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A practical and low cost Blind Spot Monitoring system is proposed. By using a single camera, the range and azimuth position of a vehicle in a blind spot are measured. The algorithm is based on the proposed RWA (Range Window Algorithm). The camera is installed on the door mirror and monitoring the side and rear of the host vehicle. The algorithm processes the image and identifies range and azimuth angle of the vehicle in the adjacent lane. This algorithm is applied to real situations. The 388 images including several kinds of vehicles are analyzed. The detection rate is 86% and the range accuracy is 1.6[m]. The maximum detection range is about 30[m].

Traditionally fog lamps use halogen filament light sources. With the emergence of high brightness white LEDs, it is now possible to develop automotive forward lighting systems with LED light sources. Six LEDs are shown to be sufficient for the implementation of a European fog lamp using a faceted reflector optical approach. Each reflector together with the LED light source forms a modular element. The optical parameters of two different lamp designs are compared and correlation between the simulation and prototype measurements is shown. Further, additional forward lighting functions can be implemented through the use of additional elements.

Flow noise from automotive HVAC (Heating, Ventilating and Air Conditioning) systems is one of the major considerations of occupant comfort. The noise generated at high blower speed is a major contributor to the vehicle interior noise. This paper reviews automotive HVAC air rush noise prediction models for estimating register, buck (air handling subsystem) and vehicle noise levels. The vehicle noise prediction method correlates well with measured noise levels at driver right ear location: with a standard deviation of 1.31 dB where standard deviation is the difference between measured and predicted noise levels for a sample size of 10 vehicles.

Customer clinics and surveys have revealed the increased importance to the customer of good defrost and demist performance in their vehicle. Achieving this level of performance, within the time and cost constraints of a modern vehicle development program, places increased reliance on computational (CAE) techniques. However, this paper describes how the optimum development process should be to combine this reliance upon CAE methods with a newly developed experimental technique. This new laser Doppler velocimetry (LDV) based methodology is employed at all stages of the development process and complements the CAE techniques perfectly. The end result is optimized airflow management within the vehicle cabin – essential if good defrost and demist performance is to be achieved in a vehicle.

The present work demonstrates the application of Design of Experiments (DOE) statistical methods to the design and optimization of a hydraulic steering pump for NVH performance. DOE methods were applied to RPM-domain data to examine the effect of several different factors, as well as the interactions between these factors, on pump NVH. Whereas most DOE analyses typically consider only a single response variable, the present work considered multiple response variables. Specifically, pump NVH performance curves for several pump rotational orders over a range of shaft speeds were analyzed. Thus, it was possible to determine the effect of the factors in question over the entire speed range of pump operation, rather than a single speed or setting. Statistical methods were applied to determine which factors and interactions had a significant effect on pump NVH. These factors were used to construct an empirical mathematical prediction model for NVH performance.

Induction noise, which radiates from the open end of the engine air induction system, can be of significant importance in reducing vehicle interior noise and tuning the interior sound to meet customer expectations. This makes understanding the source noise critical to the development of the air induction system and the vehicle interior sound quality. Given the ever-decreasing development times, it is highly desirable to use computer-aided engineering (CAE) tools to accelerate this process. Many tools are available to simulate induction noise or, more generally, duct acoustics. The tools vary in degrees of complexity and inherent assumptions. Three-dimensional tools will account for the most general of geometries. However, it is also possible to model the duct acoustics with quasi-three-dimensional or one-dimensional tools, which may be faster as well.

Estimation and prediction of residual life and reliability are serious concerns in life cycle management for aging structures. Laboratory testing replicating fatigue loading for a typical military aircraft wing skin was undertaken. Specimens were tested until their fatigue life expended reached 100% of the component fatigue life. Then, scanning electron microscopy was used to quantify the size and location of fatigue cracks within the high stress regions of simulated fastener holes. Distributions for crack size, nearest neighbor distances, and spatial location were characterized statistically in order to estimate residual life and to provide input for life cycle management. Insights into crack initiation and growth are also provided.

An algorithm, which determines the range of a preceding vehicle by a single image, had been proposed. It uses a “Range-Window Algorithm”. Here in order to realize higher robustness and stability, the pattern matching is incorporated into the algorithm. A single camera system using this algorithm has an advantage over the high cost of stereo cameras, millimeter wave radar and non-robust mechanical scanning in some laser radars. And it also provides lateral position of the vehicle. The algorithm uses several portions of a captured image, namely windows. Each window is corresponding to a predetermined range and has the fixed physical width and height. In each window, the size and position of objects in the image are estimated through the ratio between the widths of the objects and the window, and a score is given to each object. The object having the highest score is determined as the best object. The range of the window corresponding to the best object becomes an estimated range.

Engine air induction noise can play a significant role in the reduction of vehicle interior noise levels and tuning interior sound quality. Given the need to reduce prototyping and testing costs, it is important to gain an understanding of the level and frequency structure of the noise radiating from the open inlet of the air induction system. Engine simulation used independently can predict inlet noise; however, its utility is limited to systems that are largely one-dimensional. Systems that exhibit a three-dimensional nature, such as the wave dynamics in an engine air cleaner, require a more intensive approach. Boundary Element Analysis (BEA) has been demonstrated to be a tool that can be used to predict the frequency response of ducted systems and is particularly useful in highly three-dimensional systems.

Maximum Brake Torque (MBT) timing for an internal combustion engine is the minimum advance of spark timing for best torque. Traditionally, MBT timing is an open loop feedforward control whose values are experimentally determined by conducting spark sweeps at different speed, load points and at different environmental operating conditions. Almost every calibration point needs a spark sweep to see if the engine can be operated at the MBT timing condition. If not, a certain degree of safety margin is needed to avoid pre-ignition or knock during engine operation. Open-loop spark mapping usually requires a tremendous amount of effort and time to achieve a satisfactory calibration. This paper shows that MBT timing can be achieved by regulating a composite feedback measure derived from the in-cylinder ionization signal referenced to a top dead center crank angle position. A PI (proportional and integral) controller is used to illustrate closed-loop control of MBT timing.

This paper describes the applicability of optical imaging techniques to the analysis of the scratch resistance of automotive interior plastic materials. The evaluation of so-called “finger testing” has traditionally relied upon human vision for detection of the initial scratch position. Commonly performed under uniform and defined illumination conditions, the relative contrast difference signified by whitening on a surface as determined by unaided human vision is a highly variable subjective perception; thus individual inspectors may determine the “whitening” point differently. This paper compares test data obtained from both visual and instrumental evaluation methods and discusses the advantages of optical imaging techniques for surface defect analysis.