SCOPE: A Synthetic Multi-Modal Dataset for Collective Perception Including Physical-Correct Weather Conditions
This dataset addresses the need for comprehensive testing environments for researchers in autonomous driving, though it is incremental as it builds on existing synthetic data efforts.
The authors tackled the lack of datasets for collective perception in autonomous driving by creating SCOPE, a synthetic multi-modal dataset that includes realistic sensor models and physically accurate weather simulations, resulting in 17,600 frames from over 40 diverse scenarios with up to 24 collaborative agents.
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 Tübingen are included. The dataset is available at https://ekut-es.github.io/scope