292 results about "Traffic sign" patented technology

The invention discloses a guideboard surface deformation detection method and system based on a dynamic video sequence. The method comprises the following steps: acquiring road video data with a timestamp and position information; a traffic sign is automatically identified from the road video data, and geometric anomaly detection is carried out; a time sequence tracking sequence is established for the guideboard, and whether continuous abnormity exists in the guideboard is determined through multi-frame analysis; if yes, performing local texture consistency and local contour geometric analysis on the guideboard to obtain a fusion analysis result; performing state evaluation on supporting equipment corresponding to the guideboard to obtain a state evaluation result of the guideboard supporting equipment; and determining the final surface deformation state and the corresponding grade of the guideboard based on the fusion analysis result and the state evaluation result of the guideboard supporting equipment. By implementing the method provided by the invention, automatic detection and quantitative analysis of the bending or deformation condition of the road sign can be realized without manual intervention, and early warning information can be output in time.

A method includes receiving image data captured by a traffic feature detection system. The image data is representative of a traffic feature within an environment. The method includes identifying a type of the traffic feature. The method includes isolating a portion of the image data containing the traffic feature. Based on the portion of the image data containing the traffic feature, the method includes determining a pixel color value for the traffic feature. Based on the type of the traffic feature, the method includes determining an expected pixel color value for the traffic feature. Based on a comparison of the expected pixel color value and the determined pixel color value for the traffic feature, the method includes determining a health status for the traffic feature.

In various examples, systems and methods are described that may be used to generate a mask of a geographic area and corresponding vector representations of one or more environmental features included in the area. In some embodiments, the method and system may generate or obtain a first tile image representing a portion of a path surface corresponding to a geographic area. One or more features may be extracted from the data where the extracted features may indicate environmental characteristics associated with the path surface—e.g., lane boundaries, medians, traffic signs, signals, etc. Additionally, the method or system may generate a mask corresponding to the tile image and vector representations of the portion of the geographic area. In some embodiments, the locations of the environmental features associated with the mask may be reconciled with one or more previously generated masks that include some of the same environmental features.

The invention provides an automatic driving high-precision map construction method and device based on semantic understanding, and relates to the technical field of high-precision maps, and the method comprises the steps: collecting multi-source perception data and pose data along a target road through a data collection unit, the data acquisition unit at least comprises an image acquisition device, a laser radar acquisition device and a positioning device; based on the pose data and the multi-source sensing data, generating an initial point cloud map of a target road through synchronous positioning; and performing semantic segmentation and element recognition on the initial point cloud map, vectorizing static road elements at least comprising lane lines, traffic signs and a lane topological relation, and constructing a high-precision map with semantic information. The technical problem of insufficient accuracy and reliability of automatic driving high-precision map construction in the prior art is solved. The technical effect of improving the accuracy and reliability of high-precision map construction by accurately analyzing semantic association is achieved.

The invention relates to the technical field of auxiliary driving, and discloses a deep learning-based road signboard recognition method, which comprises the following steps of: acquiring a road traffic video sequence stream in a vehicle driving process; inputting the road traffic video sequence stream into a deep learning detection framework based on improved YOLOv11 for initial positioning to obtain an initial positioning result of the road signboard; performing cross-frame target tracking on the initial positioning result of the road signboard through an improved Kalman algorithm to obtain a road signboard identification result with a motion track; and displaying the identification result of the road signboard on a vehicle-mounted head-up display of the vehicle. According to the road signboard identification method and system based on deep learning provided by the invention, a scheme based on deep learning and optimized Kalman filtering is adopted, traffic signboards in a complex road traffic environment are identified, and a perfect auxiliary driving system is established, so that key signboard information can be quickly and accurately found in the complex road environment; and a driver is assisted to make correct judgment.

Systems and methods for navigating a host vehicle are disclosed. In one implementation, a system includes a processor configured to receive at least one image captured by a front view camera of the host vehicle; analyze the at least one image to detect a representation of a traffic sign in an environment of the host vehicle; determine a relevancy of the traffic sign to the host vehicle, wherein the relevancy of the traffic sign to the host vehicle is determined based on a trajectory of the host vehicle relative to a location of the traffic sign; and cause the host vehicle to implement at least one navigational action based on the relevancy of the traffic sign to the host vehicle.

