Autonomous driving is a popular research area within the computer vision research community. Since autonomous vehicles are highly safety-critical, ensuring robustness is essential for real-world deployment. While several public multimodal datasets are accessible, they mainly comprise two sensor modalities (camera, LiDAR) which are not well suited for adverse weather. In addition, they lack far-range annotations, making it harder to train neural networks that are the base of a highway assistant function of an autonomous vehicle. Therefore, we introduce a multimodal dataset for robust autonomous driving with long-range perception. The dataset consists of 176 scenes with synchronized and calibrated LiDAR, camera, and radar sensors covering a 360-degree field of view. The collected data was captured in highway, urban, and suburban areas during daytime, night, and rain and is annotated with 3D bounding boxes with consistent identifiers across frames. Furthermore, we trained unimodal and multimodal baseline models for 3D object detection. Data are available at \url{https://github.com/aimotive/aimotive_dataset}.

翻译：自主驾驶是计算机视觉研究领域中的一个热门方向。由于自动驾驶汽车对安全性高度敏感，确保其鲁棒性对于实际部署至关重要。尽管已有多个公开的多模态数据集，但它们主要包含两种传感器模态（摄像头、激光雷达），这并不适用于恶劣天气条件。此外，这些数据集缺乏远距离标注，使得训练作为自动驾驶车辆高速公路辅助功能基础的神经网络变得更加困难。因此，我们引入了一个用于鲁棒自主驾驶的长距离感知多模态数据集。该数据集包含176个场景，涵盖同步且标定的激光雷达、摄像头和雷达传感器，覆盖360度视场角。采集的数据涵盖白天、夜晚和雨天条件下的高速公路、城市和郊区场景，并使用跨帧保持一致的3D边界框进行标注。此外，我们还训练了用于3D目标检测的单模态和多模态基线模型。数据可在\url{https://github.com/aimotive/aimotive_dataset}获取。