Machine Learning Based Path Prediction System - Adapting One Model for All Intersections

Kai Qi Huang, Min Te Sun

研究成果: 書貢獻/報告類型會議論文篇章同行評審

2 引文 斯高帕斯(Scopus)

摘要

To reduce the number of accidents, this thesis proposes a vehicle path prediction system to predict the future direction when a vehicle is about to cross an intersection. The GPS sensor is used to collect the dataset of vehicle trajectories at intersections. The trend of vehicle movements are derived from the heading in the trajectories, which is then combined with the vehicle speed to generate training data. In our path prediction algorithm, two ensemble learning algorithms, i.e., Random Forests and AdaBoost, are adopted for model training. The experiment results indicate that the Random Forest algorithm exhibits the best performance, and the Adaboost algorithm performs better than the base learner (i.e., Decision Tree).

原文???core.languages.en_GB???
主出版物標題Proceedings - 2018 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2018
發行者Institute of Electrical and Electronics Engineers Inc.
頁面64-69
頁數6
ISBN(電子)9781728112299
DOIs
出版狀態已出版 - 24 12月 2018
事件2018 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2018 - Taichung, Taiwan
持續時間: 30 11月 20182 12月 2018

出版系列

名字Proceedings - 2018 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2018

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???event.eventtypes.event.conference???2018 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2018
國家/地區Taiwan
城市Taichung
期間30/11/182/12/18

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