V2X-Sim: Multi-Agent Collaborative Perception Dataset and Benchmark for Autonomous Driving

By Yiming Li, Dekun Ma, Ziyan An, Zixun Wang, Yiqi Zhong, Siheng Chen, Chen Feng. IEEE Robotics and Automation Letters, 2022

374 citations recorded by the source corpus.

Vehicle-to-everything (V2X) communication techniques enable the collaboration between vehicles and many other entities in the neighboring environment, which could fundamentally improve the perception system for autonomous driving. However, the lack of a public dataset significantly restricts the research progress of collaborative perception. To fill this gap, we present V2X-Sim, a comprehensive simulated multi-agent perception dataset for V2X-aided autonomous driving. V2X-Sim provides: (1) multi-agent sensor recordings from the road-side unit (RSU) and multiple vehicles that enable collaborative perception, (2) multi-modality sensor streams that facilitate multi-modality perception, and (3) diverse ground truths that support various perception tasks. Meanwhile, we build an open-source testbed and provide a benchmark for the state-of-the-art collaborative perception algorithms on three tasks, including detection, tracking and segmentation. V2X-Sim seeks to stimulate collaborative perception research for autonomous driving before realistic datasets become widely available.

Subfields: Automotive Engineering, Computer Vision and Pattern Recognition, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/c6dfb08c45000ad2d0017d2ce560187164538749

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Citations in the corpus

V2X-ViT: Vehicle-to-Everything Cooperative Perception with Vision Transformer (2022, background): “V2X-Sim [24] is an open V2X simulated dataset but does not simulate noisy settings and only contains a single road type.”