104 results about "Intelligent transportation system" patented technology

The invention relates to the technical field of intelligent traffic systems, and discloses an expressway multi-source traffic data grading and classification processing system, which comprises a data acquisition standardization module for acquiring and preprocessing multi-source traffic data; the data source quality evaluation module is used for evaluating the credibility of the data source; the scene emergency degree calculation module is used for calculating the flow anomaly degree, the vehicle speed anomaly degree and the meteorological risk score and identifying an emergency event; the data grading module is used for calculating a comprehensive priority score of the data and obtaining a grading data set by adopting a threshold segmentation method; the data classification module is used for carrying out self-adaptive classification on the data; the data fusion module identifies the same traffic parameter, performs conflict detection and performs fusion processing on conflict data; the result output module is used for obtaining a processing result by adopting a grading and classification output and quality feedback mechanism; according to the invention, intelligent refined processing of the highway multi-source traffic data is realized, and the emergency response speed and the data processing accuracy are improved.

The invention discloses an intelligent traffic control method and system in a low-altitude economic environment, and relates to the technical field of intelligent traffic systems, and the method comprises the steps: collecting low-altitude aircraft and ground traffic spatio-temporal data through a multi-modal sensor network, and generating a multi-source heterogeneous data set; constructing a dynamic traffic situation map through spatio-temporal feature fusion; performing three-dimensional path planning to generate a three-dimensional guiding strategy; detecting conflicts and correcting strategies according to a preset rule base, and outputting an instruction set to distribute real-time traffic flow. The technical problem that in the low-altitude economic environment, a traditional traffic management and control method is difficult to meet the cross-domain cooperation requirement of the low-altitude aircraft and the ground traffic is solved, and the technical effects of three-dimensional cooperative management and control of the low-altitude aircraft and the ground traffic and further guaranteeing safe and efficient operation of the traffic in the low-altitude economic environment are achieved.

The invention discloses a highway event detection algorithm based on data situation analysis, and belongs to the technical field of intelligent traffic systems. According to the method, a dynamic graph structure is constructed by fusing multi-source data of an ETC portal, a toll station, a traffic detector, weather and the like, spatial-temporal characteristics are extracted by utilizing graph convolution and LSTM, and fuzzy distribution prediction of a traffic state is realized; calculating a node score mutation rate based on a prediction result, screening abnormal nodes in combination with a serious congestion probability, and extracting a connected abnormal sub-graph with a compact structure as a potential event region; a multi-factor event scoring function is designed, sudden change intensity, structural compactness and a state offset direction are fused, a risk level is quantified, and an adaptive boundary learning device is introduced to dynamically discriminate an alarm, so that scene limitation of a fixed threshold value is avoided; the detection accuracy and the alarm flexibility are improved, and the method is suitable for intelligent sensing and early warning of highway traffic events.

The invention belongs to the technical field of image processing and deep learning, and particularly relates to an image defogging method based on dynamic wavelet prior and double-domain learning. Aiming at the requirements of all-weather clear imaging in the fields of intelligent traffic systems, safety monitoring and the like, and in order to overcome the defect that a static convolution kernel adopted by a traditional defogging method is difficult to adapt to different haze degradation, the invention provides a method for dynamically generating a convolution kernel by using haze priori contained in a multi-scale wavelet LL sub-band; and an efficient, robust and accurate image defogging model is constructed. According to the invention, based on a multi-scale U-shaped coding-decoding architecture, a dynamic wavelet depth separable convolution module DyWConv is embedded in front of each level of a coder to realize content adaptive feature extraction, and a double-domain feature learning module SPAFormer Block cooperatively utilizing Fourier domain global modulation and wavelet domain multi-scale decomposition is designed. And double-domain features are fully fused through an adaptive gating fusion mechanism, and finally a clear image is reconstructed and output step by step. According to the method, a method for explicitly encoding frequency domain degradation prior into dynamic convolution kernel parameters is innovatively provided, the complementary advantages of Fourier transform and wavelet transform are cooperatively utilized, spatial non-uniform haze can be effectively removed, image details can be recovered, leading performance is achieved in a synthetic data set and a real scene, and the method has a wide application prospect.

