KAN-RCBEVDepth: A multi-modal fusion algorithm in object detection for autonomous driving

Abstract-- Accurate 3D object detection in autonomous driving is critical yet challenging due to occlusions, varying object sizes, and complex urban environments. This paper introduces the KAN-RCBEVDepth method, an innovative approach aimed at enhancing 3D object detection by fusing multimodal sensor data from cameras, LiDAR, and millimeter-wave radar. Our unique Bird's Eye View-based approach significantly improves detection accuracy and efficiency by seamlessly integrating diverse sensor inputs, refining spatial relationship understanding, and optimizing computational procedures. Experimental results show that the proposed method outperforms existing techniques across multiple detection metrics, achieving a higher Mean Distance AP (0.389, 23% improvement), a better ND Score (0.485, 17.1% improvement), and a faster Evaluation As illustrated in Figure 1, these sensors' complementary LiDAR delivers high-precision 3D point cloud data crucial Accurate 3D object detection is a critical component of for accurate depth perception. By leveraging the strengths of autonomous driving systems, enabling vehicles to perceive each sensor type, sensor fusion mitigates their weaknesses, their environment in three dimensions and precisely identify thereby enhancing the overall performance of 3D object and localize surrounding objects such as vehicles, including detection systems.