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Multi-modal Sensing for Distracted Driving Mitigation Using Cameras and Crowdsourcing

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Abstract

Driving-related accidents and human casualties are on the rise in the US and around the globe (Brace et al. in Analysis of the literature: the use of mobile phones while driving, 2007). The US government spends more than 10 billion dollars a year to address the aftermath of accidents caused due to distracted driving and driving in dangerous conditions.

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Authors and Affiliations

  1. University of Maryland Baltimore County, Baltimore, MD, USA

    Amol Deshpande, Mahbubur Rahaman, Nilanjan Banerjee & Ryan Robucci

  2. University of Arkansas, Fayetteville, AR, USA

    Christophe Bobda

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  1. Amol Deshpande
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  2. Mahbubur Rahaman
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  3. Nilanjan Banerjee
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  4. Christophe Bobda
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  5. Ryan Robucci
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Correspondence to Amol Deshpande .

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Editors and Affiliations

  1. University of Arkansas, Fayetteville, Arkansas, USA

    Christophe Bobda

  2. Syracuse University, Syracuse, New York, USA

    Senem Velipasalar

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Deshpande, A., Rahaman, M., Banerjee, N., Bobda, C., Robucci, R. (2014). Multi-modal Sensing for Distracted Driving Mitigation Using Cameras and Crowdsourcing. In: Bobda, C., Velipasalar, S. (eds) Distributed Embedded Smart Cameras. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-7705-1_12

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  • DOI: https://doi.org/10.1007/978-1-4614-7705-1_12

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