimeter-wave radar, and refining fusion algorithms for robustness in diverse environments. This review provides a comprehensive understanding of the current state and challenges in target detection, serving as a foundation for future innovation in autonomous driving technology.