1714 results about "Onboard camera" patented technology

The invention provides a vehicle-mounted display control method and system based on man-machine interaction, and relates to the technical field of display control, and the method comprises the steps: collecting a face image of a driver through a vehicle-mounted camera, extracting eyeball movement features, and calculating the coordinates of a real-time fixation point; matching the fixation point coordinate with a preset interaction area, and triggering an activation state; generating interactive display content according to the activated state, and calculating a dynamic projection position; detecting a moving track of the fixation point in the activation area, and identifying an interaction gesture to trigger a control instruction; and updating a display interface according to the control instruction and sending a feedback prompt tone. According to the method, the interaction efficiency and safety of the vehicle-mounted display system are improved.

Aspects of the subject disclosure relate to a vehicle camera system for view creation of view locations. A device may include a processor that obtains first data and second data from different cameras of a vehicle. The first data may include an image representation of a scene in a first field of view being observed from a first perspective view and the second data may include an image representation of the scene in a second field of view being observed from the first perspective view in a second field of view. The processor may modify the first data and the second data by a perspective transformation to adjust the image representation to a second perspective view. The processor may create a stitched image representing the scene in a combined field of view observed from the second perspective view by stitching the modified first data with the modified second data.

The invention relates to the technical field of intelligent driving, and discloses an intelligent networked automobile multi-modal sensing and decision-making system based on deep learning, and the system comprises a multi-modal data collection module which is used for collecting image data and radar time sequence data around a vehicle through a vehicle-mounted camera and a vehicle-mounted radar sensor, the surrounding environment of the vehicle can be sensed in all directions; the image data processing module is used for processing the image data by using an improved deep learning model and extracting target object feature information in the image; the radar data processing module is used for analyzing the change trend of the radar time sequence data and making an optimal driving decision according to different traffic scenes in combination with the feature information of the target object; and the optimal decision execution module is used for executing an optimal decision action according to the optimal driving decision. According to the invention, high-precision perception and quick response to environment information in a complex traffic scene can be realized, the driving safety is improved, and the traffic efficiency is optimized.

Autonomous ground vehicles that are outfitted with onboard cameras and configured to travel within an area or region are programmed with a previously generated visual map of the area or region. The visual map includes a plurality of map points from which images were previously captured and processed to determine visual features of such images. In the event of a loss of one or more position signals, such as GPS signals, the autonomous ground vehicles capture images using one or more of the cameras, and process the images to determine visual features from such images. The visual features determined from such images are compared to the visual map. Positions of one or more of the map points are identified as most likely corresponding to a position of the autonomous ground vehicle where visual features associated with such map points are similar to the visual features determined from such images.

Aspects of the subject disclosure relate to a vehicle camera system for view creation of terrain view locations. A vehicle includes a first camera and a second camera and includes a processor configured to receive a route request to provide a route projection for the vehicle. The processor receives first data from the first camera and second data from the second camera. The processor combines the first data having a first field of view with the second data having a second field of view to create a stitched image representing a scene in a combined field of view. The processor provides overlay information mapped to pixel coordinates of the stitched image and provides, for display, a terrain view that includes the stitched image and the overlay information. The terrain view may be displayed in one of multiple selectable views based on a driving mode of the vehicle.

The invention relates to the field of computer vision, and provides a dynamic map modal semantic segmentation method and system for vehicle-mounted mining video monitoring. A mining area video is captured through a vehicle-mounted camera, video frame features are deeply extracted by using a pre-trained first convolutional neural network, multi-layer features are fused to enhance the representation capability, then a time dimension dynamic map and a space dimension dynamic map are constructed, a dynamic map mode is formed after fusion, an attention mechanism is introduced, and the attention of the mining area is enhanced. And distributing weights according to the similarity of the feature vectors, and focusing the correlation among the key areas. And extracting context information by using a global average pooling layer, and combining with the weighted original feature map to fuse global and dynamic map information. In addition, local context information is also combined to enhance feature understanding. And outputting an accurate semantic segmentation result based on the comprehensively fused feature map, and distributing a label representing an object or a background category to which each pixel belongs for each pixel, thereby realizing deep understanding and efficient analysis of mining area video information.

