Abstract

utomotive systems provide a unique opportunity for mobile vision technologies to improve road safety by understanding and monitoring the driver. In this work, we propose a real-time framework for early detection of driver maneuvers. The implications of this study would allow for better behavior prediction, and therefore the development of more efficient advanced driver assistance and warning systems. Cues are extracted from an array of sensors observing the driver (head, hand, and foot), the environment (lane and surrounding vehicles), and the ego-vehicle state (speed, steering angle, etc.). Evaluation is performed on a real-world dataset with overtaking maneuvers, showing promising results. In order to gain better insight into the processes that characterize driver behavior, temporally discriminative cues are studied and visualized.


Original document

The different versions of the original document can be found in:

https://dblp.uni-trier.de/db/conf/cvpr/cvprw2014.html#Ohn-BarTMT14,
https://www.cv-foundation.org/openaccess/content_cvpr_workshops_2014/W03/papers/Ohn-Bar_Vision_on_Wheels_2014_CVPR_paper.pdf,
https://ieeexplore.ieee.org/document/6909978,
https://dx.doi.org/10.1109/CVPRW.2014.33,
http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6909978,
http://dx.doi.org/10.1109/CVPRW.2014.33,
https://www.cv-foundation.org/openaccess/content_cvpr_workshops_2014/W03/html/Ohn-Bar_Vision_on_Wheels_2014_CVPR_paper.html,
https://openaccess.thecvf.com/content_cvpr_workshops_2014/W03/html/Ohn-Bar_Vision_on_Wheels_2014_CVPR_paper.html,
[=citjournalarticle_447086_34 https://www.safetylit.org/citations/index.php?fuseaction=citations.viewdetails&citationIds[]=citjournalarticle_447086_34],
https://academic.microsoft.com/#/detail/1978507866
http://dx.doi.org/10.1109/cvprw.2014.33
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Published on 01/01/2014

Volume 2014, 2014
DOI: 10.1109/cvprw.2014.33
Licence: CC BY-NC-SA license

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