  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.
