Digital Twins in the Automotive Sector: A Summary of Reports from the IoT4CPS Research Project

In December 2020, the Austrian 3-year project IoT4CPS (Trustworthy IoT for CPS) came to its end. The project’s aim was to develop guidelines, methods and tools for secure IoT-based applications in two areas: Connected & Autonomous Vehicles, and Industry 4.0. The focus of services developed in IoT4CPS for the Automotive sector is purely on safety and security of components and applications for connected and autonomous vehicles, along their lifecycle.

The technical Work Packages (WPs) in the project include: WP3 on Safety & Security Design and Methods, WP4 on Security & Verification Analysis, and WP5 on IoT Lifecycle Management. Our focus in this post is on the reports created under WP5. The overall goal of WP5 was to define a conceptual model and develop a prototype for the “Digital Twin” as a data management infrastructure that keeps track of car’s services and components along its entire lifecycle, from the design and production of the connected car to its disposal. Some additional requirements for the design of a Digital Twin in the context of IoT4CPS, include the following:

  • Incorporating relevant IoT data models from Smart Manufacturing and automotive domains;
  • Developing a stakeholder model which can be mapped to a technical infrastructure in order to better address safety and privacy concerns;
  • Understanding boundaries of automotive and industrial ecosystems to neighbouring systems;
  • Interlinking lifecycle data model with security & safety related concepts developed in WP3 and WP4;
  • Specifying the concept of Digital Twins in the Automotive sector and implement feedback mechanisms that capture data from physical systems and “play the data back” in a digital environment.

The list of all public reports created in WP5 includes:

D5.1 Lifecycle Data Models for Smart Automotive and Smart Manufacturing

This report explores the current state of technology progress with the impact on lifecycle data models and methods for data capturing and data management in Smart Manufacturing and Automotive sectors. The report overviews a set of currently popular standards and data models in these two sectors. The identified standards and models serve as a basis for the conceptual model of the Digital Twin prototype, which will be designed in the project to simulate and validate physical processes and their lifecycle phases in several use cases central to IoT4CPS.

The report is organised as follows. Section 1 describes the project motivation to focus on Industry 4.0 technologies, e.g. Internet of Things (IoT), Cyber Physical Systems (CPSs), Smart CPSs, Smart Data and Smart Factory. Section 2 briefly presents two technical reference architectures: the Reference Architecture Model for Industry 4.0 (RAMI 4.0) and the Industrial Internet Reference Architecture (IIRA). Section 3 overviews standards and recommendations for Industry 4.0, including Smart Manufacturing standards related to product development, production system, business process and supply chain lifecycle phases; Smart Automotive standards for Intelligent Transport systems and their functional safety, and the oneM2M open standard for IoT interoperation. It also includes the state-of-the-art in Security, Safety, Lifecycle Process Standards and Best Practices Guidelines (Appendix 1); state-of-the-art related to the Process Management Standards (Appendix 2); state-of-the-art related to Business Process Modelling Languages (Appendix 3). Section 4 presents a hypothetical scenario created to capture lifecycle data models in Smart Automotive industry. The scenario identifies assets in Smart Automotive sector, models assets and simplified lifecycle data models (Appendix 4 and Appendix 5). Section 5 concludes the report.

D5.2 Product Lifecycle Data Management (PLCDM) – Stakeholder Perspectives

This report captures multi-tenancy aspects related to connected cars and emerging standards for data and information exchange in the Automotive industry. The report creates a ground for the research in WP5 and “digital twinning” of the real-world situations and processes related to various lifecycle stages in the Automotive sector. The automotive companies typically do not publicise data and hence, in this report, we look at relevant open-source systems and public datasets, in order to compensate for missing data necessary to run data analyses for the Digital Twin prototype. The presented data model will be further enriched through task T5.4 “Identity, Security and Safety in Product Lifecycle Data Management”, to additionally address identity, security, privacy and safety aspects in the Automotive industry. In addition, the IoT4CPS data model will be semantically enriched by using standard ontologies and recently proposed ontologies for formal
description of car signals and sensors, e.g. VSSo (Vehicle Signal and Attribute Ontology) (Klotz et al., 2018). Such approach will contribute to the cross-interaction of the connected car system with the external stakeholders in the cloud.

