Deep Learning in Automotive Software

Abstract

Deep-learning-based systems are becoming pervasive in automotive software. So, in the automotive software engineering community, the awareness of the need to integrate deep-learning-based development with traditional development approaches is growing, at the technical, methodological, and cultural levels. In particular, data-intensive deep neural network (DNN) training, using ad hoc training data, is pivotal in the development of software for vehicle functions that rely on deep learning. Researchers have devised a development lifecycle for deep-learning-based development and are participating in an initiative, based on Automotive SPICE (Software Process Improvement and Capability Determination), that's promoting the effective adoption of DNN in automotive software. This article is part of a theme issue on Automotive Software.