A method includes generating a first warped image based on a pose and a depth estimated from a current image and a previous image in a sequence of images captured by a camera of the agent. The method also includes estimating a motion of dynamic object between the previous image and the target image. The method further includes generating a second warped image from the first warped image based on the estimated motion. The method still further includes controlling an action of an agent based on the second warped image.

The present disclosure relates to real-time localization error correction of an autonomous vehicle (AV). A processor for real-time localization error correction of the AV is provided. The processor is configured to retrieve a reference landmark around the AV from a map aggregating server (MAS), wherein the AV is configured to interact with the MAS for real-time localization; detect, in real time, a ground truth landmark corresponding to the reference landmark, according to image data captured by one or more image capture devices installed on the AV; and determine a deviation between the ground truth landmark and the reference landmark as a real-time correction value for the real-time localization of the AV.

A medical treatment server is configured to reduce load on physicians for work other than examination and treatment. The server is configured to send recruiting conditions and in response receive undertaking conditions that are related to a physician who does not belong to the medical facility. A controller decides whether to appoint the physician depending on whether a pickup location included in the received undertaking conditions is apart from a location of the medical facility by a distance equal to or greater than a threshold. Upon deciding to appoint the physician, the controller determines a pickup location included in the undertaking conditions to be a stopover point.

A localization and mapping system and method for a motor vehicle is disclosed and includes at least one camera configured to obtain images of an environment surrounding the motor vehicle, at least one sensor configured to obtain location information for objects surrounding the motor vehicle and a controller configured to receive the images captured by the at least one camera and the location information obtained by the at least one sensor. The controller enhances the captured images utilizing a neural network and combines the enhanced images with the location information to localize the vehicle within the mapped environment.

Provided herein is control method of an electronic apparatus, including: identifying a line region from a driving-related image data of a vehicle; generating a line information corresponding to a lane where the vehicle is located from an image data of the identified line region portion; generating a position information of a lane where the vehicle is located, using at least one of the generated line information and the lane information of a road where the vehicle is located; and performing a driving-related guide of the vehicle using the generated lane position information.

Perception sensors of a vehicle can be used for various operating functions of the vehicle. A computing device may receive sensor data from the perception sensors, and may calibrate the perception sensors using the sensor data, to enable effective operation of the vehicle. To calibrate the sensors, the computing device may project the sensor data into a voxel space, and determine a voxel score comprising an occupancy score and a residual value for each voxel. The computing device may then adjust an estimated position and/or orientation of the sensors, and associated sensor data, from at least one perception sensor to minimize the voxel score. The computing device may calibrate the sensor using the adjustments corresponding to the minimized voxel score. Additionally, the computing device may be configured to calculate an error in a position associated with the vehicle by calibrating data corresponding to a same point captured at different times.

Methods and systems for managing information about fuel stations. A system includes an imaging system and a profile management system. The imaging system is used for generating imaging data for a fuel station onboard a vehicle. The profile management system receives the imaging data. The profile management system identifies selected information for the fuel station based on the imaging data received. The selected information includes an identification of the fuel station, pricing information, and at least one of fuel inventory information, or business hours information for the fuel station. The profile management system updates a profile for the fuel station based on the selected information. The profile management system sends at least a portion of the profile for the fuel station to a target system.

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.

An operation method of a navigation apparatus includes: obtaining valid global positioning system (GPS) data at a current time point corresponding to a current position of a target device; determining first neighboring map elements corresponding to a first region indicated by the valid GPS data at the current time point from among a plurality of map elements of map data; and determining a pose parameter of the target device at the current time point based on a first direction specified by at least a portion of the first neighboring map elements.

A driving assistance device for a vehicle includes a traveling environment recognizer, a parking space detector, an evaluation value calculator, and a parking space setter. The traveling environment recognizer is configured to recognize traveling environment information related to an outside of the vehicle. The parking space detector is configured to, in a case where the vehicle has entered a parking lot, detect one or more parking spaces available to the vehicle based on the traveling environment information. The evaluation value calculator is configured to calculate one or more evaluation values for the one or more parking spaces, each of the one or more evaluation values including an exit hindrance factor as an evaluation item. The parking space setter is configured to select, with priority, a parking space for parking the vehicle from among the one or more parking spaces. The evaluation value of the parking space is relatively high.