Looking-in and looking-out vision for Urban Intelligent Assistance: Estimation of driver attentive state and dynamic surround for safe merging and braking

Tawari, Ashish; Sivaraman, Sayanan; Trivedi, Mohan M.; Shannon, Trevor; Tippelhofer, Mario

doi:10.1109/ivs.2014.6856600

Cited by 68 publications

(30 citation statements)

References 19 publications

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“…It is driven in part by the desire to increase user productivity, safety, and satisfaction by modeling attention. Knowledge of a user's attentional state can be used for a number of purposes, but common applications aim at improving productivity in real world environments such as an office space (Ba and Odobez 2006;Börner et al 2014;Dong et al 2010;Horvitz et al 1999Horvitz et al , 2003Matsumoto et al 2000;Selker 2004;Stiefelhagen et al 2001;Stiefelhagen 2002;Stiefelhagen and Zhu 2002;Vertegaal et al 2006;Voit and Stiefelhagen 2008), monitoring driver inattention to increase safety (Bergasa et al 2006;Dong et al 2011;D'Orazio et al 2007;Fletcher and Zelinsky 2009;Knipling et al 1994;Su et al 2006;Tawari et al 2014;Torkkola et al 2004;Yeo et al 2009), and improving interaction in virtual environments (Barbuceanu et al 2011;Horvitz et al 2003;Muir and Conati 2012;Navalpakkam et al 2012;Roda and Nabeth 2007;Toet 2006;Vertegaal et al 2006;Ugurlu 2014;Yonetani et al 2012).…”

Section: Attentional State Estimationmentioning

confidence: 99%

Automatic gaze-based user-independent detection of mind wandering during computerized reading

Bixler

D’Mello

2015

User Model User-Adap Inter

128

108

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Mind wandering is a ubiquitous phenomenon where attention involuntarily shifts from task-related thoughts to internal task-unrelated thoughts. Mind wandering can have negative effects on performance; hence, intelligent interfaces that detect mind wandering can improve performance by intervening and restoring attention to the current task. We investigated the use of eye gaze and contextual cues to automatically detect mind wandering during reading with a computer interface. Participants were pseudorandomly probed to report mind wandering while an eye tracker recorded their gaze during the reading task. Supervised machine learning techniques detected positive responses to mind wandering probes from eye gaze and context features in a userindependent fashion. Mind wandering was detected with an accuracy of 72 % (expected accuracy by chance was 60 %) when probed at the end of a page and an accuracy of 67 % (chance was 59 %) when probed in the midst of reading a page. Global gaze features (gaze patterns independent of content, such as fixation durations) were more effective than content-specific local gaze features. An analysis of the features revealed diagnostic patterns of eye gaze behavior during mind wandering: (1) certain types of fixations were longer; (2) reading times were longer than expected; (3) more words were skipped; and (4) there was a larger variability in pupil diameter. Finally, the automatically detected mind wandering rate correlated negatively with measures of learning and transfer even after controlling for prior knowledge, thereby providing evidence of predictive validity. Possible improvements to the detector and applications that utilize the detector are discussed.

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Section: Attentional State Estimationmentioning

confidence: 99%

Automatic gaze-based user-independent detection of mind wandering during computerized reading

Bixler

D’Mello

2015

User Model User-Adap Inter

128

108

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“…The driving data is collected from naturalistic, on-road driving using our LISA-A testbed [28]. There are two looking-in cameras facing the driver for capturing the driver's movements, and one lookingout camera for capturing the front road condition.…”

Section: A Datasets and Annotationmentioning

confidence: 99%

Where is the driver looking: Analysis of head, eye and iris for robust gaze zone estimation

Tawari

Chen

Trivedi

2014

17th International IEEE Conference on Intelligent Transportation Systems (ITSC)

Self Cite

100

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Abstract-Driver's gaze direction is a critical information in understanding driver state. In this paper, we present a distributed camera framework to estimate driver's coarse gaze direction using both head and eye cues. Coarse gaze direction is often sufficient in a number of applications, however, the challenge is to estimate gaze direction robustly in naturalistic realworld driving. Towards this end, we propose gaze-surrogate features estimated from eye region via eyelid and iris analysis. We present a novel iris detection computational framework. We are able to extract proposed features robustly and determine driver's gaze zone effectively. We evaluated the proposed system on a dataset, collected from naturalistic on-road driving in urban streets and freeways. A human expert annotated driver's gaze zone ground truth using information from the driver's eyes and the surrounding context. We conducted two experiments to compare the performance of the gaze zone estimation with and without eye cues. The head-alone experiment has a reasonably good result for most of the gaze zones with an overall 79.8% of weighted accuracy. By adding eye cues, the experimental result shows that the overall weighted accuracy is boosted to 94.9%, and all the individual gaze zones have a better true detection rate especially between the adjacent zones. Therefore, our experimental evaluations show efficacy of the proposed features and very promising results for robust gaze zone estimation.

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“…Such cues are studied using an array of range sensors that track vehicles in terms of their position and relative velocity. A commercial object tracking module [20] tracks and re-identifies vehicles across LIDAR and radar systems providing vehicle position and velocity in a consistent global frame of reference. In this work, we only consider trajectory information (longitudinal and lateral position and velocity) of the forward vehicle.…”

Section: Surround Signalsmentioning

confidence: 99%

Vision on Wheels: Looking at Driver, Vehicle, and Surround for On-Road Maneuver Analysis

Ohn-Bar

Tawari

Martin

et al. 2014

2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops

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Looking-in and looking-out vision for Urban Intelligent Assistance: Estimation of driver attentive state and dynamic surround for safe merging and braking

Abstract: This paper details the research, development, and demonstrations of real-world systems intended to assist the driver in urban environments, as part of the Urban Intelligent Assist (UIA) research initiative. A 3-year collaboration between

Cited by 68 publications

References 19 publications

Automatic gaze-based user-independent detection of mind wandering during computerized reading

Automatic gaze-based user-independent detection of mind wandering during computerized reading

Where is the driver looking: Analysis of head, eye and iris for robust gaze zone estimation

Vision on Wheels: Looking at Driver, Vehicle, and Surround for On-Road Maneuver Analysis

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