404 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.

The invention provides a YOLOv8 traffic sign real-time detection method based on edge calculation optimization, and the method comprises the steps: collecting traffic scene image data in real time, carrying out the preprocessing of the collected image data, inputting an optimized YOLOv8 network model, carrying out the feature extraction, carrying out the processing of feature maps of different scales through an SE module and a bidirectional feature fusion strategy, and carrying out the detection of a traffic sign. Entering a detection head for target detection to obtain a detection result; an obtained detection result is transmitted to a central server or an automatic driving system in a structured data format through a low-delay communication protocol; in a central server or an automatic driving system, real-time statistics and trend analysis are carried out on detection results, and through deep optimization and deployment strategy improvement on a YOLOv8 model, many limitations in an edge calculation scene in the prior art are overcome. Specifically, a lightweight optimization strategy combining model pruning, quantitative processing and an efficient inference engine is provided, and the detection precision and robustness are improved in combination with an environment adaptive image preprocessing method.

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 present disclosure provides a system and a method for jointly controlling one or multiple connected and automated vehicles (CAVs) and one or multiple human-driven vehicles (HDVs) subject to integer constraints for crossing each of multiple intersections on a road. The method comprises collecting digital representation of states of each of the CAVs, HDVs, and traffic signs, solving an optimization problem jointly optimizing traffic flows based on a macroscopic traffic flow model in a centralized traffic controller (CTC) subject to convex relaxation of the integer constraints, solving a multi-variable mixed-integer programming (MIP) problem in each of multiple intersection traffic controllers (ITCs) optimizing a cost function and minimizing tracking errors in traffic flow values of a microscopic traffic flow model with respect to relaxed traffic flow values from the CTC, and transmitting the optimized values of the control commands to the corresponding CAVs and corresponding traffic signs.

The invention discloses an intersection traffic violation total factor snapshot system based on multi-view collaboration, and relates to the technical field of intelligent traffic, and the system comprises a data collection and transmission module, a data preprocessing and splicing module, a data optimization and analysis module, a traffic violation judgment module, and a data storage and management module. According to the invention, through a panoramic video optimization unit in the data optimization and analysis module, the spliced panoramic video is deeply optimized, and tiny splicing traces or image local distortion generated in the splicing process is further eliminated, so that the panoramic video picture is clearer, smoother and more real, and high-quality image materials are provided for subsequent analysis; and the target in the video is accurately recognized through the image recognition and analysis unit, target feature information of vehicles, pedestrians and traffic signs and marked lines is extracted, traffic illegal behaviors can be more accurately judged based on the information, and the situations of misjudgment and missed judgment are reduced.

The present disclosure provides a system and method for jointly controlling one or more networked automated vehicles (CAVs) and one or more human driven vehicles (HDVs) subject to integer constraints for crossing each of a plurality of intersections on a road. The method includes collecting a digital representation of a state of each of CAV, HDV, and traffic signs, solving an optimization problem of joint optimization of traffic flow based on a macroscopic traffic flow model in a convex relaxation centralized traffic controller (CTC) subject to integer constraints, solving a multivariable mixed integer programming (MIP) problem in each of a plurality of intersection traffic controllers (ITCs) that optimizes a cost function and minimizes tracking errors of traffic flow values of the microscopic traffic flow model with respect to relaxed traffic flow values from the CTCs, and transmitting optimized values of the control commands to corresponding CAVs and corresponding traffic signs.

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.

The invention relates to the technical field of accumulated water level measurement, in particular to a sunken road accumulated water monitoring device which comprises a base and a plurality of fixing cones fixed to the bottom of the base and further comprises a plurality of vertical arms fixed to the base, a top plate is fixed to the ends, away from the base, of the vertical arms, and a display panel is fixed to the side of the top plate. A triangular display area is arranged in the display panel; the shielding mechanism is connected with the display panel and can move relative to the display panel; the floating balls are arranged on the side of the base and connected with the follow-up mechanism installed on the base, the follow-up mechanism is connected with the shielding mechanism, and the follow-up mechanism is triggered when the floating balls ascend; when the floating ball is located at different heights, the display areas correspond to different display conditions, display and reflection of the water level condition are comprehensive, in the monitoring process, the height of the floating ball changes, the height of the display panel is fixed, the height of the display panel is equal to that of a traffic sign, and therefore passing personnel can pay attention to and observe the display panel conveniently.

The invention discloses a YOLOv8n road multi-target detection method and a YOLOv8n road multi-target detection system based on an HGNet backbone network, and the method achieves the rapid and accurate extraction of road markers, such as lane markings and traffic signs, through the comprehensive analysis of multi-dimensional information, such as geometric features, coordinate information and reflection intensity, of point cloud data, and in combination with an intelligent extraction algorithm. An operator can complete intelligent tracking extraction of lane marking lines, arrows, zebra crossings and the like on a road with the length of hundreds of meters through simple clicking and basket pulling operation, and high-quality vector output is generated. According to the technology, manual intervention is remarkably reduced, the operation efficiency is improved, and important data support is provided for the fields of road asset management, intelligent traffic system construction and the like.

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.

