Automated driving systems (ADS) have significant potential to make mobility safer and more efficient as they evolve and integrate new technology. These advances continuously change the operational boundaries where ADS can operate safely as specified by the operational design domain (ODD). Nevertheless, research into ADS and ODD is still in its infancy, so that long-term concepts that cover the entire product life cycle are not yet available. Current research focuses on the initial development of ADS and ODD. A more comprehensive perspective, particularly regarding the long-term evolution of an ODD during operation and throughout the life cycle of an ADS, remains largely unaddressed. This paper describes an overview of the life cycle of an ADS and its corresponding ODD that consists of initial development, ongoing versioning, and end-of-life management. Furthermore, the paper examines how the definition and implementation of an ODD, as an essential asset for ADS development, influence the phases identified in the ADS life cycle to ensure alignment with the requirements of the stakeholders.

Developing an automated driving system (ADS) involves collaboration between various stakeholders. To support this process, the concept of operational design domain (ODD) has emerged. Nonetheless, stakeholders require variable levels of information from an ODD. A thorough investigation has identified eight main stakeholder categories. Furthermore, a stakeholder analysis is used to assess their expectations, interests, and influence. These findings briefly summarise all necessary ODD engineering requirements and deliverables for all ODD stakeholders.

Autonomous vehicles are complex distributed systems consisting of multiple software applications and computing nodes. Determining the assignment between these software applications and computing nodes is known as the application-placement problem. The input of this problem is a set of applications, their requirements, a set of computing nodes, and their provided resources. Due to the potentially large solution space of the problem, an optimization goal defines which solution is desired the most. However, the optimization goal used for the application-placement problem is not static but has to be adapted according to the current context the vehicle is experiencing. Therefore, an approach for a context-based determination of the optimization goal for a given instance of an application-placement problem is required. In this paper, we introduce C-po, an approach to address this issue. C - PO ensures that if the safety level of a system drops due to an occurring failure, the optimization goal for the successively executed application-placement determination aims to restore the safety level. Once the highest safety level is reached, C-po optimizes the application placement according to the current driving situation. Furthermore, we introduce two methods for dynamically determining the required level of safety.