143 results about "Traffic regulation" patented technology

The invention relates to the technical field of data processing, and discloses a road condition monitoring recognition method, device and equipment based on machine vision and a medium. The method comprises the following steps: carrying out multi-source data acquisition on a road environment to obtain an original data set; performing dynamic time warping and multi-modal feature fusion processing on the original data set to obtain a spatial-temporal feature tensor; performing multi-scale pyramid decomposition and depth feature extraction on the spatial-temporal feature tensor to obtain a road condition feature vector; semantic analysis is carried out on the road condition feature vectors through feature mapping and knowledge graph enhancement, and a road condition attribute set is obtained; performing multi-level risk quantification and space-time propagation simulation on the road condition attribute set to obtain a graded early warning index and a risk distribution map; and performing multi-target tradeoff and causal chain reasoning on the graded early warning indexes and the risk distribution map to obtain a self-adaptive traffic regulation and control instruction set. According to the invention, the efficiency and accuracy of road condition monitoring and identification based on machine vision are improved.

The invention relates to an automatic driving risk quantification method based on a conflict risk field. Comprising the steps of 1, constructing a basic risk field; step 2, under a basic risk field framework, constructing a conflict risk field by taking an ADV as a center; step 3, regarding the conflict risk field as a repulsive force acting on the ADV, and using the repulsive force to quantify the dynamic influence of the environmental elements and various TP states on the driving safety of the ADV; the method comprises the following steps: firstly, constructing a basic risk field on the basis of traffic vehicle distribution and motion characteristics, further forming a vehicle forward, lateral, backward and traffic regulation constraint multi-dimensional conflict risk field on the basis, and converting an abstract traffic conflict relationship into a computable repulsive force model; dynamic quantitative characterization of driving risks in a state dimension, a space dimension and a time dimension is realized, and continuous optimization and evolution of an end-to-end automatic driving algorithm are supported by means of a repulsive force model constructed based on a conflict risk field.

The invention provides an end-to-end automatic driving track generation method and system, computer equipment and a medium, and belongs to the field of path intelligent planning, and the method comprises the steps: obtaining traffic environment three-dimensional perception information and a natural language instruction; converting the traffic environment three-dimensional perception information to obtain a structured semantic mark, and adjusting the weight of the structured semantic mark through a natural language instruction; based on the weight of the structured semantic mark, visual flow features are extracted from the structured semantic mark, and language flow features are extracted from the natural language instruction; based on a cross-modal attention mechanism, the visual flow features and the language flow features are fused, fusion features are obtained, and a simulation track is generated; real-time collision time is calculated, when the real-time collision time is larger than a collision time threshold value, the simulation track is corrected based on physical constraints and a traffic regulation library, and a corrected driving track is obtained. The automatic driving simulation track constructed by the method conforms to the intention of a user, and the system reliability and the man-machine cooperation efficiency are improved.

The invention provides a network flow regulation and control system and method and a storage medium, and relates to the technical field of network communication. The network traffic regulation and control system comprises: an information collection unit, which is used for obtaining historical traffic data of all media plane network elements connected with a control plane network element and a topological relation of the media plane network elements; the AI model updating unit is used for updating the traffic regulation and control model of the control plane network element according to the historical traffic data and the topological relation, and the strategy generation unit is used for obtaining a traffic distribution strategy of the control plane network element; and the strategy execution unit is used for dynamically adjusting the media traffic of each media plane network element according to the traffic distribution strategy. According to the method, the network data is collected and analyzed in real time, and dynamic updating of the AI model is combined, so that dynamic regulation and control of the network traffic are realized, the method can quickly adapt to changes of network conditions, and the flexibility and efficiency of traffic regulation and control are improved.

Multimodal large language models (MLLMs) have excellent reasoning capabilities and are used for autonomous driving applications. For real-world applications, understanding and navigating in three-dimensional (3D) space is necessary, particularly for autonomous vehicles (AVs) to make informed decisions, anticipate future states, and interact safely with the environment. An MLLM agent system includes a 3D projector model and an adapted LLM that extends understanding and reasoning capability from 2D to 3D. Another component of the MLLM agent system is development of a benchmark visual question-answering (VQA) training dataset for training the MLLM agent. The VQA tasks include scene description, traffic regulation, 3D grounding, counterfactual reasoning, decision making, and planning.

