Abstract: A surveillance camera includes an imaging unit configured to output a video frame, and a control unit configured to set a search range in the video frame and to extract a tracking-target image by using a feature quantity of an image in the search range. In a case where an object image other than the tracking target is contained in the search range, the control unit masks a feature quantity of the object image and extracts the tracking-target image.

Abstract: Apparatus are provided for generating an object classification for an object, the apparatus comprising an image sensor, a radar sensor, and a processing unit, the processing unit configured to perform the steps of: receiving image data for the object from the image sensor, operating an image-based object classifier on the image data to generate an image-based object classification, receiving radar data for the object from the radar sensor, operating a radar-based object classifier on the radar data to generate a radar-based object classification, selecting between the image-based object classification and the radar-based object classification to output as the object classification for the object, determining if a training condition is met by the radar-based object classification, and training the radar-based object classifier using the image-based object classification when the training condition is met by the radar-based object classification.

Abstract: A light inspection system positions an autonomous vehicle (AV) in a field of view of a camera such that the camera captures an image of a light of the AV. The light inspection system instructs a camera to capture an image of the light while the light is switched on. The light inspection system receives the captured image, determines a luminance of the light based on the image, and determines to service the light in response to the luminance of the light being below a threshold luminance.

Abstract: Examples disclosed herein relate to determining a response based on hierarchical models. In one implementation, a processor applies a first model to an image of an environment to select a second model. The processor applies the selected second model to the image and creates an environmental description representation based on the output of the second model. The processor determines a response based on the environmental description information.

Abstract: Methods for performing operations for improving driver safety across a fleet of vehicles are disclosed. A plurality of safety events pertaining to a driving of a fleet of vehicles by a plurality of drivers are detected. A subset of the events is identified. The subset corresponds to one or more safety events of the plurality of safety events involving one or more vehicles of the fleet of vehicles to which drivers have not been assigned. A user interface is generated for presentation on a client device, the user interface including an interactive user interface element for accessing the subset of the events. One or more user interface elements are provided for accepting or rejecting recommendations for assignments of one of the plurality of drivers to each of the vehicles. The recommendations are generated based on an application of a machine-learned model to images of faces captured.

Abstract: A vehicular driving assist system includes a plurality of lidar sensors having respective fields of sensing exterior the vehicle, with the fields of sensing of first and second lidar sensors partially overlapping at an overlap region. The lidar sensors emit a respective pattern of light and provide a respective output to an ECU based on sensed light that is reflected by objects present in the field of sensing of the respective lidar sensor. The pattern of light emitted by the second lidar sensor differs from the pattern of light emitted by the first lidar sensor. The ECU determines misalignment of the second lidar sensor relative to the first lidar sensor based at least in part on the light sensed by each of the lidar sensors that is reflected by an object present in the overlap region when the respective pattern of light is emitted by the respective lidar sensor.

Abstract: According to an embodiment, a calculation device includes a registerer, a first calculator, a receiver, and a second calculator. The registerer registers a plurality of patterns. The first calculator calculates a degree of closeness between the registered patterns. The receiver receives an input pattern. The second calculator calculates a first similarity value between the input pattern and a first registered pattern among the plurality of registered patterns, calculates second similarity values between the input pattern and one or more second registered patterns having a neighbor relationship with the first registered pattern among the plurality of registered patterns, and calculates a combined similarity value in which the first similarity value and the one or more second similarity values are combined.

Abstract: An image processing apparatus (10) includes an interface (12) configured to acquire a surrounding image of a moveable body (1) and a processor (12) configured to overlay a display indicating a course trajectory of a specific portion of the moveable body (1) in a travel direction of the moveable body (1) on the surrounding image at a position corresponding to the height of the specific portion from a road surface (3). The processor (12) is configured to change the display indicating the course trajectory when an obstacle, included in the surrounding image and present in the travel direction of the moveable body (1), and the course trajectory of the specific portion are in contact.

