To develop the next generation of intelligent LiDARs, we propose a novel framework of parallel LiDARs and construct a hardware prototype in our experimental platform, DAWN (Digital Artificial World for Natural). It emphasizes the tight integration of physical and digital space in LiDAR systems, with networking being one of its supported core features. In the context of autonomous driving, V2V (Vehicle-to-Vehicle) technology enables efficient information sharing between different agents which significantly promotes the development of LiDAR networks. However, current research operates under an ideal situation where all vehicles are equipped with identical LiDAR, ignoring the diversity of LiDAR categories and operating frequencies. In this paper, we first utilize OpenCDA and RLS (Realistic LiDAR Simulation) to construct a novel heterogeneous LiDAR dataset named OPV2V-HPL. Additionally, we present HPL-ViT, a pioneering architecture designed for robust feature fusion in heterogeneous and dynamic scenarios. It uses a graph-attention Transformer to extract domain-specific features for each agent, coupled with a cross-attention mechanism for the final fusion. Extensive experiments on OPV2V-HPL demonstrate that HPL-ViT achieves SOTA (state-of-the-art) performance in all settings and exhibits outstanding generalization capabilities.

翻译：为发展下一代智能激光雷达，本文提出了一种并行激光雷达新框架，并在实验平台DAWN（数字自然世界）上构建了硬件原型。该框架强调激光雷达系统中物理空间与数字空间的紧密集成，其中网络化是其支持的核心特性之一。在自动驾驶背景下，V2V（车车通信）技术实现了不同智能体间的高效信息共享，显著推动了激光雷达网络的发展。然而，当前研究均在理想场景下开展——所有车辆配备相同激光雷达，忽略了激光雷达类别与工作频率的多样性。本文首先利用OpenCDA和RLS（真实激光雷达仿真）构建了名为OPV2V-HPL的新型异构激光雷达数据集。此外，我们提出了HPL-ViT，这是一种专为异构动态场景中鲁棒特征融合设计的先驱性架构。该架构采用图注意力Transformer为每个智能体提取领域特定特征，并辅以交叉注意力机制实现最终融合。在OPV2V-HPL上的大量实验表明，HPL-ViT在所有设置下均达到了SOTA（最先进）性能，且展现出卓越的泛化能力。