Eugen Ernst

Abstract

The necessity of a reconfigurable software-defined vehicle platform that is no longer sold as a one-off, but can be continuously adapted to dynamic customer requirements during its lifetime, is becoming increasingly clear. Political and economic institutions have become aware of these needs and initiated actions in the context of contested markets with declining sales figures. However, according to our research, existing vehicle architecture concepts and frameworks do not cover the upgradeability, dynamic reconfigurability at runtime and continuous adaptability aspects of vehicle sensors to the necessary extent. Vehicle sensor technology constitutes the fundamental basis for safe decisions of autonomous driving functions, we therefore consider the importance of this topic to be significant. Building upon the existing service-oriented E/E architecture framework ROSMARIN, we want to contribute a concept extension, allowing the seamless integration of intelligent sensors in vehicles. In addition to requirements for intelligent sensor technology in flexible architectures such as Plug-and-Play of sensor hardware, upgradeability through virtualization of sensor software and runtime resilience, our concept furthermore integrates a novel generalized approach for dynamic sensor calibration and fusion. We prove our concept on a physical demonstrator setup with ultrasonic, LiDAR and camera sensors and demonstrate the upgradeability and reconfigurability of sensor hardware and software. In addition, we show the dynamic online fusion of camera and LiDAR data with minimal effort, while also proving the resilience and automatic adaptability of our approach in case of unforeseeable events like sensor failures.

Abstract

The progress of digitalization and Internet of Things enables more and more complex, networked and powerful Cyber-physical Systems (CPS) operating in uncertain environments. This complexity and uncertainty, however, makes it unfeasible to model every aspect in advance. This causes the models to leave their scope and reach their capability limits. Specifically, in safe maintenance planning for highly-automated trucks, this fact causes waste of valuable resource, since maintenance models are often more rule-of-thumb (e.g. operation hours) than precise. In order to counteract this issue, we propose extending the digital twin concept by artificial intelligence such that the models become dynamic and adaptive. Having described the general approach and its architecture, we showcase and evaluate the approach in a highly-automated truck scenario.