Abstract: An optical system for monitoring a vehicle driver's eye gaze includes a first illumination source and a second illumination source for emitting light towards the driver's eye, a lens, and an image sensor for detecting an image, wherein the lens is configured to direct light reflected by the driver's eye to the image sensor, and wherein a distance between the second illumination source and an optical axis of the lens is larger than a distance between the first illumination source and the optical axis. Further, a method for monitoring a vehicle driver's eye gaze with an optical system is disclosed.

Abstract: An aerial drone system configured to serve as personal drone assistance is disclosed. The drone assistant is configured to follow the user and provide (1) audiovisual output including video, audio, and navigation, (2) environmental comfort including shade, light, misters for the benefit of the user, as well as (3) privacy and security. To provide audiovisual output, the drone is configured to track the user and maintain a constant height and distance relative to the user, preferably a few feet away in front of the user. To provide environmental comfort including shade, for example, the drone is configured to automatically maintain a position between the user and the sun, thus causing a shadow to be continually cast on the user. This shading is enhanced by specialized louvres and screens configured to prevent any direct sunlight from directly impinging on the user.

Abstract: An efficient K-nearest neighbor search algorithm for three-dimensional (3D) lidar point cloud in unmanned driving and a use of the foregoing K-nearest neighbor search algorithm in a point cloud map matching process in the unmanned driving are provided. A novel data structure for fast K-nearest neighbor search is used, such that each voxel or sub-voxel includes a proper quantity of points to reduce redundant search. The novel K-nearest neighbor search algorithm is based on a double segmentation voxel structure (DSVS) and a field programmable gate array (FPGA). By means of the novel K-nearest neighbor search algorithm, nearest neighbors are searched for only in a neighboring expected area near a search point, thereby reducing search of redundant points. In addition, an optimized data transmission and access policy is used, which makes the algorithm more fit the characteristic of the FPGA.

Abstract: Disclosed is an adaptive autonomous emergency braking (AEB) control method. An adaptive AEB control method includes identifying a front vehicle to be avoided on the basis of front-view information acquired through a front-view sensor, setting a steering avoidable area on the basis of speed information and lateral acceleration information of a host vehicle, adaptively determining an AEB activation time point on the basis of whether a vehicle is present in the set steering avoidable area, and controlling AEB activation on the basis of the adaptively determined AEB activation time point.

Abstract: Disclosed is a feedback adversarial learning framework, a recurrent framework for generative adversarial networks that can be widely adapted to not only stabilize training but also generate higher quality images. In some aspects, a discriminator's spatial outputs are distilled to improve generation quality. The disclosed embodiments model the discriminator into the generator, and the generator learns from its mistakes over time. In some aspects, a discriminator architecture encourages the model to be locally and globally consistent.

Abstract: A computer includes a processor and a memory, the memory storing instructions executable by the processor to determine respective probabilities of a direction of a gaze of a vehicle occupant toward each of a plurality of points in an image, determine a gaze distance from a center of the image based on the probabilities, and, upon determining that the gaze distance exceeds a threshold, suppress manual control of at least one vehicle component.

Abstract: The present invention relates to a computer-implemented method for improving the interpretation of the surroundings of a vehicle (100), wherein the method comprises: receiving a source image (14) of a surrounding environment (118) of the vehicle, which source image is captured by a sensor unit (12a) of the vehicle; receiving or computing a depth data image (20) comprising depth data based on the source image and at least one more source image; detecting a repetitive pattern (30) in the received source image; and based on the detection of the repetitive pattern, determining that an area (32) of the depth data image, which area (32) corresponds to an area (34) of the detected repetitive pattern in the received source image, contains unreliable depth data.

Abstract: An object detection device configured to be mounted on a vehicle includes an object extraction unit that is configured to extract a point group that is a set of points representing a part of an object as the object, a neighboring object extraction unit that is configured to extract a neighboring object in an XY-plane of a world coordinate system, wherein the neighboring object is the object which is the closest to the vehicle, a coordinate transformation unit that is configured to transform coordinates of the neighboring object in the world coordinate system into coordinates of the neighboring object in an image captured by a camera, and a person determination unit that is configured to perform a person detection process in which it is determined whether or not the neighboring object is a person on the coordinates of the neighboring object.

