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This thesis focuses on traction control techniques for electric vehicles with independently driven wheels . The aim is to maximise the grip of each wheel with the ground, improving the performance as well as the stability of the whole vehicle. Hence the estimation of the friction characteristic between tyres and ground is addressed . Accurate estimat ion is required not only to control the wheels at their best working condition but also to predict their behaviour for future manoeuvres. This information could be used to calculate the braking distance or to evaluate the maximum attainable speed during cornering . A detailed knowledge of the friction characteristic would substantially improve the functionality of anti-lock braking system (ABS), anti-slip regulation (ASR), electronic stability control (ESC) and adaptive cruise control (ACC) . Estimation of ground characteristics and traction control for electric vehi­cles have been studied before, although previous research generally relies on driving manoeuvres at the limit of adhesion to obtain meaningful information about the maximum grip offered by the ground. This is not suitable during safe driving on public roads and the present thesis addresses the problem of constantly monitoring the road conditions, and forecasting the loss of adhesion instead of waiting for it . Furthermore, my approach is robust against external forces and can be applied to vehicles on tilted grounds. Hence my novel approach supersedes previous studies that assumed the vehicle on a flat surface, mostly neglecting the effects of rolling resistance and aerodynamic drag. I also propose a framework to take into cons.ideration dynamic effects such as iner­ tial forces and the distribution of the gravitational force. Taking these effects into account would lead to a more reliable estimation, particularly effective on steep or bumpy roads where adhesion loss is more likely to happen . Some of this work resulted in the publication of an IEEE conference paper . My work belongs to a larger project aiming to develop an autonomous elec­tric vehicle whose main targets are economic and environmental sustainability. These targets led us to consider moving some of the computational require­ ments for the automatic guidance to a remote serve, wirelessly communicating with the vehicle. Together with this investigation1 original contributions of the project will include scene understanding solely based on stereo cameras and integration between the visual information and the vehicle dynamic response captured by the vehicle controller . My research is focused on this application, and a considerable amount of energy through my PhD went to the develop­ment of the system required to remotely control the vehicle. An extensive description of the development phase is included in this thesis.