1920 results about "Intelligent transport" patented technology

The invention provides an intelligent traffic decision-making method based on multi-source sensor data, and the method comprises the steps: obtaining vehicle sensor data and road information, and obtaining a standardized traffic flow feature vector through data synchronization, fusion, feature extraction and dimension reduction processing; inputting the standardized traffic flow feature vector into a traffic flow prediction model, and predicting the traffic flow through a vehicle dynamics model constraint state transition matrix; according to the predicted traffic flow and the real-time road condition information, the road congestion state is judged, and a real-time congestion map is generated; in combination with the real-time congestion map, the road network speed limit rule and the starting and ending point position information, an optimal path is planned, and path smoothing processing is performed through a driving behavior mode; and constructing a vehicle intelligent decision-making system, integrating traffic flow prediction, congestion detection and a path planning model, and outputting a vehicle driving strategy according to real-time vehicle sensor data.

The invention, which relates to the technical field of logistics transportation and intelligent transportation, discloses an intelligent sensing and active early warning system for security risks of multimodal transport of long and large goods based on multi-source data fusion, comprising: an original data input module; a service scene intelligent sensing module; an acquired data analysis module; a safety risk early warning module; an operation guidance module; a networking information receiving module; the invention further discloses a long and large cargo multimodal transport safety risk intelligent sensing and active early warning method based on multi-source data fusion. According to the invention, the complex safety challenge faced by long and large goods in the multimodal transportation process is solved, and the safety and efficiency of the transportation process are improved.

The invention discloses a built environment and traffic accident causal effect analysis method based on bi-robust learning, and belongs to the field of intelligent traffic safety and traffic accident risk assessment. By integrating satellite remote sensing, interest point distribution and traffic accident data, a spatial-temporal feature enhanced environment index system is constructed; realizing cross-regional data collaborative analysis by adopting federal learning, and guaranteeing data compliance in combination with differential privacy encryption; a generative adversarial network is introduced to generate a high-fidelity anti-fact scene, and a dual robust learning framework is coupled to accurately quantify a condition average processing effect of a built environment on accident risks. According to the scheme, a technical tool with both theoretical preciseness and practical feasibility is provided for urban traffic planning and safety management, and a scientific basis is provided for optimizing and building environment design and reducing traffic accident risks through a multi-source data fusion and privacy cooperation mechanism; and the blank in the aspects of causal robustness, insufficient anti-fact generation precision, privacy protection and multi-source data fusion in the prior art is filled.

The invention discloses an urban traffic flow prediction method and system based on time sequence deep learning, and relates to the technical field of intelligent traffic, and the method comprises the steps: 1, collecting traffic flow data and road network topology, and carrying out the preprocessing; 2, constructing a static adjacency matrix and a dynamic adjacency matrix according to the preprocessed traffic flow data and road network topology; 3, calculating a space-time attention weight by adopting a space-time attention mechanism according to the preprocessed traffic flow data; and 4, according to the static adjacency matrix, the dynamic adjacency matrix and the space-time attention weight, using the improved space-time diagram convolutional network to predict and output future traffic flow. The urban traffic flow prediction method combines the space-time diagram convolutional network, the space-time attention mechanism and the adaptive diagram generation mechanism to realize accurate urban traffic flow prediction.

The invention discloses a cargo transportation real-time path planning method based on artificial intelligence, and relates to the technical field of artificial intelligence and intelligent traffic, and the method comprises the following steps: collecting real-time data from a traffic monitoring system, a vehicle-mounted sensor, a road event platform and a weather information system in a transportation process, forming an original data set containing multi-source information; time synchronization and space alignment are carried out on source data in an original data set, and a standardized data frame is constructed to ensure the consistency of different source information in a space-time dimension. According to the method, the accuracy, the real-time performance and the intelligence of path planning are effectively improved through multi-source data consistency identification, weighted fusion and a dynamic re-planning mechanism. The system can timely identify data conflicts, dynamically adjust paths and enhance transportation safety and efficiency, has a self-learning capability, continuously optimizes path strategies and adapts to complex traffic environments.

