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Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
}
TY - GEN
T1 - Gaze-based Intention Anticipation over Driving Manoeuvres in Semi-Autonomous Vehicles
AU - Wu, Min
AU - Louw, Tyron
AU - Lahijanian, Morteza
AU - Ruan, Wenjie
AU - Huang, Xiaowei
AU - Merat, Natasha
AU - Kwiatkowska, Marta
N1 - ©2019 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
PY - 2020/1/27
Y1 - 2020/1/27
N2 - 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’ timeseries 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 safetycritical 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.
AB - 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’ timeseries 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 safetycritical 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.
U2 - 10.1109/IROS40897.2019.8967779
DO - 10.1109/IROS40897.2019.8967779
M3 - Conference contribution/Paper
SP - 6210
EP - 6216
BT - 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
PB - IEEE
T2 - IEEE/RSJ International Conference on Intelligent Robots and Systems
Y2 - 3 November 2019 through 8 November 2019
ER -