The invention relates to the technical field of behavior recognition, in particular to an image library construction and recognition algorithm under a large-scale incomplete multi-view and multi-modal scene, which comprises the following steps of: deploying a plurality of sensors and cameras in a traffic scene, and unifying multi-modal data into the same space-time coordinate system; fusing the multi-modal data subjected to space-time alignment, predicting a target state and updating a measurement value; dynamically generating multi-view candidate views based on the fused data by using an adversarial generative network under space-time constraint, and complementing missing views in combination with a target kinematics model; a target kinematic model and a deep learning technology are fused, a cross-camera target is tracked, a traffic regulation ontology library is constructed, and traffic illegal behaviors are automatically identified and classified in combination with an image identification technology. According to the method, the consistency of different sensor data in time and space dimensions is ensured, and data chaos and errors caused by space-time differences are avoided.

The invention discloses an automatic driving test method based on traffic regulation weights and electronic equipment, in a test scene generation process, importance weights of traffic regulations are introduced, and the importance weights guide an attention mechanism to distribute different attention degrees to word block vectors, so that the test scene generation efficiency is improved. For example, high attention is allocated to word block vectors corresponding to high-risk behaviors and key regulations, so that subsequent scene generation is ensured to preferentially cover violation scenes of the high-risk behaviors and key traffic regulations; and learning each word block vector and the context thereof in the word block vector sequence through an attention mechanism to obtain global context representation which can reflect global dynamic interaction information in a complex scene, so that diversified and complex test scenes can be generated, and the test efficiency is improved. Therefore, when the test scene generated by the method is used for testing the automatic driving system, the compliance performance of the automatic driving system in a complex and high-risk traffic scene can be reflected more accurately.

The invention discloses a question answering system and a construction method for a motor vehicle driving teaching knowledge graph, and aims to overcome the defects of knowledge coverage integrity, scene adaptability, reasoning depth and interaction naturalness in the prior art. A traffic regulation document, coach voice and short videos and vehicle-mounted real-time data are integrated through a data acquisition module to cover rules and experience knowledge, a knowledge construction module constructs a space-time dynamic knowledge graph containing static and dynamic nodes based on Dgraph, space-time association is achieved by means of GeoHash coding, real-time updating is completed through an MQTT protocol, and the time-space dynamic knowledge graph is stored through a storage module. The intelligent reasoning module calls a DoWhy model to construct an'operation, principle and result 'causal chain in combination with a physical formula so as to output deep interpretation, and the interaction module achieves multi-mode immersion teaching through speech synthesis, gesture recognition and 3D animation, dynamically optimizes knowledge pushing according to student operation data, and achieves multi-mode teaching. The comprehensiveness, the real-time performance, the scientificity and the interaction naturalness of driving teaching are improved, and the method is suitable for intelligent driving teaching scenes.

The embodiment of the invention discloses a multi-source big data traffic control management method and system based on a distributed architecture, and belongs to the technical field of intelligent traffic. The problem that in the prior art, traffic situation prediction precision is insufficient, and consequently traffic regulation and control are not accurate is solved. Comprising the following steps: constructing a dynamic traffic flow model based on extracted feature data and a dynamic space-time diagram corresponding to a traffic network; deducing the traffic situation through a central node in a traffic road network to obtain a plurality of traffic prediction scenes, and matching corresponding traffic regulation and control strategies; after the abnormal traffic event is detected, an affected area is delimited according to the corresponding propagation influence trend of the abnormal traffic event on the space axis; dynamically combining a plurality of operation units corresponding to the traffic regulation and control strategy through the central node, and generating a customized regulation and control scheme; and each edge node performs joint optimization on the customized regulation and control scheme by using a distributed optimization algorithm based on local traffic flow data so as to realize traffic control management.

