@inproceedings{1b35f80183c64880b713cfd2c8dd1836,
title = "Bayesian network-based identification of driver lane-changing intents using eye tracking and vehicle-based data",
abstract = "A Bayesian network decision-making method is proposed by combining driver{\textquoteright}s eye-tracking data and vehicle-based data together to identify driver lane-changing intents. First, experiments are conducted in a driving simulator with eye-tracker device to obtain the data when a subject driver makes lane-changing maneuvers. Second, collected data are analyzed in machine learning method using Bayesian decision-making approach to predict driver{\textquoteright}s lane-changing intents. Last, to show the benefits of our proposed method, comparison experiments are made between the data fusion way and only using eye tracking data or vehicle-based data. The results show that the Bayesian network with data fusion method performs better than using single information to recognize driver{\textquoteright}s lane-changing intents. At the same time, thresholds of Lane-changing probability and vehicle-based data as restricting condition choosing work is discussed in order to select the best identification parameter.",
author = "Li, {X. H.} and M. R{\"o}tting and Wang, {W. S.}",
note = "Publisher Copyright: {\textcopyright} 2017 Taylor & Francis Group, London.; 13th International Symposium on Advanced Vehicle Control, AVEC 2016 ; Conference date: 13-09-2016 Through 16-09-2016",
year = "2017",
language = "English",
isbn = "9781315265285",
series = "Advanced Vehicle Control AVEC{\textquoteright}16: Proceedings of the 13th International Symposium on Advanced Vehicle Control (AVEC'16)",
publisher = "CRC Press/Balkema",
pages = "299--304",
editor = "Johannes Edelmann and Manfred Plochl and Pfeffer, {Peter E.}",
booktitle = "Advanced Vehicle Control AVEC{\textquoteright}16 - Proceedings of the 13th International Symposium on Advanced Vehicle Control AVEC{\textquoteright}16",
}