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：自动驾驶是计算机视觉研究领域的热点课题。由于自动驾驶车辆具有极高的安全性要求，确保其鲁棒性对实际部署至关重要。尽管已有多个公开的多模态数据集可用，但它们主要包含两种传感器模态（摄像头、激光雷达），难以适应恶劣天气环境。此外，这些数据集缺乏远距离标注，导致难以训练作为高速公路辅助驾驶功能基础的神经网络。因此，我们提出一个面向远距离感知的鲁棒自动驾驶多模态数据集。该数据集包含176个场景，同步并标定了覆盖360度视场的激光雷达、摄像头和雷达传感器。采集数据涵盖白天、夜间及雨天条件下的高速公路、城区和郊区场景，并采用带有跨帧一致标识符的三维边界框进行标注。此外，我们训练了用于三维目标检测的单模态及多模态基线模型。数据可通过\url{https://github.com/aimotive/aimotive_dataset}获取。