Georg Macher

Davide Bacciu, Siranush Akarmazyan, Eric Armengaud et al.

This paper discusses the perspective of the H2020 TEACHING project on the next generation of autonomous applications running in a distributed and highly heterogeneous environment comprising both virtual and physical resources spanning the edge-cloud continuum. TEACHING puts forward a human-centred vision leveraging the physiological, emotional, and cognitive state of the users as a driver for the adaptation and optimization of the autonomous applications. It does so by building a distributed, embedded and federated learning system complemented by methods and tools to enforce its dependability, security and privacy preservation. The paper discusses the main concepts of the TEACHING approach and singles out the main AI-related research challenges associated with it. Further, we provide a discussion of the design choices for the TEACHING system to tackle the aforementioned challenges

Erik Armengaud, Georg Macher, Alexander Massoner et al.

The open and cooperative nature of Cyber-Physical Systems (CPS) poses new challenges in assuring dependability. The DEIS project (Dependability Engineering Innovation for automotive CPS. This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 732242, see http://www.deis-project.eu) addresses these challenges by developing technologies that form a science of dependable system integration. In the core of these technologies lies the concept of a Digital Dependability Identity (DDI) of a component or system. DDIs are modular, composable, and executable in the field facilitating (a) efficient synthesis of component and system dependability information over the supply chain and (b) effective evaluation of this information in-the-field for safe and secure composition of highly distributed and autonomous CPS. The paper outlines the DDI concept and opportunities for application in four industrial use cases.

Eric Armengaud, Sebastian Frager, Stephen Jones et al.

Increasingly sophisticated function development is taking place with the aim of developing efficient, safe and increasingly Automated Driving Functions. This development is possible with the use of diverse data from sources such as Navigation Systems, eHorizon, on-board sensor data, Vehicle-to-Infrastructure (V2I) and Vehicle-to-Vehicle (V2V) communication. Increasing challenges arise with the dependency on large amounts of real-time data coming from off-board sources. At the core of addressing these challenges lies the concept of a Digital Dependability Identity (DDI) of a component or system. DDIs are modular, composable, and executable components in the field, facilitating: $\bullet$ efficient synthesis of component and system dependability information, $\bullet$ effective evaluation of information for safe and secure composition of highly distributed and autonomous Cyber Physical Systems. In AVL's Connected Powertrain (TM), Automated Driving Functions are tailored to Powertrain Control Strategies that predictively increase energy efficiency according to the powertrain type and its component efficiencies. Simultaneously, the burden on the driver is reduced by optimizing the vehicle velocity, whilst minimizing any journey time penalty.In this work, the development of dependable Automated Driving Functions is exemplified by the Traffic Light Assistant, an adaptive strategy that utilizes predictions of preceding traffic, upcoming road curvature, inclination, speed limits, and especially traffic light signal phase and timing information to increase the energy efficiency in an urban traffic environment. A key aspect of this development is the possibility for seamless and simultaneous development; from office simulation to human-in-the-loop and to real-time tests that include vehicle and powertrain hardware. Driver's acceptance and comfort is rated in an advanced diver simulator mounted on a hexapod, capable of emulating longitudinal and lateral acceleration of a real vehicle. Test results from real-time function validation on a Powertrain Testbed are shown, including real traffic light signal phasing information and traffic flow representation on Graz city roads.

Andrea Höller, Tobias Rauter, Johannes Iber et al.

The ever growing demands of embedded systems to satisfy high computing performance and cost efficiency lead to the trend of using commercial off-the-shelf hardware. However, due to their highly integrated design they are becoming increasingly susceptible to hardware errors (e.g. caused by radiation-induced soft-errors or wear-out effects). Since such faults cannot be fully prevented, systems have to cope with their effects. At the same time there is the trend of multi-core processors in embedded systems. Approaches to achieve fault tolerance by using the multiple cores to establish redundancy have been presented in literature. However, typically only homogeneous redundancy techniques are considered to tolerate soft errors. However, there is a lack of appropriate reaction mechanisms for restoring the system in case of permanent hardware faults. Here, we propose the basic idea of enhancing multi-core redundancy techniques with a cost-efficient automated introduction of diversity in the executed software replicas. Recently, these automated software diversity techniques have attracted attention in the security domain. We propose to use these techniques to recover from permanent hardware faults. This is achieved by adapting the software execution in such a way that permanent faults are mitigated.