Cybersecurity has become one of the most crucial challenges in the automotive development lifecycle. The upcoming ISO/SAE 21434 standard provides only a generic framework that is insufficient to derive concrete design methods. This article proposes an actionable cybersecurity development lifecycle model that provides concrete action and work product guidance aligned with the ISO/SAE 21434 and Automotive SPICE® extension for cybersecurity. The model has been inspired by action research in “next” industry practice pilot projects, which ensures that it is actionable. It has been augmented by insights gained from literature research in cybersecurity development for embedded systems. The proposed lifecycle model complements the ISO/SAE 21434 standard and provides the basis for the company‐specific process and practice specifications. It has been validated through the integration of cybersecurity‐related aspects in an electric power steering system. A core characteristic of the model is the central role of threat modeling, vulnerability analyses, and cybersecurity requirements derivation on both system and subsystem levels. Without concrete practice guidelines, the ISO/SAE 21434 is very difficult to understand and apply at this stage. This contribution aims to fill this gap through a model inspired by cutting‐edge embedded cybersecurity practices interpreted for the current and near‐future automotive electronic architectures.

As vehicle driving evolves from human-controlled to autonomous, human–machine interaction ensures intuitive usage as well as the feedback from vehicle occupants to the machine for optimising controls. The feedback also improves understanding of the user satisfaction with the system behaviour, which is crucial for determining user trust and, hence, the acceptance of the new functionalities that aim to improve mobility solutions and increase road safety. Trust and acceptance are potentially the crucial parameters for determining the success of autonomous driving deployment in wider society. Hence, there is a need to define appropriate and measurable parameters to be able to quantify trust and acceptance in a physically safe environment using dependable methods. This study seeks to support technical developments and data gathering with psychology to determine the degree to which humans trust automated driving functionalities. The primary aim is to define if the usage of an advanced driving simulator can improve consumer trust and acceptance of driving automation through tailor-made studies. We also seek to measure significant differences in responses from different demographic groups. The study employs tailor-made driving scenarios to gather feedback on trust, usability and user workload of 55 participants monitoring the vehicle behaviour and environment during the automated drive. Participants’ subjective ratings are gathered before and after the simulator session. Results show a significant increase in trust ensuing the exposure to the driving automation functionalities. We quantify this increase resulting from the usage of the driving simulator. Those less experienced with driving automation show a higher increase in trust and, therefore, profit more from the exercise. This appears to be linked to the demanded participant workload, as we establish a link between workload and trust. The findings provide a noteworthy contribution to quantifying the method of evaluating and ensuring user acceptance of driving automation. It is only through the increase of trust and consequent improvement of user acceptance that the introduction of the driving automation into wider society will be a guaranteed success.

The Industrial Revolution drives the digitization of society and industry, entailing Cyber-Physical Systems (CPSs) that form ecosystems where system owners and third parties share responsibilities within and across industry domains. Such ecosystems demand smart CPSs that continuously align their architecture and governance to the concerns of various stakeholders, including developers, operators, and users. In order to satisfy short- and long-term stakeholder concerns in a continuously evolving operational context, this work proposes self-adaptive software models that promote DevOps for smart CPS. Our architectural approach extends to the embedded system layer and utilizes embedded and interconnected Digital Twins to manage change effectively. Experiments conducted on industrial embedded control units demonstrate the approach’s effectiveness in achieving sub-millisecond real-time closed-loop control of CPS assets and the simultaneous high-fidelity twinning (i.e., monitoring) of asset states. In addition, the experiments show practical support for the adaptation and evolution of CPS through the dynamic reconfiguring and updating of real-time control services and communication links without downtime. The evaluation results conclude that, in particular, the embedded Digital Twins can enhance CPS smartness by providing service-oriented access to CPS data, monitoring, adaptation, and control capabilities. Furthermore, the embedded Digital Twins can facilitate the seamless integration of these capabilities into current and future industrial service ecosystems. At the same time, these capabilities contribute to implementing emerging industrial services such as remote asset monitoring, commissioning, and maintenance.

Cybersecurity has become a crucial challenge in the automotive sector. At the current stage, the framework described by the ISO/SAE 21434 is insufficient to derive concrete methods for the design of secure automotive networked embedded systems on the supplier level. This article describes a case study with actionable steps for designing secure systems and systematically eliciting traceable cybersecurity requirements to address this gap. The case study is aligned with the ISO/SAE 21434 standard and can provide the basis for integrating cybersecurity engineering into company-specific processes and practice specifications.