Automated Design Space Exploration for Resource Allocation in Software-Defined Vehicles

Modern vehicles include an increasing amount of software, e.g., for autonomous driving capabilities, connectivity, and personalized user experience. The capabilities in current vehicles are still mostly provided by multiple separated embedded systems, while the current trend goes toward purely software-defined vehicles (SDV). Traditional distributed electrical/electronic (E/E) architectures have tightly coupled hardware/software, and the computational power is optimized for the included feature set. For SDVs, a centralized E/E architecture utilizing high-performance computers has been proposed. In contrast to individual embedded systems with limited and fixed functionality, combing a large set of individual software components in a single system leads to a high complexity in the proper allocation of resources. Model-based system engineering (MBSE) has been promoted in the automotive industry to handle complex system design. However, existing MBSE approaches focus mainly on traditional E/E architectures. In this work, we propose an automated and model-based approach that can address the resource allocation problem in SDVs. Users can formally describe the vehicle’s resources, safety/non-safety requirements, and optimization objectives based on existing software engineering standards. The proposed method is not restricted to specific system models, requirements, or optimization goals and is, therefore, compatible with other E/E architectures. By introducing a model-independent transformation from the model information to solver-independent optimization formulas, the resource allocation problem can be solved automatically by a wide range of state-of-the-art solvers. We demonstrate the applicability of this approach in a SDV scenario with a high-performance computer and multiple applications.