This thesis is concerned with automotive active safety, and a central theme is a new safety function called Emergency Lane Assist (ELA). Automotive safety is often categorised into passive and active safety, where passive safety is concerned with reducing the effects of accidents and active safety aims at avoiding them. ELA detects lane departure manoeuvres that are likely to result in a collision and prevents them by applying a steering wheel torque. The ELA concept is based on traffic accident statistics, i.e., it is designed to give maximum safety based on information about real life traffic accidents. The ELA function puts tough requirements on the accuracy of the information from the sensors, in particular the road shape and the position of surrounding objects, and on robust threat assessment. Several signal processing methods have been developed and evaluated in order to improve the accuracy of the sensor information, and these improvements are also analysed in how they relate to the ELA requirements. Different threat assessment methods are also studied, and a common element in both the signal processing and the threat assessment is that they are based on driver behaviour models, i.e., they utilise the fact that depending on the traffic situation, drivers are more likely to behave in certain ways than others. Most of the methods are general and can be, and hopefully also will be, applied also in other safety systems, in particular when a complete picture of the vehicle surroundings is considered, including information about road and lane shape together with the position of vehicles and infrastructure. All methods in the thesis have been evaluated on authentic sensor data from actual and relevant traffic environments.