REHEARSE: adveRse wEatHEr datAset for sensoRy noiSe modEls

Before an automated vehicle can be commercialised, it requires extensive tests, to prove its safety in the real world. One way to perform safer tests is in simulated environment, due to its comparative lower costs than real world testing. For these tests to be meaningful, the simulation-to-reality gap must be minimal. To minimise this gap, many simulation factors must be considered, one of them is how to accurately include weather in the simulated environment. This work tackles this issue by the creation of a dataset with multiple scenes, across two different test tracks, one in outdoor environment and the other in indoor environment. The selection of sensors to collect data includes RGB camera, FLIR camera, 4D RADAR, MEMS LiDAR and spinning LiDAR. The data are collected in 3 weather conditions, namely rain, fog, and clear weather, and at daytime and nighttime. The chosen targets in the scenes are EuroNCAP validated targets representing Pedestrian, Cyclist, and Car; EuroNCAP targets have RADAR and LiDAR reflections similar to the real road stakeholders. Auxiliary targets such as LiDAR and RADAR reflectors, and camera board, with known properties, are also used. In both facilities, the data are collected with targets at set distances from the sensors. A total of 412 test runs, with 30 s data collection each, has been carried out. The dataset is publicly available in the Open Simulation Interface (OSI) format, and provides a dataset creator, converting rosbag into OSI format. The REHEARSE dataset contributes to the research community as the first weather dataset focused for automated driving system, which includes the ground truth of target position as well as the label of the weather conditions on the level of rain and fog distributions, visibility, and droplet size.