The AEIF Data Collection: A Dataset for Infrastructure-Supported Perception Research with Focus on Public Transportation
<|reference_start|>The AEIF Data Collection: A Dataset for Infrastructure-Supported Perception Research with Focus on Public Transportation: This paper we present our vision and ongoing work for a novel dataset designed to advance research into the interoperability of intelligent vehicles and infrastructure, specifically aimed at enhancing cooperative perception and interaction in the realm of public transportation. Unlike conventional datasets centered on ego-vehicle data, this approach encompasses both a stationary sensor tower and a moving vehicle, each equipped with cameras, LiDARs, and GNSS, while the vehicle additionally includes an inertial navigation system. Our setup features comprehensive calibration and time synchronization, ensuring seamless and accurate sensor data fusion crucial for studying complex, dynamic scenes. Emphasizing public transportation, the dataset targets to include scenes like bus station maneuvers and driving on dedicated bus lanes, reflecting the specifics of small public buses. We introduce the open-source ".4mse" file format for the new dataset, accompanied by a research kit. This kit provides tools such as ego-motion compensation or LiDAR-to-camera projection enabling advanced research on intelligent vehicle-infrastructure integration. Our approach does not include annotations; however, we plan to implement automatically generated labels sourced from state-of-the-art public repositories. Several aspects are still up for discussion, and timely feedback from the community would be greatly appreciated. A sneak preview on one data frame will be available at a Google Colab Notebook. Moreover, we will use the related GitHub Repository to collect remarks and suggestions.<|reference_end|>
@article{vosshans2024the,
    title={The AEIF Data Collection: A Dataset for Infrastructure-Supported
  Perception Research with Focus on Public Transportation},
    author={Marcel Vosshans, Alexander Baumann, Matthias Drueppel, Omar Ait-Aider,
  Ralf Woerner, Youcef Mezouar, Thao Dang and Markus Enzweiler},
    journal={arXiv preprint arXiv:2407.08261},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.08261},
    primaryClass={cs.RO}
}