Identification and analysis of driver postures for in-vehicle driving activities and secondary tasks recognition

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dc.contributor.authorXing, Yang
dc.contributor.authorLv, Chen
dc.contributor.authorZhang, Zhaozhong
dc.contributor.authorWang, Huaji
dc.contributor.authorNa, Xiaoxiang
dc.contributor.authorCao, Dongpu
dc.contributor.authorVelenis, Efstathios
dc.contributor.authorWang, Fei-Yue
dc.date.accessioned2018-03-26T15:03:28Z
dc.date.available2018-03-26T15:03:28Z
dc.date.issued2018-12-25
dc.description.abstractDriver decisions and behaviors regarding the surrounding traffic are critical to traffic safety. It is important for an intelligent vehicle to understand driver behavior and assist in driving tasks according to their status. In this paper, the consumer range camera Kinect is used to monitor drivers and identify driving tasks in a real vehicle. Specifically, seven common tasks performed by multiple drivers during driving are identified in this paper. The tasks include normal driving, left-, right-, and rear-mirror checking, mobile phone answering, texting using a mobile phone with one or both hands, and the setup of in-vehicle video devices. The first four tasks are considered safe driving tasks, while the other three tasks are regarded as dangerous and distracting tasks. The driver behavior signals collected from the Kinect consist of a color and depth image of the driver inside the vehicle cabin. In addition, 3-D head rotation angles and the upper body (hand and arm at both sides) joint positions are recorded. Then, the importance of these features for behavior recognition is evaluated using random forests and maximal information coefficient methods. Next, a feedforward neural network (FFNN) is used to identify the seven tasks. Finally, the model performance for task recognition is evaluated with different features (body only, head only, and combined). The final detection result for the seven driving tasks among five participants achieved an average of greater than 80% accuracy, and the FFNN tasks detector is proved to be an efficient model that can be implemented for real-time driver distraction and dangerous behavior recognition.en_UK
dc.identifier.citationYang Xing, Chen Lv, Zhaozhong Zhang et al., Identification and analysis of driver postures for in-vehicle driving activities and secondary tasks recognition. IEEE Transactions on Computational Social Systems, Volume: 5, Issue: 1, March 2018, pp95-108en_UK
dc.identifier.issn2329-924X
dc.identifier.urihttp://dx.doi.org/10.1109/TCSS.2017.2766884
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/13119
dc.language.isoenen_UK
dc.publisherIEEEen_UK
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectDriver behavioren_UK
dc.subjectdriver distractionen_UK
dc.subjectfeedforward neural network (FFNN)en_UK
dc.subjectKinecten_UK
dc.subjectrandom forest (RF)en_UK
dc.titleIdentification and analysis of driver postures for in-vehicle driving activities and secondary tasks recognitionen_UK
dc.typeArticleen_UK

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