Calibration of an embedded camera for driver-assistant systems

One of the main technical goals in the automotive industry is to increase vehicle safety. The European project SPARC (secure propulsion using advanced redundant control) is developing the next generation of trucks towards this aim. The SPARC consortium intends to do so by providing the truck with active security systems. Specifically, by equipping the truck with different sensors, it can be made aware of its environment, such as other vehicles, pedestrians, etc. By combining all sensor data and processing it with internal proprioceptive information, the truck can advice, warn the driver or even override him in case of non-response. Camera systems are particularly advantageous for sensing purposes, because they are passive sensors and they provide very rich information. Moreover, they can easily be software-reconfigured to extract new or additional information from the image. Typical information that SPARC aims to extract is the position of the vehicle within the lane, the presence and distance of other vehicles or obstacles and the identification of roadsigns. In this paper, a lane-detection algorithm are presented and discussed. Some of the resulting information needs to be given in world coordinates, as opposed to image coordinates. To carry out the necessary conversion, a previous calibration is needed. The challenge is to determine a procedure to calibrate a camera mounted on a truck to precisely determine the position of obstacles situated in a 100 meter range. The two-step calibration procedure presented here has been designed to simplify the calibration of the mounted cameras in the truck production line.