The invention discloses a traffic operation and maintenance fault intelligent scheduling method and system based on an AI large model, and belongs to the technical field of traffic control. The method comprises the following steps: collecting a vehicle driving track GPS coordinate set, a traffic flow density matrix, a vehicle-mounted camera monitoring image frame sequence and a fault vehicle owner speed anomaly detection result in real time; constructing a traffic operation state analysis model, and outputting real-time traffic operation state characteristics; generating a traffic fault probability distribution curved surface in a future time window; generating a comprehensive fault positioning confidence coefficient matrix; and planning an optimal maintenance resource path according to the pheromone updating rule, and updating the optimal maintenance resource path to the visual scheduling platform in real time. According to the method, space-time diagram convolutional network dynamic modeling is constructed according to multi-source data, so that the limitation of a space blind area of a single data source is broken through, the fault positioning speed is improved, and the problems of incomplete coverage and low positioning speed in the prior art are solved.

The invention discloses an automobile data recorder fatigue driving detection and reminding method based on multi-modal data fusion, and the method comprises the following steps: S1, collecting and preprocessing a face image through a vehicle-mounted camera, and carrying out the face detection and region positioning; s2, dynamically adjusting the size and the position of an image block by adopting a dynamic image blocking technology; s3, performing facial image feature extraction by using a visual converter; s4, acquiring vehicle multi-modal data and fusing the vehicle multi-modal data with the facial features; s5, optimizing a visual converter network structure and training hyper-parameters through a wolf pack optimization algorithm; s6, performing fatigue state analysis based on the optimized visual converter; and S7, when the fatigue state is detected, reminding the driver to have a rest through sound-light alarm. The fatigue driving detection and reminding method of the automobile data recorder is high in precision and high in real-time performance through a deep learning algorithm of multi-modal data fusion and optimization, and the recognition accuracy of fatigue driving and the robustness of the system are effectively improved.

The invention relates to an automatic driving YOLO target detection method and system for coping with light influence, and the method comprises the steps: employing a vehicle-mounted camera to shoot the actual condition images of streets and roads on a plurality of streets, obtaining the vehicle actual condition images of different streets and different roads in different time periods, and forming an image data set; marking the image data set by using a LabelImg tool, and marking a vehicle target in the image, including a bounding box and category information of a vehicle; constructing an automatic driving YOLO target detection network model coping with light influence; and training an automatic driving YOLO target detection network model coping with the light influence by using the obtained image data set, wherein the trained automatic driving YOLO target detection network model coping with the light influence is used for coping with vehicle target detection under the light influence. Accurate vehicle distance adjustment and safe driving decisions can be provided for the automatic driving vehicle, and driving safety and efficiency under various light conditions are ensured.

The invention relates to the technical field of unmanned aerial vehicle navigation, and discloses an unmanned aerial vehicle navigation method and system based on computer vision, and the unmanned aerial vehicle navigation method based on computer vision comprises the following steps: collecting a current visual field image of an airborne camera of an unmanned aerial vehicle, and carrying out adaptive image enhancement; performing scene semantic understanding; semantic perception feature extraction is carried out; feature matching and outer point elimination are carried out; floor identification is carried out, and multi-sensor fusion positioning is carried out; plane segmentation, structural constraint extraction and map normalization are carried out; performing visual navigation evaluation, judging a navigation mode according to an evaluation result, and generating an exploration control instruction quantity based on the navigation mode; generating a cross-floor path sequence, optimizing a track in real time, and performing control fusion in combination with a navigation mode identifier and an exploration control vector; according to the method, the navigation problem of the unmanned aerial vehicle in complex scenes such as smoke interference, multi-story buildings and illumination dramatic change in a GPS denial environment is solved, and high-precision autonomous navigation is realized.

