This project presents the development of a collision avoidance active system for cars.There is a large interest in developing avoidance system in the automotive industry since the accidents are of such nature that can be avoided if the system works as desirable e.g., in animal crossing or having the car in front stopping without the driver noticing. A control system is designed to avoid collisions by acting on the steer and brakes of a car. An algorithm is developed to optimize a fuzzy logic controller which actuates on the steer and brakes of the car. The algorithm optimizes the inputs of the car, i.e. steer and brake, to avoid the collision with the object. The optimization of the trajectory implies that the car returns to the original lane and it is the minimum time possible inthe other lane. The object is situated at different distances and the initial speed of the car also varies depending on the situations. The results are obtained by using a car model that is developed in this project in conjunction with the tyre model, [1]. Simulations show that it performs collision avoidance manoeuvres in different conditions. Furthermore, improvements of the present work are suggested that are believed to further enhance the presented algorithm.

Driver behaviour has become an important aspect of transport research and over the years a considerable number of car following models have been developed. However, many of these models do not accurately simulate actual driving behaviour due to a lack of suitable qualitative and quantitative data. Moreover, the inclusion of socioeconomic variables in the existing models to ascertain the effect on car following behaviour is lacking. This research underlines the need to further investigate driving behaviour and car following models and to develop techniques to provide a better understanding of driver-vehicle interactions during car following. It investigates data collection techniques and develop better techniques to enhance and improve the collection of microscopic driver behaviour and traffic flow data. This study developed a novel data collection technique which involved instrumenting a private vehicle with front and rear advanced radar sensors, both forward and rear facing video-audio recorders connected to GPS based time series speed and distance measurement devices, an in-vehicle computer logging vehicle speed and a CAN monitoring interface user program to provide real time monitoring and display of data. This system has been utilised to collect a more enhanced and reliable microscopic driver behaviour data in three consecutive vehicles movements which represents an improvement from previously used systems. Three different versions of the GHR car following model were produced for: car following car, truck following car and car following truck. Further analysis of the GHR model showed that in the case of car following car, car drivers responses to the lead car are more obviously stronger than in the case of truck following a car. A distance-based car following model and distance-based two-leader car following model that predict the safe following distance of following vehicles were developed to provide a better understanding of driver behaviour. An extension of these models to include gender, corridor (road) type and vehicle occupancy showed evidence of statistical significance of these variables on driver behaviour. A bus following model that predicts the “following distance” also has been calibrated to describe the interactions between a bus and a car within urban-rural driving conditions. In addition, data analysis showed that drivers were inconsistent with their driving behaviour and that there was variability in driving behaviour across the drivers observed in keeping a safe or desired following distance. This study provides a platform for a number of future research agendas including data collection techniques for collection of driver behaviour data; evaluation of different ITS technologies; impact assessment of ACC on driver safety and improvement of traffic microscopic simulation tools in order to strengthen their ability to simulate realistic transport problems for efficient and effective transportation systems.