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To cope with the increasing number of advanced features (e.g., smart-phone integration and side-blind zone alert.) being deployed in vehicles, automotive manufacturers are designing flexible hardware architectures which can accommodate increasing feature content with as fewer as possible hardware changes so as to keep future costs down. In this paper, we propose a formal and quantitative definition of flexibility, a related methodology and a tool flow aimed at maximizing the flexibility of an automotive hardware architecture with respect to the features that are of greater importance to the designer. We define flexibility as the ability of an architecture to accommodate future changes in features with no changes in hardware (no addition/replacement of processors, buses, or memories). We utilize an optimization framework based on mixed integer linear programming (MILP) which computes the flexibility of the architecture while guaranteeing performance and safety requirements.

Current development processes for automotive Electronic Control System (ECS) architectures have certain limitations in evaluating and comparing different architecture design alternatives. The limitations entail the lack of systematic and quantitative exploration and evaluation approaches that enable objective comparison of architectures in the early phases of the design cycle. In addition, architecture design is a multi-stage process, and entails several stakeholders who typically use their own metrics to evaluate different architecture design alternatives. Hence, there is no comprehensive view of which metrics should be used, and how they should be defined. Finally, there are often conflicting forces pulling the architecture design toward short-term objectives such as immediate cost savings versus more flexible, scalable or reliable solutions. In this paper, we propose the usage of a set of metrics for comparing ECS architecture alternatives.

Recent trends in the automotive industry show growing demands for the introduction of new in-vehicle features (e.g., smart-phone integration, adaptive cruise control, etc.) at increasing rates and with reduced time-to-market. New technological developments (e.g., in-vehicle Ethernet, multi-core technologies, AUTOSAR standardized software architectures, smart video and radar sensors, etc.) provide opportunities as well as challenges to automotive designers for introducing and implementing new features at lower costs, and with increased safety and security. As a result, the design of Electrical/Electronic (E/E) architectures is becoming increasingly challenging as several hardware resources are needed. In our earlier work, we have provided top-level definitions for three relevant metrics that can be used to evaluate E/E architecture alternatives in the early stages of the design process: flexibility, scalability and expandability.

Automotive HW/SW architectures are becoming increasingly complex to support the deployment of new safety, comfort, and energy-efficiency features. Such architectures include several software tasks (100+), messages (1000+), computational and communication resources (70+ CPUs, 10+ buses), and (smart) sensors and actuators (20+). To cope with the increasing system complexity at lowest development and product costs, highest safety, and fastest time to market, model-based rapid-prototyping development processes are essential. The processes, coupled with optimization steps aimed at reducing the number of software and hardware resources while satisfying the safety requirements, enable reduction of the system complexity and ease downstream testing/validation efforts. This paper describes a novel model-based design exploration and optimization process for the deployment of a set of software tasks on a dual-processor control module implementing a fail-safe strategy.

Automotive control applications are implemented over distributed platforms consisting of a number of electronic control units (ECUs) connected by communication buses. During system development, the designer can explore a number of design alternatives: for example, software distribution, software architecture, hardware architecture, and network configuration. Exploring design alternatives efficiently and evaluating them to optimize metrics such as cost, time, resource utilization, and reliability provides an important competitive advantage to OEMs and helps minimize integration risks. We present a methodology (Platform-Based Design) and a framework (Metropolis) to support efficient architecture exploration. We have exercised the methodology and the capabilities of Metropolis for developing a library of automotive architecture components and performed design space exploration on a chassis control sub-system.

Future automobiles are required to support an increasing number of complex, distributed functions such as active safety and X-by-wire. Because of safety concerns and the need to deliver correct designs in a short time, system properties should be verified in advance on function models, by simulation or model checking. To ensure that the properties still hold for the final deployed system, the implementation of the models into tasks and communication messages should preserve properties of the model, or in general, its semantics. FlexRay offers the possibility of deterministic communication and can be used to define distributed implementations that are provably equivalent to synchronous reactive models like those created from Simulink. However, the low level communication layers and the FlexRay schedule must be carefully designed to ensure the preservation of communication flows and functional outputs.