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Abstract

Tracking driver’s gaze is an integral part of driving monitoring systems, designed to assess the driver’s state. In this work, we report a novel method that creates a ‘vocabulary’ of driver’s gaze patterns during naturalistic driving, across a variety of driving environments. We use an information-theory based unsupervised clustering method to create a vocabulary that contains mutual information between properties of the driving scenarios (e.g., approaching and crossing junctions) and the gaze clusters. Our clustering begins with a dual-staged process of a change of representation. It transforms segments from raw gaze samples into a probabilistic Gaussian Mixture Model (GMM). By using GMM, we were able to represent a gaze sequence of several seconds as a stationary distribution. These GMMs were then grouped in the second stage using information-theory divergence into clusters of segments. The cluster IDs are our ‘vocabulary’. We evaluate our model on a dataset of 15 hr of naturalistic driving, comprising urban, suburban, and highways. The evaluation demonstrates that our clusters preserve relevant driving-related information, and the vocabulary contains meaningful states of driving behaviour.

Keywords

eye gaze clustering information divergence

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References

Ahlström

Kircher

Nyström

Wolfe

(2021). Eye tracking in driver attention research—How gaze data interpretations influence what we learn. Frontiers in Neuroergonomics, 2, Article 713632. https://doi.org/10.3389/fnrgo.2021.713632

Alletto

Palazzi

Solera

Calderara

Cucchiara

(2016). Dr(eye)ve: A dataset for attention-based tasks with applications to autonomous and assisted driving. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (pp. 54–60). https://doi.org/10.1109/CVPRW.2016.15

Cover

T. M.

Thomas

J. A.

(2006). Elements of information theory (2nd ed.). Wiley-Interscience.

Fang

Yan

Qiao

Xue

(2022). DADA: Driver attention prediction in driving accident scenarios. IEEE Transactions on Intelligent Transportation Systems, 23(5), 4959–4971. https://doi.org/10.1109/TITS.2020.3044678

Gomaa

Reyes

Alles

Rupp

Feld

(2020). Studying person-specific pointing and gaze behavior for multimodal referencing of outside objects from a moving vehicle. In Proceedings of the 22nd ACM International Conference on Multimodal Interaction (pp. 402–410). https://doi.org/10.1145/3382507.3418874

Hensch

A.-C.

Beggiato

Krems

(2021). Predicting lane changes by identifying sequence patterns of implicit communication cues. In Proceedings of the International Conference on Applied Human Factors and Ergonomics (pp. 3–20). Springer. https://doi.org/10.1007/978-3-030-80012-3_1

Horiguchi

Y. A.

Sawaragi

Nakanishi

Takimoto

(2016). Extraction and investigation of dominant eye-gaze pattern in train driver’s visual behavior using Markov cluster algorithm. In 2016 Joint 8th International Conference on Soft Computing and Intelligent Systems (SCIS) and 17th International Symposium on Advanced Intelligent Systems (ISIS) (pp. 288–293). IEEE. https://doi.org/10.1109/SCIS-ISIS.2016.0160

Purves

Augustine

G. J.

Fitzpatrick

Hall

W. C.

LaMantia

A.-S.

McNamara

J. O.

Williams

S. M.

(2004). Neuroscience (3rd ed.). Sinauer Associates.

Sai Krishna

Supriya

Vardhan

Rao

. (2022). Vision transformers and YOLOv5 based driver drowsiness detection framework. arXiv Preprint arXiv:2209.01401. https://doi.org/10.48550/arXiv.2209.01401

10.

Schwehr

Willert

(2017). Driver’s gaze prediction in dynamic automotive scenes. In 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC) (pp. 1–6). IEEE. https://doi.org/10.1109/ITSC.2017.8317828

11.

Victor

T. W.

Harbluk

J. L.

Engström

J. A.

(2005). Sensitivity of eye-movement measures to in-vehicle task difficulty. Transportation Research Part F: Traffic Psychology and Behaviour, 8(2), 167–190. https://doi.org/10.1016/j.trf.2005.04.014

12.

Wang

Dong

Liao

Chen

Lee

D.-H.

(2024). Exploring eye movement patterns and driver cognitive state in partially autonomous driving simulation. In extended abstract submitted to the 2024 Conference on Emerging Technologies in Computing, Crete, Greece.

13.

Winter

Happee

Martens

Stanton

(2014). Effects of adaptive cruise control and highly automated driving on workload and situation awareness: A review of the empirical evidence. Transportation Research Part F: Traffic Psychology and Behaviour, 27, 196–217. https://doi.org/10.1016/j.trf.2014.06.016

14.

Zhuang

Kehua