Abstract: Methods, apparatus and systems for determining a region attribute, and electronic devices are provided. In one aspect, a method includes: identifying a marker line in a target map, the target map being a map of a to-be-cleaned target scene, an intelligent cleaning device relying on the target map during a cleaning process, determining an enclosed region and an unenclosed region with a first position as a reference point in the target map, based on the identified marker line and an auxiliary object in the target map, the auxiliary object including a map boundary and an obstacle, the first position being a position of a preset reference object in the target map, determining the enclosed region as a user-defined cleaning region for the cleaning process of the intelligent cleaning device, and determining the unenclosed region as a normal cleaning region for the cleaning process of the intelligent cleaning device.

Abstract: An image selection method, applied to a self-propelled apparatus, includes: collecting an image from a surrounding environment through an image collection device during the self-propelled apparatus travels; scoring the image according to a scoring rule when there is a recognizable obstacle in the collected image, wherein a value of the scoring is used to indicate an imaging quality of the recognizable obstacle in the image; and selecting an image that comprises the recognizable obstacle and that has a highest score as a to-be-displayed image in response to receive a request to view the image of the recognizable obstacle. A computer-readable storage medium and a self-propelled apparatus are further provided.

Abstract: The invention relates to a vehicle, in particular a motor vehicle, having reproduction means for displaying at least one current parameter of the vehicle, wherein the reproduction means are configured to represent the current parameter value on the reproduction means in the form of numbers or numerical words, wherein there are provided allocation means which are configured to allocate to the number or numerical word, on the basis of the value of the number or numerical word, the size of the number or numerical word and/or at least one parameter of the number or numerical word relating to the color and/or of the background thereof, and wherein the reproduction means are configured to represent the number or numerical word on the reproduction means with the allocated size and/or the color parameter.

Abstract: Various embodiments include methods and vehicles, such as an autonomous vehicle, a semi-autonomous vehicle, etc., for collaboratively directing or configuring one or more headlights of a vehicle to avoid visually interfering with operation of an oncoming vehicle. Various embodiments may include receiving, by a first vehicle processor, a first collaborative lighting message from a second vehicle, wherein the first collaborative lighting message requests that the first vehicle direct one or more headlights of the first vehicle to avoid visual interference with operation of the second vehicle and directing one or more the headlights of the first vehicle in accordance with the collaborative lighting plan. Directing the headlights to avoid visual interference with operation of the second vehicle may include switching the headlights to a low-beam configuration.

Abstract: According to one or more examples, a system includes an observer vehicle, and a vehicle in a predetermined vicinity of the observer vehicle. The observer vehicle detects an operating condition of the vehicle using one or more perception devices. The observer vehicle determines that the operating condition does not satisfy a predetermined norm. The observer vehicle generates a message indicating the operating condition of the vehicle. The observer vehicle identifies a communication identifier of the vehicle. The observer vehicle sends the message to be received by the vehicle using the communication identifier via a vehicle to everything (V2X) communication module.

Abstract: Methods for improving compliance with regulations pertaining to vehicle driving records are disclosed. One or more digital images from a camera mounted in a vehicle are received. Based on a determination that the vehicle has hours of service that have not been assigned to a driver, a subset of the one or more digital images corresponding to the hours of service are identified based on the timestamps. The subset of the one or more digital images are processed to identify a correspondence between a face of a person included in the one or more digital images and a face of a known person. Based on the correspondence transgressing a threshold level of correspondence, a user interface is generated for presentation on a device. The user interface includes an interactive user interface element for accepting a recommendation to assign the known person as the driver for the unassigned hours of service.

Abstract: Examples disclosed herein may involve a computing system that is configured to (i) obtain previously-derived perception data for a collection of sensor data including a sequence of frames observed by a vehicle within one or more scenes, where the previously-derived perception data includes a respective set of object-level information for each of a plurality of objects detected within the sequence of frames, (ii) derive supplemental object-level information for at least one object detected within the sequence of frames that adds to the previously-derived object-level information for the at least one object, (iii) augment the previously-derived perception data to include the supplemental object-level information for the at least one object, and (iv) store the augmented perception data in an arrangement that encodes a hierarchical relationship between the plurality of objects, the sequence of frames, and the one or more scenes.