Abstract: An object recognition apparatus include a storage device and a processor. The storage device stores peripheral information and tolerance information. The tolerance information is information in which the degree of tolerance for the undetected object is represented for each class of the object. The peripheral information is acquired by a sensor device provided in the vehicle. The processor performs object recognition process for recognizing an object around the vehicle. In the object recognition process, the processor identifies the object and its class to be detected based on the peripheral information, and calculates the likelihood that is a parameter representing the probability of detection of the object. Further, the processor calculates a likelihood threshold corresponding to the object based on the tolerance information, and determines whether to output the identification result of the object based on the comparative between the likelihood and the likelihood threshold.

Abstract: Embodiments of the present invention provide a display method for use in a vehicle. The method comprises: obtaining first and second images showing adjacent or overlapping regions external to the vehicle, the first and second images being captured at different points in time; obtaining (904) at least one image property for each of the first and second images; calculating (914) an image correction factor as a function of the at least one image property for each of the first and second images; adjusting (914) the appearance of the first image and/or the second image according to the calculated image correction factor; generating (908) a composite image from the first image or an adjust first image and the second image or an adjusted second image; and displaying at least part of the composite image; wherein for at least one of the first and second image, the at least one obtained image property is in respect of a group of images including the first or second image.

Abstract: A device for removing or killing weeds, comprising a first image capturing apparatus for capturing a first ground image, a data processing unit connected thereto and an agricultural machine controlled by the data processing unit, which is designed for the targeted removal or killing of weeds, wherein the data processing apparatus is configured to receive the first ground image from the first image capturing apparatus, to receive manually determined position data from at least one connected terminal, which indicate the position of weeds and/or crops and/or ground structures, e.g. rows of plants, which have been detected in the first ground image by one or more users, and, on the basis of the position data, to control the agricultural machine in such a way that it removes or kills the weeds in a targeted manner.

Abstract: A method and system for navigating vehicles based on road conditions determined in real-time is disclosed. The method includes the steps of receiving a first dataset including an image of a section of a road within a Field of View (FOV) of a camera attached to a vehicle and a second dataset associated with the road. The method further includes detecting edges and a vanishing point in the image, correcting road perspectivity in the image, and determining surface anomalies in the road based on a set of parameters, the second dataset and a Time of Flight technique (ToF), creating a digital elevation model for the image, and assigning a value, in real-time, from a predefined value range to each of a plurality of grids in the image based on a digital elevation model to generate a digital elevation image. The set of parameters includes volume associated with the surface anomalies.

Abstract: A method of detecting lanes includes the steps: capturing (S1) a camera image (K) of a vehicle environment by a camera device (2) of a vehicle (5); determining (S2) feature points (P1 to P15) in the camera image (K), which feature points correspond to regions of possible lane boundaries (M1, M2); generating (S3) image portions of the captured camera image (K) respectively around the feature points (P1 to P15); analyzing (S4) the image portions using a neural network to classify the feature points (P1 to P15); and determining (S5) lanes in the vehicle environment taking account of the classified feature points (P1 to P15).

Abstract: A method for the remote control of at least one function of a motor vehicle by means of a mobile controller involves a remote control command transmitted to the motor vehicle and executed by the motor vehicle if an approval has been issued. The method provides a remote control system that has a particularly high level of reliability, which is achieved by detecting an image of the motor vehicle by an optical detection device in the mobile controller is transmitted to the motor vehicle, and the approval is then issued by a checking device in the motor vehicle if the motor vehicle has been identified in the transmitted image by the checking device.

Abstract: The present invention relates to an occupant monitoring method and apparatus therefor. A monitoring apparatus according to one aspect of the present invention may include a camera obtaining an image in a vehicle and a processor processing the image. Moreover, the processor may include a detecting module separating each region in which an object exists from the image, a classifying module classifying the object existing in the each separated region and a recognizing module recognizing a pose of an occupant if the object corresponds to the occupant.