The invention belongs to the field of artificial intelligence and intelligent traffic systems, particularly relates to a path control system combining multi-modal perception and dynamic trajectory prediction, and aims to solve the problems of insufficient perception fusion, low trajectory prediction precision and control response lag in a complex dynamic environment. The system comprises a multi-modal perception fusion unit, a dynamic interaction modeling unit, a space-time coupling prediction unit, a risk field construction unit and an adaptive path generation unit. Multi-source sensor data are fused through confidence coefficient weighting, an interaction weight matrix under an attention mechanism is constructed, a future trajectory is predicted in combination with individual dynamics and a social force model, a four-dimensional space-time risk field is generated, and a minimum risk path is solved based on an improved fast marching algorithm. The system realizes sensing-prediction-control closed-loop cooperation, the end-to-end delay is less than 250 milliseconds, and the driving safety and comfort in a complex traffic scene are improved.

The invention relates to the field of intelligent transportation systems, discloses an intelligent highway maintenance system and method, and aims to solve the fundamental defects that in the prior art, the data acquisition dimension is single, the maintenance decision depends on artificial experience, and prospective prediction is lacked. The method comprises the following steps: acquiring multi-modal dynamic sensing data covering a whole road domain; constructing and updating a four-dimensional space-time digital twinborn model in real time; driving the causal graph neural network model to perform structure health state prediction and diagnosis; generating an active maintenance instruction through a multi-objective optimization engine; and the autonomous maintenance execution unit is dispatched to complete unmanned closed-loop operation. According to the method and the system, fundamental transformation of road maintenance from passive response to active prevention, from artificial experience to intelligent decision making and from discrete operation to closed-loop automation is realized, and the maintenance efficiency, the safety and the health level of the whole life cycle of the road are remarkably improved.

The invention relates to the technical field of computer vision and target detection, and particularly discloses a lightweight real-time two-wheeled vehicle helmet detection method. According to the method, firstly, a video stream is collected and preprocessed through a traffic monitoring camera, then feature extraction and fusion are carried out through a lightweight backbone network, an encoder and a neck network in sequence, finally, a detection result is output through a decoder, a StripCGLU module is introduced to reduce the calculation complexity, a Pola Former module is adopted to enhance the feature interaction capability, and finally, the detection result is output through a decoder. And a GLBiFPN network is designed to optimize multi-scale feature fusion. According to the method, the calculation complexity and parameter quantity of the model are remarkably reduced, the requirement of edge calculation equipment for efficiency is met while high detection precision is guaranteed, and an effective technical means is provided for safety monitoring in an intelligent traffic system.

The invention discloses a sequence-based vehicle type category correction method and device, and relates to the technical field of intelligent traffic systems, and the method comprises the steps: obtaining a vehicle detection model, and collecting a backflow video data set; traversing the set, detecting each frame of vehicle target by using the model, and associating cross-frame targets by using a target tracking technology to obtain a vehicle trajectory and form a plurality of vehicle target sequences; traversing the sequence to count vehicle types, and correcting the vehicle types according to a statistical result to obtain a plurality of corrected vehicle target sequences; identifying a missing detection frame based on an original sequence, predicting a missing detection target position, and obtaining a plurality of missing detection data sets; and iteratively updating the vehicle detection model in combination with the correction sequence, the missing detection data and the original sequence. According to the invention, the technical problems of vehicle type judgment errors and target detection deficiency due to the fact that multi-frame associated information is not combined in a traffic scene in traditional vehicle detection are solved, and the technical effects of improving the vehicle type detection accuracy and integrity and realizing continuous optimization of the detection capability are achieved.