The invention provides a driving behavior intelligent identification method and system based on video analysis, and the method comprises the steps: obtaining a driver face video stream and a road environment video stream collected by a vehicle-mounted camera, and reading the driving information recorded by a whole vehicle communication network; recognizing an eyelid closing state, a sight line direction and a head posture in the driver face video stream based on a posture recognition model, and performing fatigue distraction analysis to obtain driver state information; performing motion trail analysis on the road environment video stream and the driving information, and performing driving risk assessment in combination with the driver state information to obtain driving assessment information; and performing early warning construction according to the driving evaluation information, generating early warning prompt information, and synchronously writing the early warning prompt information, the driving evaluation information and the driver state information into a safety data protection memory. The fatigue and distraction states of the driver can be recognized more accurately, and the accuracy of state judgment is improved.

A vehicular camera includes a housing for mounting the vehicular camera at an in-cabin side of a vehicle windshield. Width of the housing is greater than height of the housing and is greater than depth of the housing. The height of the vehicular camera is less than 35 mm. Circuitry disposed at a first side of a single circuit board accommodated by the housing includes an imager. A lens barrel accommodates a lens and protrudes outward from a front portion of the housing. Circuitry disposed at a second side of the single circuit board includes a coaxial connector that extends from the single circuit board to protrude outward from the rear portion of the housing. Image data captured by the vehicular camera is transferred via the coaxial connector to an electronic control unit that is located in the vehicle remote from where the vehicular camera is mounted.

The invention discloses a vehicle-mounted driving assistance method, which relates to the technical field of driving assistance and comprises the following steps: acquiring road image data acquired by a vehicle-mounted camera and vehicle state data acquired by a vehicle-mounted sensor; performing image recognition on the road image data, extracting traffic signs, lane lines and surrounding vehicle position information, and generating road environment data; and judging a current driving risk level according to the road environment data and the vehicle state data, and feeding back early warning information to a driver according to the driving risk level. According to the invention, a comprehensive utilization and real-time feedback mechanism of multi-source data is provided, and the perception and reaction capability of a driver to potential risks is enhanced. Therefore, the system not only can deal with conventional driving situations, but also can deal with complex and dynamically changing traffic situations, so that the driving safety is greatly improved, and the possibility of accidents is reduced.

The invention discloses an autonomous navigation positioning system and method for an autonomous vehicle, and belongs to the technical field of automatic driving, and the method specifically comprises the steps: receiving a satellite signal through a GPS receiver, preliminarily determining the position of the vehicle, starting a vehicle sensor to carry out real-time measurement, and preprocessing a result; the preprocessed measurement result is converted into position information in a global coordinate system, and the position and attitude of the vehicle are calculated in combination with a sensor fusion algorithm; generating point cloud data by using a laser radar, matching the point cloud data with a high-precision map to determine the accurate position of a vehicle, and identifying obstacle and road features; a vehicle-mounted camera captures an environment image, extracts visual feature information and fuses the visual feature information with environment perception information; on the basis of fusion information and distance information, the obstacle and road conditions are sensed in real time, the optimal driving route is determined by adopting a path planning algorithm, visual navigation and automatic obstacle avoidance are realized, and the safety and reliability of automatic driving are improved.

Provided is a headlight device equipped with a variable light distribution system that performs ADB control, wherein a glare suppression function provided by ADB control is enabled without adjusting the optical axis of the headlight device at times such as when the headlight device is replaced.　An aiming system for a vehicle headlight device comprises an onboard camera that captures a forward image of a host vehicle, a headlight device that sets a light irradiation range in front of the host vehicle, and a control device that performs variable light distribution control for specifying a light reduction or a light shielding region in the light irradiation range on the basis of the image captured by the onboard camera. During initial setting, the control device: sets, in the vehicle headlight device, a specific unit of irradiation at which a specific position in the image captured by the onboard camera, which has captured an image of an illuminated surface for calibration, will be an irradiation position; detects a deviation between the specific position and the irradiation position of the specific unit of irradiation in the image captured by the onboard camera that has captured the image of the illuminated surface for calibration; and corrects the output of the variable light distribution control in accordance with the deviation.

