This work presents a scenario and a system concept for an Unmanned Aerial Vehicle (UAV) teamed with an Unmanned Ground Vehicle (UGV) and a base station for firefighting tasks. Based on a detailed scenario description, we investigate tangible design choices regarding relevant hardware and algorithms based on today's technology. We conclude that the implementation of a functional prototype appears feasible.

The automotive industry is characterized by a complex and highly distributed development network. As a result, among other things, the coordination of the strongly interlinked process landscape makes it difficult to ensure the development quality of the integrated system in a given time. Model-based systems engineering and agile methods might help to increase the speed of development, but the combination of hardware and software development must be kept synchronized. To overcome this problem, the goal of the OMEx-DriveTrain project is to develop an open, modular and experimental platform for the electric powertrain to develop, integrate and verify complex products in the field of electromobility. In this paper, the realization of the open, modular and experimental platform will be introduced.

The purpose of supply chain design is to ensure an efficient and effective logistics network. Within supply chain design, simulation and optimization tools are used in combination to improve design scenarios from different angles. Nowadays, sustainability becomes an increasingly important criterion for the evaluation of these design scenarios. To integrate ecological and economical features, an exchange of results between an optimizer and a simulation model often improves the outcomes compared to one system only. Although the communication between both systems is frequently applied, it is also error-prone as the underlying models are tool-dependent and cannot be exchanged without any adaptations. The efficient use of the simulation and optimization tools requires a holistic data model depicting the elements of the network and their dependencies. Therefore, this paper introduces a tool-independent and generalized description of the supply chain. The significant advantage of this generalized description is its ability to exchange amendments between tools automatically.

The increasing technical complexity of cars and the high number of offered options lead to new challenges in the automotive industry and especially the mid-term demand and capacity management (DCM). This requires a procedural adaptation based upon an efficient information model. In this contribution, the state of the art is analysed for both the DCM process and the underlying information models. Promising concepts for managing the steadily increasing requirements in DCM are deducted, and a modular process kit for the procedural adaptation combined into the concept SmartDCM is introduced. Additionally, a new approach of an efficient information model for managing the increasingly complex information is presented.

Software Development for Automotive Systems is becoming more and more complex. Cars are turned into software intensive and to some extent software defined products by the increasing functionality of the various electronic control units (ECUs). Major driving factors for this development are complex human-machine-interfaces (HMI), eMobility and autonomous driving. Consequently, developing the software comes along with complex development projects and several partners. Different companies participate in such projects and form a value chain for the generation of a certain software system. Development tools have to support these project specific value chains. For this purpose the tools form a project-specific information supply chain. Ideally, they are connected to an automated tool chain. AMALTHEA provides an open source tool chain platform to support the setup of project specific development systems. This contribution presents the AMALTHEA tool chain environment and the underlying software development methodology. The concepts for the support of tailoring a tool chain and the standardization activities for the respective framework are described. Furthermore, an approach for the quantification of the efficiency and effectivity gains is described and key performance indicators are estimated.

Software-Mapping, i.e. the mapping of software elements to hardware components, is especially in the context of embedded multicore systems a rather complex task. Usually, it is not sufficient to allocate tasks to hardware, since further types of allocations, e.g. communications to data paths or data to memories, exist. Accordingly, these allocations have a crucial impact on the performance. Since it is required to fulfill several constraints, e.g. deadlines or task ordering, it is furthermore necessary to select those allocations that result in a valid, but also efficient mapping. Such efficiency is usually not achieved by executing the application as quick as possible but e.g. as reliable or energy saving as possible. This can be achieved by using mathematical methods, e.g. Integer Linear Programming (ILP). ILP allows describing the mapping problem in terms of equations, which will be optimized towards a specific goal. This work describes an exemplary integration of an existing mathematical method for embedded multi-core software to hardware mapping into the AMALTHEA Tool Platform, including its evaluation as well as adaptation, in order to provide an automated software mapping functionality.