540 results about "Traffic sign" patented technology

Various embodiments of the present disclosure relate to operator assistance based on extracting natural language characters from one or more sensed objects. For instance, particular embodiments may generate a natural language utterance based on extracting natural language text in a nearby traffic sign. In an illustrative example, particular embodiments may detect, via object detection and within image data, one or more regions of the image data depicting the traffic sign. Particular embodiments can then extract one or more first natural language characters represented in the traffic sign based at least on performing optical character recognition within the one or more regions of the image data in response to detecting the one or more regions of the image data depicting the traffic sign.

The invention discloses a road traffic sign target detection method based on YOLOv8, and relates to the field of road target detection, in the method, a CDFF-YOLO model is constructed, and an MPA module is embedded in a feature fusion network of the model to enhance the feature fusion capability of C2f; an MSF module is constructed, GSConv and CARAFE up-sampling operators are embedded to realize a multi-dimensional sequence information fusion effect, and the detection precision of the model is improved while the parameter quantity and the calculation quantity of the model are reduced as much as possible. A DFF module is constructed, two-dimensional Fourier transform and inverse transform methods are adopted to enable feature information to be enhanced in a frequency domain and a space domain at the same time, and the detection performance of a network model for shielded small targets and small targets under uneven illumination is effectively improved. The three modules not only enhance the detection effect of the small target model, but also enable the network to stably cope with the detection scene of the small target. According to the method, the detection performance of the small road traffic sign target is excellent, the processing speed is high, and deployment of mobile terminal equipment is facilitated.

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.

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.

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 discloses a vehicle-mounted sign retroreflection coefficient detection method and system adaptive to a slope road. When the relative distance between a to-be-detected sign board in a to-be-detected area and a detection vehicle meets a preset shooting distance, the to-be-detected sign board in the to-be-detected area is detected; obtaining road dip angle information, collected by a dip angle sensor in real time, of the detection vehicle at each moment in the driving process in the to-be-detected area; solving a road dip angle in the detection area according to the road dip angle information at each moment; the compensation angle of the detection module is calculated according to the road dip angle, the electric adjusting structure is controlled to adjust the incident angle of the detection module into the target incident angle in real time according to the compensation angle, and the compensated detection module is obtained; using the compensated detection module to photograph a to-be-detected traffic signboard to obtain a to-be-identified signboard image; performing identification processing on the to-be-identified signboard image to obtain a retroreflection brightness coefficient corresponding to the to-be-detected traffic signboard; therefore, the effectiveness and accuracy of detection can still be maintained under complex road conditions.

The invention discloses a traffic sign target detection method, electronic equipment and a medium, and the method comprises the steps: carrying out the feature extraction of a traffic sign image through a backbone network which comprises a full-dimension dynamic convolution module and a space channel collaborative attention module, and obtaining a preliminary feature; a spatial pyramid pooling fast module is used for obtaining features of different receptive fields, channel aggregation is carried out to obtain deep features, feature enhancement is carried out through a spatial channel collaborative attention module, and then the obtained deep features are input into a bidirectional fusion network module for feature fusion. And finally, carrying out target classification and bounding box regression on the feature representations of different scales through a convolution module and a nonlinear activation function. According to the method, the space channel collaborative attention module and the full-dimensional dynamic convolution module are adopted, the model reasoning speed and accuracy are improved while multi-scale feature extraction is achieved, and low-fine-granularity information is fully extracted to help detection of traffic signs in images.

Examples of the presently disclosed technology provide computerized systems and methods that automatically update digital maps in response to detected traffic sign updates in a rapid and efficient manner. Examples achieve these advantages by cascading traffic sign updates through multi-layered digital maps using specific methodologies tailored to different types of traffic sign updates. By providing rapid and accurate digital map updates, examples can improve performance for autonomous driving systems that rely on digital maps for determining vehicle navigation paths—thereby improving traffic safety and efficiency.

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.

The invention provides an automatic driving control method and device for a trackless rubber-tyred vehicle, and the method comprises the steps: collecting an infrared image and a visible light image in the driving direction of the trackless rubber-tyred vehicle, carrying out the image registration of the infrared image and the visible light image, and obtaining a fusion image; acquiring point cloud data in a view range of the driving direction of the trackless rubber-tyred vehicle, and identifying obstacle information in the view range based on the fused image and the point cloud data; determining a driving range of the trackless rubber-tyred vehicle based on the obstacle information in the visual field range; identifying indication information of traffic signs in the visual field range, and determining a driving strategy of the trackless rubber-tyred vehicle in the driving range based on the indication information, so that the trackless rubber-tyred vehicle drives according to the driving strategy; wherein the driving strategy comprises speed control, driving path planning and a steering angle. According to the invention, the trackless rubber-tyred vehicle can automatically avoid obstacles and simultaneously meet the driving requirements, so that the reliability of automatic driving control of the trackless rubber-tyred vehicle during underground driving is improved.