The invention relates to the technical field of intelligent driving, and discloses a defogging embedded system and method for an intelligent driving vehicle-mounted camera, and the method comprises the steps: obtaining original image data collected by the vehicle-mounted camera, and analyzing the brightness distribution, color shift and texture features in an image; based on the image degradation feature information, performing preliminary enhancement processing on the image by adopting an edge guided filtering and brightness adaptive mapping algorithm to generate an initial enhanced image; carrying out fog distribution estimation on the initial enhanced image, carrying out detail restoration and color compensation in combination with a lightweight image reconstruction strategy, and dynamically improving the image quality through an iterative optimization mode; performing definition evaluation on the optimized image, and measuring the fuzzy degree change trend and the structural similarity; and in combination with the finally output clear image, performing target identification and feature extraction operation, and performing accuracy evaluation on an identification result. The method has the advantage of improving the definition of the intelligent driving image.

The invention provides an ai-driven mud head vehicle illegal behavior cooperative monitoring method and system, and the method comprises the steps: collecting the three-dimensional acceleration and three-dimensional angular velocity data of a mud head vehicle through a vehicle-mounted inertial sensor, and constructing an inertial behavior sequence; establishing an individualized behavior model based on historical normal behavior data, identifying an abnormal behavior period deviating from a normal state, and generating an abnormal score; when the abnormal behavior exists, a vehicle-mounted camera is triggered to collect an environment image, and the legality of the scene where the mud-head vehicle is located is judged through a visual semantic recognition model; and fusing the abnormal score and the scene legality mark, and generating a final violation judgment result of the violation behavior through a joint judgment model. And according to the violation judgment result, triggering a multi-stage linkage response mechanism, including vehicle-mounted real-time feedback, regional monitoring equipment linkage and violation data uploading and evidence storage.

A method, device, equipment and medium for locating water accumulation based on three-dimensional oblique photography of unmanned aerial vehicles. A three-dimensional virtual scene graph is constructed based on three-dimensional terrain data obtained by oblique photography of unmanned aerial vehicles, and a target fitting plane is generated by fitting its point cloud data through a random sample consistency algorithm. The uneven area set is determined according to the height difference between each point cloud and the target fitting plane; the road surface image obtained by a vehicle-mounted camera is input into a water accumulation detection model generated by training an adaptive dynamic channel pruning strategy to obtain a detection frame set, and the strategy is to calculate the square of the channel weight amplitude and normalize it to the sum of the weight squares of all retained channels in the same layer to obtain an adaptive dynamic channel pruning score and prune the channel according to its size; the uneven area set and the detection frame set are weightedly fused to obtain a fused detection area; the fused detection area with a confidence greater than a confidence threshold is taken as a water accumulation area and its coordinates are output, which can improve detection efficiency and reduce costs.

A vehicular camera includes a lens unit, a circuit board, an imaging element, a housing supporting the lens unit and accommodating the circuit board and the imaging element, a shield made of metal that is disposed in an internal space of the housing to surround the circuit board, and a resin member that contains a magnetic material, has a predetermined thermal conductivity, and is disposed between the circuit board and a first bottom surface portion of the shield in a manner of extending over an entire circumference of the hole including a central portion of a second shape of the first bottom surface portion of the shield. The resin member is in contact with a second surface of the circuit board, first to fourth side surface portions of the shield, and first bottom surface portion of the shield.

