3D Point Cloud Object Detection for Autonomous Driving Based on Improved PointPillars

Abstract

<div class="section abstract"><div class="htmlview paragraph">Aiming at the problem of low detection accuracy in 3D object detection for autonomous driving, this paper proposes an improved PointPillars framework that enhances feature representation while reducing computational cost. Accurate perception of surrounding vehicles, pedestrians, and obstacles is critical to ensure the safety and reliability of autonomous driving systems, yet the widely used PointPillars model is often constrained by limited global feature extraction and vulnerability to environmental interference, which restricts its effectiveness in complex real-world scenarios. To address these limitations, the backbone network is reconstructed with a lightweight MobileViTv2 module to strengthen global feature capture and robustness, enabling better modeling of long-range dependencies without significantly increasing model complexity. In addition, a dynamic upsampling strategy is introduced to replace the original upsampling module, which not only improves detection performance but also reduces the number of parameters and computational burden. The proposed method is validated on a hybrid dataset composed of the public KITTI benchmark and self-collected driving data, providing a more comprehensive evaluation under diverse conditions. Experimental results demonstrate consistent improvements compared with the original PointPillars, including a 3.74% increase in Average Orientation Similarity (AOS), a 1.02% gain in BEV detection accuracy, and a 2.41% reduction in parameter count, while maintaining real-time inference at 15.2 frames per second. Furthermore, qualitative comparisons show that the improved model exhibits significantly fewer false detections and enhanced robustness to interference. Overall, the proposed approach contributes a practical and efficient 3D object detection framework that achieves higher accuracy and reliability while meeting the real-time requirements for deployment in autonomous driving applications.</div></div>