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

Kai Qi Huang, Min Te Sun

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

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).

Original languageEnglish
Title of host publicationProceedings - 2018 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages64-69
Number of pages6
ISBN (Electronic)9781728112299
DOIs
StatePublished - 24 Dec 2018
Event2018 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2018 - Taichung, Taiwan
Duration: 30 Nov 20182 Dec 2018

Publication series

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

Conference

Conference2018 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2018
Country/TerritoryTaiwan
CityTaichung
Period30/11/182/12/18

Keywords

  • General intersections
  • Machine learning
  • Path prediction

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