  Self-driving cars and autonomous vehicles are revolutionizing the automotive
sector, shaping the future of mobility altogether. Although the integration of
novel technologies such as Artificial Intelligence (AI) and Cloud/Edge
computing provides golden opportunities to improve autonomous driving
applications, there is the need to modernize accordingly the whole prototyping
and deployment cycle of AI components. This paper proposes a novel framework
for developing so-called AI Inference Engines for autonomous driving
applications based on deep learning modules, where training tasks are deployed
elastically over both Cloud and Edge resources, with the purpose of reducing
the required network bandwidth, as well as mitigating privacy issues. Based on
our proposed data driven V-Model, we introduce a simple yet elegant solution
for the AI components development cycle, where prototyping takes place in the
cloud according to the Software-in-the-Loop (SiL) paradigm, while deployment
and evaluation on the target ECUs (Electronic Control Units) is performed as
Hardware-in-the-Loop (HiL) testing. The effectiveness of the proposed framework
is demonstrated using two real-world use-cases of AI inference engines for
autonomous vehicles, that is environment perception and most probable path
prediction.
