Abstract: In one embodiment, a match application identifies an edge corresponding to a road segment based on an observational point specifying an estimated position and an estimated heading. The match application performs search operations on a road network graph based on the estimated position to identify multiple candidate edges. For a first candidate edge, the match application computes a first quality level based on at least a distance between the estimated position and the first candidate edge and an angle between the estimated heading and a direction associated with the first candidate edge. Subsequently, the match application determines that the first quality level indicates that the first candidate edge has a higher quality than any other candidate edge. Finally, the match application transmits the first candidate edge to an application that associates content specified in relation to the estimated position with the first candidate edge.

Abstract: In one embodiment, a layer application generates navigation-related data based on observations associated with vehicles. In operation, the layer application generates an attribute based on a first observation associated with a vehicle and an observation type. The layer application then compares the attribute to an existing road database to generate a dynamic layer. Subsequently, the layer application causes the dynamic layer to be transmitted to a navigation subsystem. After receiving the dynamic layer, the navigation subsystem performs navigation operation(s) based on the road database and the dynamic layer. Because the layer application may receive and operate on real-time observations, providers of road databases may produce dynamic layers at a frequency that enables navigation applications to continually provide accurate navigation data.

Abstract: In one embodiment, a speed limit application associates speed limits with road segments based on a road graph. In operation, the speed limit application selects a source road segment that meets a target road segment at an intersection based on the road graph. The source road segment is associated with a speed limit. Subsequently, the speed limit application determines a confidence value associated with extrapolating the first speed limit to the target road segment based on the first road graph. The speed limit application then determines that the confidence value indicates that a confidence in the extrapolation satisfies a minimum confidence requirement. Consequently, the speed limit application generates an attribute that associates the first speed limit with the target road segment. Finally, the speed limit application causes a navigation-related operation to be performed based on the attribute.

Abstract: In one embodiment, a cluster application generates navigation-related data based on observations received from vehicles. In operation, the cluster application computes an oriented distance between two observed object positions based on a heading, where each observed object position is associated with a different one of two observations. The cluster application then generates a cluster that includes the two observations based on the oriented distance. Subsequently, the cluster application computes an object position that is associated with the cluster based on the two observations. The cluster application transmits the object position and at least one characteristic associated with the observations to a update application that generates an update to a road database. Because the cluster application computes the object position based on multiple observations that are likely of a single object, the object position associated with the cluster may be more reliable than the observed object positions.

Abstract: In one embodiment, a speed limit application associates speed limits with road segments based on a road graph. In operation, the speed limit application selects a source road segment that meets a target road segment at an intersection based on the road graph. The source road segment is associated with a speed limit. Subsequently, the speed limit application determines a confidence value associated with extrapolating the first speed limit to the target road segment based on the first road graph. The speed limit application then determines that the confidence value indicates that a confidence in the extrapolation satisfies a minimum confidence requirement. Consequently, the speed limit application generates an attribute that associates the first speed limit with the target road segment. Finally, the speed limit application causes a navigation-related operation to be performed based on the attribute.

Abstract: In one embodiment, a cluster application generates navigation-related data based on observations received from vehicles. In operation, the cluster application computes an oriented distance between two observed object positions based on a heading, where each observed object position is associated with a different one of two observations. The cluster application then generates a cluster that includes the two observations based on the oriented distance. Subsequently, the cluster application computes an object position that is associated with the cluster based on the two observations. The cluster application transmits the object position and at least one characteristic associated with the observations to a update application that generates an update to a road database. Because the cluster application computes the object position based on multiple observations that are likely of a single object, the object position associated with the cluster may be more reliable than the observed object positions.

Abstract: In one embodiment, a match application identifies an edge corresponding to a road segment based on an observational point specifying an estimated position and an estimated heading. The match application performs search operations on a road network graph based on the estimated position to identify multiple candidate edges. For a first candidate edge, the match application computes a first quality level based on at least a distance between the estimated position and the first candidate edge and an angle between the estimated heading and a direction associated with the first candidate edge. Subsequently, the match application determines that the first quality level indicates that the first candidate edge has a higher quality than any other candidate edge. Finally, the match application transmits the first candidate edge to an application that associates content specified in relation to the estimated position with the first candidate edge.

Abstract: In one embodiment, a layer application generates navigation-related data based on observations associated with vehicles. In operation, the layer application generates an attribute based on a first observation associated with a vehicle and an observation type. The layer application then compares the attribute to an existing road database to generate a dynamic layer. Subsequently, the layer application causes the dynamic layer to be transmitted to a navigation subsystem. After receiving the dynamic layer, the navigation subsystem performs navigation operation(s) based on the road database and the dynamic layer. Because the layer application may receive and operate on real-time observations, providers of road databases may produce dynamic layers at a frequency that enables navigation applications to continually provide accurate navigation data.