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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.

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.

Since the beamforming concept was first introduced to acoustic engineers approximately 10 years ago, the acoustic phased array has become one of the most popular techniques used to identify noise sources. Generally speaking, the spatial resolution of the phased array is proportional to the number of microphones and the array size. With more microphones or a larger array, better spatial resolution is achieved. However, to achieve better spatial resolution for a given microphone array structure, the so-called CLEAN method was recently introduced. By applying the traditional non-parametric estimation method, such as delay-and-sum, the CLEAN algorithm first computes the largest noise and removes it from the raw data samples. Then, the program estimates a new peak noise, based on the “cleaned” data samples. The process is repeated until the remaining data samples become random noise. The CLEAN method works well if the noise sources are far apart.

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.

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.

ECU designers are seeking more flexibility from HIL test systems. Often their needs are met by the development of custom hardware, either internally or by HIL test system vendors. Many systems also rely heavily on the use of multiple expensive microprocessors to achieve the required timing and synchronization performance. This paper discusses an alternative based on PC technology and reconfigurable I/O hardware. The HIL test system designer uses a graphical programming interface to reconfigure not only the real-time software portion of the system, but also the FPGA-based I/O hardware. This increases flexibility and lowers cost by providing capabilities such as generating simulated outputs synchronized to crank angle and implementing multiple serial communication protocols.

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.

Military jet aircraft expose both ground maintenance personnel and the community to high levels of noise. The U.S. Department of Defense is funding research to develop advanced modeling tools for noise reduction techniques and community noise exposure. A large-scale microphone array for portable near-field acoustic holography (NAH) and data acquisition system was created for this purpose. The system was designed for measuring high-amplitude jet noise from current and next-generation military aircraft to provide model refinement and benchmarking, evaluate performance of noise control devices, and predict ground maintenance personnel and community noise exposure. The acoustical instrumentation system was designed to be easy to use with scalable data processing as the primary focus. The data acquisition system allowed up to 152 channels simultaneously sampled at a rate of 96 kHz.

Empowering the end-user is the primary focus of most software developers, whether in the general computing industry or in automotive instrumentation applications. End-users' expectations for both ease of use and flexibility in software products are high. Products in the general computing industry, such as Microsoft Word and Excel, have set standards for what a user expects from a software product. Because of the complex nature of most analytical instrumentation applications, it is difficult to deliver a software product for these applications that is both easy to use and flexible for many applications. And, if the product does exist, it usually comes with a relatively high price tag. There are, however, some lower cost software development tools available for instrumentation applications that combine a good mix of flexibility and ease of use. These tools require some development on the part of the end-user, but they do not require a computer science background.

This is an overview of the development of a portable, real-time acoustic beamformer based on FPGA (Field Programmable Gate Arrays) and digital microphones for noise source identification. Microphone arrays can be a useful tool in identifying noise sources and give designers an image of noise distribution. The beamforming algorithm is a classic and efficient algorithm for signal processing of microphone arrays and is the core of many microphone array systems. High-speed real-time beamforming has not been implemented much in a portable instrument because it requires large computational resources. Utilizing a beamforming algorithm running on a Field Programmable Gate Array (FPGA), this camera is able to detect and locate both stationary and moving noise sources. A high-resolution optical camera located in the middle of the device records images at a rate of 25 frames per second. The use of the FPGA technology and digital microphones provides increased performance, reduced cost and weight.