An approach is provided for identifying partitions associated with erratic pedestrian behaviors and their correlations to points of interest. For example, the approach involves receiving sensor data associated with a geographic area. The approach also involves based on the sensor data, determining pedestrian-behavior parameter(s) respectively for partition(s). Each respective partition of the partition(s) represents a respective subarea of the geographic area, a respective time period, or a combination thereof. The approach further involves identifying at least one erratic partition from the partition(s) based on determining that a respective pedestrian-behavior parameter associated with the at least one erratic partition deviates from a baseline pedestrian-behavior parameter by at least a threshold extent. The approach further involves determining a correlation of the at least one erratic partition to at least one map feature of a geographic database. The approach further involves providing the correlation as an output.

A server includes a feature point position estimation section that estimates positions of feature points of a plurality of respective input maps if a position correction section has failed in position correction for an input map, an accumulated data generation section that accumulates the positions of the feature points of the plurality of respective input maps to generate accumulated data, a convergence determination section that determines whether the positions of the feature points have converged based on the accumulated data, a difference data generation section that generates, if it is determined that the positions of the feature points have converged, difference data from the positions of the feature points that have converged, and a difference data reflection section that reflects the difference data in the reference map to update the reference map.

The present disclosure provides a method and apparatus for automatically producing map data. The method includes: performing track rectification on crowdsourcing tracks based on corresponding standard tracks, and locating each map element included, based on depth information of track point images included in the rectified crowdsourcing tracks; comparing a latest map element obtained based on the rectified crowdsourcing tracks locating and an old map element at a corresponding locating position using a pre-built entity semantic map; determining, in response to a change in the latest map element compared to the old map element, a target processing method according to a processing standard of a changed map element pre-abstracted from a map element update specification; and processing the latest map element according to the target processing method to obtain a processed latest map.

Provided are an environmental map management apparatus, an environmental map management method, and a program that can correct an environmental map to achieve an accurate association between the environmental map and a map indicated by given map data provided by a given map service. A common environmental map data storage unit (64) stores environmental map data that is generated based on sensing data acquired by a tracker and that indicates an environmental map. A pattern identification unit (68) identifies a predetermined pattern in the environmental map on the basis of the environmental map data. A corresponding element identification unit (70) identifies a linear element that appears in a map indicated by given map data provided by a given map service and that is associated with the predetermined pattern. A common environmental map data update unit (72) updates the environmental map data on the basis of a location of the linear element in the map indicated by the given map data.

A system configured to, and method of, generating and providing a data product using data supplied by a multitude of vehicles. The system/method includes carrying out a re-segmentation process in which an initial set of road segments are processed so as to obtain a re-segmented set of road segments; attributing traffic data to at least a subset of the re-segmented set of road segments, wherein, for each road segment of the subset of road segments, a portion of the traffic data is attributed to the road segment based on geographical proximity; carrying out a road segment merging process on the re-segmented set of road segments in order to obtain a merged set of road segments; aggregating metrics associated with the merged set of road segments to obtain aggregated road segment data; generating the data product using the aggregated road segment data; and providing the data product to a third party.

An apparatus receives a plurality of instances of map update data during a time window. Each instance is associated with a respective geographic region of a plurality of defined geographic regions. The apparatus determines, for the time window, region rankings for the plurality of defined geographic regions. A region ranking is determined based on a volume measure, a measure of map update activity, a measure of inaccurate activity, a general priority weight, and/or an interest density corresponding to the respective geographic region. The apparatus determines, for the time window, respective ranking ranges for a plurality of data buckets and assigns respective instance rankings to the instances of map update data. The instances are assigned to data buckets based on the instance rankings and ranking ranges. The apparatus ingests the instances based on the respective data buckets to cause a digital map maintained by the apparatus to be updated.

An example control system includes a memory and at least one processor to obtain image data from a given region and perform image analysis on the image data to detect a set of objects in the given region. For each object of the set, the example control system may classify each object as being one of multiple predefined classifications of object permanency, including (i) a fixed classification, (ii) a static and fixed classification, and/or (iii) a dynamic classification. The control system may generate at least a first layer of a occupancy map for the given region that depicts each detected object that is of the static and fixed classification and excluding each detected object that is either of the static and unfixed classification or of the dynamic classification.

Disclosed herein are system and method embodiments to implement a validation of a vector map. The validation process may merge proposed and persisted high-definition mapping data, evaluate the high-definition mapping data with a set of customizable validation rules, return/persist validation results, and provide a means to acknowledge validation failures to minimize creation of problematic vector map content.

A semi-public blockchain maintained on one or more nodes in a map cloud platform comprises data for maintaining a global map of a predetermined geographic area. The blockchain also comprises a plurality of data records, where each data record is associated with an update to a global map. When a message associated with a map update to the global map is received, the nodes of the blockchain determine a consensus by evaluating the map update, where the evaluating comprises performing a plurality of proofs including a proof of location, a proof of iterations, a proof of physical delivery and a proof of safety. When consensus is attained and the map update is validated, a data record associated with the map update is generated and added to the blockchain with a timestamp and a link to prior data records in the blockchain.

Methods, apparatus and systems for map reconstruction based on wireless tracking are described. In one example, a described system comprises: a sensor configured to collect sensing data in a venue and obtain a plurality of trajectories, and a processor. Each trajectory is a time series of spatial coordinates (TSSC) representing a path traversed by a respective object in the venue. Each TSSC is accompanied by at least one respective time series of sensing data (TSSD) collected while the respective object traverses the path in the venue. The processor is configured for: segmenting each TSSC and its accompanying at least one TSSD into segments, bundling the plurality of trajectories based on similarity measures between pairs of the segments, fusing the bundled trajectories to generate fused trajectories, computing a shape of the fused trajectories, and generating a map of the venue based on the computed shape.