The rapid advancement of autonomous driving and autonomous vehicle technology has intensified the need for robust environment perception. In this work, we leverage automotive LiDAR point cloud data, which are inherently sparse, noisy, and unstructured, posing challenges to conventional classification methods. To address these difficulties, we adopt deep learning-based 3D neural networks and specifically enhance the VoxNet architecture. Through systematic adjustments—such as increasing convolutional kernel size and quantity, incorporating dropout and Batch Normalization layers, and integrating residual connections—we achieve substantial accuracy gains. In particular, our improved VoxNetVR5 model attains 97.92% accuracy, outperforming the baseline VoxNet by around 4–5 percentage points (from ~93% to ~98%). This enhancement is critical for safety-sensitive autonomous driving applications, as even modest accuracy improvements significantly reduce classification errors. Although the increased network complexity and added layers lead to longer training times, the robust performance justifies the computational cost. The proposed VoxNetVR5 architecture and its associated hyperparameter optimizations offer a powerful and efficient solution for processing large-scale, high-dimensional LiDAR data, paving the way for safer and more reliable vehicle perception systems.