The invention discloses a driving behavior risk assessment method and system, and relates to the technical field of intelligent traffic and driving safety, and the method comprises the steps: collecting the operation parameter data of a vehicle, carrying out the feature extraction of the operation parameter data, extracting feature parameters to form a feature vector, and calculating the Mahalanobis distance score of the feature vector; according to the Mahalanobis distance score of the feature vector, carrying out risk grade division by adopting an improved hierarchical fuzzy inference system to obtain a risk grade score; and establishing a driver portrait model according to the risk level score, and when the risk level score exceeds a personalized risk threshold, outputting a driving risk behavior improvement suggestion to complete driving behavior risk assessment. According to the invention, a refined grading basis is provided for individual risk monitoring of the driver, dynamic monitoring and real-time intervention of driver behaviors are realized, traffic accidents can be reduced, and driving safety is improved.

The invention provides an intelligent traffic dynamic green wave control method and system, a storage medium and a program product, and relates to the field of traffic control systems.The method comprises the steps that real-time traffic data of all lanes and emergency data published by a traffic management department are obtained; obtaining traffic flow prediction results of different directions of each road section in a future preset time based on a neural network model; outputting the congestion level and the congestion duration of each road section in combination with the emergency situation data and a traffic flow theoretical model, and further determining an adjustment sequence; and on the basis of the traffic flow prediction result, calculating the optimal green wave bandwidth of the signal lamp at each intersection in each target road section by using a particle swarm optimization algorithm to obtain an optimal timing combination scheme of the signal lamp in each target road section, and performing real-time timing control. By implementing the method, the traffic flow can be dredged more efficiently, the parking frequency and delay time of vehicles on the road are reduced, the overall traffic capacity of the urban road is improved, and energy consumption and environmental pollution are reduced.

The invention belongs to the technical field of vehicle trajectory prediction, and discloses a multi-scale interactive perception highway vehicle trajectory prediction method, which comprises the following steps: jointly modeling short-term burst features and long-term evolution trends through a convolutional neural network and a bidirectional gating cycle unit, and introducing a time sequence attention mechanism to improve the perception ability for key time slices; in combination with a dynamic graph attention mechanism including physical edge features such as relative position, relative speed and relative acceleration, a vehicle interaction relationship is updated in real time so as to improve spatial modeling precision and interpretability; in the decoding stage, the guide vector and the semantic information of the lane are fused, so that the predicted trajectory conforms to the geometric structure of the road in space and keeps smooth and continuous in time. According to the method, the robustness and adaptability of the model in the sparse adjacent vehicle environment of the expressway can be improved while the prediction precision is ensured, a more stable and reliable trajectory prediction result is provided for an intelligent traffic system, and powerful technical support is provided for traffic safety management and operation scheduling of the expressway.

The invention relates to the technical field of intelligent transportation control, in particular to a tomato transportation speed self-adaptive adjustment method based on path road condition feedback, which comprises the following steps: acquiring road images, vibration waveforms and altitude data through a multi-source sensing unit, and constructing real-time road condition information; identifying a driving mode and extracting a corresponding bumping parameter; detecting the maturity grade of the tomato by combining multispectrum and thermal imaging, and querying a maturity-compressive strength corresponding table to calculate a cargo damage threshold value; constructing a dynamic mapping model under multiple working conditions, predicting vibration response and converting the vibration response into equivalent pressure; comparing the equivalent pressure with a damage threshold value to obtain a safety margin, constructing a speed adjustment decision tree and generating a maximum allowable speed value of each road section; and dynamically generating a segmented variable-speed control instruction based on the speed decision matrix, and controlling a throttle valve and a braking system to cooperatively change speed. The method has the advantages of accurate working condition identification, dynamic fruit adaptation, closed-loop speed regulation and control and the like, and is suitable for fine speed control of a high-sensitivity fruit and vegetable transportation scene.

