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The transition to Model-Based Design must be managed carefully, both to demonstrate short-term benefits and to establish a culture that enables the full realization of the theoretical benefits of this approach. In this paper we introduce the concepts of Model-Based Design, highlight some of its benefits, and then discuss in detail the 10 best practices for adopting a Model-Based Design culture across an organization. These best practices have been gleaned from successful and not-so-successful transformations to Model-Based Design at companies from a variety of different industries.

Many of the multimedia and convenience features in today's passenger vehicles involve Human Machine Interfaces (HMIs), such as the radio face plate or the remote key fob. The functional requirements for these systems are often written in terms of the customer interaction with the interface device. In the past, design engineers would not begin to test requirements for these systems until prototype hardware was available. However, many product development organizations are shifting from this hardware-based traditional development cycle, which relies on designing via a prototype and test iteration, to Model-Based Design. Unfortunately, testing systems with complex human machine interface requirements becomes less intuitive when the prototypes are removed from the design process, because the test cases must be scripted into the modeling environment instead of being applied directly to a prototype of the interface device.

Current trends in the automotive, aerospace and other industries are resulting in the development of exceptionally large system-level models of control law or physical system behaviors. This is especially true in the propulsion systems algorithm and software development areas. The MathWorks has been actively engaged with a number of engineering groups faced with industry demands of increased software feature content with higher complexity and a shorter turn-around time. This paper discusses a roadblock encountered on a typical propulsion system software simulation due to its sheer size. It also discusses challenges, results, and lessons learned in solving the above problem from both a tool technology and engineering perspective.

Today, many leading automotive OEMs and Suppliers are adopting Model-Based Design for the development of embedded systems applications. In this paper, the authors review the challenges of performing configuration management that is adequate for use in a production environment of the models and associated files central to Model-Based Design.

The steady growth in the number of electronic control units on the average vehicle and the complexity of the algorithms that reside on these controllers has resulted in one of the most significant initiatives in the automotive industry in years. AUTOSAR - the Automotive Open System Architecture - has united more than 100 companies, automobile manufacturers, suppliers and tool vendors to develop a standard architecture for electronic control units. By the end of 2006 Version 2.1 was released, and now OEMs as well as suppliers have started to develop and integrate AUTOSAR-compliant functionality and components into vehicles. This paper will focus on the approach and challenges faced by engineers developing AUTOSAR-compliant production code using Model-Based Design.

The successful deployment of modeling packages across large organizations depends on the adoption of a uniform set of modeling standards within the organization. Further, the automation of modeling standards enforcement greatly facilitates their adoption. This paper examines several case studies of large-scale deployment of Model-Based Design and the areas where standards enforcement automation was employed. The paper further examines several areas where automation could be employed to improve the deployment process.

Methods for controlling execution order in traditional text-based languages such as C and Fortran are well established. The transition to graphical programs has revealed some of the hidden issues inherent in any scheduling routine, specifically data dependency and data protection (in multirate systems). Graphical programming languages provided built-in diagnostics that allow users to analyze the data dependencies to develop optimal schedules from a data propagation perspective. This paper examines one heuristic that can be used to develop an optimal schedule for an arbitrary model.

A key step in Model-Based Design is the deployment of an algorithm as machine code onto a target processor in the production vehicle. Modern software tools automatically generate the algorithmic source code from models. Given the many combinatorial possibilities for realizing a given algorithm within the modeling environment, the generated C source code will be a function of a realization. This dependency is an important consideration because the quality and clarity of the source code impacts the amount of verification and analysis that must be done for production software development. Other factors involved in generating the machine code from the source code, such as compiler optimization and microprocessor architecture, also contribute to this optimization. Organizations that proactively data mine and gather these optimizations into a set of best practices stand to benefit from reduced development times and lower costs.

A recent Gartner Dataquest study predicts that the total worldwide automotive semiconductor market will grow from $20.1 billion in 2007 to $25.9 billion by 2010. The study also predicts that revenue from automotive usage of FPGAs will triple to approximately $312 million during that same period[1]. Many of these FPGAs will be deployed in safety applications such as back-up cameras, lane departure warning systems, blind-spot warning system, and adaptive cruise control. FPGAs will also be deployed in next-generation engine electronics, emissions control, navigation, and entertainment applications. Automotive systems engineers are adept at using Model-Based Design for implementing some of these embedded applications on DSPs and microcontrollers. Many of these engineers are new to FPGA design and waking up to a fragmented workflow that is making it harder to meet time-to-market and cost objectives.