The invention discloses a point cloud intelligent cutting and dynamic optimization method based on edge computing, and belongs to the technical field of intelligent traffic systems and edge computing, and the method comprises the steps: collecting three-dimensional point cloud data through a multi-line laser radar disposed on an RSU; generating a three-dimensional bounding box through a three-dimensional target detection network in an RSU edge calculation unit, generating an ROI mask, expanding a buffer region, and performing adaptive region cutting on the point cloud to form an ROI point set and a background point set; a perception-driven cutting decision is realized, and ROI extraction is converted into a self-learning process based on semantic features from fixed threshold judgment; voxelizing the non-uniform resolution point cloud, generating a unique hash index for each voxel unit, comparing a hash set of a current frame with a hash set of a previous frame, extracting newly added and disappeared point sets, and constructing a differential data packet according to a change ratio; while high timeliness is kept, data redundancy is reduced, and the point cloud updating rate and the incremental transmission efficiency are improved; and dynamic optimization of communication-calculation cooperation is realized.

The invention relates to the technical field of intelligent traffic systems, in particular to a highway network global traffic flow prediction method, device and equipment, and the method comprises the steps: constructing an initial feature sequence based on the multi-source data of an ETC portal system; inputting the data into a multi-head attention model driven by a query mechanism, and outputting a spatial dependency relationship between ETC door frame nodes; inputting the spatial dependency relationship into an extraction fusion model fusing a causal convolutional network and a selective state space network, extracting short-term time sequence features through the causal convolutional network, modeling long-term time sequence features through the selective state space network, and fusing the long-term time sequence features into fused time sequence features; and a global traffic flow prediction result is generated based on the fusion time sequence features, so that the problems of high traffic flow prediction cost, data coverage missing and the like in related technologies are solved.

The invention relates to the field of intelligent traffic systems, in particular to an intelligent intersection multi-vehicle cooperative control method. The method comprises the following steps: S1, sensing a vehicle state and sharing vehicle node information by adopting a dual-mode communication mechanism; s2, fusing state data at edge computing nodes; establishing a dynamic traffic model according to the fused state data; s3, acquiring node data of the edge calculation node, calculating a vehicle passing priority index according to the node data, and performing vehicle priority decision according to the passing priority index; s4, performing grading processing on the vehicles with the overlapped tracks; s5, the RSU device broadcasts the control instruction, and if the vehicle-mounted unit receives the control instruction within the set time, an execution report is transmitted back to the RSU device; and if the vehicle-mounted unit does not receive the control instruction within the set time, a redundancy execution mechanism is started to detect and control the running state of the vehicle. According to the invention, millisecond-level synchronous transmission and high-precision perception of information are realized, and the real-time performance and accuracy of traffic environment modeling are improved.

The invention discloses a lightweight real-time mapping system based on cloud edge collaboration and a VLA-HDMap high-frequency interaction method, and aims to solve the problems of slow updating, high cost and low safety of HDMap. The model has the capabilities of real-time environment understanding, semantic analysis and end-to-end decision instruction output. The vehicle end can download the cloud HMap in real time, and also participates in crowdsourcing data acquisition and uploading. According to the local lightweight mapping method provided by the invention, a real-time circulation mechanism of'acquisition-application-correction 'is used, so that the HDMap is highly consistent with the actual environment. A cloud edge collaborative hierarchical parallel incremental update global HDMap is also provided, the essence is a time-space intersection multi-scale analysis model of minute-level traffic flow prediction and day / week-level map differentiation detection, a high-risk region of traffic flow peak + map change can be efficiently identified, and a convex hull algorithm is used to obtain a region, which needs to be locally updated, of the whole HDMap. According to the invention, a new-generation intelligent traffic system can be constructed, and the safety application of group intelligence, symbiotic economy and large-scale automatic driving can be promoted.