The invention discloses an urban road mobile source intelligent monitoring method and system based on multi-source data coupling, and relates to the technical field of environment monitoring and intelligent traffic. Utilizing a machine learning algorithm to construct a pollutant emission, carbon emission and energy consumption prediction model; acquiring vehicle inventory data in the city, and calculating the pollutant emission, energy consumption and carbon emission of the whole city; the method comprises the following steps of: constructing a dynamic distribution diagram of automobile emission in a city by using real-time position data of vehicles and urban road network information, introducing an atmospheric diffusion model, simulating pollutant migration by combining urban geographic information and environmental data, comparing and verifying a simulated migration result with actually measured data of a national control site, and optimizing parameters of the atmospheric diffusion model; and storing the result into a real-time database, and displaying the pollutant distribution, emission, energy consumption and carbon emission of the urban road network in real time through a visual interface. According to the invention, the temporal-spatial resolution and prediction precision of data are effectively improved.

The invention provides a road monitoring multi-mode sensing method and a road monitoring multi-mode sensing system adapting to a dynamic environment, and belongs to the field of road monitoring. Performing multi-modal weighted fusion to obtain a feature set after weighted fusion; performing classification processing on the fusion features by using a multi-layer perceptron network; generating a comprehensive evaluation result; dynamically adjusting the working priority of each sensor; alignment is carried out in time and space; automatically adjusting a data fusion strategy; and carrying out data cooperative processing by utilizing edge computing and cloud computing to generate a local model generated by the edge computing and a global model and an optimization result generated by the cloud computing. According to the method, the defects of a traditional road monitoring system in the aspects of environmental adaptability, sensor fusion, calculation efficiency and compatibility are overcome, the overall performance and reliability of the system in a complex and changeable environment are remarkably improved, and the method has wide application prospects especially in the fields of automatic driving and intelligent traffic.

The invention relates to the technical field of intelligent traffic, and discloses a road transportation key operating vehicle intelligent supervision system, which comprises a data acquisition and fusion module used for acquiring multi-source heterogeneous data of a vehicle terminal, an external environment and the like in real time and carrying out standardization processing; the cognitive digital twinning construction module is used for constructing a dynamic heterogeneous graph representation human-vehicle-road-environment system and outputting a cognitive state vector through a graph neural network; the risk prediction and evaluation module is used for predicting a risk evolution trend based on the state vector sequence and quantifying the uncertainty of prediction by using a Monte Carlo discarding method; and the self-adaptive intervention decision module is used for combining risk prediction and uncertainty, making a decision through a reinforcement learning model and executing an optimal active intervention instruction. By constructing cognitive digital twinning and introducing uncertainty quantification and closed-loop self-checking, prospective prediction and self-adaptive intervention of driving risks are realized, and the accuracy and robustness of supervision are improved.

The invention relates to the field of intelligent traffic safety, in particular to a hazardous chemical substance transportation path dynamic risk prevention and control system and method based on space-time fusion, and provides a method for calculating a multi-dimensional risk index matrix through a risk index quantification module and combining a space-time environment sensitivity prediction matrix generated by an environment sensitivity analysis module. According to a risk spreading probability cloud picture of the risk spreading prediction module, accurate evaluation of the dynamic risk of the road section is realized; the system establishes a collaborative risk factor assessment model, adjusts a safety threshold according to historical accident data, and generates a space-time road network risk scoring matrix; the risk prevention and control decision module determines an early warning level, selects prevention and control measures, optimizes a transportation path, formulates an emergency response plan for a high-risk road section, and outputs an intelligent decision instruction set; according to the system, comprehensive quantification of hazardous chemical substance characteristics is realized, and the comprehensiveness of risk assessment and the intelligence of prevention and control decision making are improved.

