@inproceedings{588055fd34a64dc9b753bce31c68fc75,
title = "AgriDrive: Data, Benchmarks and Analysis",
abstract = "With the rapid development of autonomous driving technology in the recent years, its application has been extended from urban driving to agricultural scenes.Obstacle detection and scene segmentation are the key parts of autonomous driving technology.Because of the difference between the agricultural and the urban scene, the existing algorithms of obstacle detection and feasible area segmentation may not be directly applied to the agricultural scene.In this paper, we construct a dataset of agricultural scene autonomous driving, which is used for obstacle detection and scene segmentation tasks.Besides, we evaluate the current state of the art deep learning models on this dataset.The result shows this dataset presents challenges for existing algorithms of obstacle detection and scene segmentation.Our dataset can be used for further research and algorithm development.",
keywords = "autonomous driving, obstacle detection, scene segmentation",
author = "Zebei Tong and Hongchang Chen and Ying Li and Dongpu Cao",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 2024 IEEE International Conference on Unmanned Systems, ICUS 2024 ; Conference date: 18-10-2024 Through 20-10-2024",
year = "2024",
doi = "10.1109/ICUS61736.2024.10840115",
language = "English",
series = "Proceedings of 2024 IEEE International Conference on Unmanned Systems, ICUS 2024",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1539--1544",
editor = "Rong Song",
booktitle = "Proceedings of 2024 IEEE International Conference on Unmanned Systems, ICUS 2024",
address = "United States",
}