Map-based curvilinear coordinates for autonomous vehicles

Localization within high definition maps is a key problem for autonomous navigation as vehicles need to extract information from them. In addition, many navigation tasks are defined with respect to map features. For instance, estimating the cross-track and along-track gaps of a vehicle with respect to a given path is critical for lane keeping or intersection management. Map-based localization is also important for cooperative tasks like platooning in curved roads or lane changing. This work studies different methods to compute map-based coordinates defined as curvilinear abscissa, lateral distance and heading with respect to paths in high definition maps. Four approaches using polylines, lanelets and splines are compared. Thanks to real experiments, the discontinuity issues of polylines used in current high definition maps are evaluated and we discuss advantages and drawbacks of splines-based and lanelet methods. We also report experimental results corresponding to a platoon of two vehicles in curved roads and evaluate the effects of the use of low cost GNSS receivers.