Wu, M, Louw, T orcid.org/0000-0001-6577-6369, Lahijanian, M et al. (4 more authors) (2020) Gaze-based Intention Anticipation over Driving Manoeuvres in Semi-Autonomous Vehicles. In: Proceedings of the 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2019). IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2019), 03-08 Nov 2019, Macau, China. IEEE , pp. 6210-6216. ISBN 978-1-7281-4004-9
Abstract
Anticipating a human collaborator's intention enables safe and efficient interaction between a human and an autonomous system. Specifically, in the context of semiautonomous driving, studies have revealed that correct and timely prediction of the driver's intention needs to be an essential part of Advanced Driver Assistance System (ADAS) design. To this end, we propose a framework that exploits drivers' time-series eye gaze and fixation patterns to anticipate their real-time intention over possible future manoeuvres, enabling a smart and collaborative ADAS that can aid drivers to overcome safety-critical situations. The method models human intention as the latent states of a hidden Markov model and uses probabilistic dynamic time warping distributions to capture the temporal characteristics of the observation patterns of the drivers. The method is evaluated on a data set of 124 experiments from 75 drivers collected in a safety-critical semi-autonomous driving scenario. The results illustrate the efficacy of the framework by correctly anticipating the drivers' intentions about 3 seconds beforehand with over 90% accuracy.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Uploaded in accordance with the publisher's self-archiving policy. |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Environment (Leeds) > Institute for Transport Studies (Leeds) > ITS: Safety and Technology (Leeds) |
Funding Information: | Funder Grant number EU - European Union 610428 |
Depositing User: | Symplectic Publications |
Date Deposited: | 23 Sep 2019 10:07 |
Last Modified: | 27 Mar 2020 15:42 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/IROS40897.2019.8967779 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:151186 |