Abstract: Various embodiments include methods and scanning systems for photonically detecting an object of high-interest having selective wavelength reflection. Various embodiments include sequentially scanning the environment by projecting a coherent pulsed electromagnetic beam of light of a first wavelength. Reflected light of the first coherent beam is received onto a photoelectric detector, which outputs digital intensity data. Various embodiments further include sequentially scanning the environment by projecting a coherent pulsed electromagnetic beam of light of a second wavelength different from the first wavelength. Reflected light of the second coherent beam is received onto a photoelectric detector, which outputs digital intensity data. The intensity of the reflected light of the first wavelength may be compared with the intensity reflected light of the second wavelength, and an alert may be sent to an autonomous vehicle system in response to the intensity difference exceeding a threshold.

Abstract: The present application discloses a traffic monitoring device capable of switching camera modes, comprising a sensor group, a infrared camera group, a visible light camera group, a visible light camera head, a infrared camera head, a head driver and a digital signal processor. The sensor group comprises pressure sensor, photoelectric displacement sensor and photosensitive sensor; the infrared camera and the visible light camera collect image information. The above devices are used as the detection front ends connected to the digital signal processor. The head driver adjusts the angle of the visible light camera and the infrared camera in real time. The system described in this application has two working modes: continuous shooting and interval shooting, which shortens the working time of the system, extends the life of the system, and reduces the cost of the system.

Abstract: An image processing apparatus includes: a determiner that determines whether or not a vehicle in which a camera is mounted is located at a specific place where a traveling direction of the vehicle changes, the camera picking up an image of an entire image pickup range with first image quality and picking up an image of a partial region in the image pickup range with second image quality that is higher image quality than the first image quality; and a region changer that, when it is determined that the vehicle is located at the specific place, changes a position of the partial region in the image pickup range such that an image of a region of attention that is included in the image pickup range and an image of which is to be picked up with the second image quality is picked up.

Abstract: A vision system for detecting free space in front of a motor vehicle includes a mono imaging apparatus (11) adapted to capture images (30) from the surrounding of a motor vehicle, and an electronic processing device (14) adapted to perform image processing of images (30) captured by the mono imaging apparatus (11) in order to detect objects in the surrounding of a motor vehicle. The electronic processing device (14) is adapted to calculate a horizontal component of the optical flow (31), and to determine transitions (33, 35) between regions of essentially constant horizontal optical flow and regions of essentially non-constant horizontal optical flow.

Abstract: A road surface area detection device includes a normalized speed calculation portion configured to calculate a normalized speed based on a movement of a feature point in an image captured by a camera that is disposed in a vehicle; a determination range calculation portion configured to calculate a road surface determination range, which is indicated by a magnitude of the normalized speed, based on the normalized speeds of at least two feature points at different positions in a width direction of the vehicle in a predetermined central area where the vehicle is positioned in a center thereof in the width direction perpendicular to a vehicle traveling direction; and a road surface area identification portion configured to identify, as a road surface area on which the vehicle travels, a position in the width direction that includes the feature point whose normalized speed is within the road surface determination range.

Abstract: The present embodiments relate to efficient localization of a vehicle within a lane region. An imaging device onboard the vehicle may capture an image stream depicting an environment surrounding the vehicle. An image may be inspected to identify pixel bands indicative of lane markings of a road depicted in the image. Based on the identified pixel bands, a lane region indicating a lane of a roadway can be extracted. A lane drift metric may be generated that indicates an offset of the vehicle relative to a center of the lane region. An output action may be initiated based on the offset indicated in the lane drift metric. The lane region can be translated from a first frame to a second frame providing a top-down perspective of the vehicle within the lane region using a transformation matrix to assist in efficiently deriving the lane drift metric.

