Driver's gaze prediction in dynamic automotive scenes | IEEE Conference Publication | IEEE Xplore

Driver's gaze prediction in dynamic automotive scenes

Publisher: IEEE

Abstract:

For future Advanced Driver Assistance Systems (ADAS), knowledge about what the driver perceived in his surrounding environment is important to estimate the driver's situa...View more

Abstract:

For future Advanced Driver Assistance Systems (ADAS), knowledge about what the driver perceived in his surrounding environment is important to estimate the driver's situation awareness. This estimate can then be used for example to adapt the systems' warning and intervention strategies according to the driver's needs. We propose a Dynamic Bayesian Network (DBN) which operates in the ground plane at pixel level and simultaneously tracks two gaze motion models to model the driver's focus of attention. We introduce a new time variant transition probability for motion hypotheses of fixations and saccades combining spatial and temporal domain motivated by human gaze motion characteristics. For environment perception, we solely rely on series sensors, while for gaze tracking, a commercial eye tracker is employed. Our system efficiently smooths the measured gaze target point during estimated fixations while preserving the characteristics of saccadic jump behavior. Thereby, the driver's gaze target in the world is effectively extracted.
Date of Conference: 16-19 October 2017
Date Added to IEEE Xplore: 15 March 2018
ISBN Information:
Electronic ISSN: 2153-0017
Publisher: IEEE
Conference Location: Yokohama, Japan

References

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