@inproceedings{dce656300c8541799be93147f8dfebec,
title = "Small-scale intersection scan model for UGV in urban environment",
abstract = "Intersection detection is a critical capacity for an Unmanned Ground Vehicle (UGV) to drive safely in structured urban environment. Large-scale intersections stamped on maps have plenty of features for detection while some unmapped small-scale urban intersections are hard to be identified. In this paper, we propose a novel intersection detection method conducted on the basis of Hidden Markov Model (HMM). This method is based on the intersection scan model to obtain the traversable directions to classify the intersection. The scan model is effective in dealing with both LIDAR and visual data. Combination of the scan model and the HMM can accurately estimate the traversable directions in consideration of both real-time and historic data. Results from simulations and real-world experiments have shown the functionality of the presented approach.",
author = "Yi Yang and Li Hao and Hao Zhu and Lyu Ningyi and Song Wenjie",
note = "Publisher Copyright: {\textcopyright} 2017 American Automatic Control Council (AACC).; 2017 American Control Conference, ACC 2017 ; Conference date: 24-05-2017 Through 26-05-2017",
year = "2017",
month = jun,
day = "29",
doi = "10.23919/ACC.2017.7963605",
language = "English",
series = "Proceedings of the American Control Conference",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "4229--4235",
booktitle = "2017 American Control Conference, ACC 2017",
address = "United States",
}