Abstract: A vehicular trailer assist system includes a camera disposed at a rear portion of a vehicle and having an exterior field of view that includes at least a portion of a trailer hitched to the vehicle. A control, responsive to processing of frames of image data captured by the camera and during a calibration maneuver by the vehicle, determines a trailer template of trailer hitched to the vehicle. The control, during a turning portion of the calibration maneuver, determines a trailer collision angle based on the determined trailer template. The control, after completion of the calibration maneuver, determines a current trailer angle of the trailer relative to the vehicle as the vehicle is driven along a road. The control alerts an operator of the vehicle if the current trailer angle is within a threshold amount of the determined trailer collision angle.

Abstract: A vehicle control system includes an imaging device, a map generating unit, and an own lane identifying unit. The own lane identifying unit is configured to identify one lane as an own lane on a map in a case where the one lane is an only lane present in a specific area on the map, compare a type of a delimiting line of a captured own lane with types of delimiting lines of a plurality of lanes on the map in a case where the plurality of lanes are present in the specific area on the map, and identify one of the plurality of lanes on the map as the own lane on the map in a case where the type of the delimiting line of the captured own lane matches only a type of a delimiting line of the one of the plurality of lanes on the map.

Abstract: A point data acquisition unit acquires point data indicating a reflection point obtained by an optical sensor that receives reflected light of an emitted light beam reflected at the reflection point. An image data acquisition unit acquires image data of an area around the reflection point indicated by the point data. A luminance calculation unit calculates smoothness of luminance of the image data. A fog determination unit determines the density of fog based on a distance, which is determined from the point data, from the optical sensor to the reflection point and the smoothness of the luminance.

Abstract: Aspects of the disclosure relate to detecting and responding to malfunctioning traffic signals for a vehicle having an autonomous driving mode. For instance, information identifying a detected state of a traffic signal for an intersection. An anomaly for the traffic signal may be detected based on the detected state and prestored information about expected states of the traffic signal. The vehicle may be controlled in the autonomous driving mode based on the detected anomaly.

Abstract: System and techniques for vehicle environment modeling with a camera are described herein. A device for modeling an environment comprises: a hardware sensor interface to obtain a sequence of unrectified images representative of a road environment, the sequence of unrectified images including a first unrectified image, a previous unrectified image, and a previous-previous unrectified image; and processing circuitry to: provide the first unrectified image, the previous unrectified image, and the previous-previous unrectified image to an artificial neural network (ANN) to produce a three-dimensional structure of a scene; determine a selected homography; and apply the selected homography to the three-dimensional structure of the scene to create a model of the road environment.

Abstract: The traffic monitoring system is provided with: a camera which captures an image of a monitoring area including a road and generates image data; a millimeter-wave radar which scans a scanning area included in the monitoring area and generates millimeter-wave data; and an information processing server which is connected to the camera and the millimeter-wave radar and acquires the image data and the millimeter-wave data. The information processing server is provided with: a data synchronization unit which synchronizes the image data with the millimeter-wave data so that the difference between a timing at which the image data is generated and a timing at which the millimeter-wave data is generated is equal to or smaller than a certain value; and a screen generation unit which associates the image data and the millimeter wave, which have been synchronized, with each other and generates a monitoring screen that indicates the road conditions.

Abstract: A vehicle air vent system includes a dashboard defining an air vent opening. An air register assembly is disposed within the air vent opening of the dashboard. The air register assembly includes a frame and a vane rotatably coupled to the frame. An imager is coupled to the dashboard. The imager captures data within a field of view. A controller is communicatively coupled to the imager and the air register assembly. The controller receives the data from the imager. The controller determines a position of an object within the field of view in response to the data.

Abstract: Disclosed are methods, systems, and non-transitory computer-readable medium for determining color labeling for a vehicle search service. For instance, the method may include: obtaining vehicle images for a plurality of vehicles; filtering the vehicle images to obtain exterior and/or interior images of the plurality of vehicles, determining super-pixel values for the exterior images and/or the interior images; generating a standard color mapping by associating the super-pixel values to standard color names; and storing searchable vehicle information at least based on the standard color mapping.

Abstract: A vehicle information detection method, a method for training a detection model, an electronic device and a storage medium are provided, and relates to the technical field of artificial intelligence, in particular to the technical field of computer vision and deep learning. The method includes: performing a first target detection operation based on an image of a target vehicle, to obtain a first detection result for target information of the target vehicle; performing an error detection operation based on the first detection result, to obtain error information; and performing a second target detection operation based on the first detection result and the error information, to obtain a second detection result for the target information.

