Map Line Interface for Autonomous Driving

Delivering an accurate representation of the lane ahead of an autonomously driving vehicle is one of the key functionalities of a good ADAS perception system. This statement holds especially for driving on a highway. Functions such as lane keeping assistance rely on a proper representation of the lanes from the perception subsystem. To achieve such a proper representation, information from various sensors such as cameras, LiDARs, radars, HD maps are taken into consideration. Up to now HD map information has been mainly used for longitudinal control, e.g. in cases when there are speed limits or curves ahead. Using map information for lateral control is more difficult, as the quality of the information derived from the map heavily depends on the precision of the positional and heading information of the ego vehicle. Uncertainty in the ego vehicle position and pose directly results in uncertainty in the line information retrieved from the digital map. We examine the influence of an uncertain Gaussian ego position and pose for the resulting map line information which is not necessarily Gaussian. In order to transport the map line information to other subsystems such as the lane fusion module, we need to approximate the map line distribution by a suitable data structure which is both accurate and compact. We discuss and evaluate suitable approximations of the resulting map line distributions such as mean values of map lines only, mean values combined with standard deviation values and mean values combined with the full covariance matrices. We show that the usage of mean values and covariance matrices approximate the true distributions rather accurately, and therefore are both from an accuracy point of view and from a bandwidth point of view the way to represent map lines in interfaces.