Abstract: An approach is provided for determining a negative observation of a road feature (e.g., traffic sign). The approach involves, for example, querying a spatial data structure for a set of location points based on a spatial radius around a location of the road feature. The spatial data structure stores a plurality of location points from a plurality of location traces that did not have a road feature observation. The approach also involves map matching each location trace in the set of location points to a matched path of road links. The approach further involves determining that said each location trace is a negative observation of the road feature based on determining that the location of the road feature falls between two map-matched location points of said each location trace that are on the matched path of road links.

Abstract: An approach is provided for traffic sign learning. The approach involves, for example, receiving a plurality of traffic sign observations generated using sensor data collected from a plurality of vehicles. Each of the plurality of traffic sign observations includes location data and sign property data for an observed traffic sign corresponding to said each of the plurality of traffic sign observations. The approach also involves clustering the plurality of traffic speed sign observations into at least one cluster based on the location data and the sign property data. The approach further involves determining a learned sign for the at least one cluster, and determining a learned sign value indicated by the learned sign based on the location data, the sign property data, or a combination of the plurality of traffic sign observations aggregated in the at least one cluster.

Abstract: An approach is provided for determining a negative observation of a road feature (e.g., traffic sign). The approach involves, for example, querying a spatial data structure for a set of location points based on a spatial radius around a location of the road feature. The spatial data structure stores a plurality of location points from a plurality of location traces that did not have a road feature observation. The approach also involves map matching each location trace in the set of location points to a matched path of road links. The approach further involves determining that said each location trace is a negative observation of the road feature based on determining that the location of the road feature falls between two map-matched location points of said each location trace that are on the matched path of road links.

Abstract: An approach is provided for traffic sign learning. The approach involves, for example, receiving a plurality of traffic sign observations generated using sensor data collected from a plurality of vehicles. Each of the plurality of traffic sign observations includes location data and sign property data for an observed traffic sign corresponding to said each of the plurality of traffic sign observations. The approach also involves clustering the plurality of traffic speed sign observations into at least one cluster based on the location data and the sign property data. The approach further involves determining a learned sign for the at least one cluster, and determining a learned sign value indicated by the learned sign based on the location data, the sign property data, or a combination of the plurality of traffic sign observations aggregated in the at least one cluster.