Automobile Safety Technology

The purpose of this project was to evaluate the educational level of the WPI community on automobile safety devices and develop an interactive medium through which visitors can establish a better understanding of the technology. A video presentation and museum exhibit were constructed together to educate the community on the criteria of history, purpose, and functionality for several automotive technologies. The presentation component incorporates pictures, videos, and diagrams to portray the educational material about each technology, while the actual exhibit includes physical components from each category to aide in visualization of these devices. This project produced positive feedback from members of the community as well as several recommendations for future revisions of this project

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

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

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

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

We need a global feminist campaign against Artificial Intelligence bias

Ruhi Khan, ESRC Researcher at the LSE, argues that Artificial Intelligence is embedded with patriarchal norms and thus poses many harms to women. She calls for a global feminist campaign to tackle the issue of gender and race bias in AI systems through awareness, inclusion and regulation