A vehicular camera monitoring system includes an electronic control unit (ECU) at a vehicle and a support arm movably disposed at a side portion of the vehicle, with the support arm having a base end attached at the side portion of the vehicle and a distal end opposite the base end. A camera is disposed at the distal end of the support arm. The support arm is movable between a stowed position and an extended position. A cover element covers an aperture at the side portion at least when the support arm is in the extended position. The camera, when the support arm is in the extended position, captures image data and provides captured image data to the ECU, which processes the provided image data for (i) display of video images derived from provided image data and / or (ii) detection of an object in the field of view of the camera.

The invention discloses a multi-degraded image restoration method based on semantic guidance. According to the method, for degraded images captured by a vehicle-mounted camera under severe weather conditions, potential features of the images are extracted by adopting a trunk network based on Transform, multi-modal semantic information is extracted in combination with a CLIP visual language model, and semantic guidance is provided for different degradation types through a dynamic text prompt generation mechanism. A self-adaptive feature fusion module is designed, channel attention and space attention mechanisms are combined to realize effective integration of multi-modal features, and a degradation feature extraction and fusion module is introduced to enhance the generalization ability of the model. According to the semantic guidance multi-type image recovery network SGIRN provided by the invention, the global modeling capability of the Transform and the cross-modal representation capability of the CLIP visual language model are combined, so that high-quality recovery of various weather degradation types is realized.

The invention provides an automobile lamp control system and method based on DLP projection, and relates to the technical field of automobile lamp projection control. Comprising a vehicle-mounted camera module, a navigation module, a vehicle-mounted controller, a millimeter-wave radar module and a DLP photographing module, the vehicle-mounted camera module is used for capturing gesture information and sight line information of a driver in real time; an image processing module and a control module are integrated in the vehicle-mounted controller; the image processing module is used for analyzing and recognizing the received gesture information and sight line information of the driver; the millimeter-wave radar module is used for detecting vehicle surrounding environment information; the navigation module is used for sending navigation information to the control module when a vehicle starts a navigation function; the control module is used for carrying out fusion processing on the received information based on a preset algorithm, a rule base and a gesture base, generating a control instruction corresponding to the information and sending the control instruction to the DLP photographing module. The driving safety is improved, the interaction convenience is enhanced, and the information display is optimized.

Provided is an image processing device that, by performing highly accurate correction of a parallax error caused by a vertical offset between left and right cameras, makes it possible to accurately distinguish between a control object such as an object dropped on a road surface and road surface paint, and accurately detect a control object having a low step. The image processing device includes a specific region extraction unit that extracts a specific region on a road surface from a plurality of images captured by each of a plurality of in-vehicle cameras; a road surface parameter estimation unit that estimates a road surface structure represented by a road surface parameter, from a road surface portion other than the specific region, the road surface portion being a road surface in the image; a specific region parallax estimation unit that obtains, from the road surface structure, an ideal parallax to be obtained in the specific region; a parallax image generation unit that obtains a parallax from the plurality of images; and a vertical offset calculation unit that obtains a vertical offset amount of the plurality of images by using an actual parallax of the specific region, the ideal parallax of the specific region, and arrangement information in the image of the specific region.

Various systems and methods are presented regarding utilizing technology onboard a vehicle to mitigate the effects of communication signals being deleteriously affected / lost when operational surroundings cause occlusion / loss of receipt of positioning / location signals at the vehicle. The vehicle can be operating non-autonomously, partially autonomously, or autonomously. To supplement the lost / occluded signals position information of other systems proximate to the vehicle can be obtained / utilized. The other systems can include another vehicle, a mobile device, an internet, and suchlike. Also, onboard devices can be utilized to determine the location, such as an onboard camera system providing information regarding street signs, building facades, and suchlike.

