The increasing trend towards the use of image sensors in transportation is driven both by legislation and consumer demands for higher safety and a better-driving experience. Awareness of the environment that surrounds a vehicle can make driving and manoeuvring of the vehicle much safer for all road users. The authors present an image processing method to detect lane departure in forward-facing video specifically designed to be in accordance with proposed automotive lane departure warning standards. Our system uses a novel lane-marking segmentation algorithm in accordance with national standards for lane markings. This method does not demand the high computational requirements of inverse perspective mapping unlike methods proposed in related research. The authors present a method for lane boundary modelling based on subtractive clustering and Kalman filtering in the Hough transform domain, which is within the constraints of automotive standards. Finally, using the cameras intrinsic and extrinsic parameters, the width of the vehicle and guidelines issued by the International Organisation for Standardisation, we show how lane departure can be identified. Results are presented that verify the systems high detection rate and robustness to various background interference, lighting conditions and road environments.