Abstract: Fatigue driving monitoring, reminding and early-warning method and system based on computer vision are provided. The system includes: a fatigue real-time monitoring system for identifying fatigue characteristic postures of a driver and obtaining a fatigue level according to frequencies of the fatigue characteristic postures and a driving time; a fatigue automatic reminding system for receiving the fatigue level, obtaining a vehicle real-time position information and providing the driver with a corresponding grade of reminder according to the fatigue level; and a fatigue information early-warning system for receiving the fatigue level and the vehicle real-time position information to obtain a fatigue safety index, so that a cloud map of fatigue road conditions is obtained according to the fatigue safety index and shared with traffic management departments and drivers for early-warning. It reduces the situation of fatigue driving on freeway and the hazard of traffic safety accidents caused by fatigue driving.

Abstract: A technique for rendering an under-vehicle view including obtaining a first location of a vehicle, the vehicle having a set of cameras disposed about the vehicle, capturing a set of images; storing images of the set of images in a memory, wherein the images are associated with a time the images were captured, moving the vehicle to a second location, obtaining the second location of the vehicle, determining an amount of time for moving the vehicle from the first location to the second location, generating a set of motion data, the motion data indicating a relationship between the second location of the vehicle and the first location of the vehicle, obtaining one or more stored images from the memory based on the determined amount of time, rendering a view under the vehicle based on the one or more stored images and set of motion data, and outputting the rendered view.

Abstract: Systems and methods for navigating a host vehicle are disclosed. In one implementation, a system includes a processor configured to receive a first image acquired by a first camera and a second image acquired by a second camera onboard the host vehicle; identify a first representation of an object in the first image and a second representation of the object in the second image; input to a first trained model at least a portion of the first image; input to a second trained model at least a portion of the second image; receive the first signature encoding determined by the first trained model and the second signature encoding determined by the second trained model; input to a third trained model the first signature encoding and the second signature encoding; and receive an indicator of a location of the object determined by the third trained model.

Abstract: A method for operating a vehicle includes detecting a traffic light located at a first spatiotemporal location based on a first digital video stream captured by a first camera and a second digital video stream captured by a second camera. It is determined that the vehicle is located at a second spatiotemporal location by validating first location data received from sensors against second location data obtained by filtering the first location data. It is determined that the traffic light is expected at the first spatiotemporal location based on a semantic map referenced by the second spatiotemporal location. Responsive to determining that the traffic light is expected at the first spatiotemporal location, a traffic signal of the traffic light is detected based on the two digital video streams. A trajectory is determined in accordance with the traffic signal. A control circuit operates the vehicle in accordance with the trajectory.

Abstract: An autonomous vehicle is described herein. The autonomous vehicle includes a lidar sensor system. The autonomous vehicle additionally includes a computing system that executes a lidar segmentation system, wherein the lidar segmentation system is configured to identify objects that are in proximity to the autonomous vehicle based upon output of the lidar sensor system. The computing system further includes a deep neural network (DNN), where the lidar segmentation system identifies the objects in proximity to the autonomous vehicle based upon output of the DNN.

Abstract: A vehicular control system includes a forward viewing camera disposed at an in-cabin side of a windshield of a vehicle and viewing forward of the vehicle. Road curvature of a road along which the vehicle is traveling is determined responsive at least in part to processing of image data captured by the camera. Responsive at least in part to processing of captured image data, speed of the vehicle is controlled by an adaptive cruise control system of the vehicle. Upon approach of the vehicle to a curve in the road along which the vehicle is traveling, speed of the vehicle is reduced by the adaptive cruise control system to a reduced speed for traveling around the curve in the road. Speed of the vehicle is increased by the adaptive cruise control system when the vehicle is traveling along a straighter section of road after the curve in the road.

Abstract: A method and an apparatus for detecting a lane is provided. The lane detection apparatus according to an embodiment includes: an acquisition unit configured to acquire a front image of a vehicle; and a processor configured to input the image acquired through the acquisition unit to an AI model, and to detect information of a lane on a road, and the AI model is trained to detect lane information that is expressed in a plane form from an input image. Accordingly, data imbalance between a lane area and a non-lane area can be solved by using the AI model which learns/predicts lane information that is expressed in a plane form, not in a segment form such as a straight line or curved line.

