In recent years, the electrical and/or electronic (E/E) architecture of vehicles has evolved significantly, driven by the demand for increased computational power to support safety-critical applications and advanced driver assistance systems (ADAS) functionalities. This evolution has led to the adoption of centralized architectures with high-performance computing units. These architectures also require high-bandwidth and deterministic communication protocols to handle numerous sensors and actuators, especially in mixed-criticality systems. However, configuring and integrating essential applications into a vehicle's E/E architecture while meeting safety requirements, guaranteeing reliable communication, and considering optimization objectives are time-consuming, complex, and error-prone tasks. This paper presents a novel model-based framework, called E/E Designer, to facilitate the synthesis of a car's E/E architecture supporting automotive embedded systems modeling. The framework automates mapping of software components to hardware elements and computes schedules for application threads. It establishes network message routing and schedules communication tasks within the car's topology while addressing safety requirements such as redundancy. The E/E Designer also optimizes the system model using multi-objective optimization, and utilizes a single-step approach to solve mixed-integer programming (MIP) constraints in order to reduce the solving time and consider the relations among various constraints. We use an experimental setup to investigate the framework's performance through design-time and run-time evaluations. The results of our design-time experiments indicate that our formulations can scale to systems of reasonable size. During our run-time evaluations, we observed no timing deadline violations after deploying the design-time solutions on the experimental setup.
CITATION STYLE
Askaripoor, H., Mueller, T., & Knoll, A. (2023). E/E Designer: a Framework to Design and Synthesize Vehicle E/E Architecture. IEEE Transactions on Intelligent Vehicles. https://doi.org/10.1109/TIV.2023.3324617
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