The invention discloses a planning system and method based on a traffic space-time adjustment demand, the system comprises a spatial database, a stream processing framework, a distributed search engine, a data processing module and a dispersion force scheduling module, and the system calculates a traffic dispersion required by a congested road section by calculating a congestion index Cit of each road section in a future period of time; inquiring the currently deployed traffic dispersion personnel of the congested or seriously congested road section; calculating a dredging force tension index, rendering the road section with multiple colors, and generating a dredging force tension index trend chart; the optimal dispersion force scheduling scheme is automatically calculated for the road section with insufficient dispersion force configuration, so that the operation efficiency of the existing traffic regulation system is greatly improved, and the travel efficiency of urban residents is greatly improved.

The invention relates to an automatic driving simulation evaluation method and device, and belongs to the technical field of intelligent driving, and the method comprises the steps: constructing a digital twin 3D map; a vehicle simulation model is loaded in the digital twin 3D map, an automatic driving module used for loading an automatic driving algorithm is constructed, and an automatic driving evaluation unit based on traffic regulations is constructed; an automatic driving algorithm to be tested is loaded, the automatic driving module generates a control instruction based on environment data collected by the environment sensing assembly and issues the control instruction to the vehicle motion controller, and the vehicle motion controller controls the vehicle motion model to operate in the digital twin 3D map; the automatic driving evaluation unit collects controlled motion state parameters of the vehicle motion model and intervention degree parameters of the manual controller, and evaluates an automatic driving algorithm. According to the method, the defect that evaluation cannot be carried out according to the traffic regulations in the automatic driving algorithm evaluation process is effectively overcome, meanwhile, advantages and disadvantages of different algorithms can be compared, and the test cost is effectively reduced.

The invention discloses a traffic safety early warning system and method based on thunder-vision fusion. The system comprises an acquisition system, an extraction module, a construction module, a prediction module and an alarm module. The acquisition module is used for acquiring and processing image data in an urban traffic scene; the extraction module is used for performing feature extraction on the image data to obtain fusion features; the construction module constructs a prediction model based on the fusion features; the prediction module obtains a prediction result by using the prediction model; and the alarm module is used for giving an alarm when the prediction result shows that the behavior violating the traffic regulations occurs. According to the invention, by fusing visual and radar sensor data, the accuracy and robustness of early warning can be improved, false alarms caused by environmental changes and sensor limitations are reduced, meanwhile, the requirement of real-time monitoring is met, the traffic accident rate is effectively reduced, the traffic management efficiency is improved, and a basis is provided for future technical improvement and function extension.

The invention relates to the technical field of path planning, discloses an optimal path planning method in an intelligent traffic system, and aims to solve the defects in the aspects of dynamic response, target optimization, multi-scene adaptation and the like in the prior art. According to the technical key points, real-time traffic data, historical traffic data and environmental data are collected, multi-source information is fused, a multi-target optimization model is constructed, and factors such as passing time, cost and carbon emission are comprehensively considered. The system adopts a reinforcement learning algorithm and a dynamic traffic prediction model, and combines a real-time feedback mechanism to realize dynamic update of a path. According to the method, path selection can be optimized in different scenes, such as blockage avoidance in peak hours, flow regulation and control in festivals and holidays, logistics distribution path planning, school bus scheduling, new energy vehicle path optimization and the like. Compared with a traditional method, the method can significantly improve the accuracy, real-time performance and resource utilization efficiency of path planning, has good social and economic benefits, is suitable for various complex traffic scenes, and has wide popularization and application prospects.

The invention provides a double-end cooperative network-on-chip flow control method and device and electronic equipment, and the method comprises the steps: receiving first flow counting information synchronously transmitted by a destination end through a rapid signal, and determining second flow counting information of a source end, the source end and the destination end are respectively a starting node and an ending node of a complete link in the network-on-chip; estimating the link state of the network-on-chip according to the first flow counting information and the second flow counting information; and controlling the flow injection rate of the source end according to the link state. According to the method, counting information synchronization of a source end and a destination end is realized by utilizing the characteristics of low cost and high speed of a control signal in the network-on-chip, so that the link state of the network-on-chip is accurately estimated, the process ensures that no obvious flow accumulation exists on a path, bandwidth resource waste caused by excessive speed limitation is avoided, and the link state of the network-on-chip is accurately estimated. Quicker and more accurate flow management is supported; and more timely and accurate flow regulation is realized.