Techniques for detecting and labeling traffic lights and / or traffic signs and the traffic lanes with which such lights and signs are associated in an environment are disclosed. Images may be evaluated to identify pixels that may be associated with a light, sign, or lane. Associations between lights and / or signs and lanes in the environment may be determined along with probabilities for the individual pixels that the pixels may be associated with one of the light / sign and lane associations. Those pixels having a sufficient probability of be associated with a light / sign and lane association may be assigned a corresponding label. An output image with such labels can be provided for vehicle control and for other operations, such as top-down segmentation and trajectory determination.

A method of controlling an autonomous vehicle includes capturing video data of an environment surrounding the autonomous vehicle. The method includes detecting a state of a traffic light and identifying whether the state is a flashing signal or regular cyclic state colors. The method includes analyzing congestion at an intersection; calculating distance from nearby vehicles; and determining whether there is sufficient green light time to safely cross the intersection without blocking traffic. The method includes executing a stop instruction to stop the autonomous vehicle when a presence of the pedestrian is detected. The method includes operating a surrounding scanning circuitry when the traffic light is in a flashing state. The method includes analyzing the surroundings at an intersection; detecting pedestrians, nearby vehicles, and traffic signs. The method includes executing a control circuitry to control the behavior of the autonomous vehicle and navigate safely.

The invention discloses a safe takeover and man-machine cooperation automatic driving control method. The method comprises the following steps: acquiring a face image, eye movement data, a head posture, a sight direction, a blinking frequency and a steering wheel holding state of a driver in real time through a vehicle-mounted camera and a steering wheel torque sensor; the collected driver state information is input into a driver ability evaluation module constructed based on a time sequence model, a driver ability level C is output, and the ability level C is divided into four levels of C0 to C3; and acquiring current driving scene information through an environment sensing sensor carried by the vehicle. According to the invention, all-weather and full-scene environment understanding is realized, the camera provides semantic information of lane lines and traffic signs, the millimeter-wave radar can still accurately measure distance at rainy and foggy nights, the laser radar generates high-precision three-dimensional point cloud, depicts the contour of an obstacle, and injects prior knowledge into a high-precision map.

The invention discloses a traffic safety facility night vision evaluation method and system, and relates to the technical field of image processing, and the method comprises the steps: obtaining a to-be-evaluated traffic safety facility image; if the to-be-evaluated traffic safety facility image is the to-be-evaluated traffic sign image, determining a retroreflection coefficient of each color area of a traffic sign in the to-be-evaluated traffic sign image; based on the retroreflection coefficient and the gray value uniformity of the specific distribution area of the traffic sign, performing night vision evaluation on the traffic sign; if the to-be-evaluated traffic safety facility image is a to-be-evaluated traffic marking image, determining a retroreflection brightness coefficient of a traffic marking in the to-be-evaluated traffic marking image and gray value uniformity of a specific image area where the traffic marking is located; and performing night vision evaluation on the traffic marking based on the retroreflection brightness coefficient and the gray value uniformity of the specific distribution area of the traffic marking. The traffic safety facility night vision detection method is high in detection precision and high in reliability.

The invention discloses an instruction tamper-proofing device for a traffic information board system, relates to the field of traffic information security, and solves the three technical problems of key clone risk, high computing power dependence and poor environmental adaptability of a traditional encryption scheme. A tamper-proof system is constructed through a triple cooperation mechanism: a unique unclonable physical feature is extracted based on a physical unclonable circuit, efficient integrity verification is realized through a segmented cascade parity check circuit, and a timestamp is accurately compared in combination with a temperature compensation clock; and multi-dimensional features are fused, spliced and integrated, tampering recognition is performed through feature comparison, and ultimate protection is formed if irreversible hardware is triggered to fail if tampering recognition is performed. The device achieves pure hardware millisecond-level response, thoroughly eliminates software dependence, maintains zero misjudgment rate in an extreme environment, ensures full-life-cycle safety of instructions through physical-level protection, and provides unduplicated safety guarantee for an information board system.