The invention discloses a road berth charging and control system for realizing multi-level fault tolerance and a method thereof, and belongs to the technical field of intelligent traffic systems and Internet of Things. The system adopts an edge computing and distributed sensing collaborative architecture, and is composed of an edge controller (ECU) and a sensing and control unit (SCU) deployed in a berth. High availability of the system is ensured through fault-tolerant design of three core dimensions: firstly, perception layer fault tolerance dynamically adjusts data fusion weights of geomagnetism, millimeter wave radar and visual AI according to real-time environment data such as rainfall and electromagnetic interference by integrating an environment perception sub-module; secondly, network layer fault tolerance utilizes an immutable transaction log and a Saga distributed transaction compensation mechanism to realize local charging and digital RMB double offline payment when cloud connection is interrupted, and data consistency is ensured after network recovery; and finally, a hardware layer establishes a neighborhood cooperation protocol based on a signature agent command through fault tolerance, and a healthy node is allowed to act as an agent fault node through safety verification to execute an unlocking instruction. According to the invention, the problems of environmental interference, network interruption, single-point hardware failure and the like in unattended parking management are effectively solved, and the robustness and financial security of the system are remarkably improved.

The invention discloses a traffic flow prediction method based on cross-modal interactive geographic image coding, and relates to the technical field of intelligent traffic systems and data processing. The method comprises the following steps: firstly, acquiring historical traffic data and a road network geographic image, and respectively constructing an embedded representation containing space-time periodicity and extracting node-level visual features; secondly, through a hybrid cross attention mechanism, utilizing a learnable global visual token as an abstract agent, compressing visual features and performing cross-modal alignment with the dynamic space-time representation to generate an enhanced space-time representation; meanwhile, a visual relation mode is constructed based on local visual patches between the nodes, and refining is carried out through a general relation matrix; and finally, dynamically fusing time, space and visual relation characteristics by using an adaptive gating mechanism, and predicting future traffic flow. Visual modes such as road geometry can be effectively captured, the problem of dynamic and static heterogeneous mode alignment is solved, and the accuracy of traffic prediction is remarkably improved while the calculation efficiency is guaranteed.

The invention relates to a traffic facility attribute mining method and system based on multi-modal network open source data. The method comprises the following steps: extracting traffic facility information from a webpage to construct a knowledge graph; extracting positions and appearances of traffic facilities from the images, and analyzing streetscape images to obtain attributes such as road traffic; collecting map images, tiles and vector data to construct a road network topology and attribute database; associating webpage texts, pictures, streetscape images and network map multi-source data according to the spatial position of the traffic facility; through comprehensive and deep attribute mining, different modal data are integrated to improve the accuracy and reliability of attribute mining, the real-time and dynamic updating capability, the convenient visualization and interaction operation, the data sharing and integration convenience, and the traffic facility management efficiency and collaboration are improved. According to the method, comprehensive, accurate and real-time traffic facility attribute data support can be provided for urban traffic planning, traffic management and intelligent traffic system construction, so that the efficiency and the intelligent level of traffic facility management are remarkably improved.

The invention belongs to an intelligent traffic system and artificial intelligence technology, and discloses a tunnel emergency speed limit decision support system and method. The method comprises the steps that a natural language instruction of a traffic management terminal is received and analyzed, and an unstructured current emergency scene is converted into a standardized parameter; based on the current emergency scene after parameter standardization, similarity retrieval is carried out in the historical simulation case library, a historical case with the highest matching degree is recalled, and an optimal control scheme and a recommendation index thereof are extracted from the historical case and recommended to a traffic management terminal; and performing result integration and output on the optimal control scheme recommended to the traffic management terminal and the recommendation index thereof. According to the method, a complete historical simulation case library and an efficient AI Agent retrieval matching engine are constructed, so that full-process automation from scene recognition to scheme recommendation is realized.

