## 1 Introduction  
With the continuous development of technology, autonomous driving has become one of the most compelling problems in the field of computer vision. The implementation of autonomous driving cannot be achieved without the perception and understanding of the environment [1]. In autonomous driving systems, depth information is crucial to real-time perception and decision processes. Depth information can provide important information about object localization, distance and shape, which can help autonomous driving systems make accurate judgments and decisions. How to obtain accurate depth information has become an important problem in the research of autonomous driving. Currently, LiDAR is a widely used hardware device among the depth information collection methods. LiDAR calculates the distance of an object by emitting a laser beam and measuring its return time. Compared with other sensors, LiDAR has high precision and can provide reliable depth information. However, LiDAR has some limitations, the most prominent of which is the sparsity of the data due to the sampling nature of this method. The point cloud of the LiDAR, which emits 64 beams, are projected into the camera's coordinate system, and the depth information accounts for about 5% of the total pixels [2]. In practical applications, the point cloud data collected using LiDAR are usually limited and do not provide a complete pixel-level depth map. To overcome the sparsity of LiDAR data, depth completion has become an important algorithm. Depth completion is a method that fills a sparse depth map into a dense depth map. It estimates the missing depth information by using a multi-sensor fusion approach. Therefore, depth completion has a wide range of promising applications in autonomous driving systems and has an important impact on the safety of the system. To improve the performance of depth completion, researchers have proposed an impressive array of methods, including sparse invariant convolution [2] and multimodal fusion strategies [3; 4; 5; 6; 7; 8]. However, it is difficult to fill the depth map by relying on only 5% of the depth information. To overcome this challenge, significant progress has been made in recent years in the use of RGB images to guide depth completion. These methods use RGB images as an additional information source to improve the performance of the depth completion. The main methods on RGB images include supervised learning [9; 10; 11; 12; 13; 14; 15; 16; 17], reconstructing stereo images [18; 19; 20; 21; 22], temporal relations [23; 24; 25], and other related sequence relations [26]. In a related study, [PERSON] et al. [27] provided powerful guidance information for depth completion by using RGB information to generate sharp depth boundaries. They improved the performance of the network by using geodesic distance as a constraint. In addition, S2D [28] used a self-supervised deep neural network based on residual blocks to fuse LiDAR data and RGB images in the same feature space, thus effectively improving the prediction accuracy. In addition, DeepLiDAR [29] provides a better guide for depth completion by predicting surface normal vectors using RGB data. These encouraging research works show that depth completion using image guidance has great potential to improve the accuracy of prediction results and has attracted increasing research interest. However, despite the progress made, there are still some challenges and problems to be addressed. For example, existing depth completion methods still have performance limitations in small objects. Therefore, this paper proposes a depth completion method based on adaptive feature fusion, aiming to improve the performance of existing methods, especially for small objects. Inspired by [30], the core idea of the method in this paper is to propose an end-to-end architecture with adaptive spatial feature fusion by encoder-decoder (ASFF-ED) module, and this architecture is able to achieve adaptive adjustment during feature extraction by optimizing pixel-level weights.