The invention discloses a rail transit hub emergency regulation and control method and system based on real-time simulation, and relates to the technical field of intelligent traffic and rail transit emergency management, and the method comprises the steps: carrying out the time synchronization processing of real-time passenger flow data and train operation state data, and generating a hub state data set; constructing a multi-level simulation model, and simulating a passenger flow distribution evolution process by presetting passenger flow evacuation behaviors and train operation constraint conditions; based on an output result of the multi-level simulation model, positioning capacity limiting nodes in each evacuation path by adopting a bottleneck identification algorithm, calculating passenger flow carrying capacity, and generating an optimal path regulation and control parameter with shortest evacuation time as a target; and the optimal path regulation and control parameters are converted into a train automatic control system instruction and a passenger flow guide display instruction, the instructions are issued to each line control unit through a hub centralized dispatching system, and the train operation frequency and the passenger flow evacuation direction are adjusted. According to the invention, intelligence and collaboration of emergency regulation and control of the rail transit hub are realized.

The invention discloses an urban traffic event semantic recognition method based on a knowledge graph, and relates to the technical field of intelligent traffic and artificial intelligence, and the method comprises the steps: obtaining the multi-modal traffic data of urban traffic, and constructing a knowledge graph model; preprocessing and feature extraction are carried out on the multi-modal traffic data, the extracted multi-modal features are mapped to entity nodes of a knowledge graph model, a fusion feature vector is generated, and semantic embedding coding is carried out on the fusion feature vector through a graph neural network; constructing an event inference rule base based on a semantic embedding coding result, and performing multi-layer inference calculation on the fusion feature vector by using a graph convolutional neural network to obtain a matching strength score of the candidate traffic event and a standard event mode in the knowledge graph; and in combination with the event space-time constraint condition and the historical event mode, outputting a traffic event recognition result, confidence evaluation and disposal suggestions. According to the invention, the accuracy and practicability of urban traffic event identification are improved.

The invention relates to the technical field of intelligent traffic and energy-saving illumination, in particular to an intelligent management and control method and system for energy-saving illumination of a highway tunnel and a computer program, and is suitable for an intelligent illumination control system for improving tunnel driving safety and energy efficiency of an illumination system. Dynamic response and fine energy consumption adjustment of tunnel lighting are realized through out-of-tunnel brightness multi-source fusion prediction, entrance section feedforward, closed-loop dimming control, a segmented intelligent lighting strategy based on vehicle detection and an in-tunnel brightness closed-loop and stepless dimming mechanism. The system has a multi-sensor redundancy mechanism and can automatically return to a safety mode when equipment fails, so that the continuity and the safety of illumination are guaranteed; and meanwhile, data summarization and energy-saving statistics are combined, control parameters are optimized in cooperation with a digital twinning or self-learning algorithm, the energy-saving effect and the operation and maintenance efficiency are further improved, and the purposes of traffic safety and low-carbon operation are considered.

The invention relates to the technical field of intelligent traffic monitoring, in particular to a vehicle identification and traffic incident detection method and system based on the view angle of an unmanned aerial vehicle, and the method comprises the steps: obtaining the aerial traffic video data of the unmanned aerial vehicle and the flight control data of the unmanned aerial vehicle; performing target identification on the image in the video data by adopting a target detection algorithm to obtain vehicle information; tracking the vehicle in the video data by using a target tracking algorithm, and mapping the identified vehicle to a unified coordinate system in combination with the flight control data to generate vehicle trajectory data; constructing a comprehensive road description model based on the vehicle trajectory data and the vehicle information; and forming a traffic incident recognition module based on the comprehensive road description model, and recognizing the input aerial video according to a preset traffic incident recognition rule. According to the invention, by optimizing the vehicle identification algorithm, constructing the road description model and designing the efficient traffic event identification module, the application efficiency of the unmanned aerial vehicle aerial video in the traffic monitoring field is effectively improved.