The invention provides a traffic flow prediction method based on deep learning, and belongs to the field of intelligent traffic systems and traffic management. The method comprises the following steps: collecting traffic flow data of a road, and obtaining regional road network map data; constructing a memory tensor enhanced Transform fusion model, wherein the memory tensor enhanced Transform fusion model comprises a data embedding layer, a feature extraction layer, a jump connection layer and an output layer; inputting the traffic flow data and the regional road network map data into a data embedding layer for preprocessing to obtain preprocessed data; inputting the preprocessed data into a feature extraction layer for space-time memory triple attention and multi-layer perceptron processing to obtain a plurality of space-time memory feature extraction layer outputs; accumulating the output and input jump connection layers of each space-time memory feature extraction layer to obtain a jump connection result; and inputting the jump connection result into an output layer, and carrying out convolution operation twice to obtain a traffic flow prediction result. According to the method, a more accurate traffic prediction basis can be provided for decision making, and the method has a remarkable engineering application prospect.

The invention relates to the technical field of intelligent traffic systems, in particular to a marine traffic data fusion method based on a big data platform, and the method comprises the steps: carrying out the type division of marine traffic data, obtaining metadata information, respectively inputting the metadata information to a batch synchronization module and a real-time flow collection module, and obtaining the information after batch synchronization and the information after collection; processing the offline data, wherein the processing comprises storage decision, analysis and format conversion; the information subjected to batch synchronization and the processed offline data enter a batch processing layer to obtain data subjected to batch processing, and the collected information is input to a stream processing layer to obtain data subjected to stream processing; storing the batch synchronized information after data cleaning, the processed off-line data, the batch processed data and the data after the stream processing layer into a data storage center; and on the basis of the data stored in the data storage center, constructing a spatio-temporal joint index model to retrieve the maritime traffic data. According to the invention, technical support is provided for ship trajectory prediction business.

The invention discloses a road plane alignment recovery method based on laser point cloud, and belongs to the technical field of road engineering surveying and mapping and digital modeling. The method comprises the following steps: firstly, preprocessing laser point cloud data, and extracting and smoothly fitting a road marking point set; then, a road center line is obtained through uplink and downlink symmetrical marking line fitting, and a continuous and smooth center line model is formed through moving average and B spline interpolation; and finally, constructing a direction angle-mileage curve, realizing automatic identification of a straight line segment, a circular curve segment and an easement curve segment based on recursive linear regression and threshold discrimination, and performing parameterized reconstruction by respectively adopting least square fitting or B spline fitting, thereby obtaining a complete, continuous and accurate road plane line shape. According to the method, the linear parameters of the existing road can be rapidly recovered without traditional design data, the precision is high, the automation degree is high, and the method is suitable for road maintenance management, digital twin modeling and intelligent traffic systems.

This invention discloses a coal source intelligent transportation system based on multi-data fusion, belonging to the field of coal source intelligent transportation technology. The core technology of this invention lies in the communication connection between the intelligent transportation center and multiple types of sensors to realize distributed monitoring of the entire transportation process in the coal source area. The intelligent transportation center performs data fusion processing to form targeted transportation control strategies. The slope stability assessment unit is responsible for monitoring the slope safety in the coal source area, identifying risks such as overloading and foundation wear, and providing feedback on control suggestions. The path dynamic optimization unit optimizes the transportation path based on slope detection results and real-time road conditions, balancing transportation efficiency and safety. The loading and unloading efficiency assessment unit comprehensively evaluates the loading and unloading effect based on multi-dimensional data, optimizes loading strategies, and ensures smooth transportation links.

The invention relates to a traffic flow speed estimation method based on a Gaussian mixture model and hierarchical Bayesian, and belongs to the technical field of intelligent traffic systems. The method comprises four steps of data preparation and preprocessing, traffic state clustering division, hierarchical Bayesian random parameter model construction, and model verification and performance evaluation. Through deep fusion of the Gaussian mixture model and the hierarchical Bayesian model, data-driven traffic state identification and random parameter estimation are realized, traffic flow heterogeneity can be effectively described, model parameter explosion is avoided, and model prediction precision and generalization ability are improved.

