A server device includes: a data collection unit that collects vehicle behavior data; a scene extraction unit that extracts, from the collected vehicle behavior data, driving scenes corresponding to the behavior of the vehicle and a scene feature amount of each of the driving scenes; an abnormality detection unit that calculates a degree of abnormality which represents an extent of deviation of the scene feature amount of each of the extracted driving scenes relative to a driving model, and detects a driving scene including a location of an abnormality using the calculated degree of abnormality; a section determination unit that extracts driving scenes satisfying a predetermined condition from among the detected driving scene and a plurality of driving scenes continuous to the driving scene, and determines, as an abnormal behavior section, a time range defined by the total continuation time of the extracted driving scenes; an image request unit that requests, from the vehicle, one or more captured images according to the determined abnormal behavior section; and a display control unit that performs control to display the images acquired from the vehicle according to the request.

An object detection system may include an imager configured to generate image data indicative of an environment in which a machine is present, and a sensor configured to generate sensor data indicative of the environment in which the machine is present. The object detection system may further include an object detection controller including one or more object detection processors configured to receive an image signal indicative of the image data, identify an object associated with the image data, and determine a first location of the object relative to the position of the imager. The one or more object detection processors may also be configured to receive a sensor signal indicative of the sensor data, and determine, based at least in part on the sensor signal, the presence or absence of the object at the first location.

The present disclosure analyzes the image around the main body, detects the depth of the floor surface and the height of the floor surface beyond the obstacle, and determines whether to climb the obstacle.

An iterative method for estimating the movement of at least one material body in a surrounding space discretized into a grid of cells, includes the following steps: a) obtaining an inconsistency grid in iteration t (IG.sub.t), generated in response to detection of a change of occupancy state in at least one of the cells of the grid of cells between an iteration t-1 and iteration t, b) recurrently generating a filtered movement grid in iteration t (FMG.sub.t.sup.d, FMG.sub.t.sup.s) comprising, for each cell of the inconsistency grid in iteration t (IG.sub.t), a posterior probability (P(d.sub.t,i.sup.A|z.sub.1:t), of the material body having performed a movement with at least one movement component characterizing the movement, the movement component forming part of a discrete set of components around the cell, the filtered movement grid in iteration t (FM.sub.t.sup.d, FMG.sub.t.sup.s) being generated on the basis of the filtered movement grid in iteration t-1 (FMG.sub.t-1.sup.d, FMG.sub.t-1.sup.s), of an inconsistency grid from iteration t-1 (IG.sub.t-1), and of the inconsistency grid in iteration t(IG.sub.t).

Methods and apparatus for matching portions of images are described as well as using depth information generated from the matching results. In various embodiments, lower portions of images of a scene area, which are more likely to include objects closer to a vehicle on which cameras are mounted, than are portions of images corresponding to an upper portion of the scene area, are processed and used for depth determination. In this way, depths to objects, which are likely to be closer to the vehicle than objects in the upper portion of the scene area, are determined first and used to control a vehicle reducing the risk of collision as compared to systems where the depth determination of an entire scene is completed before depth information is used for control purposes with the order of processing being such that nearer objects are likely to be detected prior to more distant objects.

Sensors, including time-of-flight sensors, may be used to detect objects in an environment. In an example, a vehicle may include a time-of-flight sensor that images objects around the vehicle, e.g., so the vehicle can navigate relative to the objects. The sensor may generate first image data at a first configuration and second image data at a second configuration. An estimated depth of an object may be determined from the first image data, and an actual depth of the object may be determined from the second image data, based on the estimated depth. In examples, the first and second configurations have different modulation frequencies such that a nominal maximum depth in the first configuration is greater than the nominal maximum depth in the second configuration.

There is provided an image capturing apparatus that captures a plurality of images, calculates a three-dimensional position from the plurality of images, and outputs the plurality of images and information about the three-dimensional position. The image capturing apparatus includes an image capturing unit, a camera parameter storage unit, a position calculation unit, a position selection unit, and an image complementing unit. The image capturing unit outputs the plurality of images using at least three cameras. The camera parameter storage unit stores in advance camera parameters including occlusion information. The position calculation unit calculates three dimensional positions of a plurality of points. The position selection unit selects a piece of position information relating to a subject area that does not have an occlusion, and outputs selected position information. The image complementing unit generates a complementary image, and outputs the complementary image and the selected position information.

A carrier includes a body, a sensor, and an output unit. The body has the ability to travel autonomously and includes a holding mechanism for holding a burden. The sensor is provided for the body and detects a situation surrounding the body. The output unit outputs detected information, collected by the sensor, about the situation to a management device that manages operation of another carrier.

Techniques for determining an error model associated with a system/subsystem of vehicle controller are discussed herein. To determine whether a complex system (which may be uninspectable) is able to operate safely, errors can be introduced into operating regimes (scenarios) to validate the safe operation of such a system. By comparing captured and/or generated vehicle data with ground truth data, an error of the system can be statistically quantified and modeled. The statistical model can be used to introduce errors to the scenario to perturb the scenario to test, for example, a vehicle controller. Based on a simulation of the vehicle controlled in the perturbed scenario, a safety metric associated with the vehicle controller can be determined, as well as causes for any failures.

A display device for a vehicle, including: a rear imaging section that captures a rear image; a rear lateral imaging section that captures rear lateral images; and a control section that generates a combined image in which a rear processed image obtained by processing the rear image with a first parameter, and rear lateral processed images obtained by processing rear lateral images with a second parameter, are combined, wherein the control section further identifies an object existing in the rear image or the rear lateral images; acquires relative information including relative position and relative speed of the object with respect to the vehicle, and blind spot regions of the combined image; and, based thereon, if the object will disappear from the combined image due to entering into the blind spot regions, changes the blind spot regions by adjusting the first and second parameters.