The application provides a road traffic sign extraction method and device, electronic equipment and storage medium, comprising: acquiring point cloud data of a target scene, and determining a target projection image corresponding to the target scene based on the point cloud data; wherein the content displayed by the target projection image at least includes a road traffic sign; through a pre-trained target detection model, target detection processing is performed on the target projection image to obtain sign attribute data corresponding to the road traffic sign; wherein the target detection model is obtained by distillation training on a lightweight detection model; based on the sign attribute data, a target local image and a target template image corresponding to the road traffic sign are determined, and a sign boundary corresponding to the road traffic sign is extracted from the target local image according to the target template image. The application can effectively reduce the requirement for hardware equipment, and can also better balance the efficiency and effect of extracting the road traffic sign.

The embodiment of the invention discloses a vehicle data processing method and device, electronic equipment and a readable storage medium, and the method comprises the steps: obtaining the position sensing information of a vehicle, and responding to the position sensing information which comprises at least one traffic marker. Determining a target reference object corresponding to the at least one traffic marker and a distance between the vehicle and the target reference object, determining a second distance between a positioning point of the vehicle on the navigation page and the mapping reference position based on the map data, and determining a longitudinal error according to a difference value between the second distance and the first distance, and adjusting the position of the positioning point on the navigation page according to the longitudinal error. Therefore, through the method, the longitudinal error existing in vehicle positioning can be determined and improved, and the accuracy of vehicle positioning is improved.

The utility model particularly relates to an intelligent traffic cone which comprises a base, a plurality of conical barrels are arranged above the base, the sizes of the conical barrels are sequentially and gradually reduced from bottom to top, the adjacent conical barrels are connected in a clamped mode, and an ejector rod is arranged in the conical barrel at the uppermost end and can move up and down. The lower end of the ejector rod is connected with the conical barrel at the uppermost end in a clamped mode, the outer side face of the ejector rod is made of transparent materials, a lamp is arranged in the ejector rod, a lifting handle groove is formed in the upper end of the ejector rod, and a lifting handle is rotatably arranged in the lifting handle groove. The traffic cones can be rapidly placed, traffic signs can be marked on the traffic cones through the display mechanism, the traffic cones can be conveniently observed at night through the lamps, and the traffic cones can be rapidly folded when not used through the foldable conical barrels and the lifting mechanism. Large space is prevented from being occupied, and follow-up carrying and the like are facilitated.

The invention provides a simulation method and device for an intelligent driving test, electronic equipment, a program product and a storage medium. The simulation method comprises the steps that a to-be-tested problem set and a traffic sign data set of a target area are acquired; according to each to-be-tested problem in the to-be-tested problem set, processing an acquired data set of a vehicle, and determining an acquired data subset corresponding to each to-be-tested problem; on the basis of the collected data subset corresponding to each to-be-tested problem, real vehicle simulation of each to-be-tested problem is generated; traversing and combining all traffic sign data in the traffic sign data set to generate corresponding road network simulation; and combining the real vehicle simulation of each to-be-tested problem with the road network simulation to obtain target simulation of the intelligent driving test. Therefore, integrated verification of high authenticity, high coverage, compliance and repeatability of intelligent driving is realized.

The invention discloses a composite active light-emitting digital visual real-time display traffic sign, and belongs to the field of road traffic signs, the composite active light-emitting digital visual real-time display traffic sign comprises a display screen, a reflecting layer, a light absorption layer and a microstructure layer which are sequentially arranged from bottom to top, and the reflecting layer and the light absorption layer form an image-text layer for displaying fixed traffic information. The brightness of the display screen can be adaptively adjusted according to the illumination intensity, and various modes such as fixed information display, image-text and digital composite display, high-definition and high-brightness display and emergency information display are realized; the reflecting layer and the light absorbing layer are designed in a partitioned manner, so that light reflection and transmission requirements are met; the microstructure layer guarantees the basic luminosity performance of the product. The active light-emitting traffic sign solves the potential safety hazard problem of a traditional active light-emitting traffic sign (the basic traffic indication function is lost under the condition of failure / power failure), has the advantages of being high in production efficiency, high in all-environment adaptability, good in application effect and the like, and is beneficial to large-scale application and popularization.

The application discloses a kind of intelligent unmanned vehicle environment perception systems based on multi-sensor information fusion, it is related to unmanned vehicle technical field.The system includes: sensor module, for collecting multi-source heterogeneous data, including GPS / IMU, laser radar, camera and millimeter wave radar;Data acquisition and fusion module, through the Kalman filter to multi-source data space-time synchronous fusion;Environment perception module, based on deep learning identification road boundary, obstacle and traffic sign, constructs dynamic perception model;High-precision map construction module, using SLAM technology and semantic information constructs and updates semantic three-dimensional map;Positioning algorithm module, fusion visual odometry, fusion data and high-precision map, calculates vehicle real-time pose;And control module, according to positioning and perception result carries out path planning and navigation control, the application provides a kind of intelligent unmanned vehicle environment perception systems based on multi-sensor information fusion, can be autonomously navigated and stably driven under complex and severe environment.