Abstract: A hand trajectory recognition method for a following robot based on hand velocity and trajectory distribution comprises: sampling and photographing an operator by a kinect camera to obtain hand projection plane data; smoothing the hand projection plane data by moving average, establishing velocity vectors, and processing the velocity vectors to obtain a hand movement descriptor; establishing a hand movement area, traversing hand three-dimensional positions of all frames in an order of sampling and photographing, assigning a mesh where the hand three-dimensional position of each frame is located, and calculating centroid positions of all assigned meshes; establishing centroid directing vectors, and processing the centroid directing vectors to obtain a hand trajectory shape descriptor; and processing cosine values of two angles to obtain a common similarity of the movement descriptor and the trajectory shape descriptor to standard descriptors, and using a standard gesture with a maximum common similarity as a r

Abstract: When a main power from a main battery of a vehicle is interrupted (or cut off), an auxiliary power supplied from an auxiliary battery may be used to drive electrical components of the vehicle. The vehicle includes: a main battery to supply the main power; the auxiliary battery to supply an auxiliary power; and an image recording controller to operate with the main power, capture and record an image of surroundings of the vehicle. In particular, when the supply of the main power is interrupted, the image recording controller performs capturing and recording the image by receiving the auxiliary power, controls a supply path of the auxiliary power so that the auxiliary power is supplied to a predetermined electrical component of the vehicle to continuously operate the predetermined electrical component using the auxiliary power.

Abstract: A radar apparatus installed in a vehicle includes a transceiver that transmits a radar signal to an outside of the vehicle and receives a radar signal reflected from an object; a signal processing unit that processes the reflected radar signal to detect the object; a fusion data generation unit that generates fusion data based on radar data and camera data; and a classification unit that classifies the detected object using an artificial intelligence module trained based on the generated fusion data.

Abstract: A driving support system for a vehicle includes an information obtainer, a target vehicle speed calculator, a comparison processor, and a determination processor. The information obtainer is configured to obtain curvature information about a curvature of a driving lane of the vehicle and about a curvature of an adjacent lane. The adjacent lane is adjacent to the driving lane. The target vehicle speed calculator is configured to calculate a target vehicle speed in the driving lane and a target vehicle speed in the adjacent lane using the curvature information. The comparison processor is configured to compare a difference between the target vehicle speed in the driving lane and the target vehicle speed in the adjacent lane with a threshold. If the difference is greater than or equal to the threshold, the determination processor is configured to determine that a road branches off in a traveling direction of the vehicle.

Abstract: A method for calibrating at least one position sensor in a vehicle, comprising: saving a map in the vehicle with at least one item of virtual location information from the environment of a predefined calibration route, moving the vehicle on the predefined calibration route, detecting at least one item of actual location information via the at least one position sensor while the vehicle moves on the predefined calibration route, carrying out a comparison between the at least one item of virtual location information and the at least one item of actual location information, calculating a map-based movement of the vehicle within the saved map based on the comparison, calculating an odometry-based movement of the vehicle, and calibrating the at least one position sensor based on a comparison between the map-based movement and the odometry-based movement.

Abstract: A system, method, and computer-program product includes detecting, via a localization machine learning model, a target object within a scene based on downsampled image data of the scene, identifying a likely position of the target object within original image data of the scene, extracting, from the original image data of the scene, a target sub-image containing the target object, classifying, via an object classification machine learning model, the target object to a probable object class of a plurality of distinct object classes, routing the target image resolution of the target sub-image to a target object-condition machine learning classification model of a plurality of distinct object-condition machine learning classification models, classifying, via the target object-condition machine learning classification model, the target object to a probable object-condition class, and displaying, via a graphical user interface, a representation of the target object in association with the probable object-condition

Abstract: Example implementations are provided for an arrangement of co-aligned rotating sensors. One example device includes a light detection and ranging (LIDAR) transmitter that emits light pulses toward a scene according to a pointing direction of the device. The device also includes a LIDAR receiver that detects reflections of the emitted light pulses reflecting from the scene. The device also includes an image sensor that captures an image of the scene based on at least external light originating from one or more external light sources. The device also includes a platform that supports the LIDAR transmitter, the LIDAR receiver, and the image sensor in a particular relative arrangement. The device also includes an actuator that rotates the platform about an axis to adjust the pointing direction of the device.

Abstract: A method includes calculating a stereo-disparity map between the initial image and the final image, for each column of the stereo-disparity map, calculating an average value of the stereo-disparities of the pixels of the column, calculating the slope and/or constant factor of a linear function approximating variations of said average values; and calculating said information relative to the relative speed between the object and the camera, based on the slope and/or the constant factor.

