The present teaching relates to method, system, medium, and implementation of a global model update center. At least one model is established at the model update center for detecting objects surrounding each of autonomous driving vehicles of a fleet. A plurality of labeled data items are received, from the fleet of autonomous driving vehicles, where each of the labeled data items is detected, based on the at least one model, from sensor data characterizing surroundings of the autonomous driving vehicles. The labeled data items are generated automatically on-the-fly by the autonomous driving vehicles. Based on the received labeled data items, at least some of the models are updated and model update information is accordingly generated. Such generated model update information is then distributed to the fleet of autonomous driving vehicles.

The present teaching relates to method, system, medium, and implementation of in-situ perception in an autonomous driving vehicle. A plurality of types of sensor data are acquired continuously via a plurality of types of sensors deployed on the vehicle, where the plurality of types of sensor data provide information about surrounding of the vehicle. One or more items surrounding the vehicle are tracked, based on a model, from a first of the plurality of types of sensor data from a first type of the plurality of types of sensors. For a tracked item, cross temporal validation is performed by obtaining an estimated label for the item, retrieving previous labels corresponding to the item with corresponding previous time stamps, and assigning the estimated label to the item if the estimated label is validated based on previous labels to generate a labeled item. The labeled items are to be used to generate model updated information, which is then used to update the model.

The present teaching relates to method, system, medium, and implementation of in-situ perception in an autonomous driving vehicle. A plurality of types of sensor data are acquired continuously via a plurality of types of sensors deployed on the vehicle, where the plurality of types of sensor data provide information about surrounding of the vehicle. One or more items surrounding the vehicle are tracked, based on some models, from a first of the plurality of types of sensor data from a first type of the plurality of types of sensors. A second of the plurality of types of sensor data are obtained from a second type of the plurality of sensors and are used to generate validation base data. Some of the one or more items are labeled, automatically, via validation base data to generate labeled at least some item, which is to be used to generate model updated information for updating the at least one model.

Systems and methods for controlling or guiding one or more automated vehicles and/or people (VOP) in an environment using virtual approved pathways (VAPs). Methods include determining a set of parameters associated with automated vehicles and/or people operating within an environment, including periodically obtaining a first set of parameters from a plurality of data sources deployed within an environment, in real-time.

A conveyance system comprising a conveyance vehicle that conveys an object to be conveyed and a control device that controls operation of the conveyance vehicle, wherein the conveyance vehicle includes a guide line detecting unit that detects a guide line laid along a travel route; a travel controlling unit that makes the conveyance vehicle travel along the guide line; and an own conveyance vehicle position estimating unit that estimates a position of an own conveyance vehicle; and the control device includes a recording unit that records map information including position information of the guide line; and makes the conveyance vehicle travel to the guide line in the map information, when the position of the own conveyance vehicle estimated by the own conveyance vehicle estimating unit is more than a predetermined distance away from the position of the guide line in the map information.

A map generation system generates map data for an agricultural machine to automatically travel on a road around a field, and includes a storage to store feature block images associated with different types of road features, and a processor configured or programmed to acquire position distribution data on one or more types of road features, the position distribution data being generated based on at least one of sensor data from a LiDAR sensor and image data from an imager output while a movable body including at least one of the LiDAR sensor and the imager is moving along the road; read from the storage one or more types of feature block images associated with the one or more types of features; and align the feature block images in accordance with the position distribution data to generate map data on a region including the road.

The present teaching relates to method, system, medium, and implementation of a global model update center. At least one model is established at the model update center for detecting objects surrounding each of autonomous driving vehicles of a fleet. A plurality of labeled data items are received, from the fleet of autonomous driving vehicles, where each of the labeled data items is detected, based on the at least one model, from sensor data characterizing surroundings of the autonomous driving vehicles. The labeled data items are generated automatically on-the-fly by the autonomous driving vehicles. Based on the received labeled data items, at least some of the models are updated and model update information is accordingly generated. Such generated model update information is then distributed to the fleet of autonomous driving vehicles.

A return method and device for an aerial vehicle are provided. The method includes: during a flight process of the aerial vehicle, performing real-time planning on a return path from a current position of the aerial vehicle to a return position; performing real-time transmission of the return path to a terminal device to display the return path on a display interface. The aerial vehicle plans the return path in real-time during flight and sends it in real-time to the terminal device for display. This allows users to timely understand the planned return path of the aerial vehicle. Even in the event of a loss of connection between the aerial vehicle and the terminal device, the terminal device can display the return path based on the previously received information, thereby enhancing the safety of aerial vehicle return.

An operation management system including a computer configured to manage operations of registered mobilities. The computer stores road surface information in association with a position in map data based on at least a detection result of a surroundings monitoring device and positional information received from a measurement mobility. The computer stores, as a detail detection area, at least one of a poor detection area in the map data in which detection of the road surface information by the measurement mobility is poor and a predetermined area in the map data. The computer transmits, to the measurement mobility that is scheduled to travel or that is traveling the detail detection area, a speed change command, such that the measurement mobility, which travels at a normal command speed by automated driving, travels the detail detection area at a specific speed lower than the normal command speed.

A method and an apparatus for determining a position of a shelf are provided. The method may include: obtaining a number of automated guided vehicles with shelf scanning devices; determining, based on the number of the automated guided vehicles, a scanning area of a place to which each automated guided vehicle belongs; determining, based on the scanning area, a scanning route of the scanning area to which each automated guided vehicle belongs; transmitting the scanning route of the scanning area to which the automated guided vehicle belongs, to the automated guided vehicle; and determining a position of a shelf in the scanning area to which the automated guided vehicle belongs based on scanning information of a shelf scanning device on the automated guided vehicle and position information of the automated guided vehicle.