V2AIX: A Multi-Modal Real-World Dataset of ETSI ITS V2X Messages in Public Road Traffic
<|reference_start|>V2AIX: A Multi-Modal Real-World Dataset of ETSI ITS V2X Messages in Public Road Traffic: Connectivity is a main driver for the ongoing megatrend of automated mobility: future Cooperative Intelligent Transport Systems (C-ITS) will connect road vehicles, traffic signals, roadside infrastructure, and even vulnerable road users, sharing data and compute for safer, more efficient, and more comfortable mobility. In terms of communication technology for realizing such vehicle-to-everything (V2X) communication, the WLAN-based peer-to-peer approach (IEEE 802.11p, ITS-G5 in Europe) competes with C-V2X based on cellular technologies (4G and beyond). Irrespective of the underlying communication standard, common message interfaces are crucial for a common understanding between vehicles, especially from different manufacturers. Targeting this issue, the European Telecommunications Standards Institute (ETSI) has been standardizing V2X message formats such as the Cooperative Awareness Message (CAM). In this work, we present V2AIX, a multi-modal real-world dataset of ETSI ITS messages gathered in public road traffic, the first of its kind. Collected in measurement drives and with stationary infrastructure, we have recorded more than 285 000 V2X messages from more than 2380 vehicles and roadside units in public road traffic. Alongside a first analysis of the dataset, we present a way of integrating ETSI ITS V2X messages into the Robot Operating System (ROS). This enables researchers to not only thoroughly analyze real-world V2X data, but to also study and implement standardized V2X messages in ROS-based automated driving applications. The full dataset is publicly available for non-commercial use at v2aix.ika.rwth-aachen.de.<|reference_end|>
@article{kueppers2024v2aix:,
    title={V2AIX: A Multi-Modal Real-World Dataset of ETSI ITS V2X Messages in
  Public Road Traffic},
    author={Guido Kueppers, Jean-Pierre Busch and Lennart Reiher, Lutz Eckstein},
    journal={arXiv preprint arXiv:2403.10221},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.10221},
    primaryClass={cs.MA}
}