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The current automotive electronic and electrical (EE) architecture has reached a scalability limit and in order to adapt to the new and upcoming requirements, novel automotive EE architectures are currently being investigated to support: a) an Ethernet backbone, b) consolidation of hardware capabilities leading to a centralized architecture from an existing distributed architecture, c) optimization of wiring to reduce cost, and d) adaptation of service-oriented software architectures. These requirements lead to the development of Zonal EE architectures as a possible solution that require appropriate adaptation of used security mechanisms and the corresponding utilized hardware trust anchors. 1 The current architecture approaches (ECU internal and in-vehicle networking) are being pushed to their limits, simultaneously, the current embedded security solutions also seem to reveal their limitations due to an increase in connectivity.

This paper explains why software for efficient model-based development is needed to improve the efficiency of automakers and suppliers when implementing solutions with next generation automotive embedded systems. The resulting synergies are an important contribution for the automotive industry to develop safer, smarter, and more eco-friendly cars. To achieve this, it requires implementations of algorithms for machine learning, deep learning and model predictive control within embedded environments. The algorithms’ performance requirements often exceed the capabilities of traditional embedded systems with a homogeneous multicore architecture and, therefore, additional computing resources are introduced. The resulting embedded systems with heterogeneous computing architectures enable a next level of safe and secure real-time performance for innovative use cases in automotive applications such as domain controllers, e-mobility, and advanced driver assistance systems (ADAS).

In recent years, we see more and more ECUs integrating a huge number of application software components. This process mostly results from the increasing amount of so called in-house software in various fields like electric-drive, chassis and driver assistance systems. The software development for these systems is partially moved from the supplier to the car manufacturers. Another important trend is the introduction of new network architectures intending to meet the growing communication requirements. For such ECUs the software integration scenarios become more complicated, as more quality of service requirements with regards to timing, safety and security need to be considered [2]. Multi-core microcontrollers offer even more potential variants for integration scenarios. Understanding the interaction between the different software components, not only from a functional, but also from a timing view, is a key success factor for modern electronic systems [6,7,8,9].

The infrastructure in modern cars is a heterogeneous and historically grown network of different field buses coupling different electronic control units (ECUs) from different sources. In the past years, the amount of ECUs in the network has rapidly grown due to the mushrooming of new functions which historically were mostly implemented on a one-ECU-per-function basis resulting in up to a hundred ECUs in fully equipped luxury cars. Additionally, new functions like parking assist systems or advanced chassis control functions are getting increasingly complex and require more computing power. These two facts add up to a complex challenge in development. The current trend to host several functions in single ECUs as integration platforms is one attempt to address this challenge. This trend is supported by the increased computing power of current and upcoming multi-core microcontrollers.