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}.
@article{arxiv.2211.09445,
title = {aiMotive Dataset: A Multimodal Dataset for Robust Autonomous Driving with Long-Range Perception},
author = {Tamás Matuszka and Iván Barton and Ádám Butykai and Péter Hajas and Dávid Kiss and Domonkos Kovács and Sándor Kunsági-Máté and Péter Lengyel and Gábor Németh and Levente Pető and Dezső Ribli and Dávid Szeghy and Szabolcs Vajna and Bálint Varga},
journal= {arXiv preprint arXiv:2211.09445},
year = {2023}
}
Comments
The paper was accepted to ICLR 2023 Workshop Scene Representations for Autonomous Driving