Paper Info
Title
Deep Learning in Automotive: Challenges and Opportunities.
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.
Year
DOI
Venue
2017
10.1007/978-3-319-67383-7_21
Communications in Computer and Information Science
Keywords
Field
DocType
Deep learning,Automotive SPICE,Software development lifecycle,ADAS (advanced driver assistance systems),W model
Functional safety,Manufacturing engineering,Automotive software engineering,Artificial intelligence,Software development process,Deep learning,Engineering,Automotive software,Automotive industry
Conference
Volume
ISSN
Citations 
770
1865-0929
3
PageRank 
References 
Authors
0.41
2
2
Name
Order
Citations
PageRank
Fabio Falcini1223.73
Giuseppe Lami219522.98