The invention discloses an automatic driving evaluation benchmark construction method, system and device, and belongs to the technical field of automatic driving evaluation. The method comprises the following steps: analyzing original traffic regulations into atomic rules, and generating candidate rule pairs with hierarchical labels; performing space-time coexistence verification on the candidate rule pair, and constructing a hierarchical rule set; converting the hierarchical rule set into a text scene description and generating a structured semantic representation, then mapping the structured semantic representation into an automatic driving simulator, rendering and capturing a multi-view image sequence, and generating high-fidelity visual scene data; constructing a multi-choice question and answer pair based on the generated high-fidelity visual scene data, and forming an evaluation data set containing different cognitive levels; and training or evaluating an automatic driving model by using the evaluation data set, and optimizing a planning strategy of the model according to an evaluation result. The technical problem of automatic driving model evaluation is solved.

The invention discloses a highway edge calculation flow regulation and control method based on OpenHarmony, and belongs to the technical field of intelligent traffic control systems. The method comprises the following steps: traffic flow detection adopts a lightweight improved YOLOv8 model, a backbone network is replaced by an OfficientViT, a neck network is optimized by phantom convolution, a feature fusion module is enhanced by SCConv, and a dynamic non-monotonic focusing loss function is introduced; according to traffic flow prediction, a spatio-temporal feature fusion Transform model is constructed, a multi-head convolution low-rank decomposition attention mechanism is adopted to capture time dependence, spatial topological features are extracted in combination with attention graph convolution, spatio-temporal information is fused through a gating unit, and a prediction error is controlled within 1.5-3.0 times of historical standard deviation; a triple lightweight LSTM-AutoEncoder is adopted for abnormal early warning, so that the false alarm rate is reduced; the OpenHarmony distributed task scheduling is cooperated with the above modules, the variable information sign and the dynamic speed limit sign are driven to execute regulation and control, the response delay is reduced, the problem of real-time accurate regulation and control under the condition that the computing power of the edge device is limited is solved, and the traffic efficiency is improved.

The invention discloses a traffic regulation knowledge graph enhanced driving decision reasoning question and answer method and device, relates to the technical field of traffic scene understanding of an auxiliary driving system in intelligent traffic, and aims at an online simulation driving test scene. The semantic confusion problem of the traffic schematic diagram under the condition of limited sample scale is solved, and the interpretability of traffic schematic diagram questions and answers is enhanced. The method comprises the following steps: receiving a to-be-answered traffic schematic diagram problem input by a user by using electronic equipment; inputting the to-be-answered traffic schematic diagram question into a trained traffic schematic diagram driving decision inference question and answer model to output an inference answer; and displaying the reasoning answer by using the electronic equipment so that a user can refer to the reasoning answer to complete a driving decision. According to the method, the accuracy of traffic schematic diagram questions and answers is guaranteed under the condition that semantic confusion exists in the samples, and good technical support is provided for an auxiliary driving system, automatic driving and other applications in an intelligent traffic scene.

The present invention provides a centralized RoCEv2 network congestion control method, which relates to the technical field of network congestion control, including: S1, when a data center network is started, a network controller calculates and saves a switch forwarding table and an alternative forwarding table according to the network topology; S2, the network controller determines the network operation state according to link information; S3, if the network operation state is a network congestion state, a flow rate adjustment method is used to perform flow control, and if the congestion situation is improved after flow control, the method goes to step S2; S4, the network controller notifies the congested switch according to the comprehensive link cost to set the alternative forwarding table as the positive forwarding table for data forwarding, and goes to step S2. The present invention generates a global flow control strategy through the network controller, ensures the consistency and effectiveness of the strategy, and automatically switches the forwarding path when the global flow control strategy fails, thereby improving the efficiency and accuracy of congestion control.