The invention discloses an intelligent traffic flow dynamic regulation and control method and system based on multi-source data fusion, and relates to the technical field of data processing. The method comprises the following steps: executing multi-source data acquisition through a data acquisition interface to obtain a multi-source traffic data set; performing spatial fusion on the multi-source traffic data set to obtain a flow marking map; performing current traffic state analysis based on the flow marking map, identifying local and global congestion, and constructing a traffic situation map; and performing traffic flow state prediction in a preset future time zone based on the traffic situation map, performing regulation and control decision based on a prediction result, and generating a traffic flow regulation and control strategy. The technical problem that traffic flow prediction and regulation are not accurate enough in the prior art is solved, and the technical effects that intelligent traffic flow dynamic regulation is achieved through multi-source data fusion and space-time analysis, and the traffic management precision and the response speed are improved are achieved.

The invention discloses a road congestion prediction system and method based on spatial-temporal feature extraction, and belongs to the field of intelligent traffic. The system adopts a layered distributed architecture, and comprises a multi-source data acquisition module, a data preprocessing unit, a double-flow spatio-temporal feature extraction network, a two-stage spatio-temporal attention mechanism module, a congestion prediction model and a result feedback interface. Multi-modal data such as a vehicle-mounted GPS track, checkpoint flow, video monitoring and meteorological data are integrated, a double-flow feature extraction network is constructed by adopting a graph convolutional network and a bidirectional gating circulation unit, and a key space-time region is dynamically focused in combination with a multi-head self-attention and time weighted dot product attention mechanism; and finally, optimizing the generalization ability of the model through a composite loss function. According to the method, a dynamic adaptive learning framework and multi-source data combined modeling mode is adopted for urban road traffic flow characteristics, the space-time precision and the real-time response capability of road network congestion prediction are remarkably improved, and reliable decision support is provided for intelligent traffic control.

The invention provides a traffic flow spatio-temporal feature adaptive extraction method based on a dynamic Kolmogorov-Arnold network, and aims to solve the defects of a traditional model in the aspects of dynamic spatio-temporal modeling, structural adaptability and feature expression efficiency. According to the method, a network structure is dynamically adjusted through differential topology search and a parameterized primary function library, a double-flow coupling architecture is designed to extract space and time dependent features respectively, and adaptive fusion of spatial and temporal features is realized by using a gating mechanism. The method comprises the following specific steps: performing space-time normalization and graph structure coding to generate a node feature matrix; extracting multi-scale spatial-temporal features in parallel by the dynamic graph convolution KANs and the time sequence convolution KANs; feature alignment and cooperative enhancement are realized based on a local correlation matrix and a bidirectional interactive attention mechanism; and the lightweight KAN decoder is combined with the dynamic basis function library to output a prediction result. Experiments show that the MAE is reduced to 1.78 vehicles per minute and the reasoning speed is improved by 1.8 times in a traffic accident emergency scene on a PeMS08 data set, and the method is suitable for an intelligent traffic management and vehicle-road cooperation system.

The invention discloses a traffic flow prediction method and system based on improved space-time coupling KA-LSTM, and belongs to the technical field of intelligent traffic and artificial intelligence. According to the method, for the problems that a traditional model is poor in adaptability, low in prediction precision and insufficient in calculation efficiency in a complex traffic scene, a KA-LSTM model is constructed, a forgetting gate, an input gate and a memory updating mechanism of LSTM are dynamically optimized through KANs, the capturing capacity for long-period dependence is enhanced, and a system comprises a data collection module, a model training module, an anomaly detection module and a real-time optimization module. Experiments show that the indexes such as MAE and RMSE are reduced by more than 40.1% compared with those of traditional LSTM, the training efficiency is remarkably improved, and the method is suitable for urban traffic management and optimization. The method has the core advantages that through dynamic gating optimization, multi-modal data fusion and an online updating strategy, the method has good performance effects in prediction precision, real-time performance and complex scene adaptability, and an efficient and reliable solution is provided for traffic flow prediction.