Abstract: According to an embodiment, a device for detecting fog on a road comprises an imaging device installed to capture a two-way road and capturing a fog on the two-way road, a network configuring device provided under the imaging device and transmitting an image captured by the imaging device, a fog monitoring device receiving the image from the network configuring device, analyzing the image to thereby detect the fog, and outputting an alert per predetermined crisis level, and a display device displaying the alert output from the fog monitoring device and transmitting the alert via a wired or wireless network.

Abstract: This disclosure provides an apparatus and method for performing object detection based on images captured by a fisheye camera and an electronic device. The apparatus includes a memory and a processor coupled to the memory. The processor according to an embodiment is configured to: project an original image captured by the fisheye camera onto a cylindrical or spherical projection model, and perform reverse mapping to obtain at least two reversely mapped images, angles of view of the at least two reversely mapped images being towards different directions, detect objects in the reversely mapped images, respectively, and detect an object that is the same among the objects detected in the reversely mapped images. According to this disclosure, information in the wide field of view images obtained by capturing by the fisheye camera may be fully utilized.

Abstract: A system for assisting in aligning a vehicle for hitching with a trailer includes a steering system that adjusts a steering angle of the vehicle, a braking system that adjusts a speed of the vehicle, an imaging system that receives image data of a trailer disposed in an area proximate the vehicle, and a controller. The controller detects, within a threshold distance defined from a stationary point on the vehicle and the trailer, a position of a coupler from the image data, and, responsive to the position being less than the threshold distance, a status of the trailer based on the image data being indicative of a position of a hitch ball relative to the coupler within a second threshold. The controller maneuvers, via the steering and braking systems, the vehicle based on the status.

Abstract: The present disclosure is directed generating candidate trajectories and selecting one of them to be implemented. In particular, a computing system can obtain an initial travel path for an autonomous vehicle. The computing system can obtain sensor data describing objects within an environment of the autonomous vehicle. The computing system can generate a plurality of trajectories for the autonomous vehicle based on the sensor data and the initial travel path. The computing system can determine whether the initial travel path, an offset profile, and a velocity profile meet flatness criteria. In response to determining that the initial travel path, the offset profile, and the velocity profile meet the flatness criteria, the computing system can combine the initial travel path, the offset profile, and the velocity profile into the respective trajectory. The computing system can determine a trajectory from the plurality of trajectories.

Abstract: A vehicle-mounted device is configured to identify another vehicle from an image generated by photographing the surroundings of a host vehicle using a photographing device. The vehicle-mounted device is configured to detect identification information of the another vehicle on the basis of a signal received via inter-vehicle communication with a vehicle-mounted device of the another vehicle located in a communication range of the inter-vehicle communication. The vehicle-mounted device is configured to determine whether the identification information corresponding to the another vehicle identified from an image has been acquired.

Abstract: A movement distance calculation device includes: a first movement distance calculation unit which calculates a first movement distance of a movable body based on plural rotation speeds of plural wheels of the movable body and a steering angle of the movable body; a vector detection unit which detects a movement vector of an object included in the acquired images as defined herein; a second movement distance calculation unit which calculates a second movement distance of the movable body based on the movement vector; a first reliability determining unit which determines reliability of the calculated first movement distance as defined herein; a second reliability determining unit which determines reliability of the calculated second movement distance as defined herein; and a movement distance determining unit which determines a movement distance using at least one of the calculated first movement distance and the calculated second movement distance as defined herein.

Abstract: An approach is provided for detecting and map-coding a tunnel based on probes and image data. The approach involves, for example, identifying a gap in probe data collected from one or more location sensors of a plurality vehicles. The gap represents a probe gap segment along which at least one probe point of the probe data does not occur or occurs below a threshold number. The approach also involves retrieving image data depicting a geographic area based on location coordinate data associated with the gap. The approach further involves processing the image data to identify one or more end points of a road network depicted in the image data. The approach further involves locating a tunnel start point and/or a tunnel end point based on the one or more endpoints. The approach further involves providing the tunnel start point and/or the tunnel end point as a map data output.

