Thesis (M.S.)--University of Texas at El Paso, 2009. / Title from title screen. Vita. CD-ROM. Includes bibliographical references. Also available online.

Driving simulators are used to conduct experiments on for example driver behavior, road design, and vehicle characteristics. The results of the experiments often depend on the traffic conditions. One example is the evaluation of cellular phones and how they affect driving behavior. It is clear that the ability to use phones when driving depends on traffic intensity and composition, and that realistic experiments in driving simulators therefore has to include surrounding traffic. This thesis describes a model that generates and simulates surrounding vehicles for a driving simulator. The proposed model generates a traffic stream, corresponding to a given target flow and simulates realistic interactions between vehicles. The model is built on established techniques for time-driven microscopic simulation of traffic and uses an approach of only simulating the closest neighborhood of the driving simulator vehicle. In our model this closest neighborhood is divided into one inner region and two outer regions. Vehicles in the inner region are simulated according to advanced behavioral models while vehicles in the outer regions are updated according to a less time-consuming model. The presented work includes a new framework for generating and simulating vehicles within a moving area. It also includes the development of enhanced models for car-following and overtaking and a simple mesoscopic traffic model. The developed model has been integrated and tested within the VTI Driving simulator III. A driving simulator experiment has been performed in order to check if the participants observe the behavior of the simulated vehicles as realistic or not. The results were promising but they also indicated that enhancements could be made. The model has also been validated on the number of vehicles that catches up with the driving simulator vehicle and vice versa. The agreement is good for active and passive catch-ups on rural roads and for passive catch-ups on freeways, but less good for active catch-ups on freeways.

M.Ing. (Electrical and Electronic Engineering) / Adaptive Cruise Control (ACC) is proposed to assist drivers tedious manual acceleration or braking of the vehicle, as well as with maintaining a safe headway time gap. This thesis proposes, simulates, and implements a vision-based ACC system which uses a single camera to obtain the clearance distance between the preceding vehicle and the ACC vehicle. A three-step vehicle detection framework is used to obtain the position of the lead vehicle in the image. The vehicle coordinates are used in conjunction with the lane width at that point to estimate the longitudinal clearance range. A Kalman filter filters this range signal and tracks the vehicle’s longitudinal position. Since image processing algorithms are computationally intensive, this document addresses how adaptive image cropping improves the update frequency of the vision-based range sensor. A basic model of a vehicle is then derived for which a Proportional-Integral (PI) controller with gain scheduling is used for ACC. A simulation of the system determines whether the ACC algorithm will work on an actual vehicle.