The invention discloses a vehicle trajectory similarity calculation method based on semantic fusion and enhanced comparative learning, comprising the following steps: acquiring and preprocessing original trajectory data of a vehicle, and constructing a POI semantic region; constructing a track heterogeneous graph fusing time, space, weather and POI semantics; generating track embedding by using an embedding network fused by multidimensional features of time, space, weather and POI semantics; a meta-path is generated through reinforcement learning to capture a high-order semantic relationship, and final embedding is generated through dynamic weight fusion; optimizing an embedding space by utilizing a comparative learning framework; calculating the similarity between trajectory embedding vectors by adopting cosine similarity; according to the method provided by the invention, weather is used as an independent dimension to be fused into trajectory coding, four-dimensional joint characterization is realized, key semantic association paths are mined through reinforcement learning, the problem of scarcity of annotation data is relieved by utilizing comparative learning, the accuracy of trajectory similarity calculation is improved, and the high-precision requirement of fields such as intelligent transportation on trajectory analysis can be met.

The invention, which relates to the technical field of intelligent traffic and illumination control, discloses a street lamp vehicle-road cooperative control system comprising a data acquisition module, a data processing module, a control execution module and a decision management module. The data acquisition module is used for acquiring traffic signal lamp phase, vehicle position and speed, street lamp working state and environment illumination intensity data; according to the invention, through a cross-system collaborative algorithm, the traffic flow direction is predicted in combination with the phase of the traffic signal lamp, the brightness of the street lamp illumination area is adjusted in advance, the energy distribution according to needs is realized, the energy consumption is effectively reduced, and through a vehicle-lamp-cloud three-level decision-making mechanism, the computing power priority is dynamically distributed according to the traffic condition. Traffic signal lamp control and vehicle driving path planning are optimized, the road passing efficiency is improved, traffic congestion is relieved, by pushing the lighting optimization path to the vehicle, the vehicle is driven under the proper lighting condition, the emergency brake risk is reduced, and the road traffic safety level is remarkably improved.

The invention discloses a bridge ship collision early warning system based on artificial intelligence, and relates to the field of intelligent traffic infrastructure safety protection. The method is used for solving the problems of difficulty in multi-source data fusion and low risk identification accuracy in a complex water area. The method comprises the following steps: firstly, uniformly mapping a radar point cloud, an AIS signal and a visual trajectory to a coordinate system taking a pier as a center through a Lie group transformation algorithm, and constructing a ship motion state tensor; then, ship behavior characteristics are extracted through a space-time diagram convolutional network, and intention probability distribution and behavior deviation degree are output; thirdly, an orthogonal constraint variational auto-encoder is adopted to decouple the autonomous control behavior and the environment disturbance factor, and a collision risk quantized value is generated according to the norm ratio of the autonomous control behavior and the environment disturbance factor; and finally, constructing a dynamic alarm threshold value according to the behavior deviation degree and the disturbance characteristics, realizing graded early warning, and driving a behavior pattern library to update through a feedback mechanism to form a self-adaptive closed-loop early warning system.

The invention relates to the technical field of intelligent traffic, and discloses an expressway parking risk dynamic early warning method and system based on spatial-temporal characteristics and an adaptive threshold, and the method comprises the steps: collecting vehicle driving data through a multi-type sensor, obtaining vehicle internal data through 5G communication, and employing a D-S evidence theory for fusion; performing spatio-temporal feature processing on the fused data, constructing a spatio-temporal matrix, and extracting features by using a spatio-temporal convolutional neural network; constructing a parking risk assessment model by combining a gating circulation unit of an attention mechanism based on the extracted features, and outputting a parking risk probability in a period of time in the future; according to road sections, time periods and weather conditions, an early warning threshold value is dynamically adjusted by using a reinforcement learning algorithm, multi-channel early warning is triggered when the threshold value is exceeded, and the system comprises a data acquisition and transmission subsystem, a data processing and analysis subsystem and an early warning decision and release subsystem. The objective of the invention is to realize prediction and timely and effective early warning of the parking risk of the high-speed vehicle.

