The European Green Deal defines 4 key elements for a sustainable mobility and automotive industry, namely: climate neutrality, zero pollution Europe, sustainable transport, and the transition to a circular economy. Digital technologies are a significant enabler for attaining the sustainability goals in mobility and transportation. The EC is taking initiatives to ensure that digital technologies such as AI, 5G, IoT and cloud/edge computing can accelerate the transition of the automotive industry to electrical, autonomous, connected, and shared vehicles. The current COVID-19 situation accelerates this trend.
The AI4CSM project will develop advanced electronic components and systems (ECS) and architectures for future massmarket
ECAS vehicles. This fuels the digital transformation in the automotive sector to support the mobility trends and accelerate the transition towards a sustainable green and digital economy. Having assembled some of Europe’s best partners from industry, research and academia, AI4CSM will deliver key innovations in technical areas including: sensor fusion and perception platforms; efficient propulsion and energy modules; advanced connectivity for cooperative mobility applications; vehicle/edge/cloud computing integration concepts; new digital platforms for efficient and federated computing; and intelligent components based on trustworthy AI techniques and methods. ECAS vehicles enabled by embedded
intelligence and functional integration for future mobility, becomes the pivotal factor for the automotive sector to address the Green Deal principles.
AI4CSM consists of 8 collaborative R&D clusters, gathering 41 partners from 10 countries. AI4CSM will reinforce user acceptance and affordability by convenience and services for the major transition to a diverse mobility. AI4CSM addresses the increasing demand of mobility, supporting future traffic concepts and strengthen the European automotive manufacturing base as a global industry leader.
In the area of digital transformation, when Flexibility in manufacturing of complex products becomes the key competitive advantage, Artificial Intelligence (AI) is the accepted method to drive the digitalization for the transformation of the industry and their industrial prod-ucts. These products with highest complexity based on multi-dimensional requirements need flexible and adaptive manufacturing lines as well as flexible com-ponents. One of the examples is the automotive indus-try and the products based on high semiconductor con-tent for functional integration, such as highly auto-mated cars, aircrafts, and also the related industrial and manufacturing itself. This change will enable new in-novative industrial and manufacturing models. New process management approaches emerge with the use of data science as a core strategy in the organization and management of these networking manufacturing sites. With deterministic data, the classical approach performs best. However, when only empirical data are available, AI be-comes the option of choice, especially in networking manufacturing sites which are typical in the semiconductor manufacturing and the novel ISLAND approach of the automotive industry away from the linear production with less potential for further flexibility and faster response times.
Independent validation is fundamental to emphasise the capability and safety of any solution in the electric, connected and automated (ECA) vehicles space. It is vital that appropriate and audited testing takes place in a controlled environment before any deployment takes place. As the software and hardware components come from multiple vendors and integrate in numerous ways, the various levels of validation required must be fully understood and integration with primary and secondary parts must be considered.
The key targets of ArchitectECA2030 are
- the robust mission-validated traceable design of electronic components and systems (ECS)
- the quantification of an accepted residual risk of ECS for ECA vehicles to enable type approval, and
- an increased end-user acceptance due to more reliable and robust ECS.
The proposed methods include automatic built-in safety measures in the electronic circuit design, accelerated testing, residual risk quantification, virtual validation, and multi-physical and stochastic simulations.
The project will implement a unique in-vehicle monitoring device able to measure the health status and degradation of the functional electronics empowering model-based safety prediction, fault diagnosis, and anomaly detection. A validation framework comprised of harmonized methods and tools able to handle quantification of residual risks using data different sources (e.g. monitoring devices, sensor/actuators, fleet observators) is provided to ultimately design safe, secure, and reliable ECA vehicles with a well-defined, quantified, and acceptable residual risk across all ECS levels.
The project brings together stakeholders from ECS industry, standardization and certification bodies, test field operators, insurance companies, and academia closely interacting with ECSEL lighthouse initiative Mobility.E to align and influence emerging standards and validation procedures for ECA vehicles.
In order to strengthen European aviation industry for the future and to increase its competitiveness the European Commission released its vision for aviation Flightpath 2050 in 2011. Among other goals, it aims at the reduction of CO2 emissions by 75 % compared to 2000. In order to achieve this goal the efficiency of modern aero-engines has to be improved considerably, whereas artificial intelligence (AI) and digitalization will play a key role (BMK, 2020).