This invention relates to the field of traffic sign equipment technology, specifically disclosing a flip-type variable traffic sign and its operating method. The sign includes a base plate and a controller. The controller is installed at the rear of the base plate and contains a main control board and a power module. A matrix of circular signage plates is fixed to the front of the base plate. Each circular signage plate includes a flip-type reflector and a fixed reflector. The back of the fixed reflector is tilted and fixed to the front of the base plate. The flip-type reflector is rotatably mounted on the base plate. The flip-type reflector and the fixed reflector are positioned opposite each other and form a circle when flipped open. The back of the flip-type reflector is the same color as the base plate and is a reflective surface. Both the flip-type and fixed reflectors have a reflective front surface, the color of which differs from the reflective surface. A magnet is provided on the back of the flip-type reflector. This invention enables switching between the back and reflective surfaces to display different signage information, providing a variety of information. Furthermore, the controller only powers the coil when the sign is flipped, resulting in greater energy efficiency.

In various examples, machine learning models may be trained and used to determine associations between traffic control devices (e.g., traffic signs, traffic lights, etc.) and lane segments of a driving surface. The systems and methods of the present disclosure may effectively combine rule-based methods and machine-learning based methods for traffic control device to lane association. For instance, training data may be synthetically generated based on traffic regulations relating to placement of traffic lights, and machine learning models may be trained to associate traffic lights to respective lanes using the training data with ground truth generated by rules. As such, image data may not be needed for the machine learning models to predict light to lane associations. Instead, given a set of non-image features indicative of lane segment and traffic light geometry and / or semantics, the machine learning models may predict the associated lane segment for each traffic light.

The application provides a kind of based on edge computing's vehicle-mounted map data distributed storage method and system, it is related to intelligent transportation and edge computing technical field, the application is by first obtaining vehicle-mounted map data source and hierarchical processing, separate out the basic layer data containing basic road network and the detail layer data containing lane line, traffic sign;Again, road side edge computing unit and supporting communication unit are deployed, the effective value of communication is evaluated by physical layer processor, and the effective signal coverage area is defined by optimizing parameter;Subsequently, in combination with edge computing unit position, coverage area and basic road network information, the detail layer data is dynamically sliced, and the sliced data is distributedly stored in the optimal node to form a storage network;Finally, when vehicle enters coverage area, relevant map information is obtained and distributed based on optimization parameter, which can realize distributed storage of vehicle-mounted map data and efficiently provide relevant map information for vehicle.

The application discloses a kind of high-precision vector map automatic generation method, mainly according to laser point cloud intensity, geometric structure feature extraction lane marking, through ground filtering, ground, non-ground point cloud is segmented;Adopt block grating to convert three-dimensional point cloud into two-dimensional projection image;Using image filtering, edge extraction operator, dynamic adaptive threshold segmentation, morphological analysis and other processing extraction road marking pixel, based on point cloud image mapping relationship extraction marking point cloud;Using the point cloud contour matching method based on knowledge template library carries out marking classification.Based on deep learning convolutional neural network, road sign detection classification is carried out to road image, and the point cloud region of interest is established by image point cloud fusion;Using clustering screening candidate point cloud, based on principal component analysis carries out local projection, based on knowledge template library point cloud matching extraction traffic sign;Finally, based on GPS data, calibration, parameter fitting, vectorization, data association are carried out, and the construction of high-precision vector map is completed.

The application discloses a kind of sign detection method, device, nonvolatile storage medium and computer equipment.Therein, the method includes: obtaining historical frame, target frame and the motion information of vehicle, wherein historical frame and target frame are the image obtained by the camera of vehicle shooting;According to motion information and historical frame in historical detection frame, determine target area, wherein historical detection frame is used to mark traffic sign in historical frame;According to target area and target frame, generate target detection frame, wherein target detection frame is used to mark target traffic sign in target area in target frame.The application solves the technical problem that a large amount of computing power is required to identify traffic sign from video images in real time while driving.