Abstract: A gate-based vehicle image capture system for analyzing vehicle image data is presented. The system may include a portable imaging gate apparatus configured to capture vehicle image data of a vehicle. The portable imaging gate apparatus may include a plurality of imaging assemblies positioned at a plurality of viewing angles. The system may further include an external processing server configured to receive the vehicle image data from the portable imaging gate apparatus. The external processing server may also analyze the vehicle image data to identify a plurality of vehicle features, and determine a first vehicle feature from the plurality of vehicle features. The first vehicle feature may be related to a vehicle incident. The system may also include a provider server configured to receive the first vehicle feature from the external processing server, and update an aspect of a risk evaluation based on the first vehicle feature.

Abstract: Aspects described herein may allow for the application of an artificial neural network architecture to identify intrusion in a database. Changes to components of a data table of the database may be tracked as a snapshot of the changes over a period of time. Any change in the data table may be associated with a user. Utilizing multiple snapshots, a background substitution technique may be utilized to generate a matrix of the changes to the data table over a period of time. A model having an artificial neural network architecture may utilize the matrix as an input set to identify the user as an unauthorized user accessing the database.

Abstract: Provided is a technology for recording effective data, as a result of inspection on a to-be-inspected object. This inspection assistance device is provided with: a reception unit that receives information about a to-be-inspected object; an acquisition unit that acquires image data captured by an imaging device; and a recording control unit that, in the case when the imaging time of the image data falls within a prescribed time range based on the time at which the information was received being set as a reference, and when the to-be-inspected object indicated by the received information coincides with the to-be-inspected object recognized from the image data, records the information pertaining to the result of inspection on the to-be-inspected object and the information about the to-be-inspected object in association with each other.

Abstract: The vehicle control system/method for adapting a control function based on a user profile may comprise: a gesture recognition module; a user profile module; a function control module; a processor; a non-transitory storage element coupled to the processor; encoded instructions stored in the non-transitory storage element, wherein the encoded instructions when implemented by the processor, configure the system to: identify a user; retrieve a user profile for the identified user; receive at a gesture recognition module, an input indicating a gesture from the user; identify a control function request corresponding to the gesture input; send a verification of the control function request; and receive at a function control module characteristics parsed from the user profile that effect the control function request by the user profile module to adapt a control function command for an adapted control function output by the function control module.

Abstract: In exemplary embodiments, methods and systems are provided for controlling an auto high beam functionality for headlights of a vehicle. In an exemplary embodiment, a method includes: obtaining camera data pertaining to an object in front of the vehicle; identifying, via a processor, a radial gradient of pixels in a region of interest from the camera data; and automatically controlling, via the processor, the auto high beam functionality for the headlights based on the radial gradient.

Abstract: In one embodiment, a system receives an initial reference line representing a route from a first location to a second location associated with an autonomous driving vehicle (ADV), where the initial reference line includes a number of reference line segments. The system smooths the line segments by applying a quadratic programming (QP) spline smoothing algorithm to the line segments. The system determines that the QP spline smoothing algorithm for the line segments fails to converge. The system adjusts (or enumerates) a location of an end point of a line segment. The system smooths the line segments by applying a QP spline smoothing algorithm to the adjusted (or enumerated) line segments. The system generates a smoothed reference line based on the smoothed line segments to be used as a reference line of the route to control the ADV.

Abstract: Vehicles and methods for detecting a three-dimensional (3D) position of a traffic sign and controlling a feature of the vehicle based on the 3D position of the traffic sign. An image is received from a camera. The image is processed using a neural network. The neural network includes a traffic sign class block regressing a traffic sign class for a traffic sign included in the image and a rotation block regressing an orientation for the traffic sign. Dimensions for the traffic sign are retrieved from an information database based on the traffic sign class. A 3D position of the traffic sign is determined based on the dimensions of the traffic sign and the orientation of the traffic sign. A feature of the vehicle is controlled based on the 3D position of the traffic sign.

