An automatic vehicle positioning management system includes an on-vehicle apparatus and a portable device. The on-vehicle apparatus, installed on a vehicle, acquires a first location of the vehicle through wireless positioning. The first location is sent to the portable device which acquires a second location of the vehicle through GPS. When multiple vehicles form a fleet, each vehicle respectively sends its first and second locations to a server through its portable device. The second location of each vehicle is corrected by operations of point error analysis, image overlay and point error correction, so that the fleet can be managed more precisely.

A microservice node can include a network real-time kinematics (RTK) device to receive raw satellite data associated with a physical reference station via a first message in a first message queue, to receive static virtual location data associated with a static virtual reference station (VRS) agent, to generate corrections data for the static VRS agent based on the raw satellite data and the static virtual location data, and to transmit the corrections data to the static VRS agent. The microservice node can include the static VRS agent to publish the corrections data in a second message in a second message queue. The microservice node can include an adapter device to determine that the client device is located within a geographic area associated with the static VRS agent and to transmit the corrections data from the second message queue to the client device.

A microservice node can include a network real-time kinematics (RTK) device to receive raw satellite data associated with a physical reference station via a first message in a first message queue, to receive static virtual location data associated with a static virtual reference station (VRS) agent, to generate corrections data for the static VRS agent based on the raw satellite data and the static virtual location data, and to transmit the corrections data to the static VRS agent. The microservice node can include the static VRS agent to publish the corrections data in a second message in a second message queue. The microservice node can include an adapter device to determine that the client device is located within a geographic area associated with the static VRS agent and to transmit the corrections data from the second message queue to the client device.

Described are navigational systems for vehicles including modular, field-swappable and field-configurable components and a plurality of operational modes.

Described are navigational systems for vehicles including modular, field-swappable and field-configurable components and a plurality of operational modes.

The present disclosure describes a system for dynamically determining an accurate location of a light electric vehicle. For example, if a light electric vehicle is within a predetermined distance of a location for which an accurate location determination is needed or required, a light electric vehicle management system may update the determined location of the light electric vehicle with a location correction factor that is based, at least in part, on a reference location provided by a stationary reference point.

The present disclosure describes a system for dynamically determining an accurate location of a light electric vehicle. For example, if a light electric vehicle is within a predetermined distance of a location for which an accurate location determination is needed or required, a light electric vehicle management system may update the determined location of the light electric vehicle with a location correction factor that is based, at least in part, on a reference location provided by a stationary reference point.

A method of automatic labeling of images for supervised machine learning includes obtaining images of roadside objects with a camera mounted to a vehicle, recording a position and orientation of the vehicle within a defined coordinate system while obtaining the images recording position information for each roadside object with the same defined coordinates system as used while recording the position and orientation of the vehicle, and correlating a position of each of the obtained images of the roadside objects with the position information of each roadside object in view of the recorded position and orientation of the vehicle. The images are labeled to identify the roadside objects in view of the correlated position of each of the obtained images of the roadside objects.

A method of automatic labeling of images for supervised machine learning includes obtaining images of roadside objects with a camera mounted to a vehicle, recording a position and orientation of the vehicle within a defined coordinate system while obtaining the images recording position information for each roadside object with the same defined coordinates system as used while recording the position and orientation of the vehicle, and correlating a position of each of the obtained images of the roadside objects with the position information of each roadside object in view of the recorded position and orientation of the vehicle. The images are labeled to identify the roadside objects in view of the correlated position of each of the obtained images of the roadside objects.

This invention relates to a system of lawn mowing and caring services. It comprises a lawn profile data information management system having lawn profile data stored therein; at least one mobile device for registered users, which communicates wirelessly with the lawn profile data information management system for requesting services; and at least one robotic lawn mower or lawn robot which works with the mobile device to acquire requisite lawn profile data from the lawn profile data information management system for the services before the robotic lawn mower or lawn robot can perform the requisite mowing or caring services on the specific piece of lawn.