Methods and systems for updating digital maps by refining map feature positions and improving map position accuracy are disclosed. Geographic map updating may include a base map element and an update element where the base map element may include a representation of the map area displaying features thereof and the update element may include a geographic feature identification element and one or more positioning elements. One embodiment includes identifying a point of interest's position data and updating the map data within a map region surrounding the point of interest.

An autonomous driving device includes a map recording a content having different type for each position while one or a plurality of contents and positions are associated with each other, an acquisition unit acquiring the content corresponding to a first position on the map, a specification storage unit storing a plurality of autonomous driving modes of the vehicle and the type of content necessary for the execution of the modes in association with each other, a selection unit selecting an executable autonomous driving mode based on the type of content acquired by the acquisition unit and the type of content stored in the specification storage unit, and a control unit controlling the vehicle at the first position in the selected autonomous driving mode, the selection unit determines one autonomous driving mode based on an order of priority set in advance when there is a plurality of executable autonomous driving modes.

An example robot performs a scan to obtain image data of a given region. The robot performs image analysis on the image data to detect a set of undesirable objects, and generates a reference map that excludes the set of undesirable objects, where the reference map is associated with the location of the robot at the time of the scan.

An example robot performs a scan to obtain image data of a given region. The robot performs image analysis on the image data to detect a set of undesirable objects, and generates a reference map that excludes the set of undesirable objects, where the reference map is associated with the location of the robot at the time of the scan.

A system for automatically annotating a map includes: a robot; a server operably connected to the robot; file storage configured to store files, the file storage operably connected to the server; an annotations database operably connected to the server, the annotations database comprising map annotations; an automatic map annotation service operably connected to the server, the automatic map annotation service configured to automatically do one or more of create a map of an item of interest and annotate a map of an item of interest; a queue of annotation requests operably connected to the automatic annotation service; and a computer operably connected to the server, the computer comprising a graphic user interface (GUI) usable by a human user.

A system for automatically annotating a map includes: a robot; a server operably connected to the robot; file storage configured to store files, the file storage operably connected to the server; an annotations database operably connected to the server, the annotations database comprising map annotations; an automatic map annotation service operably connected to the server, the automatic map annotation service configured to automatically do one or more of create a map of an item of interest and annotate a map of an item of interest; a queue of annotation requests operably connected to the automatic annotation service; and a computer operably connected to the server, the computer comprising a graphic user interface (GUI) usable by a human user.

A cluster generation apparatus includes processing circuitry configured to acquire position information and time information for a plurality of users; generate a cluster based on spots visited by the users by using the position information for the users, the cluster being a classification of the users; and generate, for each generated cluster, a movement route based on a history of movement of the cluster between the spots, from the time information and the position information for the users belonging to the cluster.

A cluster generation apparatus includes processing circuitry configured to acquire position information and time information for a plurality of users; generate a cluster based on spots visited by the users by using the position information for the users, the cluster being a classification of the users; and generate, for each generated cluster, a movement route based on a history of movement of the cluster between the spots, from the time information and the position information for the users belonging to the cluster.

A vehicle behavior monitor installed in a host vehicle that monitors the behavior of other vehicles. When the behavior of the other vehicles appears to deviate from an expected trajectory of an in-vehicle map, a geo-fenced region is monitored by external sensors. The external sensor data stream is input to a pre-trained anomaly detector. The clusters from the feature space of the encoder are compared to a database of known behaviors. A confidence level is determined based on the number of vehicles which exhibit the behavior. If the confidence level is equal to or greater than a confidence level threshold, a persistence value is calculate based on the type of behavior. The behavior and the persistence value are used to update the in-vehicle map. Based on the persistence value, the update is transmitted to a map server when the host vehicle enters an area of high data connectivity or is dropped.

A vehicle behavior monitor installed in a host vehicle that monitors the behavior of other vehicles. When the behavior of the other vehicles appears to deviate from an expected trajectory of an in-vehicle map, a geo-fenced region is monitored by external sensors. The external sensor data stream is input to a pre-trained anomaly detector. The clusters from the feature space of the encoder are compared to a database of known behaviors. A confidence level is determined based on the number of vehicles which exhibit the behavior. If the confidence level is equal to or greater than a confidence level threshold, a persistence value is calculate based on the type of behavior. The behavior and the persistence value are used to update the in-vehicle map. Based on the persistence value, the update is transmitted to a map server when the host vehicle enters an area of high data connectivity or is dropped.