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As Advanced Crash Avoidance Technologies (ACATs) such as Forward Collision Warning (FCW), Crash Imminent Braking Systems and other advanced technologies continue to be developed, the need for full-scale test methodologies which can minimize hazards to test personnel and damage to equipment has rapidly increased. The challenge of evaluating such ACAT systems is twofold. First, the evaluation system must be able to deliver a potential Collision Partner (CP) reliably and precisely along a trajectory which would ultimately result in a crash in a variety of configurations, such as rear-ends, head-ons, crossing paths, and sideswipes. Second, and more importantly, the collision partner must not pose a substantial physical risk to the test driver, other test personnel, equipment, or to test vehicles in the event that the collision is not avoided.

The Advanced Crash Avoidance Technologies (ACAT) program initiated by the National Highway Traffic Safety Administration had two major overall objectives. These were to develop a standardized Safety Impact Methodology (SIM) tool to evaluate the effectiveness of advanced technologies in avoiding and mitigating specific types of vehicle crashes; and to develop and demonstrate objective tests that are used in the SIM to verify the safety impact of a real system. Honda and Dynamic Research Inc. (DRI) had been developing and applying such SIMs for several years and had a Cooperative Agreement with NHTSA to further develop a SIM in order to determine the feasibility of developing estimates of effectiveness for specific not-yet-deployed safety technologies in the absence of data from real world or field operational tests, and linking it to the results from objective tests.

This paper attempts to integrate a derivative-free probability analysis method to Reliability-Based Robust Design Optimization (RBRDO). The Eigenvector Dimension Reduction (EDR) method is used for the probability analysis method. It has been demonstrated that the EDR method is more accurate and efficient than the Second-Order Reliability Method (SORM) for reliability and quality assessment. Moreover, it can simultaneously evaluate both reliability and quality without any extra expense. Two practical engineering problems (vehicle side impact and layered bonding plates) are used to demonstrate the effectiveness of the EDR method.

In the last decade, considerable advances have been made in reliability-based design optimization (RBDO). One assumption in RBDO is that the complete information of input uncertainties are known. However, this assumption is not valid in practical engineering applications, due to the lack of sufficient data. In practical engineering design, information concerning uncertainty parameters is usually in the form of finite samples. Existing methods in uncertainty based design optimization cannot handle design problems involving epistemic uncertainty with a shortage of information. Recently, a novel method referred to as Bayesian Reliability-Based Design Optimization (BRBDO) was proposed to properly handle design problems when engaging both epistemic and aleatory uncertainties [1]. However, when a design problem involves a large number of epistemic variables, the computation task for BRBDO becomes extremely expensive.

This paper presents an innovative approach for quality engineering using the Eigenvector Dimension Reduction (EDR) Method. Currently industry relies heavily upon the use of the Taguchi method and Signal to Noise (S/N) ratios as quality indices. However, some disadvantages of the Taguchi method exist such as, its reliance upon samples occurring at specified levels, results to be valid at only the current design point, and its expensiveness to maintain a certain level of confidence. Recently, it has been shown that the EDR method can accurately provide an analysis of variance, similar to that of the Taguchi method, but is not hindered by the aforementioned drawbacks of the Taguchi method. This is evident because the EDR method is based upon fundamental statistics, where the statistical information for each design parameter is used to estimate the uncertainty propagation through engineering systems.

This paper describes the heat transfer model of a composite aluminum brake rotor and compares the predicted temperatures to dynamometer measurements taken during a 15 fade stop trial. The model is based on meshed surface geometry which is simulated using RadTherm software. Methods for realistically modeling heat load distribution, surface rotation, convection cooling and radiation losses are also discussed. A comparison of the simulation results to the dynamometer data shows very close agreement throughout the fade stop trial. As such, the model is considered valid and will be used for further Steel Clad Aluminum (SCA) rotor development.

The introduction of a thin fluid layer between two layers of sheet metal offers a highly effective and economical alternative to the use of constrained viscoelastic damping layers in sheet metal structures. A diesel engine gear cover, which is constructed of two sheet metal sections spot welded together, takes advantage of fluid layer damping to produce superior vibration and sound radiation performance. In this paper, the bending of a double layered plate coupled through a thin fluid layer is modeled using a traveling wave approach which results in a impedance function that can be used to assess the vibration and sound radiation performance of practical double layered plate structures. Guided by this model, the influence of fluid layer thickness and inside-to-outside sheet thickness is studied.

To reduce the number and severity of accidents, automakers have invested in active safety systems to detect and track neighboring vehicles to prevent accidents. These systems often employ RADAR and LIDAR, which are not degraded by low lighting conditions. In this research effort, reflections from deer were measured using two sensors often employed in automotive active safety systems. Based on a total estimate of one million deer-vehicle collisions per year in the United States, the estimated cost is calculated to be $8,388,000,000 [1]. The majority of crashes occurs at dawn and dusk in the Fall and Spring [2]. The data includes tens of thousands of RADAR and LIDAR measurements of white-tail deer. The RADAR operates from 76.2 to 76.8 GHz. The LIDAR is a time-of-flight device operating at 905 nm. The measurements capture the deer in many aspects: standing alone, feeding, walking, running, does with fawns, deer grooming each other and gathered in large groups.

Surrogate targets are used throughout the automotive industry to safely and repeatably test Advanced Driver Assistance Systems (ADAS) and will likely find similar applications in tests of Automated Driving Systems. For those test results to be applicable to real-world scenarios, the surrogate targets must be representative of the real-world objects that they emulate. Early target development efforts were generally divided into those that relied on sophisticated radar measurement facilities and those that relied on ad-hoc measurements using automotive grade equipment. This situation made communication and interpretation of results between research groups, target developers and target users difficult. SAE J3122, “Test Target Correlation - Radar Characteristics”, was developed by the SAE Active Safety Systems Standards Committee to address this and other challenges associated with target development and use. J3122 addresses four topics.

This paper describes the development and application of a newly developed metric for evaluating and quantifying the capability of a vehicle/controller (e.g., Automated Vehicle or human driver) to avoid collisions in nearly any potential scenario, including those involving multiple potential collision partners and roadside objects. At its core, this Collision Avoidance Capability (CAC) metric assesses the vehicle’s ability to avoid potential collisions at any point in time. It can also be evaluated at discrete points, or over time intervals. In addition, the CAC methodology potentially provides a real-time indication of courses of action that could be taken to avoid collisions. The CAC calculation evaluates all possible courses of action within a vehicle’s performance limitations, including combinations of braking, accelerating and steering.