Gaze-based Intention Anticipation over Driving Manoeuvres in Semi-Autonomous Vehicles

Wu, Min and Louw, Tyron and Lahijanian, Morteza and Ruan, Wenjie and Huang, Xiaowei and Merat, Natasha and Kwiatkowska, Marta (2020) Gaze-based Intention Anticipation over Driving Manoeuvres in Semi-Autonomous Vehicles. In: 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, CHN, pp. 6210-6216. ISBN 9781728140049

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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’ 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.

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ID Code:
136931
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Deposited On:
24 Sep 2019 12:55
Refereed?:
Yes
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Published
Last Modified:
25 Oct 2020 08:26