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

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

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  • Min Wu
  • Tyron Louw
  • Morteza Lahijanian
  • Wenjie Ruan
  • Xiaowei Huang
  • Natasha Merat
  • Marta Kwiatkowska
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Publication date1/09/2019
Host publicationThe 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS'19), Macao, China, Nov 4-8, 2019
Number of pages7
Original languageEnglish

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.

Bibliographic note

©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.