A method of operating a moving object having a plurality of identity devices is provided. The method includes generating data in the moving object, transmitting first data of a plurality of data to a first node through a first identity device of the moving object and transmitting second data of the plurality of data to a second node through a second identity device of the moving object, receiving the first data from the first node and receiving the second data from the second node, and operating the moving object based on the first data and the second data.

A method may include designating, by a server, a designated geographic region and identifying a set of user accounts associated with mobile electronic devices located within the designated geographic region. The method may also include obtaining information from third party servers related to interactions of the user accounts with the third party servers. The method may also include receiving, from an advanced user account, a request for an automatically generated list of distinct geographic regions. The method may also include using a machine learning process to select geographic regions as the list of distinct geographic regions, the designated geographic region included in the list of distinct geographic regions based on the machine learning process acting on the information from third party servers and a quantity of user accounts. The method may also include transmitting the generated list to an electronic device associated with the advanced user account.

Information corresponding to one or more traversable map elements (TMEs) within a zone of interest is accessed from the geographic database. A respective category of a plurality of categories is determined for each of the one or more TMEs based at least in part on the information corresponding to the TME. A first category encoding data structure is generated based at least in part on map version agnostic identifiers corresponding to TMEs determined to be in a first category of the plurality of categories, wherein the first category encoding data structure is a probabilistic data structure configured to not provide false negatives for TMEs within the zone of interest. The first category encoding data structure is provided such that a mobile apparatus receives the first category encoding data structure.

A sensor calibration system periodically receives scene data from a detector in a calibration scene. The calibration scene includes calibration targets. The sensor calibration system generates a calibration map based on the scene data. The calibration map is a virtual representation of the calibration scene and includes features of the calibration targets that can be used as ground truth features for calibrating AV sensors. The sensor calibration system can periodically update the calibration map. For instance, the sensor calibration system receives the scene data at a predetermined frequency and updates the calibration map every time it receives new scene data. The predetermined frequency may be a frequency of the detector completing a full scan of the calibration scene. The sensor calibration system provides a latest version of the calibration map for being used by an AV to calibrate a sensor on the AV 110.

The present disclosure relates to a method of augmenting human perception of the surroundings, comprising: receiving a first set of data from a user device associated with the user, the first set of data comprising at least location information; receiving a second set of data from a local distributed database associated with the location information, the second set of data comprising a position of one or more entities; constructing a digital model representing the user and the one or more entities using the first and second set of data; projecting a respective course for the user and for the or each entity by inputting the digital model into a first machine learning algorithm (MLA) to infer one or more events; identifying at least one inferred event relevant to the user by inputting the one or more inferred events to the first MLA; and communicating the at least one relevant inferred event to the user.

This document describes change detection criteria for updating sensor-based maps. Based on an indication that a registered object is detected near a vehicle, a processor determines differences between features of the registered object and features of a sensor-based reference map. A machine-learned model is trained using self-supervised learning to identify change detections from inputs. This model is executed to determine whether the differences satisfy change detection criteria for updating the sensor-based reference map. If the change detection criteria is satisfied, the processor causes the sensor-based reference map to be updated to reduce the differences, which enables the vehicle to safely operate in an autonomous mode using the updated reference map for navigating the vehicle in proximity to the coordinate location of the registered object. The map can be updated contemporaneously as changes occur in the environment and without hindering performance, thereby enabling real-time awareness to support controls and to improve driving-safety.

An approach is provided for speculative navigation routing in incomplete maps. The approach involves, for example, generating an offline navigation route to a destination using offline map data cached at a device. The approach also involves transmitting a routing request to a routing server for an online navigation route to the destination. The approach further involves providing the online navigation route or a portion of the online navigation route based on determining that the online navigation route or the portion of the online navigation is received within a timeout period. The approach further involves providing the offline navigation route or a portion of the offline navigation route generated during the timeout period based on determining that the online navigation route or the portion of the online navigation route is not received before the timeout period ends.

An apparatus and a method allowing a pilot or more generally a crew to enrich a database of user obstacles directly inside the aircraft, on the ground and even during flight. Generally, the apparatus is based on a new software component, which allows new services to be provided for the functional avionic components and for the human machine interfaces HMI of the avionics. This new software component is configured to allow: user obstacles to be created, changed, deleted at the request of components of HMIs of the avionics; user obstacles to be sent to clients of the services of this new software component, which are either other functional components of the avionics or other components of HMIs of the avionics; user obstacles to be recorded to a nonvolatile memory and restored from this nonvolatile memory.

An information processing device 100 according to the present disclosure includes a reception unit 133 that receives private map information including a private land area and a generation unit 134 that generates connection map information in which the private map information and public road map information are connected to each other, based on a reference point, the public road map information including information regarding a public road, and the reference point indicating a common position between the private map information received by the reception unit 133 and the public road map information.

A computer is programmed to allocate respective connectivity quality data of a geographic area to a first map or a second map. The computer is further programmed to assign one of a plurality of subsets of the first map and one of a plurality of subsets of the second map to a first vehicle, identify respective locations of the first and second vehicles and one of the first or second maps that includes the locations of the first and second vehicles. The computer is further programmed to send, to the first and second vehicles, a map dataset that is a result of applying an XOR function to (1) the subset of the identified map that includes the location of the first vehicle assigned to the first vehicle and (2) the subset of the identified map that includes the location of the second vehicle assigned to the second vehicle.