D5.3 Cross-Platform Interoperation Model

This report investigates the IoT platform interoperability landscape and its relevance to the Automotive industry. The connected cars and their applications bring along unique opportunities as well as risks. For example, sharing data with many parties in the vehicle ecosystem can improve a variety of driving features, e.g. it can increase driving comfort and safety, improve driving experience, optimise lifecycle processes related to manufacturing and the use of vehicles, contribute to societal targets related to sustainable manufacturing, reduction of fuel consumption, road safety, and more. At the same time, sharing vehicle data can compromise the privacy of various stakeholders, e.g. revealing behavioural patterns. Thus, the objective of this report is to raise awareness of good data practices in the Automotive industry, and identify challenges and possible solutions for ensuring the quality of data interoperation among vehicle infrastructures.

D5.4.1 Identity, Security and Safety in Product Lifecycle Data Management

This report captures identity, security and safety aspects of two automotive scenarios. The data model created in D5.2 “Product Lifecycle Data Management (PLCDM) Stakeholder Perspectives” is extended by integrating the security and safety features based on the use cases (defined in D5.2) and threats which are identified in WP4 “Security Verification and Analysis” of IoT4CPS. The resulting, extended data model ensures the inclusion of both multi-stakeholder and IoT-/ CPS-based assets (and their services) along lifecycle phases of the connected cars, and adds the third cybersecurity perspective to it. With such a model, we aim to enable “digital twinning” of the real-world situations and processes related to lifecycle phases in the Automotive sectors, while taking into consideration a range of automotive safety and security indicators.

D5.4.2 Identity, Security and Safety in Product Lifecycle Data Management

This report is a successor of the D5.4.1 report that captures identity and security aspects of two automotive driving scenarios and extends the model created in D5.2 “Product Lifecycle Data Management (PLCDM) Stakeholder Perspectives” by adding a set of security threats defined in WP4 “Security Verification and Analysis” of IoT4CPS. The extended model ensures the inclusion of both multi-stakeholder and IoT-/ CPS-based assets (and their services) along lifecycle phases of connected car scenarios and adds the cybersecurity perspective to it. In this report, an additional safety
and privacy analysis of Connected and Automotive Mobility (CAM) use cases (defined in D5.4.1) is provided. D5.4.2 also includes the localisation techniques for safety and addresses trust and ethics in CAM applications.

D5.5.1 Lifecycle Data Management Prototype I

This report presents our methodology to design Digital Twins for security and safety validations. It is a summary of several recent IoT4CPS publications in which the authors address various aspects of the design of the Digital Twins for security safety, and privacy aspects for connected car applications.

D5.5.2 Lifecycle Data Management Prototype II

This report documents the second iteration of the Digital Twin prototype that is created to connect “loosely coupled” components (client applications) and share data with third parties, keeping stakeholders in control over subsets of the data by the clients. This concept has been implemented through the creation of a data-streaming platform around the scalable open-source framework “Apache Kafka”, in which each of the “data producer” to “data consumer” relation is defined by a separate communication “topic”. Kafka Streaming Applications, which can be configured with additional filter functions, connect publishers and subscribers with each other in order to exchange the data streams.
The source code of the prototype is released under a permissive open source license and can be found on the
project GitLab instance (https://git-service.ait.ac.at/im-IoT4CPS/WP5-lifecycle-mgmt). To provide readers with the access to the open source code, a public GitHub fork has been created: https://github.com/iotsalzburg/panta_rhei

D5.5.3 Lifecycle Data Management Prototype III

This report documents the final version of the Lifecycle Data Management Prototype in IoT4CPS. The novelty of the proposed approach is that live-data sharing can happen “on thy fly”. Customisation can be achieved by connecting data streams through customisable filtering mechanisms from a single data source or by injecting more complex streaming
applications when joining data from multiple data sources. This concept was implemented as a configurable
platform and is based on “Apache Kafka”. To provide readers with the access to the open source code, a public GitHub fork has been created: https://github.com/iotsalzburg/panta_rhei

The project details and further documentation are available from: https://iot4cps.at/