Automated Driving is revolutionizing many of the traditional ways of operation in the automotive industry. The impact on safety engineering of automotive functions is arguably one of the most important changes. There has been a need to re-think the impact of the partial or complete absence of the human driver (in terms of a supervisory entity) in not only newly developed functions but also in the qualification of the use of legacy functions in new contexts. The scope of the variety of scenarios that a vehicle may encounter even within a constrained Operational Design Domain, and the highly dynamic nature of Automated Driving, mean that new methods such as simulation can greatly aid the process of safety engineering. This paper discusses the need for early verification of the Functional Safety Concepts (FSCs), details the information typically available at this stage in the product lifecycle, and proposes a co-simulation platform named AD-EYE designed for exploiting the possibilities in an industrial context by evaluating design decisions and refining Functional Safety Requirements based on a reusable scenario database. Leveraging our prior experiences in developing FSCs for Automated Driving functions, and the preliminary implementation of co-simulation platform, we demonstrate the advantages and identify the limitations of using simulations for refinement and early FSC verification using examples of types of requirements that could benefit from our methodology.

The internet of things (IoT) system integrates heterogeneous systems using centric services to provide a single open solution to process sensor data. During the whole life cycle of IoT systems, developers face the problems of interface management and data interoperability led by increasing complexity. Such problems decrease the efficiency of IoT system development and implementation, such as interface configurations for domain specific systems are difficult if there is not a unified specification. This paper proposed an Open Services for Lifecycle Collaboration (OSLC) approach supporting IoT system development and implementation. The approach integrates domain specific data across the whole lifecycle including development models and sensor data. Moreover, it enables interface management for IoT system development and real-time monitoring for implementations. From the case study, we find an OSLC-based tool, Datalinks, supports data integration and interface management which improves the development efficiency and data interoperability of IoT systems. The integrated data based on OSLC acts the mid-wares of data exchange between physical space and virtual space of IoT system. Moreover, the OSLC-based interfaces are developed based on unified specifications whose reusability is promoted for the future development.

Digitalization refers to enabling, improving, and transforming operations, functions, models, processes, or activities by leveraging digital technologies. Furthermore, digitalization is considered one of the most powerful drivers of innovation with the potential to trigger the next wave of innovation. Today, the importance of digitalization is well-understood in Swedish government agencies and industry. Although there are several initiatives working to actively drive change, one question is key: What is the next step? Data analytics is a promising way to turn information into outcomes, enhance decision-making, make data-driven discoveries, minimize risk, and unearth valuable insights that would otherwise remain hidden. This paper presents survey results on data analytics adoption and usage within Swedish industry, to highlight post-digitalization industry needs. To this end, a questionnaire was designed and distributed. Answers from more than 100 respondents from the manufacturing, technology, engineering, telecommunications, and automotive industries in Sweden were collected and analyzed. The assessment results show that Swedish industry has a high resources readiness score. This suggests that the necessary tools, and human resources are in place. Moreover, its cultural readiness level, which focuses on the acceptance of data-driven decision-making, scores between high and very high. At the same time, the information systems readiness level is in between medium and high, except in the telecommunication domain. However, the organizational readiness level is between medium and low, which shows that the organizations are not structured to enable the adaptation of data analytics and the business impacts of data analytics are not in place yet. These findings suggest that the industry should use the advantages of the current cultural, information systems, and resources readiness capabilities and concentrate efforts on exploring the business impacts of data analytics, ensuring the support from executive managers, and implementing data analytics protocols to improve organizational readiness. Moreover, the industry should consider structural changes in organizations, in addition to systematically initiating proper planning, timing, budgeting, and setting of clear key performance indicators/metrics in order to ameliorate the organizational readiness of data analytics.