Abstract: A method for implementing parametric models for scene representation to improve autonomous task performance includes generating an initial map of a scene based on at least one image corresponding to a perspective view of the scene, the initial map including a non-parametric top-view representation of the scene, implementing a parametric model to obtain a scene element representation based on the initial map, the scene element representation providing a description of one or more scene elements of the scene and corresponding to an estimated semantic layout of the scene, identifying one or more predicted locations of the one or more scene elements by performing three-dimensional localization based on the at least one image, and obtaining an overlay for performing an autonomous task by placing the one or more scene elements with the one or more respective predicted locations onto the scene element representation.

Abstract: A terminal apparatus transmits, to a server, setting information including specification information of a camera based on input manipulation of the user, environment information indicating an environment in which the camera is installed, and a map image of a place where the camera is installed. The server calculates a capturing area of the camera in the map image, places a subject model that serves as a capturing target of the camera at a prescribed position in the capturing area and generates a synthesized image, generates a face image and a whole body image of the subject model located at the prescribed position that is inferred to be captured by the camera, transmits the synthesized image and the face image and the whole body image of the subject model to the terminal apparatus. The terminal apparatus displays the synthesized image, the face image and the whole body image of the subject model.

Abstract: The present disclosure describes method, apparatus, and storage medium for obtaining object information. The method includes obtaining a to-be-tracked image comprising at least one object and at least one reference image comprising a plurality of objects; extracting a to-be-tracked image block comprising a plurality of to-be-tracked points from the to-be-tracked image and extracting a reference image block comprising a plurality of reference points from a reference image of the at least one reference image; constructing a point transformation relationship between the to-be-tracked image block and the reference image block based on a position relationship between the plurality of to-be-tracked points and a position relationship between the plurality of reference points; and obtaining a position of a reference point in the reference image corresponding to a to-be-tracked point based on the point transformation relationship, to determine an object in the reference image corresponding to the at least one object.

Abstract: There is provided with an imaging apparatus that contains a plurality of imaging sections. An obtaining unit obtains, from an information processing apparatus, identification information designating two or more imaging sections among the plurality of imaging sections. A determination unit determines, based on pieces of profile information each containing setting information regarding a captured image from each of the imaging sections corresponding to the two or more imaging sections, setting information regarding the captured images upon collectively distributing the captured images from the two or more imaging sections. A generation unit generates a virtual profile containing the setting information determined. A sending unit sends information regarding the virtual profile to the information processing apparatus.

Abstract: An automatic door opening and closing system includes a distance measuring device measuring a distance between an external surface of a swing door of a vehicle and an object lying on a circumference of the swing door, a door lock device locking the swing door, a drive device opening and closing the swing door, a door opening operation input device receiving an operation to cancel the locking of the swing door and to open the swing door, and a control device controlling the door lock device and the drive device based on the distance measured in response to receiving the operation. The distance measuring device includes a plurality of distance sensors placed dispersively over the swing door, a measurement target by the distant measuring device including a whole of the external surface of the swing door.

Abstract: Provided is an image processing method. The image processing method includes receiving images acquired from a plurality of vehicles positioned on a road; storing the received images according to acquisition information of the received images; determining a reference image and a target image based on images having the same acquisition information among the stored images; performing an image registration using a plurality of feature points extracted from each of the determined reference image and target image; performing a transparency process for each of the reference image and the target image which are image-registered; extracting static objects from the transparency-processed image; and comparing the extracted static objects with objects on map data which is previously stored and updating the map data when the objects on the map data which is previously stored and the extracted static objects are different from each other.

Abstract: A method controls drive units for adjusting vehicle seat components. According to the method, a vehicle speed-dependent and crash-related nominal position or a vehicle speed-dependent and crash-related nominal position range are defined for at least one component. Starting from an actual position that is the same as the nominal position or from an actual position within the nominal position range, the actual position cannot be manually or automatically adjusted in the first instance or can only be adjusted in the nominal position range in the second instance, independently from the identification of a crash situation. Starting from an actual position that is different from the nominal position or outside the nominal position range, the actual position is automatically adjusted towards the nominal position or the nominal position range, preferably according to the identified defined situations, independently from the identification of a crash situation.