Method and system for supervised learning of road signs

An apparatus for predicting a location of a road sign, the apparatus comprising:

• at least one processor;
• and
• at least one memory including computer program code for one or more programs,
• the at least one memory and the computer program code configured to, with the at least one processor, cause the apparatus to perform at least the following:
• receive a set of pre-processed road observations;
• extract a plurality of features from the set of pre-processed road observations, wherein the plurality of features comprise at least a plurality of sensor based features and a plurality of map based features;
• associate a set of sensor based features from the plurality of sensor based features and a set of map based features from the plurality of map based features with at least one of a plurality of ground truth points in ground truth data;
• train a machine learning model based on the association of the ground truth data with the set of sensor based features and the set of map based features with at least one of a plurality of ground truth points in the ground truth data;
• and
• predict the location of the road sign based on the trained machine learning model.

A computer program product comprising at least one non-transitory computer-readable storage medium having computer-executable program code instructions stored therein, the computer-executable program code instructions comprising program code instructions for:

• receiving a set of pre-processed road observations;
• extracting a plurality of features from the set of pre-processed road observations, wherein the plurality of features comprise at least a plurality of sensor based features and a plurality of map based features;
• associating a set of sensor based features from the plurality of sensor based features and a set of map based features from the plurality of map based features with at least one of a plurality of ground truth points in ground truth data;
• training a machine learning model based on the association of the ground truth data with the set of sensor based features and the set of map based features with at least one of a plurality of ground truth points in the ground truth data;
• and
• predicting the location of the road sign based on the trained machine learning model.