@inproceedings{e7983bcb380c49f1892cf4914ad4566b,
title = "Monocular Camera Ranging based on Vehicle Attitude Estimation",
abstract = "Assisted and autonomous driving technologies are gaining popularity. A key component of autonomous driving technology is affected by the measurement of the distance in front of the vehicle. Distance prediction using monocular cameras is an efficient and low-cost ranging method. There are some limitations to existing monocular camera-based vehicle distance estimation methods. If known object features are used as the calculation standard, the calibration information is easily lost in real-world road conditions. In this paper, we proposed a method for estimating vehicle distances based on vehicle pose estimation and RGB images based on known vehicle metrics without using external references. This method estimates the distance of the vehicle by using the vehicle's characteristics as the calibration value. Good results are obtained after testing on the KITTI dataset.",
keywords = "Mask-RCNN, YoloV5, autonomous driving, measurement of distance, monocular cameras",
author = "Bo Li and Zhi Song and Xiaoliang Guo",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 10th International Conference on Information Systems and Computing Technology, ISCTech 2022 ; Conference date: 28-12-2022 Through 30-12-2022",
year = "2022",
doi = "10.1109/ISCTech58360.2022.00122",
language = "English",
series = "Proceedings - 2022 10th International Conference on Information Systems and Computing Technology, ISCTech 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "742--747",
editor = "Lei Zhang and Lixin Li",
booktitle = "Proceedings - 2022 10th International Conference on Information Systems and Computing Technology, ISCTech 2022",
address = "United States",
}