The MobSTr dataset contains a number of artifacts for an autonomous driver assistance system, ranging from textual requirements to models for system design and models relevant to safety assurance. The artifacts provided are connected with traceability links created and managed with Eclipse Capra, an open source traceability management tool. The dataset builds upon a custom traceability information model that provides type safety and semantics for the trace links. MobSTr is intended for researchers that work on software and systems traceability as well as on model-based safety assurance. It is already being used in a number of studies, including research on trace link consistency, change impact analysis, and automated analysis of safety and timing requirements.

Traceability management relies on a supporting model, the traceability information model (TIM), that defines which types of relationships exist between which artifacts and contains additional constraints such as multiplicities. Constructing a TIM that is fit for purpose is crucial to ensure that a traceability strategy yields the desired benefits. However, which design decisions are critical in the construction of TIMs and which impact they have on the usefulness and applicability of traceability is still an open question. In this paper, we use two cases of TIMs constructed for safety-critical, automotive systems with industrial safety experts, to identify key design decisions. We also propose a comparison scheme for TIMs based on a systematic literature review and evaluate the two cases as well as TIMs from the literature according to the scheme. Based on our analyses, we thus derive key insights into TIM construction and the design decisions that ensure that a TIM is fit for purpose.

In this paper, we propose an approach how connected and highly automated vehicles can perform cooperative maneuvers such as lane changes and left-turns at urban intersections where they have to deal with human-operated vehicles and vulnerable road users such as cyclists and pedestrians in so-called mixed traffic. In order to support cooperative maneuvers the urban intersection is equipped with an intelligent controller which has access to different sensors along the intersection to detect and predict the behavior of the traffic participants involved. Since the intersection controller cannot directly control all road users and - not least due to the legal situation - driving decisions must always be made by the vehicle controller itself, we focus on a decentralized control paradigm. In this context, connected and highly automated vehicles use some carefully selected game theory concepts to make the best possible and clear decisions about cooperative maneuvers. The aim is to improve traffic efficiency while maintaining road safety at the same time. Our first results obtained with a prototypical implementation of the approach in a traffic simulation are promising.