Visualization of Crash Channel Assignments in a Tabular Form

Passive safety systems try to lessen the effects of an accident. Airbags are a passive safety feature. They are designed to protect occupants of a vehicle during a crash. These systems have to be configured correctly in order to deploy airbags at the right time in case of a collision. Airbag application tools are used to simulate and interpret crashes. Some factors influence when an airbag should deploy. Based on different parameters, the logic for firing airbags is also different. Under every circumstance, an airbag has to be deployed at the right time in order to prevent injuries and fatalities. During the process of simulation, the data which is simulated is written to a database. During interpretation, this data is extracted from the database. Then, the required information can be analyzed and interpreted for further use. This data contains crash related information. For example, the type of crash, crash code and crash channel assignments. For every crash present in the airbag project, crash channels are assigned to the sensors. Each sensor present has a crash channel assigned to it. This is called the crash channel assignment. An airbag application tool is developed to show the crash channel assignments. This tool should handle the information extraction, and visualization of crash channel assignments. The final output should be in a tabular format, which includes user specific customizations

Text Messaging and Distracted Driving: Using Voice Dictation to Make Roads Safer

http://deepblue.lib.umich.edu/bitstream/2027.42/98081/1/Shah_lhc489_W2013_muir.pd

Finite element simulation of the airbag deployment in frontal impacts

Virtual modeling and simulation are increasingly used to help develop restraint systems, and airbag simulation is the necessary steps during airbag research and design progress. In this work, the squeezed airbag has been simulated by a uniform pressure method in which the pressure of the airbag is considered as constant. The main aim of this study is evaluate the performance of deploying of passenger side airbag using finite element methods (FEM) to handle different collision scenarios

AN EMPIRICAL STUDY OF STRATEGIES FOR UNDERSTANDING QUANTITATIVE DECISION MODELS

Demos is a modeling environment designed to help a co-operating team design, analyze, critique and refine quantitative models for policy research. Earlier research found that readers of Demos models tended to become disoriented while exploring models online. In response we have designed and implemented a graphical interface to Demos named Demaps. Demaps displays diagrams of the model structure, both dependence networks and abstraction hierarchies, to provide graphic context and direct manipulation style of interaction. We describe a study of the use of Demaps to understand and compare multiple versions of models. The study employs verbal protocol analysis to evaluate the design of Demaps and to discover expert strategies for model understanding and criticism. Subjects were able to learn to use Demaps effectively in about an hour to review and compare policy models and perform sensitivity analyses. The study describes two strategies used in reading models and suggests the desirability of additional facilities for recording model critiques and accessing detailed background information on models. *We gratefully acknowledge the contributions of many people, including Jill Larkin, Jim Morris, Granger Morgan, Andrew Appel. and our subjects. This work was supported by the Information Technology Center and the National Science Foundation under grant IST-8316890

Investigation of low-cost infrared sensing for intelligent deployment of occupant restraints

In automotive transport, airbags and seatbelts are effective at restraining the driver and passenger in the event of a crash, with statistics showing a dramatic reduction in the number of casualties from road crashes. However, statistics also show that a small number of these people have been injured or even killed from striking the airbag, and that the elderly and small children are especially at risk of airbag-related injury. This is the result of the fact that in-car restraint systems were designed for the average male at an average speed of 50 km/hr, and people outside these norms are at risk. Therefore one of the future safety goals of the car manufacturers is to deploy sensors that would gain more information about the driver or passenger of their cars in order to tailor the safety systems specifically for that person, and this is the goal of this project. This thesis describes a novel approach to occupant detection, position measurement and monitoring using a low-cost thermal imaging based system, which is a departure from traditional video camera-based systems, and at an affordable price. Experiments were carried out using a specially designed test rig and a car driving simulator with members of the public. Results have shown that the thermal imager can detect a human in a car cabin mock up and provide crucial real-time position data, which could be used to support intelligent restraint deployment. Other valuable information has been detected such as whether the driver is smoking, drinking a hot or cold drink, using a mobile phone, which can help to infer the level of driver attentiveness or engagement

Potential Effects of Autonomous Vehicles on the Insurance Industry

The implementation of autonomous vehicles, or self-driving cars, promises to radically change much of the normal way of life. While it may seem inconsequential to start small with a vehicle of relatively low level of automation, there are many factors to consider. Some of these factors include security, moral dilemmas, and even the insurance field. One can look back at previous implementations of new technology, such as air bags, and see that it can be difficult to predict consequences and adapt. However, actuaries have been suggesting solutions to make autonomous vehicles a safe reality. While the solutions may vary, one thing is clear: communication between the vehicle and the insurance field is imperative

Identifying and Quantifying Factors Affecting Injury Severity of Young Drivers Involved in Single Vehicle Crashes Occurring within Curves on Rural Two-Lane Roads in Louisiana

This study investigates factors affecting young driver injury levels for single vehicle crashes occurring within curves on rural two-lane roads in Louisiana. Although the number of fatal and serious injury crashes involving young drivers is declining, young drivers are still overrepresented in crashes and crashes are still the leading cause of death for young drivers. Driver injury prediction models are formulated using binary logistic regression and Bayesian Network (BN) modeling. Binary logistic regression models have commonly been used in safety studies to analyze injury levels of occupants involved in crashes over the past few decades. More recently, a few safety studies have begun to use BN models to evaluate injury levels. This study identifies eight significant factors affecting youth driver injury levels: air bag, distracted, ejected, gender, protection system, substance suspected, violation, and most harmful event. Of these factors distracted, protection system, substance suspected, and violation are human factors which can be modified through educational programs. While both models are able to identify statistical significant variables, more insight is gained from the BN model. For instance, both models found gender to be statistically significant. While the logistical regression model finds males are 0.751 times less likely to be injured than female, the BN finds gender only has a 0.02% direct effect on injury. The BN shows that it is not gender itself that affects driver injury level, but the different behavior characteristics of males versus females which affect injury levels. Males are less likely to wear seatbelts and more likely to be suspected of alcohol in crashes. It is these driver behaviors, not the gender of the driver, which affects injuries. This study also has a number of theoretical and practical implications. As the first study to utilize BN modeling in evaluating driver injury levels in Louisiana, it expands the literature of BN models being used for analyzing injury levels in car crashes. The findings are also important to driver educational and safety professionals. By identifying factors affecting young driver injury levels, educational and training programs can be enhanced to target specific human behaviors to save more lives