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Understanding the variation in the deployment times for crash sensor systems is important to ensure robust performance of a crash sensor system. Increases in both the numbers of crash modes and deployable devices have reduced the margins for the decisions about when to deploy any given device. Currently, the industry practice is to run a sweep over the potential sources of variation, recording the minimum and maximum deployment time. Questions such as: “How often do the extremes occur?” or “Are there multiple peaks in the deployment time?” can not be answered. This work uses numerical analysis methods to build on the current sweep methodology to obtain information on the distribution of the deployment times so that questions such as these can be answered when evaluating sensor calibrations. The end result is better informed engineering decisions during the calibration development.

This work presents a study of crash energy and severity in frontal offset Vehicle-To-Vehicle (VTV) crash tests. The crash energy is analyzed based on analytical formulations and empirical data. Also, the crash severity of different VTV tests is analyzed and compared with the corresponding full frontal rigid barrier test data. In this investigation, the Barrier Equivalent Velocity (BEV) concept is used to calculate the initial impact velocity of frontal offset VTV test modes such that the offset VTV tests are equivalent to full frontal impact tests in terms of crash severity. Linear spring-mass model and collinear impact assumptions are used to develop the mathematical formulation. A scale factor is introduced to account for these assumptions and the calculated initial velocity is adjusted by this scale factor. It is demonstrated that the energies due to lateral and rotational velocity components are very small in the analyzed frontal VTV tests.

This paper presents two brake control functions which are initiated when there is an impact force applied to a host vehicle. The impact force is generated due to the host vehicle being collided with or by another vehicle or object. The first function - called the post-impact braking assist - initiates emergency brake assistance if the driver is braking during or right after the collision. The second function - called the post-impact braking - initiates autonomous braking up to the level of the anti-lock-brake system if the driver is not braking during or right after the collision. Both functions intend to enhance the current driver assistance features such as emergency brake assistance, electronic stability control, anti-brake-lock system, collision mitigation system, etc.

This study demonstrates the use of pressure sensing technology to predict the crash severity of frontal impacts. It presents an investigation of the pressure change in the front structural elements (bumper, crush cans, rails) during crash events. A series of subsystem tests were conducted in the laboratory that represent a typical frontal crash development series and provided empirical data to support the analysis of the concept. The pressure signal energy at different sensor mounting locations was studied and design concepts were developed for amplifying the pressure signal. In addition, a pressure signal processing methodology was developed that relies on the analysis of the air flow behavior by normalizing and integrating the pressure changes. The processed signal from the pressure sensor is combined with the restraint control module (RCM) signals to define the crash severity, discriminate between the frontal crash modes and deploy the required restraint devices.

An Arbitrary Lagrangian Eulerian (ALE) approach was adopted in this study to predict the responses of side crash pressure sensors in an attempt to assist pressure sensor algorithm development by using computer simulations. Acceleration-based crash sensors have traditionally been used to deploy restraint devises (e.g., airbags, air curtains, and seat belts) in vehicle crashes. The crash pulses recorded by acceleration-based crash sensors usually exhibit high frequency and noisy responses depending on the vehicle's structural design. As a result, it is very challenging to predict the responses of acceleration-based crash sensors by using computer simulations, especially those installed in crush zones. Therefore, the sensor algorithm developments for acceleration-based sensors are mostly based on physical testing.

In an attempt to predict the responses of side crash pressure sensors, the Corpuscular Particle Method (CPM) was adopted and enhanced in this research. Acceleration-based crash sensors have traditionally been used extensively in automotive industry to determine the air bag firing time in the event of a vehicle accident. The prediction of crash pulses obtained from the acceleration-based crash sensors by using computer simulations has been very challenging due to the high frequency and noisy responses obtained from the sensors, especially those installed in crash zones. As a result, the sensor algorithm developments for acceleration-based sensors are largely based on prototype testing. With the latest advancement in the crash sensor technology, side crash pressure sensors have emerged recently and are gradually replacing acceleration-based sensor for side impact applications.