The aim of this research is to determine the effect of unsignalised intersection geometry on the rates of the various types of accidents occurring at unsignalised intersections. A literature review has identified that there is little consistency between the results of previous studies. Some studies found that particular parameters had an opposite effect to what was expected. With this in mind, the research identified reasons for these results and developed two basic approaches to mitigate some of the problems with multi-factor type studies. These approaches are 'maximise the efficiency of data collection' and 'develop techniques for analysing less than perfect data'. A database consisting of 206 unsignalised intersection sites from throughout Queensland was used for analysis. The outcome of this research confirms the validity of several of the current design standards for unsignalised intersections, in addition to identifying new engineering procedures.

For several years, there has been a remarkable increase in efforts to develop an autonomous car. Autonomous car systems combine various techniques of recognizing the environment with the help of the sensors and could drastically bring down the number of accidents on road by removing human conduct errors related to driver inattention and poor driving choices. In this research thesis, an algorithm for jointly ego-vehicle motion and road geometry estimation for Advanced Driver Assistance Systems (ADAS) is developed. The measurements are obtained from the inertial sensors, wheel speed sensors, steering wheel angle sensors, and camera. An Unscented Kalman Filter (UKF) is used for estimating the states of the non-linear system because UKF estimates the state in a simplified way without using complex computations. The proposed algorithm has been tested on a winding and straight road. The robustness and functioning of our algorithm have been demonstrated by conducting experiments involving the addition of noise to the measurements, reducing the process noise covariance matrix, and increasing the measurement noise covariance matrix and through these tests, we gained more trust in the working of our tracker. For evaluation, each estimated parameter has been compared with the reference signal which shows that the estimated signal matches the reference signal very well in both scenarios. We also compared our joint algorithm with individual ego-vehicle and road geometry algorithms. The results clearly show that better estimates are obtained from our algorithm when estimated jointly instead of estimating separately.