The invention relates to the technical field of intelligent traffic, in particular to an urban traffic flow prediction method fusing a dynamic graph convolutional network and a Transformer, which comprises the following steps: firstly, collecting traffic data in a road network to form a data set, then constructing a space-time dynamic GCN unit to capture dynamic evolution of road network topology, and extracting multi-scale space-time characteristics through expansion time convolution; then, constructing a Transform unit for modeling a global time sequence dependency relationship of the traffic flow; finally, in combination with a gating fusion prediction unit, spatial-temporal features are fused through a spatial-temporal cross attention mechanism, and the prediction precision and robustness are effectively improved. Through verification and evaluation of real data, the method is suitable for a traffic flow prediction scene in an urban road network dynamic environment, and especially has good performance in sudden road conditions and peak hours.

The invention relates to the technical field of intelligent traffic infrastructure engineering, in particular to a roadbed settlement high polymer grouting repair grouting parameter optimization method, which comprises the following steps of: firstly, constructing a multi-source fusion training data set, and learning a prediction model based on a training machine; establishing a driving mapping relation between the grouting parameters and the road lifting effect and the maximum stress data of the repair area; then, based on the mapping relation, a multi-objective optimization model including pavement settlement repair precision, road stress, economic cost control and environmental friendliness evaluation is constructed; solving the model by adopting a Bayesian optimization algorithm to obtain an optimized grouting parameter combination; a marginal contribution of each parameter to a prediction result is calculated in combination with an SHAP interpretability analysis method, and an engineering decision basis is provided for parameter selection; constructing a transfer learning adaptation framework to adapt to different disease, geology and road types; and finally, establishing a real-time monitoring system, and realizing optimal control by combining sensor data and model feedback.

Provided are a map updating method and apparatus, a model training method and apparatus, an electronic device, and a medium, relating to the technical field of artificial intelligence, and in particular to the fields such as automatic driving, intelligent traffic, computer vision, and image processing. A specific implementation solution is: for a same piece of position information, determining associated historical map data and M pieces of collected image data based on a time sequence, wherein M is an integer greater than or equal to 1; respectively encoding the historical map data and the M pieces of collected image data to obtain a historical map feature and M collected image features; determining position information and category information of a target instance on the basis of the historical map feature and the M collected image features, wherein the target instance represents lane information; and updating the map data on the basis of the position information and the category information of the target instance.

The invention discloses an intelligent automobile interpretable abnormity diagnosis method and system, and relates to the technical field of intelligent traffic. The method comprises the steps of collecting multi-dimensional sensor data based on an intelligent automobile test platform, and constructing a directed causal graph and a causal adjacency matrix which are used for describing a causal relationship between the sensor data; designing a causal constrained graph attention mechanism based on the causal adjacency matrix, and constructing a causal constraint enhanced graph attention anomaly diagnosis model; and based on the directed causal graph and the graph attention anomaly diagnosis model, constructing a hierarchical anomaly diagnosis strategy integrating a feature reconstruction error, a variable causal relationship and a graph attention network weight, positioning an anomaly root cause and identifying a propagation path of the anomaly in the sensor network. According to the invention, the problems of false correlation and lack of exception explanation ability of graph attention network learning in the prior art can be overcome, and reliable exception detection and root cause diagnosis of intelligent automobile multi-sensor data are realized.

The invention relates to the technical field of intelligent traffic, in particular to a traffic network robustness evaluation method and system based on multi-source data fusion, and the method specifically comprises the following steps: obtaining a plane axis diagram according to an obtained research map and road data, and marking the obtained data information; importing the plane axis diagram into the depthmapX to construct a traffic road network topology model, and performing spatial syntactic analysis; exporting a traffic road network topology model and a space syntactic analysis result, accessing real-time data in the acquired data, and mapping the traffic road network topology model to a road network space coordinate system through a space-time alignment algorithm; constructing a nonlinear coupling model to calculate the robustness weight of each road section, and optimizing parameters in the model through a loss function and a gradient descent method; and combining the robustness weight of each road section after parameter optimization with the disaster probability factor, and outputting a comprehensive evaluation result. The method solves the problems of data isolation and evaluation lag of a traditional method, and is suitable for urban road network optimization and emergency management.