The invention discloses a traffic dynamic shortest path distribution method and system based on a triangular fuzzy number weight, and relates to the technical field of intelligent traffic systems.Fuzzy information in a path evaluation attribute is represented through a triangular fuzzy number, a comprehensive weight is analyzed and calculated in combination with deviation maximization and a moisture value, and the decision reliability is effectively improved; a K-Means algorithm is adopted to perform dynamic path search, and through a representative vertex subset pre-calculation and increment updating strategy, the path retrieval efficiency is remarkably improved; a travel time prediction model related to departure time and real-time flow is established, and accurate response to accidental congestion is realized; and finally, outputting an optimal path scheme through a multi-objective decision-making method. Compared with a traditional algorithm, the speed is improved on a real road network data set, the path distribution accuracy is improved, meanwhile, the path safety, comfort and efficiency are effectively balanced, real-time path planning under a large-scale road network is supported, and an efficient solution is provided for intelligent traffic management.

The invention discloses a mine electric locomotive unmanned intelligent driving system based on the 5G technology, which comprises a data acquisition and processing center, an electric locomotive data processing center, a signal centralized closing data processing center and a centralized control information center, and all the centers realize data interaction through a network switch. The system aims to form an intelligent transportation system control platform with mechanization, automation, informatization and intellectualization of a mine electric locomotive rail transportation system through electric locomotive transformation, video monitoring system construction, remote operation system construction and the like on the basis of informatization of a rail transportation signal collection and closure system. During operation, the electric locomotive realizes the functions of interval speed adjustment, automatic lifting of a pantograph, automatic position calibration and the like according to real-time positioning, and the upper control system monitors and records the operation state and operation parameters of the electric locomotive in real time and monitors the whole process, so that the problem of intelligence and automation of a mine electric locomotive driving system is solved; omnibearing monitoring and intelligent control of the mine electric locomotive driving system are achieved.

The application discloses a target detection method, a detection system, a vehicle and a visual environment monitoring device thereof, and relates to the technical field of computer vision and artificial intelligence. The method comprises the following steps: extracting first inference features from an input foggy image through a main detection flow; inputting the first inference features into an inference-time fusion adapter in the main detection flow to generate second inference features and a pseudo-transmittance map; taking the pseudo-transmittance map as a physical space guide, and performing cross-attention fusion on the first inference features and the second inference features based on the physical guide through a PI-CAF module in the main detection flow to generate enhanced inference features and output a predicted target detection result. The application can solve the training-inference inconsistency problem in the prior art, and realize efficient and physically interpretable feature recognition based on a physically-perceived, interpretable and spatially-adaptive enhancement mechanism, so that real-time and accurate environment perception can be provided for an intelligent transportation system under adverse weather conditions such as foggy weather.

The invention provides a confluence area cooperative variable speed limit control method based on deep reinforcement learning, and belongs to the intelligent traffic system technology, and the method comprises the steps: arranging a coil detector based on a ramp confluence area simulation environment, obtaining traffic state information through an access interface, setting a demand scene, and setting a road test unit and a speed limit panel; according to the traffic state information, defining a state space, carrying out simultaneous operation on main line and ramp actions, obtaining an action space, obtaining a single selection action, introducing ramp stay time, constructing a reward function, and obtaining a speed limit control model; and in combination with a single selection action, training the speed limit control model, and performing simulation by using the trained speed limit control model to obtain a control strategy of the ramp confluence area. The method solves the problems that an existing synergy variable speed limit control method for the confluence area mainly focuses on one kind of control means, synergy control of the main line and the ramp is insufficient, and a linkage control mechanism between the main line and the ramp is not perfect.

The invention relates to the technical field of traffic engineering and intelligent traffic systems, in particular to a traditional car-following model parameter calibration method and system based on a variational auto-encoder. Comprising the following steps: S1, acquiring vehicle following trajectory data including basic time sequence information such as speed, acceleration and vehicle spacing; s2, carrying out preprocessing and sample construction on original data, segmenting a track, and forming a training and verification data set; s3, constructing a calibration model based on a variational auto-encoder, wherein the model is composed of an encoder based on a cross attention mechanism, a parameter generator and a differentiable car-following model module; and S4, training the model by adopting a joint optimization method of reconstruction loss, KL divergence loss and prediction loss. And S5, inputting to-be-calibrated data into the trained model, outputting time-varying car-following model parameters by a parameter generator, and constructing a calibration process through a differentiable car-following model to realize high-precision fitting of an actual track. The method has the advantages of high calibration precision, excellent efficiency and physical interpretability of parameter results.

