A signal processing apparatus including a first position calculation unit that calculates a three-dimensional position of a target on a first coordinate system from a stereo image captured by a stereo camera, a second position calculation unit that calculates a three-dimensional position of the target on a second coordinate system from a sensor signal of a sensor capable of obtaining position information of at least one of a lateral direction and a longitudinal direction and position information of a depth direction, a correspondence detection unit that detects a correspondence relationship between the target on the first coordinate system and the target on the second coordinate system, and a positional relationship information estimating unit that estimates positional relationship information of the first coordinate system and the second coordinate system on the basis of the detected correspondence relationship.

A tracking system for tracking water-surface objects includes a stereo camera on a hull, at least one memory, and at least one processor coupled to the at least one memory. The at least one processor is configured or programmed to detect at least one object based on a first image and a second image captured by a first imaging unit and a second imaging unit of the stereo camera, and set one detected object as a tracking target in a third image captured by the first imaging unit, the second imaging unit or another imaging unit. The at least one processor is further configured or programmed to track the tracking target using a temporal change in a feature of the tracking target, and use at least one object detected based on the first image and the second image during tracking to correct the tracking result.

A method for autonomous navigation of a movable robotic unit, in particular at least as part of a SLAM method. A plurality of virtual points from the surroundings around the detection unit is detected using a detection unit. An optimization problem of graph theory is solved using a control and/or regulation unit for ascertaining a position and/or a movement of the robotic unit and/or for detecting the surroundings as a function of the detected virtual points. At least one virtual plane is ascertained, using the control and/or regulation unit, as a function of at least one group of virtual points of the plurality of virtual points. For ascertaining a position and/or a movement of the robotic unit and/or for representing the surroundings, the ascertained virtual plane is used for solving the optimization problem of graph theory using the control and/or regulation unit.

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.

A method for risk based processing, the method may include detecting, based on first sensed information sensed at a first period, a suspected risk within an environment of a vehicle; selecting, from reference information, a situation related subset of the reference information, wherein the situation related subset of the reference information is related to the situation; selecting, from reference information, a suspected risk related subset reference information, wherein the potential risk related subset of the reference information is related to the potential risk; and determining whether the suspected risk is an actual risk, based at least on part on the suspected risk related subset reference information.

A vehicular vision system includes a camera disposed at an in-cabin side of a windshield of a vehicle and viewing forward of the vehicle. The control, responsive at least in part to image processing by an image processor of multiple frames of captured image data, detects an object present exterior of the equipped vehicle that is moving relative to the equipped vehicle. The control receives vehicle motion data indicative of motion of the vehicle when the vehicle is moving. The control, responsive at least in part to the received vehicle motion data, and via image processing of multiple frames of captured image data, determines motion of the detected object relative to the moving vehicle by (i) determining corresponding object points in at least two frames of captured image data and (ii) estimating object motion trajectory of the detected object based at least in part on the determined corresponding object points.

A driving support system includes a first monitoring device on a first object, the first monitoring device having a first controller, a first camera, and a first display, a second monitoring device on a second object, the second monitoring device having a second controller and a second camera, and a server in communication with the first and second monitoring devices. The first and second controllers are each detect a target in images acquired from the respective first or second camera, calculate target information for the target, and transmit the target information to the server. The server generates list information including the target information from the first and second monitoring devices, and transmits the list information to the first and second monitoring devices. The first controller further generates a map according to the list information received from the server, and displays the map on the first display.

Embodiments of the present disclosure relate to a method and apparatus for generating a driving path. The method includes acquiring a two-dimensional image of a driving site obtained by a camera provided on an unmanned aerial vehicle through aerial photography, and a two-dimensional image of a site in front of a vehicle photographed by a camera provided on the vehicle; generating a global map based on the two-dimensional image of the driving site, and generating a local map based on the two-dimensional image of the site in front of the vehicle; and performing path planning based on the global map and the local map, to generate a global path and a local path, the local path following a direction of the global path.

A vehicle control device includes a tracking unit estimating a motion of a moving object, a model selection unit selecting a motion model corresponding to a moving object type, an abnormality determination unit determining a presence or absence of an abnormality of the estimation of the motion of the moving object based on the estimated moving object motion and the motion indicated by the motion model, and a control unit. A control mode in which the control unit controls traveling of a host vehicle when the abnormality determination unit determines that the abnormality is present differs from a control mode in which the control unit controls the traveling of the host vehicle when the abnormality determination unit determines that the abnormality is absent.

A model generation apparatus trains, through machine learning, a neural network module that includes an extraction operation to extract an element satisfying a predetermined condition from a set of targets. In the machine learning, the model generation apparatus performs the extraction operation in a phase of forward propagation with the neural network module, and replaces, in a phase of backpropagation, the extraction operation with a differentiable alternative operation and differentiates the alternative operation to compute an approximate gradient corresponding to differentiation for the extraction operation.