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/

翻译：确保自动驾驶车辆在恶劣天气下安全可靠运行仍是一项重大挑战。为此，我们开发了一个综合数据集，包含在真实测试跑道中采集并通过实验室复现相同测试场景的传感器数据。该数据集提供了在恶劣天气条件（雨、夜晚和雪天）下记录的摄像头、雷达、激光雷达、惯性测量单元（IMU）和GPS数据。我们使用感兴趣的物体（如汽车、骑行者、卡车和行人）记录了测试场景，其中部分场景借鉴了EURONCAP（欧洲新车评价规程）。实验室生成的传感器数据是通过在硬件在环环境中执行基于仿真的测试获取的，且每个真实测试场景均配有数字孪生。数据集包含超过2小时的记录，总计超过280GB数据。因此，它是自动驾驶领域研究人员在恶劣天气条件下测试和改进算法以及探索仿真与真实差距的宝贵资源。该数据集可在以下链接下载：https://twicedataset.github.io/site/