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2017 | Supplement | Chapter

Deep Learning in Automotive: Challenges and Opportunities

Authors : Fabio Falcini, Giuseppe Lami

Published in: Software Process Improvement and Capability Determination

Publisher: Springer International Publishing

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Abstract

The interest of the automotive industry in deep-learning-based technology is growing and related applications are going to be pervasively used in the modern automobiles. Automotive is a domain where different standards addressing the software development process apply, as Automotive SPICE and, for functional safety relevant products, ISO 26262. So, in the automotive software engineering community, the awareness of the need to integrate deep-learning-based development with development approaches derived from these standards is growing, at the technical, methodological, and cultural levels. This paper starts from a lifecycle for deep-learning-based development defined by the authors, called W-model, and addresses the issue of the applicability of Automotive SPICE to deep-learning-based developments. A conceptual mapping between Automotive SPICE and the deep learning lifecycles phases is provided in this paper with the aim of highlighting the open issues related to the applicability of automotive software development standards to deep learning.
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Metadata
Title
Deep Learning in Automotive: Challenges and Opportunities
Authors
Fabio Falcini
Giuseppe Lami
Copyright Year
2017
DOI
https://doi.org/10.1007/978-3-319-67383-7_21

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