## 1 Introduction  
LiDAR (Light Detection and Ranging) has gained widespread acceptance owing to its capability to capture three-dimensional information on objects regardless of lighting conditions. Consequently, recognizing 3D objects from LiDAR point clouds has attracted significant attention in the field of autonomous driving. Nevertheless, LiDAR point clouds possess unique challenges such as sparsity, disorderliness, and unstructured data, with the sparsity issue posing an even greater hurdle for distant and small objects. This has resulted in the recognition of distant and small objects becoming a noteworthy challenge. Currently, point-based methods [1; 2; 3; 4; 5; 6; 7] and grid-based methods [8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22] are two popular categories of recognition algorithms for point clouds. Point-based methods generally take original point clouds as an input and directly recognize objects from the point cloud. This category maximizes the retention of the original information from the point clouds. However, when dealing with large-scale point cloud data, such methods can potentially consume significant computational resources. The voxelization of point clouds serves as an effective solution to address this challenge. Grid-based methods typically encode point clouds into voxels or pillars and then process them separately using different approaches. Voxel-based methods [8; 9; 10; 11; 12; 13; 14] usually convert the input point cloud data into a voxel space and then use 3D convolution or sparse convolution [9] to extract features from voxels and complete the recognition task. Pillars are a specialized type of voxel that do not take height information into account. Pillar-based methods [15; 16; 17; 18; 19; 20; 21; 22] usually encode point clouds into 2D pillars before projecting the point clouds as a 2D pseudo image. Then, a 2D CNN can be employed to recognize it. However, encoding point clouds into voxels/pillars inevitably results in spatial information loss. Moreover, as point clouds' locations become more distant and objects get smaller, the number of points within an individual voxel decreases, leading to even more severe information loss and recognition accuracy being affected as a consequence. Currently, the prevalent methods typically adopt a single-scale voxel/pillar to encode point clouds, rendering them powerless in addressing the exacerbated issue of spatial information loss at long distances. But using multiple multi-scale pillars can help solve this difficulty. Consequently, this article introduces Adaptive Scale and Correlative Attention PointPillars (ASCA-PointPillars). The cornerstone of ASCA-PointPillars lies in the adaptive scale pillars (ASP) module and the correlative point attention (CPA) module. The ASP module employs various sizes of pillar to encode the point clouds based on the sparsity of the point clouds. The smaller the size of the pillar, the stronger the feature representation ability of each point, resulting in less spatial loss. Sparser point clouds in the distance are then encoded using smaller pillars to mitigate the spatial information loss of distant objects and enable the network to capture richer feature information for these objects. After encoding the point clouds into multi-scale pillars, the CPA module is utilized to enhance the feature association within the pillars, providing richer contextual features. The CPA module incorporates a self-attention mechanism that effectively establishes connections between pieces of contextual information [23]. Consequently, the CPA module not only strengthens the feature association within small pillars representing distant objects, but also enhances the feature correlation among point clouds representing small objects, ultimately improving the recognition performance for both distant and small objects.