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To develop the next generation of intelligent LiDARs, we propose a novelframework of parallel LiDARs and construct a hardware prototype in ourexperimental platform, DAWN (Digital Artificial World for Natural). Itemphasizes the tight integration of physical and digital space in LiDARsystems, with networking being one of its supported core features. In thecontext of autonomous driving, V2V (Vehicle-to-Vehicle) technology enablesefficient information sharing between different agents which significantlypromotes the development of LiDAR networks. However, current research operatesunder an ideal situation where all vehicles are equipped with identical LiDAR,ignoring the diversity of LiDAR categories and operating frequencies. In thispaper, we first utilize OpenCDA and RLS (Realistic LiDAR Simulation) toconstruct a novel heterogeneous LiDAR dataset named OPV2V-HPL. Additionally, wepresent HPL-ViT, a pioneering architecture designed for robust feature fusionin heterogeneous and dynamic scenarios. It uses a graph-attention Transformerto extract domain-specific features for each agent, coupled with across-attention mechanism for the final fusion. Extensive experiments onOPV2V-HPL demonstrate that HPL-ViT achieves SOTA (state-of-the-art) performancein all settings and exhibits outstanding generalization capabilities.

HPL-ViT: A Unified Perception Framework for Heterogeneous Parallel LiDARs in V2V