SCOPE: A Synthetic Multi-Modal Dataset for Collective Perception Including Physical-Correct Weather Conditions
<|reference_start|>SCOPE: A Synthetic Multi-Modal Dataset for Collective Perception Including Physical-Correct Weather Conditions: Collective perception has received considerable attention as a promising approach to overcome occlusions and limited sensing ranges of vehicle-local perception in autonomous driving. In order to develop and test novel collective perception technologies, appropriate datasets are required. These datasets must include not only different environmental conditions, as they strongly influence the perception capabilities, but also a wide range of scenarios with different road users as well as realistic sensor models. Therefore, we propose the Synthetic COllective PErception (SCOPE) dataset. SCOPE is the first synthetic multi-modal dataset that incorporates realistic camera and LiDAR models as well as parameterized and physically accurate weather simulations for both sensor types. The dataset contains 17,600 frames from over 40 diverse scenarios with up to 24 collaborative agents, infrastructure sensors, and passive traffic, including cyclists and pedestrians. In addition, recordings from two novel digital-twin maps from Karlsruhe and T\"ubingen are included. The dataset is available at https://ekut-es.github.io/scope<|reference_end|>
@article{gamerdinger2024scope:,
    title={SCOPE: A Synthetic Multi-Modal Dataset for Collective Perception
  Including Physical-Correct Weather Conditions},
    author={J"org Gamerdinger, Sven Teufel, Patrick Schulz, Stephan Amann,
  Jan-Patrick Kirchner, Oliver Bringmann},
    journal={arXiv preprint arXiv:2408.03065},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.03065},
    primaryClass={cs.CV}
}