An intelligent pedestrian flow analysis method based on subway station video monitoring comprises the following steps: setting a decomposition frequency to continuously decompose and screen station video data to obtain a screening frame element library, setting a linear activation kernel and carrying out convergence extraction to obtain a coverage feature value, and carrying out element replacement on the linear activation kernel according to the coverage feature value to obtain a pedestrian flow analysis result; obtaining a replacement capture core; carrying out mark capture and nuclide matching on the screening frame element library by replacing the capture kernel to obtain a net value matching kernel, and constructing an element net value library according to the net value matching kernel; carrying out identity analogy on the element net value library to obtain a similar identity net value library, carrying out pattern positioning on the net value matching core and carrying out similar identification on the net value matching core and the similar identity net value library to obtain a target class identification node, and carrying out position tracking and heterogeneous analysis on the target identification node to obtain a traffic adjustment strategy; the passenger flow is automatically detected and tracked, and the passenger flow management efficiency of the subway station is improved.

The invention discloses an automatic driving vehicle driving behavior compliance simulation test method, which comprises the following steps of S1, decomposing a natural language traffic regulation into an initial premise and a constraint condition, digitalizing the initial premise and the constraint condition by utilizing measurement sequential logic, and generating a mathematical formula which can be analyzed by a machine; s2, constructing a simulation scene according to the initial premise; s3, accessing the tested automatic driving algorithm to the self-vehicle in the simulation scene, and collecting vehicle state and environment data in real time; and S4, dynamically adjusting a violation detection threshold based on the constraint condition and the current weather and road conditions, and outputting a detection result. The method is different from a traditional real vehicle testing method, and various possible scenes including extreme weather, road conditions and the like can be covered more comprehensively; different from a traditional violation detection method, the method considers the influence of road and weather factors on violation detection conditions for different test scenes, and is higher in universality.

The invention relates to the technical field of traffic control optimization, and discloses a traffic jam control optimization algorithm based on a regional population scale and an economic structure. The algorithm comprises the following steps: firstly, obtaining population distribution data, economic industry data and real-time traffic flow original data of a target area, and generating an area traffic characteristic representative data set through multi-dimensional clustering processing; generating a traffic regulation and control allocation instruction set based on the data set; then constructing a dynamic traffic state hierarchical optimization model containing a top layer road network stability model and a bottom layer resource cost model; and finally, determining a cooperative constraint condition of the two models, inputting an instruction set into the models, performing cross-layer iteration updating solution according to the constraint condition, and outputting an optimal traffic control strategy configuration scheme. According to the algorithm, multi-dimensional data are fused, collaborative optimization of road network stability and resource cost is realized, the accuracy and adaptability of traffic jam treatment are improved, and regional traffic jam is effectively relieved.

The present invention discloses a driving decision-making reasoning question-answering method and device enhanced by a traffic law knowledge graph. The method relates to the technical field of traffic scene understanding for assisted driving systems in intelligent transportation. Aimed at online simulated driving test scenarios, the method solves the semantic confusion problem of traffic diagrams under limited sample size and enhances the interpretability of traffic diagram question-answering. The method comprises: using an electronic device to receive a traffic diagram question to be answered input by a user; inputting the traffic diagram question to be answered into a trained traffic diagram driving decision-making reasoning question-answering model to output a reasoning answer; and using the electronic device to display the reasoning answer so that the user can make a driving decision by referring to the reasoning answer. The present invention ensures the accuracy of traffic diagram question-answering in the presence of semantic confusion in the sample, providing good technical support for applications such as assisted driving systems and autonomous driving in intelligent transportation scenarios.

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 invention relates to the technical field of unmanned vehicle path planning, in particular to a path planning method and device for an unmanned vehicle and electronic equipment. The method effectively solves the problem of loss of connectivity of map narrow channels caused by a traditional fixed corrosion radius through divide-and-conquer corrosion treatment and dynamic skeleton line extraction, ensures the feasibility of path planning, and innovatively utilizes the contour extraction and reference path scanning technology to improve the accuracy of path planning. According to the method, the right-side driving traffic rule is converted into the continuous right-side channel geometric constraint, so that the planned path naturally conforms to the traffic regulations and is personified in behavior, the safe, smooth and efficient optimal path is generated while the kinematics constraint of the vehicle is strictly met, and the navigation safety and intelligence of the unmanned vehicle in the structured environment are remarkably improved.