The utility model relates to the technical field of traffic sign installation, in particular to a traffic sign installation and maintenance auxiliary device which comprises a frame, an electric telescopic rod is fixedly connected to the inner wall of the frame, a top plate is fixedly connected to the output end of the electric telescopic rod, and a bottom frame is fixedly connected to the inner wall of the frame. A sign body is installed on the surface of the bottom frame, a stand column is fixedly connected to the side face of the sign body, a positioning device is arranged in the frame and comprises two sleeves, the two sleeves are both installed in the frame, and connecting plates are fixedly connected to the side faces of the two sleeves. The surface of the connecting plate is sleeved with a sleeve plate, and the lower surface of the sleeve plate is fixedly connected with a supporting plate. According to the auxiliary device for installing and maintaining the traffic sign, the problem that in the process that the auxiliary device for installing and maintaining the traffic sign is used for maintaining the traffic sign, when the sign body and the stand column need to be assembled, the position between the stand column and the sign body is often difficult to position is solved.

The utility model provides a traffic signboard anchoring member, which relates to the technical field of traffic signboards, and comprises a signboard rod, an assembling plate and a pre-fixing plate, the bottom of the signboard rod is fixedly connected with the assembling plate, the pre-fixing plate is arranged at the bottom of the assembling plate, a fastening assembly is arranged between the assembling plate and the pre-fixing plate, and the pre-fixing plate is fixedly connected with the pre-fixing plate. The fastening assembly comprises elastic clamps, an inserting rod, a fastening bolt and a fastening nut, the two sides of the bottom of the pre-fixing plate are fixedly connected with a set of elastic clamps, and the top end of the inserting rod is fixedly connected with the fastening bolt; the two sets of inserting rods are inserted into the two sides of the pre-fixing plate respectively, the inserting rods are inserted into the pre-fixing plate, the outer walls of the inserting rods open the elastic clamps firstly, and when the elastic clamps are parallel to the grooves, the elastic clamps reset through elasticity of the elastic clamps, so that the elastic clamps are clamped into the grooves, and the inserting rods and the pre-fixing plate are fixed into a whole. And the concrete and the elastic clamp are used for fixing the inserting rod, so that the structure is more stable.

The disclosed systems and techniques facilitate efficient detection and classification of traffic signs in driving environments. The disclosed techniques include, obtaining, using a sensing system of a vehicle a first set of perspective camera images of an environment and a second set of radar images of the environment. The techniques further include generating, using a first neural network, one or more camera features characterizing the first set of images, generating, using a second neural network, one or more radar features characterizing the second set of images, and processing the one or more camera features and the one or more radar features to obtain an identification of one or more traffic signs in the environment.

In various examples, multilabel hierarchical classification of objects for autonomous systems and applications is described herein. Systems and methods are disclosed that use one or more neural networks to classify objects, such as traffic signs, using multilabel classification and / or hierarchical classification. For instance, a multilabel subnetwork of the neural network(s) may classify an object based at least on one or more attributes associated with the object. As such, the output from the multilabel subnetwork may include at least a classification associated with the object and an attribute classification(s) associated with the object. A hierarchical subnetwork of the neural network(s) may also classify the object using one or more class labels, where a class label indicates another classification and / or a class group associated with the object. The systems and methods may then use the classification, the attribute classification(s), and / or the class label(s) to determine a final classification associated with the object.

1. The name of the design product: pendant (key light-traffic signs-sound). 2. The use of the design product: pendant for decoration, play and entertainment. 3. The design points of the design product: in shape. 4. The picture or photo that best indicates the design points: perspective drawing.

A method and system for fabricating reflective traffic signs for scale model applications. A scaled traffic sign image is generated using graphic design software and printed onto a transparent or reflective medium. The printed signage layer is bonded to a reflective substrate and affixed to a rigid backing material such as aluminum. The sign is then mounted on a scale stand formed from metal, plastic, or wood. Various fabrication techniques may be used, including water transfer of printed images using clear tape, printing onto acetate, or direct printing onto reflective material. In some embodiments, 3D-printed structural supports are used to house light-emitting diodes (LEDs) to simulate illuminated signage. The resulting scale signs replicate the appearance and reflective characteristics of full-size traffic signs and are suitable for use in model railroads, dioramas, architectural layouts, and educational displays.