Abstract

This article provides an interdisciplinary perspective on driver monitoring systems by discussing state-of-the-art signal processing solutions in the context of road safety issues identified in human factors research. Recently, the human factors community has made significant progress in understanding driver behaviors and assessed the efficacy of various interventions for unsafe driving practices. In parallel, the signal processing community has had significant advancements in developing signal acquisition and processing methods for driver monitoring systems. This article aims to bridge these efforts and help initiate new collaborations across the two fields. Toward this end, we discuss how vehicle measures, facial/body expressions, and physiological signals can assist in improving driving safety through adaptive interactions with the driver, based on the driver's state and driving environment. Moreover, by highlighting the current human factors research in road safety, we provide insights for building feedback and mitigation technologies, which can act both in real time and postdrive. We provide insights into areas with great potential to improve driver monitoring systems, which have not yet been extensively studied in the literature, such as affect recognition and data fusion. Finally, a high-level discussion is given on the challenges and possible future directions for driver monitoring systems.


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

http://www.scopus.com/inward/record.url?scp=84996537873&partnerID=8YFLogxK
http://dx.doi.org/10.1109/msp.2016.2602379
https://dblp.uni-trier.de/db/journals/spm/spm33.html#AghaeiDLHLPCS16,
https://www.narcis.nl/publication/RecordID/oai%3Apure.tue.nl%3Apublications%2Fa7beee28-6be3-4a5a-80b7-8247a4fc0093,
https://ui.adsabs.harvard.edu/abs/2016ISPM...33...35S/abstract,
https://research.tue.nl/en/publications/smart-driver-monitoring-when-signal-processing-meets-human-factor,
https://doi.org/10.1109/MSP.2016.2602379,
http://ieeexplore.ieee.org/document/7736132,
https://academic.microsoft.com/#/detail/2548005016
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Published on 01/01/2016

Volume 2016, 2016
DOI: 10.1109/msp.2016.2602379
Licence: CC BY-NC-SA license

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