The invention relates to the technical field of traffic regulation and control, and discloses an AI-based global traffic adaptive regulation and control method and system, and the method comprises the steps: determining the type of information needing to be collected for a target intersection based on a current date and a current time period, and collecting the multi-dimensional information of the target intersection based on the information type; and according to the multi-dimensional information of the target intersection and a traffic control AI model which is trained to converge in advance, generating a target traffic light control parameter for the target intersection, and executing a corresponding traffic light control operation based on the target traffic light control parameter corresponding to the target intersection. And the traffic flow congestion condition and the pedestrian flow congestion condition of the target intersection are slowed down through the adjusted traffic light. Therefore, the accuracy and the reliability of the traffic light control parameters can be improved, the traffic regulation and control accuracy, the reliability, the efficiency and the convenience of the intersection are further improved, and the vehicle driving smoothness and the road safety of the intersection and related roads are improved.

The invention discloses an active speed limiting system and method for an automatic driving vehicle, which are used for solving the problem of how to enable the automatic driving vehicle to flexibly adjust the driving speed according to a real-time environment on the premise of obeying traffic laws and regulations. The active speed limiting system comprises a planner, a controller, a sensor and a high-precision map, wherein the controller, the sensor and the high-precision map are connected with the planner. The road condition of the automatic driving vehicle is sensed through the sensor, and peripheral obstacle information is generated and transmitted to the planner; map information of the autonomous vehicle is obtained from the high-precision map through a planner, risk speed limit information is calculated based on surrounding obstacle information, a driving track is generated in combination with the risk speed limit information and the map information, and the driving track is converted into a control instruction to be issued to a controller so that the controller can execute an active speed limit action based on the control instruction.

The application discloses a scene library data authorization management system for intelligent automobile traffic regulation compliance test verification, comprising a traffic regulation compliance test application scene library platform, a terminal device, a scene library management module arranged on the terminal device and a scene library application client; the traffic regulation compliance test application scene library platform is used for responding to an access request of the terminal device, authenticating the terminal device, and sending encrypted scene library data to the terminal device that passes the authentication; the scene library management module is used for receiving and storing the encrypted scene library data, and is also used for responding to an access request of the scene library application client, authenticating the scene library application client, decrypting the encrypted scene library data, and calling the decrypted scene library data by the scene library application client that passes the authentication; and the scene library application client is used for reading the scene library data and performing simulation operation on the scene library data. The application can effectively prevent illegal access of non-authorized devices to the scene library data, and improves access security.

To reduce time required for searching a route after locating a departing place and a destination and generate a route reflecting traffic regulation.SOLUTION: A route searching method includes a prior search procedure for generating label data on the basis of road data and regulation data, and a route determination procedure for determining a route between spots on the basis of the label data, the prior search procedure includes a procedure for generating information on a hub comprising multiple nodes on the basis of road data, and a procedure for holding a passable direction in a hub when setting a route between respective nodes and the hub as label data for the respective nodes on the basis of road data and regulation data, and the route determination procedure includes a procedure for determining a route corresponding to a combination in which a passing direction in the hub, a passable direction in the hub when entering the hub, and a passable direction in the hub when exiting the hub do not contradict from among as a route between spots in a combination of a hub, a route entering the hub, and a route exiting the hub.SELECTED DRAWING: Figure 1

The invention relates to a comprehensive inspection, management and repair method and system for an inspection well, computer equipment and a storage medium. The method comprises the following steps: acquiring a target position and a fault type of a to-be-repaired target inspection well; obtaining a starting point position of the repairing personnel, and based on the starting point position and the target position, performing multi-target inspection well path planning by adopting a genetic algorithm to obtain a target path; the target path is used for indicating the repairing personnel to pass through each target inspection well according to the optimal path; when the position information of the repairing personnel is matched with the target position, a downhole operation instruction is generated according to the fault type; the well descending operation instruction is used for indicating the repairing personnel to repair the target inspection well according to the fault type. The multi-target inspection well path planning is carried out by comprehensively considering factors such as road conditions and traffic rules through a genetic algorithm, so that the purposes of accurately positioning target inspection wells and faults, planning an optimal repair path to improve the working efficiency, ensuring that the inspection wells can be recovered to be normal in time and ensuring the stable operation of related infrastructure systems are achieved.