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Given the fast changing market demands, the growing complexity of features, the shorter time to market, and the design/development constraints, the need for efficient and effective verification and validation methods are becoming critical for vehicle manufacturers and suppliers. One such example is fault-tree analysis. While fault-tree analysis is an important hazard analysis/verification activity, the current process of translating design details (e.g., system level and software level) is manual. Current experience indicates that fault tree analysis involves both creative deductive thinking and more mechanical steps, which typically involve instantiating gates and events in fault trees following fixed patterns. Specifically for software fault tree analysis, a number of the development steps typically involve instantiating fixed patterns of gates and events based upon the structure of the code. In this work, we investigate a methodology to translate software programs to fault trees.

In many measurement applications, there is a need to correlate data acquired from different systems or synchronize systems together with precise timing. Signal Based and Time Based are the two basic methods of synchronizing instrumentation. In Signal Based synchronization, clocks and triggers are physically connected between systems. Typically this provides the highest precision synchronization. In many NVH applications size and distance constrains physically connecting the systems needed for making measurements though the inter-channel phase information of simultaneously sampled signals is crucial. In Time Based synchronization, system components have a common reference of what time it is. Events, triggers and clocks can be generated based on this time.

Modular instrumentation is being widely used in noise and vibration measurement systems that demand higher channel counts and the wider dynamic range that 24-bit delta-sigma ADCs make available at lower costs. This is an overview how flexible modular instrumentation employing the latest software technology can be used in making high precision noise and vibration measurements where higher sampling rates, higher channel counts, increased dynamic range, and distributed architectures were needed in smaller packages. An example where this is being used is in acoustic beam forming in aircraft pass by noise tests to measure and distinguish engine and airframe noise sources.

Companies typically invest significant time and money in choosing the proper test equipment for new automotive test systems. Yet, the architecture for proper data storage and management of the mounds of data these systems produce is often times an afterthought. Although data management may not appear as an obstacle during initial design, as the system expands, changes, and interfaces with other systems, the ability to easily access and exchange technical data becomes a critical challenge to overcome. This paper will introduce new technologies giving engineers the power to search and mine data sets to find key information and trends in the data for to rapidly turn the raw data into results.

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.

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.

LIN is a low-cost, low-speed vehicle communication sub-bus becoming increasingly pervasive in automotive subsystems. It is a simple, UART-based master-slave protocol designed as a low-speed supplement to a CAN or FlexRay bus. Its primary application is cabin comfort and human interface hardware such as dashboard controls, power seat harnesses, and power door/window systems. As automotive network designers attempt to reduce wiring complexity and lower system cost, modular, inexpensive sub-buses like LIN become an attractive option. This paper presents an overview of the LIN standard and its applications, and then proposes an architecture for rapid development of LIN networks via hardware simulations of LIN nodes. Using inexpensive, off-the-shelf hardware, LIN sensor and actuator applications can be tested in-place without microcode development, speeding overall network development time.

The increasing number of features and complexity of today's automotive software architectures bring new challenges to the product development cycle. As a product is being developed, there is a need for information created during the early phases to flow seamlessly into subsequent phases. For example, information defined for an ECU during the design phase should be re-used when that ECU is tested during manufacture. Challenges often arise from the fact that one vendor's tools may be appropriate for design, but a different vendor's tools are best suited for manufacturing test. This paper discusses business and technical issues surrounding the transfer of information between such tools. Two case studies are used for discussion. One deals with databases describing signals transferred over an in-vehicle network and the other discusses simulation models as both transition from early designs through various test phases.

Almost every automotive noise, vibration, and harshness (NVH) engineer who has ever looked at a fast Fourier transform knows the difference between instruments with 90 dB and instruments with 120 dB of dynamic range. NVH engineers understand instrumentation specs such as 24-bit resolution analog-to-digital converters and alias-free signal bandwidth. However, with modern noise and vibration instrumentation systems now being almost completely built on the PC, these specs neglect the most important X factor: the PC itself. No other aspect can affect the performance of an instrumentation system for a sensor array more than the components of the PC. Fortunately, a variety of off-the-shelf PC technologies built on industry standards are available to make it easier and less expensive than ever before to instrument and manage data from large systems. But an NVH engineer must wade through a sea of options to choose the right PC technologies for desired instrumentation system performance.