Looking at Mind Wandering During Driving Through the Windows of PCA and t-SNE

Abstract

Mind wandering is a poorly understood phenomenon that can undermine driving safety. Driving performance measures have been found to be associated with mind wandering (e.g., steering wheel movements, standard deviation of lateral position, and speed variation). However, no one measure can fully describe the driver behavior associated with mind wandering. Therefore, in this paper we explore the effect of mind wandering on nine steering measures with data collected from a study that included nine drivers over two sessions of driving over five days. Participants were periodically probed to report their attentional state–whether they were mind wandering or focusing on the task. We used two dimensionality-reduction techniques—Principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE)—to visualize the dimensions underlying the nine measures. Comparing PCA to t-SNE highlights the benefits of t-SNE in revealing the fine structure that differentiates driving behavior. These visualizations show that a) driver engagement increased during roadway curve segments, and b) mind wandering manifests itself through several types of steering behavior.

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References

Baldwin

C. L.

Roberts

D. M.

Barragan

Lee

J. D.

Lerner

Higgins

J. S.

(2017). Detecting and quantifying mind wandering during simulated driving. Frontiers in Human Neuroscience, 11(August), 406. https://doi.org/10.3389/FNHUM.2017.00406

Bencich

Gamboz

Coluccia

Brandimonte

M. A.

(2014). When the Mind “Flies”: the Effects of Mind-Wandering on Driving. In Proceedings of the Trieste Symposium on Perception and Cognition.

Das

Zhou

Lee

J. D.

(2012). Differentiating alcohol-induced driving behavior using steering wheel signals. IEEE Transactions on Intelligent Transportation Systems, 13(3), 1355–1368.

Galera

Orriols

M’Bailara

Laborey

Contrand

Ribereau-Gayon

… Lagarde

(2012). Mind wandering and driving: Responsibility case-control study. British Medical Journal, 345. https://doi.org/e810510.1136/bmj.e8105

Becic

Lee

Y.-C.

McCarley

J. S.

(2011). Mind Wandering Behind the Wheel: Performance and Oculomotor Correlates. Human Factors: The Journal of the Human Factors and Ergonomics Society, 53(1), 13–21. https://doi.org/10.1177/0018720810391530

Becic

Lee

Y. C.

McCarley

J. S.

(2011). Mind wandering behind the wheel: Performance and oculomotor correlates. Human Factors, 53(1), 13–21. https://doi.org/10.1177/0018720810391530

Hubert

Rousseeuw

P. J.

Vanden Branden

(2005). ROBPCA: A new approach to robust principal component analysis. Technometrics, 47(1), 64–79. https://doi.org/10.1198/004017004000000563

Keogh

Line

Truppel

(2003). Clustering of Time Series Subequences is Meaningless: Implications for Previous and Future Research. Icmd, 8(2), 154–177. https://doi.org/10.1109/ICDM.2003.1250910

Kutila

M. H.

Jokela

Makinen

Viitanen

Markkula

Victor

T. W.

(2007). Driver cognitive distraction detection: feature estimation and implementation. Proceedings of the Institution of Mechanical Engineers Part D-Journal of Automobile Engineering, 221(D9), 1027–1040. https://doi.org/10.1243/09544070jauto332

10.

Langner

Eickhoff

S. B.

(2013). Sustaining attention to simple tasks: A meta-analytic review of the neural mechanisms of vigilant attention. Psychological Bulletin, 139(4), 870–900. https://doi.org/10.1037/a0030694

11.

Liang

Y. L.

Lee

J. D.

(2013). Using a layered algorithm to detect driver cognitive distraction. In Driving Assessment: International driving symposium on human factors in driver assessment, training, and vehicle design. University of Iowa.

12.

Liang

Y. L.

Reyes

M. L.

Lee

J. D.

(2007). Real-time detection of driver cognitive distraction using Support Vector Machines. IEEE Intelligent Transportation Systems, 8(2), 340–350. https://doi.org/10.1109/tits.2007.895298

13.

Maaten

Van Der Hinton

(2008). Visualizing Data using t-SNE. Journal of Machine Learning Research 1, 620(1), 267–84. https://doi.org/10.1007/s10479-011-0841-3.

14.

Nakayama

Futami

Nakamura

Boer

E. R.

(1999) Development of a steering entropy method for evaluating driver workload. Proceedings of JSAE Annual Congress, (724), 5–8. https://doi.org/10.4271/1999-01-0892

15.

Patel

F. N.

(2016). Large high dimensional data handling using data reduction. In International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT). Retrieved from 10.1109/ICEEOT.2016.7754940

16.

Robertson

I. H.

Manly

Andrade

Baddeley

B. T.

Yiend

(1997). “Oops!”: Performance correlates of everyday attentional failures in traumatic brain injured and normal subjects. Neuropsychologia, 35(6), 747–758. https://doi.org/10.1016/S0028-3932(97)00015-8

17.

Smallwood

Schooler

J. W.

(2006). The restless mind. Psychological Bulletin, 132(6), 946–958. https://doi.org/10.1037/0033-2909.132.6.946

18.

Smallwood

Schooler

J. W.

(2015). The Science of Mind Wandering: Empirically Navigating the Stream of Consciousness. Annual Review of Psychology, 66(1), 487–518. https://doi.org/10.1146/annurev-psych-010814-015331

19.

Wang

J. J.

Gong

Y. H.

(2010). Real-time driving danger-level prediction. Engineering Applications of Artificial Intelligence, 23(8), 1247–1254. https://doi.org/10.1016/j.engappai.2010.01.001

20.

Warm

J. S.

Parasuraman

Matthews

(2008). Vigilance Requires Hard Mental Work and Is Stressful. Human Factors, 50(3), 433–441. https://doi.org/10.1518/001872008X312152

21.

Yanko

M. R.

Spalek

T. M.

(2013). Driving with the wandering mind: The effect that mind-wandering has on driving performance. Human Factors: The Journal of the Human Factors and Ergonomics Society, 56(2012), 260–269. https://doi.org/10.1177/0018720813495280