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Deep Learning for Sensor Fusion
Howard, Shaun Michael

2017, Master of Sciences (Engineering), Case Western Reserve University, EECS - Computer and Information Sciences.
The use of multiple sensors in modern day vehicular applications is necessary to provide a complete outlook of surroundings for advanced driver assistance systems (ADAS) and automated driving. The fusion of these sensors provides increased certainty in the recognition, localization and prediction of surroundings. A deep learning-based sensor fusion system is proposed to fuse two independent, multi-modal sensor sources. This system is shown to successfully learn the complex capabilities of an existing state-of-the-art sensor fusion system and generalize well to new sensor fusion datasets. It has high precision and recall with minimal confusion after training on several million examples of labeled multi-modal sensor data. It is robust, has a sustainable training time, and has real-time response capabilities on a deep learning PC with a single NVIDIA GeForce GTX 980Ti graphical processing unit (GPU).
Wyatt Newman, Dr (Committee Chair)
M. Cenk Cavusoglu, Dr (Committee Member)
Michael Lewicki, Dr (Committee Member)
171 p.

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Howard, S. (2017). Deep Learning for Sensor Fusion. (Electronic Thesis or Dissertation). Retrieved from https://etd.ohiolink.edu/

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Howard, Shaun. "Deep Learning for Sensor Fusion." Electronic Thesis or Dissertation. Case Western Reserve University, 2017. OhioLINK Electronic Theses and Dissertations Center. 15 Dec 2017.

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Howard, Shaun "Deep Learning for Sensor Fusion." Electronic Thesis or Dissertation. Case Western Reserve University, 2017. https://etd.ohiolink.edu/

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Full text release has been delayed at the author's request until February 18, 2018