Abstract: A system for asset identification and mapping is configured to information related to a plurality of objects with at least one sensor. The captured information may be processed to identify one or more assets among the objects, and a map generated including a geographic area proximate to at least one of the one or more assets. The at least one of the one or more assets may then be identified on the map.

Abstract: Sensor data and data from V2V messages are used to detect obstacles. Additional obstacles are identified in map data according to a vehicle's position. In response to detecting a turn intent, a controller calculates a turn path according to the detected obstacles and properties of the vehicle. The turn path is presented to a user, such as by display on a HUD or superimposing the turn path on an image from a forward facing camera. A desired steering angle to traverse the turn path may be displayed, such as relative to the current steering angle of the vehicle. Notifications regarding the path and turning intent of other vehicle may be displayed along with the turn path.

Abstract: A lane keeping assist system includes: a camera configured to provide an image around a vehicle as image information; a lane information generator configured to generate image reliability information and first lane information, based on the image information; an image storage configured to store the image information for each predetermined time among predetermined times; a neural network learning device configured to generate second lane information based on the image reliability information and the image information stored for each predetermined time; and a steering controller configured to select either one of the first lane information and the second lane information as lane information, based on the image reliability information, and generate steering information based on the selected lane information.

Abstract: Systems and methods described herein relate to training a machine-learning-based monocular depth estimator.

Abstract: A system for capturing location-based data includes an interface and a processor. The interface is configured to receive a location-based data description for capturing the location-based data. The processor is configured to determine a location based at least in part on the location-based data description, create a location-based data identification job based at least in part on the location, cause execution of the location-based data identification job to a set of vehicle event recorder systems, wherein the location-based data identification job is executed by a vehicle event recorder system of the set of vehicle event recorder systems to acquire sensor data in response to a vehicle being able to acquire the sensor data related to the location of the location-based data identification job, receive the sensor data from the vehicle event recorder system, and determine the location-based data based at least in part on the sensor data.

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: A method for measuring an inter-vehicle distance using a processor is provided. The method includes acquiring a driving image photographed by a photographing device of a first vehicle; detecting a second vehicle from the driving image and calculating a ratio between an image width of the detected second vehicle and an image width of a lane in which the second vehicle is located; determining a size class of the second vehicle among a plurality of size classes based on the calculated ratio; determining a width of the second vehicle based on the determined size class of the second vehicle; and calculating a distance from the photographing device to the second vehicle based on the determined width of the second vehicle, a focal length of the photographing device, and the image width of the second vehicle.

Abstract: System and techniques for vehicle environment modeling with a camera are described herein. A time-ordered sequence of images representative of a road surface may be obtained. An image from this sequence is a current image. A data set may then be provided to an artificial neural network (ANN) to produce a three-dimensional structure of a scene. Here, the data set includes a portion of the sequence of images that includes the current image, motion of the sensor from which the images were obtained, and an epipole. The road surface is then modeled using the three-dimensional structure of the scene.

Abstract: A map update system includes a data transmission device mounted on a vehicle and a map server which stores map data. The data transmission device generates sensor data representing a road environment of surroundings of the vehicle in a predetermined position, calculates a matching degree between the road environment of the surroundings of the vehicle and a road environment in the predetermined position represented by the map data, and causes a communication circuit mounted on the vehicle to transmit the sensor data and information representing the matching degree to the map server. The map server transmits the map data by utilizing sensor data, among the sensor data received via a communication device, having a matching degree less than a matching degree threshold with a higher priority than sensor data having a matching degree or greater than the matching degree threshold.

Abstract: In a traffic emergency, there is no time for a human to integrate multiple sensor data streams and devise a plan for avoiding a collision. Only the electronic reflexes of a trained automatic system can provide evasive action in time. Disclosed is an artificial intelligence (AI) model trained to recognize an imminent collision based on sensor data, rapidly devise and test a large number of possible sequences of actions, some drawn from a library of previously-successful strategies and others invented by the AI model. If any sequence can avoid the collision, the AI model implements that sequence immediately. If none of the sequences can avoid the collision, the AI model calculates the harm caused by each sequence and picks the one that causes the least harm (fatalities, injuries, etc.) for implementation. AI is needed to find a possible solution in time to implement it and thereby mitigate the imminent collision.

