The massive volume of data generated by LiDAR sensors in autonomous vehicles creates a bottleneck for real-time processing and vehicle-to-everything (V2X) transmission. Existing lossless compression methods often force a trade-off: industry standard algorithms (e.g., LASzip) lack adaptability, while deep learning approaches suffer from prohibitive computational costs. This paper proposes LiZIP, a lightweight, near-lossless zero-drift compression framework based on neural predictive coding. By utilizing a compact Multi-Layer Perceptron (MLP) to predict point coordinates from local context, LiZIP efficiently encodes only the sparse residuals. We evaluate LiZIP on the NuScenes and Argoverse datasets, benchmarking against GZip, LASzip, and Google Draco (configured with 24-bit quantization to serve as a high-precision geometric baseline). Results demonstrate that LiZIP consistently achieves superior compression ratios across varying environments. The proposed system achieves a 7.5%-14.8% reduction in file size compared to the industry-standard LASzip and outperforms Google Draco by 8.8%-11.3% across diverse datasets. Furthermore, the system demonstrates generalization capabilities on the unseen Argoverse dataset without retraining. Against the general purpose GZip algorithm, LiZIP achieves a reduction of 38%-48%. This efficiency offers a distinct advantage for bandwidth constrained V2X applications and large scale cloud archival.

翻译：自动驾驶车辆中LiDAR传感器生成的海量数据为实时处理和车联万物（V2X）传输造成了瓶颈。现有无损压缩方法常需做出权衡：行业标准算法（如LASzip）缺乏适应性，而深度学习方法则面临高昂的计算成本。本文提出LiZIP，一种基于神经预测编码的轻量级近无损零漂移压缩框架。通过利用紧凑的多层感知机（MLP）从局部上下文预测点坐标，LiZIP仅对稀疏残差进行高效编码。我们在NuScenes和Argoverse数据集上评估LiZIP的性能，并与GZip、LASzip及Google Draco（采用24位量化配置以作为高精度几何基准）进行对比。结果表明，LiZIP在不同环境中均能实现更优的压缩比。与行业标准LASzip相比，所提系统在文件大小上减少7.5%-14.8%；在不同数据集上，性能优于Google Draco 8.8%-11.3%。此外，该系统在未见过的Argoverse数据集上无需重新训练即展现出泛化能力。相较于通用GZip算法，LiZIP实现了38%-48%的压缩率降低。这一效率为带宽受限的V2X应用和大规模云归档提供了显著优势。