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Alcohol-related collisions cause numerous deaths and injuries. The purpose of this paper is to review the technological changes that could be made and are being made that could reduce rates of impaired driving collisions. These can be classified into four categories: 1) interlock systems based on testing drivers’ blood alcohol concentration, 2) systems for monitoring driver behaviors such as head and eye or pupil movements, 3) systems that monitor vehicle dynamics and behavior, and 4) remote detection and stopping vehicles technologies. Each of these technologies has some efficiency and uses and varying levels of social and individual acceptance. Innovation may come from looking at novel ways of combining these technological designs to achieve targets to reduce the devastating effects of impaired driving for many communities.

Proliferation of real time inter-vehicular communications provides new sources for exploitable positioning data. Vehicles can, under numerous situations, have GPS satellite shortages but there will always be vehicles in their vicinity, with a different set of satellites, to provide them with useful navigation information. In this paper, we propose a cooperative positioning technique making use of reliable positions of some vehicles to enhance positioning estimates of some others. We will exploit the useful inter-vehicle data flow to extract good position measurements from vehicles with good GPS satellite LOS (line of sight), in order to enhance low positioning accuracy of other vehicles, in the neighborhood. The integration of such information will be done using geometric data fusion approach.

In positioning navigation systems, at any time, any of the sensors can break down or stop sending information, temporarily or permanently. To ensure a practical solution for use in guidance and navigation systems, faulty sensors must be detected and isolated such that their erroneous data will not corrupt the global position estimates. It is well known that Kalman filter is usually being used for data fusion applications. An interesting novel alternative is to use it for fault detection architecture as well. This paper describes the research conducted to evaluate the potential of combining fault detection and data fusion into a single architecture to make a robust positioning navigation system.

Land navigation systems need a precise and continuous position in order to function properly. The sensors commonly found in those systems are differential odometer, global positioning system and 2 or 3 axis inertial measurement unit respectively. Two or more of these complementary positioning methods must be integrated together to achieve the required performance at low cost. The integration, which implies the fusion of noisy data provided by each sensor, must be performed in some optimal manner. Most positioning system designers choose the Kalman filter as the data fusion method. An interesting alternative to the Kalman filter is the artificial neural network (ANN). This paper describes the research conducted to evaluate the potential of an ANN as a centralized fusion method and as nonlinear filters for land vehicle positioning.