Semantic segmentation generally refers to a machine learning process that associates a label or category with every pixel in an image. This can be used to recognize a collection of pixels that form distinct categories of objects, which may have applications in autonomous driving for example where the vehicle needs to identify other vehicles, pedestrians, traffic signs, pavement, and other road features from captured images of a surrounding environment. While using pixel-level annotations may be ideal for fully-supervised training of the semantic segmentation model, collecting such annotations is time-consuming and expensive, and therefore limits the scalability and practicality of fully-supervised training methods. The present disclosure enables weakly supervised training of a semantic segmentation model aided by learned prompts embedded with semantic knowledge.

The invention discloses a road traffic sign updating method based on crowd source data, and belongs to the field of road traffic sign updating, and the method comprises the steps: processing vehicle-mounted video image data through employing a pre-trained deep learning model, recognizing the type and attribute of the road semantic information of a traffic sign in a video, and obtaining the space coordinate of the traffic sign; performing spatial registration on the identified information of the traffic signs and an open source road data map; calculating a sign similarity value of the traffic sign after space registration, and judging whether the newly added traffic sign replaces the existing traffic sign or the newly added traffic sign; and generating an inference map after the traffic signs are updated, and updating the open source road data map. According to the method, the updating of the road traffic sign information is promoted, the updating efficiency and accuracy of the map data are remarkably improved, richer and more timely semantic support is provided for applications such as automatic driving, and the accuracy of traffic sign positioning and the consistency of semantic information updating are ensured.

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.

The invention discloses a road traffic sign and marking line identification and positioning method and system for automatic driving, and relates to the technical field of automatic driving. Comprising the following steps: acquiring internal parameters of a camera, external parameters between the camera and a laser radar, an AI data set, a traffic sign marking identification model, and fusion positioning of the camera and the laser radar; on the basis that the AI data set is made, when the automatic driving unmanned vehicle runs on a road, a camera is used for capturing a front video stream picture, a traffic sign marking line in the video stream picture is recognized through a traffic sign marking line recognition model, and coordinates of a detection frame of the traffic sign marking line in an image plane coordinate system are obtained; a plurality of laser radars are used for collecting original point cloud data, and the original point cloud data are analyzed through fusion positioning of a camera and the laser radars, so that a positioning result is obtained. The identification accuracy and the positioning precision are improved, and the cost is reduced.

The utility model discloses a signboard supporting device easy to install, which relates to the technical field of road traffic engineering and comprises a supporting bottom plate. A supporting frame is arranged on the surface of the upper end of the supporting bottom plate, a rotating rod is rotationally installed on the upper surface of the front end of the supporting frame, a friction gasket is arranged on the surface of the rear end of the supporting frame, a threaded groove is formed in the outer surface of the rear end of the rotating rod, and a rotating sleeve is spirally installed on the outer surface of the threaded groove. A rotating sleeve is arranged on the surface of the front end of the rotating rod, a friction gasket is arranged between the rotating sleeve and the friction gasket, a supporting plate is arranged on the surface of the front end of the rotating rod, and a lifting groove is formed in the surface of the upper end of the supporting plate. The angle of the supporting plate can be adjusted according to different use scenes, traffic signs on the surface of the sign placing plate can be conveniently displayed, and meanwhile the traffic signs can be folded and stored to be convenient to carry and install.

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 utility model discloses a road light-emitting traffic sign which comprises a connecting beam, the connecting beam is provided with a rapid installation mechanism with a second hoop rapid installation function, the rapid installation mechanism comprises a connecting cylinder, a first hoop and a second hoop, the connecting cylinder is rotationally connected to a fixing rod, and the connecting cylinder is rotationally connected to the fixing rod. The connecting cylinder is fixedly connected to one end of a first hoop, and a first inserting hole is formed in the first hoop. According to the light-emitting traffic sign, in the rapid installation mechanism, the hoop is divided into two parts, the second hoop is fixed to the connecting beam, the installation mode of the light-emitting traffic sign is more flexible, and meanwhile the trouble that the separated hoop and the connecting beam are sequentially fixed through screws is avoided; and a fan and a linear module in the cleaning mechanism are arranged, so that the situation that dust generated due to vehicle reciprocation is adsorbed on the surface when the light-emitting traffic sign works on a road for a long time can be avoided, and the cleanliness of the front face of the light-emitting traffic sign can also be guaranteed.

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.

A facility for forming a so-called emergency lane on a motorway or road with structurally separated traffic lanes in each direction that is congested by vehicles or similar, whereby emergency pillars on the roadside of motorways and expressways are used, repurposed and extended for this facility. by equipping the emergency columns with one or more warning signs / traffic signs that are integrated into or connected to these emergency columns or only connected to their wiring network, where the warning signs are designed as electrical or electronic alternating signs with various warning and / or prohibition and / or mandatory signs, where these signs are a mandatory sign “emergency lane” and a sign “no entry”, where the signs are activated as a specific sign when required, enabling emergency vehicles to reach an incident location on a section of road that is located ahead of a traffic jam in the opposite direction of travel on the same carriageway where the traffic jam is taking place, by maintaining the emptiness of this section of the route through the driving ban for regular traffic, until it is reopened, i.e. usually until the end of the operation.

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.