Sensor fusion to connect gaze fixation with dynamic driving context for driver attention management-Reference-Cited by-同舟云学术

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Sensor fusion to connect gaze fixation with dynamic driving context for driver attention management

Published:2024-11 Issue: Volume:107 Page:578-588
ISSN:1369-8478
Container-title:Transportation Research Part F: Traffic Psychology and Behaviour
language:en
Short-container-title:Transportation Research Part F: Traffic Psychology and Behaviour

Author:

Yang Shiyan^ORCID,Wilson Kyle M.,Shiferaw Brook,Roady Trey,Kuo Jonny,Lenné Michael G.

Funder

ACT Government

Publisher

Elsevier BV

Reference55 articles.

1. A gaze-based driver distraction warning system and its effect on visual behavior;Ahlström;IEEE Transactions on Intelligent Transportation Systems,2013

2. Eye tracking in driver attention research: How gaze data interpretations influence what we learn;Ahlström;Frontiers in Neuroergonomics,2021

3. Sampling frequency and eye-tracking measures: How speed affects durations, latencies, and more;Andersson;Journal of Eye Movement Research,2010

4. Towards a context-dependent multi-buffer driver distraction detection algorithm;Ahlström;IEEE Transactions on Intelligent Transportation Systems,2021

5. Bürkner, P. C. (2017). Advanced Bayesian multilevel modeling with the R package brms. arXiv preprint arXiv:1705.11123.

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