Embedded systems have become a key innovation driver for industry enabling new product features and services that had not been possible before, thereby reaching an innovation speed even in traditional products that was unthinkable a decade ago. In addition, the flexibility of embedded software and the configurability of embedded systems networks enable continuous change after product deployment. Software updates of vehicles in the shop are becoming as common as software updates of smart phones. Such updates are often used to solve design-time problems, such as late specification changes or software quality issues, but they are increasingly used for function updates and changes, system optimization or adaptation to changed environments or application requirements. Effectively, the ability to change increases product life time, such that an increased innovation speed does no more require wasteful product replacement.
However, while PC or smart phone updates are often automatic and incremental, software updates of cars or other complex and safety critical systems are thoroughly lab-tested using models and prototypes under controlled conditions before they are released to the field. Despite its high cost, lab based integration is so far necessary because the possible side effects of changes in such complex systems are hard to predict and can possibly have life threatening consequences.
Unfortunately, lab integration and test become increasingly difficult. There is a convergence of many applications from different domains (vehicle control, infotainment, traffic control, …) which share an embedded system platform (ESP) consisting of (multicore) computing nodes and a network for node communication. New adaptive, autonomous, or even evolutionary applications change their platform requirements concurrently, thereby raising issues of platform self-adaptation, robustness, security or fault handling. Finally, lab test can become impossible where embedded systems become parts of larger open networks with no single owner and no down times. Examples are traffic control or smart grids. This trend is predicted in many roadmaps, such as the European ARTEMIS SRA or the German NREMS.
CCC Research Approach – Explained by Rolf Ernst