Infineon Technologies AG

Infineon’s contributions cover the following tasks: Model-based validation and automation of the insertion of HW-based safety measures into automotive micro-controller units. A built-in-vehicle monitoring device which observes HW and SW on all ECS levels to improve the mobility stack of Autonomous Vehicles and to calculate residual risk for the remaining life of the electronic components

BUT will provide expertise in diagnostics of powertrain and perception systems as well as experience with design of fail-safe and fail-operational systems

IFAT will act as the main partner and contributor in the  HW (semiconductor) domain and support all related tasks to design, development and functionality of semiconductors (mainly sensors and the related interfaces) needed for the project goals. IFAT will act as a Leader of Supply Chain 1 Failure modes, fault detection and Residual risk of perception systems.

The IMA role is to work on analysis of related standards for embedded and controlled software. Requirements collection on test methodology implementation for electronic control units. Requirements on tests design, tests cycle management, test tool and integration tests. Requirements definition on validation and evaluation framework.

NXTech will develop the requirements for the safety validation of connected automated road vehicles at the V2X communication electronic sub-system and system level considering various situations analysis (e.g. weather analysis, EMC disturbance, buildings/obstacles disturbance, bad reflection, location signal availability, etc.). Ensuring and validating the reliability of sensors, sub-systems and systems is a critical aspect for safety of the automated road vehicles.

NXP will work on methods to achieve fail-prediction and fail-operational performance on component- and system-level during the lifetime , by on-device and inter-device lifetime testing and monitoring, focusing on in-vehicle networking and vehicle connectivity. The on-chip/on-device and inter-chip/inter-device detection methods, fault injection methods and aging-monitoring and -prediction methods.

TracSense will provide the means and knowledge to obtain and interpret information on road surface conditions. This is a unique ability that contributes to the overall system performance. Further down the supply chain the provided data will be interpreted in order to reduce uncertainty, by combining the information with other sensor readings through advanced sensor fusion methods. There are a large number of residual risks involved with current self-driving technology. Quantifying the risk of pending road surface conditions is a piece of a large and complex puzzle.

SBA has developed a deep expertise in combinatorial testing for software and systems security, with already proven results in kernel software, security protocols, web and cryptographic applications. We aim to apply this knowledge to automotive security testing and facilitate the collaboration with US National Institute of Standards of Technology (NIST) within this project.

SINTEF will lead work package WP5 and supply chain SC3. SINTEF will have a key role in the integration of the residual risk framework, as well as the work on failure modes, fault detection and residual risk for safety and security in the connectivity systems.

Our main work is usually performed under WP7 related dissemination, exploitation and standardization activities where TeraGlobus work towards Exploitation route and dissemination plan: TeraGlobus will use its expertise in technology transfer and commercialization to ascertain appropriate markets, partners / investors, and distribution channels for the results of the technical investigation of developed technologies.

TUG bring in its expertise in fault detection and localization based on models and verification and validation. TUG will work in SC2 providing a framework for diagnosis and supporting research on providing an appropriate verification and validation methodology. TUG will also provide research activities in the context of residual risk prediction, and organizational tasks. 

Provide access to an existing Living Lab to test synchronized mobility concepts in a complex real-world urban/suburban environment.

VIF has positioned itself as an independent, international platform for research and development in the automotive industry. It addresses the gap between academic research and the needs of industrial R&D departments.

The ILK contribution aims at the validation of automated driving functionalities that are implemented according to the AI paradigm (SAE Level 4-5). In this case, the attention is focused on application of supervised machine learning algorithms for inductive identification of the system control software from learning data describing the intended behaviour of an automated vehicle under real driving conditions. The outcome of such learning algorithms in form of e.g. neural networks, rule based classifiers or support vector machines could be validated using standardised techniques yielding quantitative validation results with traceable statistical confidence.

The main role of the CONVECS project team of Inria in the project will be related to the formal modelling and validation of safety-critical software and protocols at an appropriate abstraction level.

HUA will leverage on existing assets related to non-causal reasoning and to cognitive management and decision making algorithms and will apply this to propose a residual architecture encompassing state of the art methods and tools for realizing all concepts and visions related to the ECA2030 vision.

Participate in WP3, work on digital twin technology. Development of novel digital twinning methodology which will be demonstrated in collaboration with an industrial partner (NXP).

Volkswagen is interested in the application of new developments in all automotive areas not only to meet all relevant technical and legal requirements but also to satisfy customer demands on a consistently high level. This is the reason why Volkswagen follows a model based system engineering approach. Volkswagen is interested in the development of continuous simulation methods for HW and SW in the development process.