The Institute for Thermal Turbomachinery and Machine Dynamics at Graz University of Technology has been investigating the aerodynamics of intermediate turbine ducts, a key component of modern aero-engines, for many years. This research provides the institute with a large and well evaluated data basis. It shall be used for AI application in the project ARIADNE. Together with an informatics institute and two Austrian SMEs following goals shall be pursued to provide tools for the optimization of future intermediate turbine ducts in aero-engines:
• Setup of a data bank of the aeronautics of intermediate turbine ducts, based on measurements and simulation of different designs at various inflow conditions. The structure of the data bank shall allow a fast and efficient utilization for AI application.
• Development of methods for data reduction for efficient AI application based on POD methods and Machine Learning
• Development of a method for the fast flow prediction of new designs observing the physics of fluid mechanics
• Development of a tool for the evaluation of measurements in turbine ducts in order to find possible sensor errors
• Development of a tool for the evaluation of flow simulations of turbine ducts in order to find possible model errors or computational mesh problems
• Application of the developed tools to obtain innovative knowledge of principles in the flow of intermediate turbine ducts
• Finally, the developed tools shall be combined with an optimizer with the goal of fast and efficient design optimization, much faster than with flow simulation based optimizing methods
The Quality Assurance Methodologies for autonomous Cyber-Physical Systems (QAMCAS) aims at methods for enabling substantial quality improvements of interacting communicating systems that interact with humans and the physical environment. QAMCAS is intended to carry out research improving quality assurance methods during development together with methods that assure quality criteria like safety as well as reliability and also robustness during operation. The latter deals with fail-operational methodologies based on artifacts obtained during development and also measurements gained from previous similar systems during operation. In QAMCAS we treat quality assurance from a holistic point of view investigating methods to be used at development time as well as methods to be applied during operation of the cyber-physical system. To solve the challenges corresponding to the holistic view, we suggest to integrate testing methodologies like combinatorial testing and model-based testing and to combine them with machine learning approaches for model and test data generation. Furthermore, we carry out research for transferring development artifacts like models to fail-operational systems that follow the model-based reasoning paradigm. For this purpose, we have to work on smart monitoring systems that are capable of identifying failures and triggering fault localization and repair procedures for obtaining truly fail-operational systems. Although, the main ingredients are available, their integration is challenging and requires several research issues to be solved. In this proposal we discuss these issues in detail. In order to show that the proposed methods and techniques contribute to quality assurance of cyberphysical systems, we carry out the development of prototypical implementations that are further on used for providing an experimental evaluation in the context of autonomous driving and other mobile and autonomous systems.
Spreadsheets are omnipresent in organizations. They are used for a variety of purposes and in many cases, calculations in spreadsheets directly serve as a basis for reporting and for subsequent managerial decision making. One reason for the widespread use of spreadsheets is that they can be developed (“programmed”) by end users, e.g., an employee in the controlling department. Being able to develop one’s own data aggregation and decision support tools increases the flexibility for the end users. However, one potential problem with spreadsheets is that they are typically not subject to quality assurance (QA) processes that are common in traditional
software development, like code inspections or systematic testing. In many cases, spreadsheet developers also lack the awareness regarding the importance of software testing. Moreover, today’s spreadsheet environments provide only limited functionality for fault avoidance, detection, and removal.
As a result, faults are not uncommon in spreadsheets and numerous cases have been reported where such faults led to substantial financial losses for companies1 or to miscalculations in scientific investigations, for a prominent example in the field of economics.
Due to the risks that can arise from faulty spreadsheets, researchers have proposed a variety of approaches to provide better tool support for spreadsheet developers. These techniques range from advanced spreadsheet visualizations, over algorithmic spreadsheet testing and debugging methods, to solutions that aim at the automated repair of erroneous spreadsheets.
The proposed project continues these lines of research and specifically focuses on improved debugging support for spreadsheets. While existing work in that area often focused on algorithmic fault localization, recent research indicates that to be truly helpful for users, debuggers should offer more functionality than only providing ranked lists of fault candidates. In this project, we will therefore explore novel mechanisms for interactive spreadsheet debugging, which, for example, proactively guide the user to the true location of the fault. From a methodological perspective, one key aspect of our joint research project is that we follow a dual approach. Besides traditional computational experiments that, e.g., compare the fault ranking performance and the computation times of different techniques, we plan to run different controlled user studies, where study participants solve debugging tasks with the help of the tools that will be developed in the project.