Data for Cooperative perception for 3D object detection in driving scenarios using infrastructure sensors

In contrast, the late fusion scheme fuses the independently detected bounding boxes from multiple spatially diverse sensors. We evaluate the performance of both schemes, and their hybrid combination, using a synthetic cooperative dataset created in two complex driving scenarios, a T-junction and a roundabout. The evaluation shows that the early fusion approach outperforms late fusion by a significant margin at the cost of higher communication bandwidth.

The results demonstrate that cooperative perception can recall more than 95% of the objects as opposed to 30% for single-point sensing in the most challenging scenario. To provide practical insights into the deployment of such system, we report how the number of sensors and their configuration impact the detection performance of the system.<br><br>Cooperative 3D Object Detection using Infrastructure Sensors Dataset ==================================================================== Files Description ------------------ File: dataset.tar.xz Description: contains the Tar compressed dataset files used to train and evaluate the model on the T-junction and Roundabout scenarios.

File: saved_models.tar.xz Description: contains the Tar compressed pre-trained pytorch weights for the T-junction and roundabout models. These were the models used for evaluation in the paper. Code Information ----------------- For more information on how to use these files, please read the official code repository at github.com/eduardohenriquearnold/coop-3dod-infra Disclaimer ----------- If you use this dataset, the pre-trained models or our code, please cite our work using @article{arnold_coop3dod, author={Arnold, Eduardo and Dianati, Mehrdad and de Temple, Robert and Fallah, Saber}, journal={IEEE Transactions on Intelligent Transportation Systems}, title={Cooperative Perception for 3D Object Detection in Driving Scenarios Using Infrastructure Sensors}, year={2020}, pages={1-13}, doi={10.1109/TITS.2020.3028424} }