This invention pertains to the field of pedestrian transportation in coal mine roadways, specifically disclosing an intelligent personnel transportation system for mine roadways. The system includes a track installed at the top of the roadway, with a forward movement device slidably connected below the track. The forward movement device is powered by a drive unit connected below it. An automatic boarding / alighting device is detachably connected below the drive unit. The automatic boarding / alighting device includes a passenger vehicle and a propulsion mechanism for separating / connecting the passenger vehicle and the drive unit. The intelligent transportation system also includes an intelligent allocation system for scheduling the passenger vehicles and automatically navigating routes according to instructions. This intelligent personnel transportation system can transport pedestrians to any designated location in the underground roadway via the track installed at the top of the roadway, allowing for scheduled times and designated routes. The automatic boarding / alighting device overcomes the limitation of not being able to perform other operations while transporting people in the roadway, and features high safety, high intelligence, and high labor efficiency.

The invention relates to the technical field of mine collaborative transportation, and discloses a multi-machine collaborative scheduling mine intelligent transportation system and method, and the method comprises the following steps: collecting and cleaning the working conditions and geometric data of locomotives, parking lots and rail sections, calculating health scores, refreshing traction / braking dynamic upper limits, judging hydraulic availability, and shielding translation. According to load gravity center shift and wheel tread deviation, curve speed limit / acceleration is dynamically tightened, an obstacle scene set is constructed, digital twinning is injected, a robust / scene MILP model is established, and minimization is carried out with a weighted target composed of construction period, energy consumption, risk occupation, parking lot load and tail risk. According to the method, on the premise of ensuring the safety boundary and the operation principle, the plan stability and the time window achievement rate are improved, conflicts, deadlocks and energy consumption / thermal peaks are reduced, the recovery duration and the tail risk in an obstacle scene are shortened, and through the digital twinning and immutable auditing chain, the operation closed loop is realized, and the efficiency, the risk and the compliance are considered.

This invention discloses a method and system for predicting the trajectory of electric vehicles at signalized intersections, relating to the field of intelligent transportation technology. The method and system provided by this invention improve the accuracy and efficiency of electric vehicle trajectory prediction in complex intersection scenarios by utilizing Mamba to capture long-term temporal dependencies in signalized intersection videos and incorporating kinematic physical constraints using PINN. By constructing counterfactual scenarios of trajectory feature parameters for counterfactual inference prediction, the causal quantification of key factors affecting trajectory prediction results is achieved, enhancing model interpretability. Furthermore, Bayesian methods are used to perform uncertainty analysis on the Mamba-PINN fusion model, quantifying the uncertainty of trajectory prediction and improving model reliability. The optimized model exhibits strong adaptability and can provide decision-making basis and technical support for traffic signal optimization and intelligent transportation systems.

The application provides a vector map construction method and device, a storage medium and electronic equipment, comprising: performing low-light image enhancement processing on N frames of current images to obtain N frames of enhanced images; performing multi-view BEV conversion processing on the N frames of enhanced images to obtain BEV current features; performing fusion processing on the BEV current features and BEV features in a historical window to obtain BEV fusion features; generating a vector curve based on the BEV fusion features, and completing vector map construction based on the vector curve. In a low-light environment and a dynamic scene, the perception ability of an automatic driving system, the stability of map construction and the calculation efficiency can be effectively improved; through fusion of high-definition enhanced images and space-time information, high-precision and low-delay vector map prediction is realized, the robust application requirements of automatic driving technology in a complex environment are met, and more reliable technical support is provided for the development of a future intelligent transportation system.