As autonomous driving technology matures towards series production, it is necessary to take a deeper look at various aspects of electrical/electronic (E/E) architectures for autonomous driving.
This paper describes a functional architecture for autonomous driving, along with various considerations that influence such an architecture. The functionality is described at the logical level, without dependence on specific implementation technologies.
Engineering design has been used as the research method, which focuses on creating solutions intended for practical application. The architecture has been refined and applied over a five year period to the construction of protoype autonomous vehicles in three different categories, with both academic and industrial stakeholders.
The architectural components are divided into categories pertaining to (i) perception, (ii) decision and control, and (iii) vehicle platform manipulation. The architecture itself is divided into two layers comprising the vehicle platform and a cognitive driving intelligence. The distribution of components among the architectural layers considers two extremes: one where the vehicle platform is as "dumb" as possible, and the other, where the vehicle platform can be treated as an autonomous system with limited intelligence. We recommend a clean split between the driving intelligence and the vehicle platform. The architecture description includes identification of stakeholder concerns, which are grouped under the business and engineering categories. A comparison with similar architectures is also made, wherein we claim that the presence of explicit components for world modeling, semantic understanding, and vehicle platform abstraction seem unique to our architecture.
The concluding discussion examines the influences of implementation technologies on functional architectures and how an architecture is affected when a human driver is replaced by a computer. The discussion also proposes that reduction and acceleration of testing, verification, and validation processes is the key to incorporating continuous deployment processes.
Elsevier, 2016. Vol. 73, 136-150 p.