To provide a position estimation device capable of accurately estimating a position of a feature shown in an onboard image taken by a camera installed in a vehicle.SOLUTION: A position estimation device includes: a vehicle detection unit 41 that detects a first position of a vehicle 2, which has travelled over a predetermined road section, at a first time on the basis of a stationary sensor 3 installed on the predetermined road section or near the predetermined road section; a vehicle position estimation unit 42 that estimates a second position of the vehicle 2 at a second time, which is a time when an onboard image showing a predetermined feature is generated by an onboard camera 11 installed in the vehicle 2, on the basis of the first position of the vehicle 2 at the first time; and a feature position estimation unit 44 that estimates the position of the predetermined feature in a real space on the basis of the estimated second position.SELECTED DRAWING: Figure 5

Methods and apparatus for onboard camera calibration are disclosed. A disclosed apparatus for use with an aircraft includes interface circuitry communicatively coupled to a camera of the aircraft, the camera to capture an image, machine readable instructions, and programmable circuitry to at least one of instantiate or execute the machine readable instructions to generate randomized images from images stored in a data storage carried by the aircraft, determine at least one relationship between an image captured by the camera while the aircraft is in flight and the randomized images, and determine a parameter of the camera based on the at least one relationship to calibrate the camera.

The invention discloses a multi-source fusion real-time traffic perception and emergency early warning system and method based on vehicle and road cloud cooperation, and relates to the technical field of traffic management. The vehicle end system comprises an information acquisition and transmission module, a local decision module, a data receiving module, an emergency response module and a data feedback module, and the information acquisition and transmission module acquires vehicle peripheral information through a vehicle-mounted radar and a vehicle-mounted camera. According to the invention, a vehicle end collects local information such as obstacles around a vehicle and dynamic states of adjacent vehicles through a vehicle-mounted radar and a camera, a road end collects regional information such as road section traffic flow density, road damage and traffic facility states through a roadside camera and a sensor, and a cloud end integrates data at the two ends and carries out time-space synchronization through a multi-source fusion technology. The defect of a single vehicle end or road end sensing blind area is overcome, and complete sensing of the traffic state of the whole road network is achieved.

A vehicular camera includes a lens unit, a circuit board, an imaging element, a plurality of electronic components disposed on first and second surfaces of the circuit board, a circuit board connector connection portion disposed on the second surface of the circuit board, a housing including a second tubular portion that accommodates at least the circuit board and the imaging element, a metallic first planar member covering at least a part of the plurality of electronic components disposed on the second surface of the circuit board and electrically connected to the circuit of the circuit board, a connector including at least one terminal that extends across an inside and an outside of the housing, and a metallic tubular member that surrounds at least a part of the at least one terminal of the connector around the direction along the optical axis and has a tubular shape.

The present disclosure provides a method and apparatus for generating an aerial view of a vehicle, a storage medium, an electronic device, and a vehicle. The method comprises: obtaining a road scenario image set of a plurality of directions acquired by a vehicle-mounted camera of a target vehicle; preprocessing the road scenario image set into a target image set of a target size, and obtaining camera parameters of the vehicle-mounted camera; using a convolutional network to extract multi-scale image features of the target image set, and using the central point of the target vehicle as an original point to create a three-dimensional coordinate grid; and using the multi-scale image features, the camera parameters, and the three-dimensional coordinate grid to generate an environment aerial view of the target vehicle.

The invention discloses a driving environment identification method and system based on a vehicle-mounted camera, and the method comprises the steps: obtaining detection image data, and carrying out the preprocessing of the detection image data, and obtaining the preprocessed image data; inputting the preprocessed image data into a preset driving environment recognition model, and determining driving environment recognition data, a recognition target and a risk level of the recognition target; dividing the detection area into a plurality of sub-detection areas, adjusting first attention scores of the sub-detection areas based on an attention mechanism, and analyzing next detection image data according to the first attention scores; comparing the detection image data of the adjacent acquisition time, and determining a newly added target and a disappearing target; and extracting local image features of the disappearing target, adjusting a second attention score of the sub-detection region, and analyzing next detection image data according to the second attention score. According to the invention, by adjusting the attention score of each sub-recognition area, the recognition efficiency of the recognition target in the driving environment is improved.