Runtime Software Selection for Adaptive Automotive Systems

Abstract

As automotive systems become more intelligent than ever, they need to handle many functional tasks, resulting in more and more software programs running in automotive systems. However, whether a software program should be executed depends on the environmental conditions (surrounding conditions). For example, a deraining algorithm supporting object detection and image recognition should only be executed when it is raining. Supported by the advance of over-the-air (OTA) updates and plug-and-play systems, adaptive automotive systems, where the software programs are updated, activated, and deactivated before driving and during driving, can be realized. In this paper, we consider the upcoming environmental conditions of an automotive system and target the corresponding software selection problem during runtime. We formulate the problem as a set cover problem with timing constraints and then propose a heuristic approach to solve the problem. The approach is very efficient so that it can be applied during runtime, and it is a preliminary step towards the broad realization of adaptive automotive systems.