Modelling Machine Learning Components for Mapping and Scheduling of AUTOSAR Runnables

The race towards autonomous driving provoked a paradigm shift as safety became a critical objective in the development of novel functionalities. The safety-critical part of these functionalities is predominantly realized in software complying to the AUTOSAR standard in which code fragments called runnables are configured at design-time to run according to a certain order and on a certain core. As a key technology that enables autonomous driving, machine learning is expected to play a significant role in automotive applications. Since machine learning algorithms inherently exhibit faults, e.g. a classifier's prediction is wrong with a relatively high rate, to enforce safety, fault tolerance techniques have to be used. Therefore, this paper proposes that this information is systematically used in the automatic configuration of an AUTOSAR system. Not to disrupt the usual software development process, the information is appended to already mapped and scheduled runnables. Then, a heuristic is presented to generate execution alternatives during design-time which are then selected at run-time to skip the intervals reserved for fault tolerance mechanisms in the prevailing case when no fault occurred. This novel idea considerably reduces execution time as demonstrated on real-world engine control software.