Abstract: A list for accurately identifying objects with different sizes that are the same attributes can be generated automatically. A classification unit classifies, from a group of images consisting of images including objects with any of a plurality of attributes, each of the images including the objects that have an identical attribute and have different real sizes into an identical cluster. An output unit outputs each of clusters into which at least two of the images are classified as a list of images with different real sizes of the objects for the identical attribute.

Abstract: A computer-implemented method for checking the correct deployment of an airbag device comprises, in a vehicle or test vehicle model, optionally arranging a dummy or model of a vehicle occupant in the vicinity of the airbag device, triggering the deployment of the air chamber of the airbag device, taking at least one image of a scene including the air chamber at a plurality of discrete times following the triggering of its deployment and verifying the volume of inflation and/or deployment of the air chamber at the plurality of discrete times following the triggering of its deployment on the basis of the at least one image.

Abstract: A measurement apparatus operable to perform measurement concerning a predetermined item for a target subject which is a measurement target. The apparatus comprises an acquisition unit configured to, for a captured image acquired by capturing of an image capturing range that includes the target subject, acquire distance information indicating a distribution of a subject distance from an image capturing apparatus that performed the capturing and, at least, normal line information that indicates a normal line of a surface of the target subject; and a presentation unit configured to present a notification that indicates an image capturing direction of the image capturing apparatus for performing measurement of the predetermined item based on the normal line information acquired by the acquisition unit.

Abstract: Measuring speed of a vehicle in a road environment. During calibration, multiple images are captured of a calibration vehicle traveling at a known ground speed. A calibration image feature is located in the image of the calibration vehicle. An optical flow of the calibration image feature is computed to determine a model between an image speed of the calibration image feature and the known ground speed of the calibration vehicle. During speed measurement, multiple images are captured of a target vehicle traveling along a road surface at unknown ground speed. A target image feature may be located in an image of the target vehicle. An image speed may be computed of the target image feature. The model may be applied to determine the ground speed of the target vehicle from the image speed of the target image feature.

Abstract: A system for mapping traffic lights and for determining traffic light relevancy for use in autonomous vehicle navigation. The system may include at least one processor programmed to: receive at least one location identifier associated with a traffic light; receive a state identifier associated with the traffic light; receive navigational information indicative of one or more aspects of motion of the first vehicle along the road segment, and determine, based on the navigational information, a lane of travel traversed by the first vehicle along the road segment. The processor may also determine whether the traffic light is relevant to the lane of travel traversed by the first vehicle; update an autonomous vehicle road navigation model relative to the road segment; and distribute the updated autonomous vehicle road navigation model to a plurality of autonomous vehicles.

Abstract: A method and apparatus for recognizing an object are provided, including extracting a feature from an input image and generating a feature map in a neural network. In parallel with the generating of the feature map, a region of interest (ROI) corresponding to an object of interest is extracted from the input image, and a number of object candidate regions used to detect the object of interest is determined based on a size of the ROI. The object of interest is recognized from the ROI based on the number of object candidate regions in the neural network.

Abstract: A system, such as a driver assistance system, arranged to form images of outside a vehicle for viewing from an eye box region within the vehicle. The system comprises an image capture device, a picture generating unit and an optical element. The image capture device is arranged to be mounted to the vehicle. The image capture device is arranged to capture images outside the vehicle. The optical element is disposed in front of the replay plane. The optical element has a focal length. The distance between the replay plane and the optical element is less that the focal length of the optical element such that the optical element forms a virtual image of each picture for viewing from the eye box region. The picture generating unit may be a holographic projector. The holographic projector may be arranged to form pictures on a replay plane. Each picture may correspond to an image captured by the image capture device.

Abstract: A lamp controller interlocking system of a camera built-in headlamp inventive concepts includes a headlight module integrated with a camera and a light source, a camera controller generating a single frame image by composing an image captured in a short exposure section, in which a shutter opening time of the camera is relatively short, and an image captured in a long exposure section, in which the shutter opening time of the camera is relatively long, and a lamp controller controlling the light source to emit more light in the long exposure section more than in the short exposure section in synchronization with timings of the long exposure section and the short exposure section of the camera.

Abstract: An image processing device has circuitry, which is configured to obtain image data, the image data being generated on the basis of a non-linear mapping defining a mapping between an object plane and an image plane; and to process the image data by applying a kernel of an artificial network to the image data based on the non-linear mapping.