A device, method and system for determining a primary location of a public-safety unit is provided. An example device receives, from a plurality of devices associated with a public-safety unit, a plurality of locations associated with the public-safety unit, the public-safety unit comprising the plurality of devices and one or more public-safety personnel. The example device determines a context associated with the public-safety unit. The example device selects a single location, from the plurality of locations, that represents a primary location of the public-safety unit based on the context as determined. The example device one or more of: causes the primary location to be provided at an electronic map; and provides the primary location to one or more of a computer-aided dispatch (CAD) device and a public-safety server.

A device, method and system for determining a primary location of a public-safety unit is provided. An example device receives, from a plurality of devices associated with a public-safety unit, a plurality of locations associated with the public-safety unit, the public-safety unit comprising the plurality of devices and one or more public-safety personnel. The example device determines a context associated with the public-safety unit. The example device selects a single location, from the plurality of locations, that represents a primary location of the public-safety unit based on the context as determined. The example device one or more of: causes the primary location to be provided at an electronic map; and provides the primary location to one or more of a computer-aided dispatch (CAD) device and a public-safety server.

A driver-assistance device includes: a memory; and a processor including hardware. The processor is configured to: acquire both line-of-sight information on a line of sight of a driver and image data resulting from imaging surroundings of a vehicle; set a display position and display contents of an image generated from the image data based on the line-of-sight information; and output the display position and the display contents together with the image.

A driver-assistance device includes: a memory; and a processor including hardware. The processor is configured to: acquire both line-of-sight information on a line of sight of a driver and image data resulting from imaging surroundings of a vehicle; set a display position and display contents of an image generated from the image data based on the line-of-sight information; and output the display position and the display contents together with the image.

An anomaly detection device includes: an obtainer that obtains vehicle information related to the status of a vehicle and including location data indicating the location of the vehicle; a model storage that stores, for each of a plurality of cells of a grid imposed on a map, an evaluation model for evaluating the vehicle information of the vehicle located at the cell; and a determiner that calculates, based on the vehicle information and evaluation models each being the evaluation model and corresponding to evaluation cells including a first cell including the location of the vehicle indicated in the location data and one or more second cells each having a predetermined positional relationship with the first cell, an anomaly level indicating a degree of anomaly of the vehicle information, determines, based on the anomaly level, whether the vehicle information is anomalous, and outputs a determination result.

An anomaly detection device includes: an obtainer that obtains vehicle information related to the status of a vehicle and including location data indicating the location of the vehicle; a model storage that stores, for each of a plurality of cells of a grid imposed on a map, an evaluation model for evaluating the vehicle information of the vehicle located at the cell; and a determiner that calculates, based on the vehicle information and evaluation models each being the evaluation model and corresponding to evaluation cells including a first cell including the location of the vehicle indicated in the location data and one or more second cells each having a predetermined positional relationship with the first cell, an anomaly level indicating a degree of anomaly of the vehicle information, determines, based on the anomaly level, whether the vehicle information is anomalous, and outputs a determination result.

A vehicle dispatch service boarding location determination method uses a vehicle dispatch information data server and a terminal to determine a boarding location for a dispatched vehicle based on a vehicle dispatch request from the terminal. When there is a vehicle dispatch request, position information of a vehicle dispatch request point is acquired. Boarding location candidates present in a periphery of the vehicle dispatch request point are calculated based on the position information point and map information. A boarding location recognition difficulty, which is a difficulty for the user in recognizing the boarding location candidate as a boarding location, is calculated for each of the boarding location candidates. A boarding location candidate for which the boarding location recognition difficulty is low among the boarding location candidates is determined as the boarding location where the user will be picked up by the dispatched vehicle.

Systems and methods for grouping telematics vehicle data in one or more zones are provided herein. Systems and methods for generating zones and providing vehicle information associated with the one or more zones are also provided. Systems and methods for parallelizing zone generation and addressing zones disposed adjacent a boundary of a geographical are also provided.

The present disclosure is directed to collecting and processing data from computing devices of a plurality of autonomous vehicles (AVs). The data received from each of these AV computing devices may include raw sensor data or data that has been generated using data received by one or more sensors at respective AVs. Once this data is collected and associated with discrete locations and times, the data may be evaluated and used to generate mappings of various sorts. These mappings may include mappings of underground features generated based on an evaluations of vibration data. Alternatively, or additionally, these mapping may include mappings of landscape features, atmospheric features, or the locations of aircraft from data associated with certain types of sensing apparatus, for example radar apparatus or light detecting and ranging (LiDAR) apparatus.

The present disclosure is directed to collecting and processing data from computing devices of a plurality of autonomous vehicles (AVs). The data received from each of these AV computing devices may include raw sensor data or data that has been generated using data received by one or more sensors at respective AVs. Once this data is collected and associated with discrete locations and times, the data may be evaluated and used to generate mappings of various sorts. These mappings may include mappings of underground features generated based on an evaluations of vibration data. Alternatively, or additionally, these mapping may include mappings of landscape features, atmospheric features, or the locations of aircraft from data associated with certain types of sensing apparatus, for example radar apparatus or light detecting and ranging (LiDAR) apparatus.