TWICE Dataset: Digital Twin of Test Scenarios in a Controlled Environment
This provides a valuable resource for autonomous vehicle researchers to test algorithms under adverse conditions, though it is incremental as it focuses on dataset creation rather than new methods.
The researchers tackled the challenge of testing autonomous vehicles in adverse weather by creating a dataset with over 2 hours of sensor data from real test tracks and lab simulations, totaling more than 280GB, to help improve algorithms and study simulation-to-reality gaps.
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/