Research Progress on Multi-Modal Fusion Object Detection Algorithms for Autonomous Driving: A Review

As the number and complexity of sensors in autonomous vehicles continue to rise, multimodal fusion-based object detection algorithms are increasingly being used to detect 3D environmental information, significantly advancing the development of perception technology in autonomous driving. To further promote the development of fusion algorithms and improve detection performance, this paper discusses the advantages and recent advancements of multimodal fusion-based object detection algorithms. Starting from single-modal sensor detection, the paper provides a detailed overview of typical sensors used in autonomous driving and introduces object detection methods based on images and point clouds. For image-based detection methods, they are categorized into monocular detection and binocular detection based on different input types. For point cloud-based detection methods, they are classified into projection-based, voxel-based, point cluster-based, pillar-based, and graph structure-based approaches based on the technical pathways for processing point cloud features. Additionally, multimodal fusion algorithms are divided into Camera-LiDAR fusion, Camera-Radar fusion, Camera-LiDAR-Radar fusion, and other sensor fusion methods based on the types of sensors involved. Furthermore, the paper identifies five key future research directions in this field, aiming to provide insights for researchers engaged in multimodal fusion-based object detection algorithms and to encourage broader attention to the research and application of multimodal fusion-based object detection.