Various aspects of a system, a method, and a computer program product for determining a distance to the object on a road are disclosed herein. In accordance with an embodiment, the system includes a memory and a processor. The processor may be configured to receive visual data, location data and motion data of the vehicle corresponding to the first instance in time, and map data corresponding to the location data. The processor may be configured to calculate a distance of the vehicle from the object based on the visual data. The processor may be further configured to validate the location data, the motion data, and the calculated distance of the vehicle from the object, based on the map data. The processor may be further configured to generate output data corresponding to the object, based on the validated location data, the validated motion data, and the validated distance of the vehicle from the object.

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.

Methods and systems for jointly estimating a pose and a shape of an object perceived by an autonomous vehicle are described. The system includes data and program code collectively defining a neural network which has been trained to jointly estimate a pose and a shape of a plurality of objects from incomplete point cloud data. The neural network includes a trained shared encoder neural network, a trained pose decoder neural network, and a trained shape decoder neural network. The method includes receiving an incomplete point cloud representation of an object, inputting the point cloud data into the trained shared encoder, outputting a code representative of the point cloud data. The method also includes generating an estimated pose and shape of the object based on the code. The pose includes at least a heading or a translation and the shape includes a denser point cloud representation of the object.

A server for providing a passenger transportation service, may include a registered location database configured to store position information and image information related to each of previously registered get-on-and-off locations, a registered location management module configured to receive image data photographed from vehicles providing passenger transportation service during running and additional information including position information related to each of the image data, and update the image information based on the additional information by use of the image data, and a service providing module configured to, upon receiving a service provision request including position information and destination information related to a user from a user terminal, select a get-on location and a get-off location from among the get-on-and-off locations based on the position information and the destination information related to the user, and transmit get-on-and-off information including image data of the get-on location to the user terminal.

An object detection device is configured to execute a first process, a second process, and an object detection process (third and fourth processes). The first process estimates a shape of a road surface in a real space on the basis of a first disparity map. The first disparity map is generated on the basis of an output of a stereo camera that captures an image including the road surface, and is a map in which a disparity obtained from the output of the stereo camera is associated with two-dimensional coordinates formed by a first direction corresponding to a horizontal direction of the image captured by the stereo camera and a second direction intersecting the first direction. The second process removes from the first disparity map a disparity for which a height from the road surface in the real space corresponds to a predetermined range on the basis of the estimated shape of the road surface to generate a second disparity map. The object detection process (third and fourth processes) detects an object on the basis of the second disparity map.

The disclosure relates to a method for determining object information relating to an object in an environment of a multi-part vehicle having at least one towing vehicle and at least one trailer and a control unit and vehicle associated with it, with at least one trailer camera being arranged on the trailer, having at least the following steps: capturing the environment with a trailer camera from a first position and, in dependence thereon, creating a first image having first pixels; changing the position of the trailer camera; capturing the environment with the trailer camera from a second position and creating a second image having second pixels; and, determining object information relating to an object in the captured environment.

The disclosure is directed to a method for controlling a vehicle in a depot. The method includes the steps: allocating a three-dimensional target object to the vehicle; detecting a three-dimensional object in the environment around the vehicle and determining depth information for the detected three-dimensional object; classifying the detected three-dimensional object on the basis of the determined depth information and checking whether the determined three-dimensional object has the same object class as the three-dimensional target object; identifying the detected three-dimensional object if the determined three-dimensional object has the same object class as the three-dimensional target object by detecting an object identifier assigned to the three-dimensional object and checking whether the detected object identifier matches a target identifier assigned to the target object; outputting an approach signal to move the vehicle closer to the detected three-dimensional target object in an automated manner or manually if the object identifier matches the target identifier.

An object detection device is configured to execute a road surface detection process, an object disparity determination process, and an object detection process. The road surface detection process estimates a position of a road surface on the basis of a first disparity map. The first disparity map is generated on the basis of an output of a stereo camera and is a map in which a disparity is associated with two-dimensional coordinates formed by a first direction corresponding to a horizontal direction of an image captured by the stereo camera and a second direction intersecting the first direction. The object disparity determination process determines disparities as object disparities when the number of occurrences of each of the disparities for respective coordinate ranges in the first direction of the first disparity map exceeds a predetermined threshold corresponding to the disparity. The object detection process detects an object by converting information on the object disparities into points on an x-z coordinate space and by extracting a group of points.

A method for adjusting point cloud density, an electronic device, and a storage medium are provided. In the method an initial point cloud map and a distance determination threshold of a robot are obtained. A plurality of target regions in the initial point cloud map are determined, and an environmental complexity value of each target region is calculated. The initial point cloud map is divided into submaps, and a point cloud density coefficient of each submap is determined. The initial point cloud map is adjusted according to the point cloud density coefficient and the target point cloud map is obtained. By utilizing such method, adjustment efficiency and an accuracy of point cloud density can be improved.

A method for directionally warning as to a target object behind a vehicle acquires images of environment surrounding the vehicle. Objects in the environment images are identified and high-risk target object or objects from all the objects is labeled. A relative direction and a relative location of the target object with the transportation are confirmed. A warning device is adjusted based on the relative direction and the relative location. The warning device gives directional warning as to the target object. A collision risk warning system and a transportation applying the method are also disclosed.