To address the problem of low detection accuracy for cars and cyclists in the PointPillars three-dimensional (3D) object detection network, an improved PointPillars method based on density clustering and a dual attention mechanism is proposed. This method improves PointPillars in two key areas: 1) introducing a density clustering algorithm in the point cloud processing module to screen and filter out non-clustered points, reduce the influence of irrelevant point cloud information while preserving effective point clouds as much as possible; 2) integrating an attention mechanism in the column feature extraction module of the column feature extraction network. A self-attention mechanism is used to establish connections between points within columns, and a cross-attention mechanism is employed to strengthen connections between columns after column feature extraction, thereby expanding the receptive field and enabling better focus on crucial point cloud information while preserving directional data. The experimental results obtained on the KITTI autonomous driving dataset reveal that PointPillars++ improves the 3D car detection accuracy and the average directional similarity (AOS). Compared to the original network, improvements in detection accuracy were 2.41, 3.48, and 4.87 percentage points, and AOS improved by 2.47, 2.06, and 0.74 percentage points across the simple, moderate and difficult levels, respectively. For 3D cyclist detection, accuracy and AOS increased by 6.26, 1.40, and 1.64 percentage points, and by 6.13, 6.53, and 6.37 percentage points, respectively, across the same difficulty levels.