TWICE Dataset: Digital Twin of Test Scenarios in a Controlled
  Environment
Leonardo Novicki Neto Fabio Reway Yuri Poledna Maikol Funk Drechsler Eduardo Parente Ribeiro Werner Huber Christian Icking
Ensuring the safe and reliable operation of autonomous vehicles under adverse
weather remains a significant challenge. To address this, we have developed a
comprehensive dataset composed of sensor data acquired in a real test track and
reproduced in the laboratory for the same test scenarios. The provided dataset
includes camera, radar, LiDAR, inertial measurement unit (IMU), and GPS data
recorded under adverse weather conditions (rainy, night-time, and snowy
conditions). We recorded test scenarios using objects of interest such as car,
cyclist, truck and pedestrian -- some of which are inspired by EURONCAP
(European New Car Assessment Programme). The sensor data generated in the
laboratory is acquired by the execution of simulation-based tests in
hardware-in-the-loop environment with the digital twin of each real test
scenario. The dataset contains more than 2 hours of recording, which totals
more than 280GB of data. Therefore, it is a valuable resource for researchers
in the field of autonomous vehicles to test and improve their algorithms in
adverse weather conditions, as well as explore the simulation-to-reality gap.
The dataset is available for download at: https://twicedataset.github.io/site/