Predicting driver maneuvers by learning holistic features | IEEE Conference Publication | IEEE Xplore

Predicting driver maneuvers by learning holistic features


Abstract:

In this work, we propose a framework for the recognition and prediction of driver maneuvers by considering holistic cues. With an array of sensors, driver's head, hand, a...Show More

Abstract:

In this work, we propose a framework for the recognition and prediction of driver maneuvers by considering holistic cues. With an array of sensors, driver's head, hand, and foot gestures are being captured in a synchronized manner together with lane, surrounding agents, and vehicle parameters. An emphasis is put on real-time algorithms. The cues are processed and fused using a latent-dynamic discriminative framework. As a case study, driver activity recognition and prediction in overtaking situations is performed using a naturalistic, on-road dataset. A consequence of this work would be in development of more effective driver analysis and assistance systems.
Date of Conference: 08-11 June 2014
Date Added to IEEE Xplore: 17 July 2014
Electronic ISBN:978-1-4799-3638-0
Print ISSN: 1931-0587
Conference Location: Dearborn, MI, USA

References

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