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Numerous control techniques, including full state-feedback sliding mode control, have addressed the split-m braking manoeuvre. The controllers presented in this thesis extend previous work by using only certain measured outputs. These controllers work in conjunction with an anti-lock braking system (ABS) to provide safe, effective braking through steer-by-wire. Two strategies are presented, each using two measured outputs (yaw rate and lateral deviation) and front road wheel steering angle as the sole input. The first scheme estimates two further states by employing a sliding mode observer. It benefits from sliding mode robustness properties, and is demonstrated by simulation on a nonlinear model to be robust to large variations in tyre stiffness. The second scheme consists of a compensator-based, sliding mode controller. This controller, tested under the same conditions as the first, does not perform as well as the observer-based controller from a robustness perspective, but it produces a closed-loop system of lower order, which may be advantageous from an implementation perspective. Potentially dangerous vehicle scenarios, such as severe understeer, oversteer and split-m braking, are currently detected using a large combination of measurements, seeking to estimate vehicle states robustly. In this thesis, a simpler approach is adopted, which, rather than estimating all the vehicle states, looks for `signature differences' between the behaviour of an ideal linear vehicle model and the actual measured behaviour, in terms of vehicle yaw moments and lateral forces.

Over 300,000 individuals are injured every year by vehicle related accidents in the United Kingdom alone. Government and the vehicle manufacturers are not only bringing new legislation but are also investing in vehicle safety research to bring this figure down. A private self-driven car is an important factor in maintaining the independence and quality of life of the third age individuals. However, since older people brings deterioration of cognitive, physical and visual abilities, resulting in slower reaction times and lapses while driving. The third age individuals are involved in more vehicle related accidents than middle aged individuals. This scenario is corrected by the fact that the number of third age individuals is increasing, especially in developed countries. It is expected that the percentage of third age individuals in the United Kingdom will increase to 20% of the total population by 2010. Several safety systems have been developed by the automotive industry including intelligent airbags, Electronic Stability Control (ESC) and pre-tensioned seat belts, but nothing has been specifically developed for the third age related problems. This thesis proposes a driver posture identification system using low resolution infrared imaging. The use of a low resolution thermal imager provides a reliable non-contact based posture identification system at a relatively low cost and is shown to provide robust performance over a wide range of conditions. The low resolution also protects the privacy of the driver. In order to develop the proposed safety system an Artificial Intelligent Thermal Imaging algorithm (AITl) is created in MatLAB. Experimentation is conducted in real and simulated environment, with human subjects, to evaluate the results of the algorithm. The result shows that the safety system is able to identify eighteen different driving postures. The system also provides other valuable information about the driver such as driver physical built, fatigue, smoking, mobile phone usage, eye-height and trunk stability. It is clear that in incorporating this safety system in the overall automotive central strategy, better safety for third age individual can be achieved. This thesis provides various contributions to knowledge including a novel neural network design, a safety system using low resolution infrared imager and an algorithm that can identify driver posture.