TWICE Dataset: Digital Twin of Test Scenarios in a Controlled Environment

TL;DR

The TWICE Dataset provides synchronized sensor data from real and simulated test scenarios of autonomous vehicles under adverse weather, facilitating research on safety, perception, and simulation-to-reality transfer.

Contribution

It introduces a comprehensive, multimodal dataset capturing real and simulated test scenarios in adverse weather for autonomous vehicle research.

Findings

Over 2 hours of sensor data collected

Includes real and simulated test scenarios

Enables testing in adverse weather conditions

Abstract

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/