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.

The invention provides a traffic sign depth estimation method, device and equipment and a vehicle, and the method comprises the steps: obtaining a plurality of frames of continuous environment images, and determining the position, type and first depth information of a target object in each frame of environment image; based on the position and the category of the target object, selecting a preset number of environment images which contain the same target object and are continuous in time from multiple frames of continuous environment images; determining second depth information of the same target object in each frame of selected environment image according to a preset parameter of a time node corresponding to each frame of environment image in the preset number of environment images; and determining target depth information of the same target object according to the first depth information and the second depth information. According to the invention, the depth estimation precision of the traffic sign is improved, and the positioning accuracy of the traffic sign is improved.

A system for providing information to a vehicle occupant of a vehicle includes an automated driving system, a first display configured to provide information to the vehicle occupant of the vehicle, and a controller in electrical communication with the automated driving system and the first display. The controller is programmed to identify a planned change in driving behavior initiated by the automated driving system. The controller is further programmed to identify a traffic sign interpretation error using the automated driving system. The controller is further programmed to notify the vehicle occupant using the first display in response to identifying at least one of: the planned change in driving behavior and the traffic sign interpretation error.

A method of headlight evaluation can include receiving from a camera mounted on a vehicle, one or more images that include at least a portion of an illumination pattern projected by a headlight of the vehicle upon a surface of an object. The object can be a stationary object or a moving object such as, for example, a traffic sign or another vehicle. The surface can be a front portion of the traffic sign or a rear vertical surface of the other vehicle. The illumination pattern may be evaluated based on a positional relationship of the camera, the headlight, and the surface of the object. A headlight position status and / or a headlight orientation status can be output based on evaluating the illumination pattern.

The invention discloses a multi-area coverage inspection system using an unmanned aerial vehicle cluster, and particularly relates to the technical field of unmanned aerial vehicle cluster inspection. The system comprises an inspection area division module, an unmanned aerial vehicle cluster setting module, a road surface damage monitoring module, a traffic sign monitoring module, a monitoring response module, a working state judgment module, an inspection display early warning module and a database. According to the invention, the unmanned aerial vehicle cluster multi-area inspection is adopted, in a cluster mode, a plurality of unmanned aerial vehicles can work cooperatively, large-range and multi-area efficient inspection is realized through cooperative flight and division and cooperation, the inspection speed and precision are greatly improved, and the labor cost and the safety risk are reduced at the same time. According to the method, the inspection areas of the expressway are divided, the inspection speed and precision are improved in a targeted manner, the inspection precision of the risk area is ensured, and the inspection efficiency of the general area is improved.

The invention discloses a safety early warning signal sending control method applied to intelligent monitoring equipment, and belongs to the technical field of artificial intelligence. According to the invention, when a traffic accident occurs, monitoring data, meteorological data and geographic data of a place where the traffic accident occurs are acquired to form traffic accident data; extracting visual features, weather features and road environment features; identifying whether the vehicle violates a rule specified by the traffic sign, judging whether the driving track of the vehicle accords with a lane rule, and forming a traffic violation risk index; combining weather characteristics and road environment characteristics to form an environment interference intensity index; checking the damage condition of traffic signs around the accident site and the shielding condition of buildings to pedestrians to obtain a road facility association index; training the multi-layer perceptron model, inputting index data, and outputting actual proportions of human factors and natural factors of traffic accidents; and when the proportion of the natural factors exceeds a threshold value, providing a reconstruction suggestion of a traffic accident occurrence place.

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.

A novel full-color mixing optic using known RGB LEDs is proposed. Thanks to its small size, it is particularly suitable for pixels in variable message signs and display boards, but also for any other applications. Based on a known design according to WO 2012 / 068603, which presents complete mixing of the LED light colors and has since been considered state of the art, limiting the number of chips used in the light sources and their arrangement reveals a previously unknown freedom in the design of the light guide and the upstream converging lens, and thus in the achievable light distribution. This not only allows for improved color mixing and more precise adaptation of the light distribution to specifications, but can also open up new applications.The chips used (R, G, B) must be arranged in a linear, dense configuration of the same size and emission characteristics, which has become standard practice anyway. In this case, color mixing is only necessary in this linear direction. For this purpose, the optical fiber (3) has two reflection planes (4a, 4b) on either side of the linear direction and symmetrically to each other. These planes generate an endless series of mirror images of the light source (1) for mixing. Freely shaped connecting surfaces (5a, 5b) are present in the transverse direction. The converging lens (7) has a focal point (Fh) on its exit surface (6) only in the linear direction. In the transverse direction, it has a different focal point, focal spot, or focal length profile. The lens tip (8) thus forms a freeform surface.