Estimating high definition map parameters with convolutional neural networks

Sebastian Bittel, Timo Rehfeld, Michael Weber, J. Marius Zöllner

Abstract: In this paper, we present a method to estimate abstract parameters of high definition (HD) maps from sensor data. Parameters we estimate include the distance from ego-vehicle to road boundary, orientation of the ego-vehicle with respect to lanes, number of lanes, and street type. Our method is realized as a Convolutional Neural Network (CNN) that takes pre-processed sensor information in the form of grid map images as input. The estimated parameters of the network can then either be used for localization or to validate existing map data. To generate ground truth training samples, we use a semi-automatic procedure based on a good localization method to align the HD map with the sensor information from the vehicle. Our experiments yield some first promising results of the concept.