1050 results about "Intelligent transport" patented technology

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 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 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 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.

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 provides a tunnel event and warning lamp acousto-optic linkage strategy generation system based on AI decision, and relates to the technical field of intelligent traffic, and the method comprises the steps: collecting tunnel traffic event data, vehicle structured data, vehicle coordinates, vehicle flow, vehicle speed, and brightness data inside and outside a tunnel in real time, so as to obtain multi-source heterogeneous data; according to the multi-source heterogeneous data, taking the data matching degree, the space-time consistency and the event confidence as multi-objective optimization dimensions, constructing a weighted objective function, generating a Pareto optimal solution set through a multi-objective optimization algorithm, screening the solution set based on space-time constraint conditions, and removing conflict hypotheses; and determining the final event type and the structured event description of the three-dimensional geographic coordinates. The problems of large illumination energy consumption and poor system linkage in existing tunnel safety management are solved.

The invention provides a traffic abnormity and congestion cause analysis method and system based on a knowledge graph, and relates to the technical field of intelligent traffic perception, and the method comprises the steps: carrying out the target detection through employing preprocessed laser radar point cloud data, recognizing traffic participants, carrying out the continuous frame tracking of the traffic participants, and extracting the motion track and behavior characteristics in an event dimension; identifying a behavior event of the traffic target based on the motion trail and the behavior characteristics, binding the identified behavior event of the traffic target to a corresponding target entity, and performing target behavior modeling to form standardized structured information; based on structured information of a traffic target, an intersection-oriented traffic state knowledge graph is constructed, a rule-based abnormal event judgment module is utilized to perform semantic analysis on behavior and event nodes in the traffic state knowledge graph, abnormal traffic events are identified, and potential causes causing traffic congestion are traced. According to the invention, refined understanding and active perception of the intersection traffic state can be realized.

The invention discloses an intelligent control system and method based on an intelligent indication board, and relates to the technical field of intelligent traffic and Internet of Things control, and the method comprises the following steps: building a unified time mark baseline, collecting an exposure sequence, a saturation threshold sequence and a track residual error, and generating an exposure-track coupling spectrum for calibrating a rain and snow triggering time window; and calculating phase residual mapping under the constraint of an exposure-trajectory coupling spectrum, identifying an exposure oscillation root cause, extracting a saturated source region and a trajectory breakpoint set, and generating a fault anchor point. According to the method, rain and snow interference is identified through a time mark base line and a coupling spectrum, a fault anchor point is constructed, an optical path is played back to assess risks, multi-source signals are fused to generate a steady track, conjugate correction and feedforward constraint are adopted to suppress oscillation, a time reversal closed-loop instruction and multi-strategy joint debugging are combined, and self-adaptive optimization of exposure and induction information is achieved. And the stability and the control precision of the intelligent indication board in extreme weather are improved.

The invention relates to the technical field of intelligent traffic control, and discloses a self-balancing two-wheeled vehicle queue formation control system which comprises a cloud scheduling platform, a formation control unit and a vehicle end execution unit. And the cloud scheduling platform generates a global avoidance path based on the accumulated gravitational potential energy change and the pavement flatness coefficient, and executes dynamic position complementing. The formation control unit constructs a communication topology and executes formation switching based on environmental perception; the cooperative control module adopts an attitude position coupling algorithm, an expected acceleration is corrected through a gain function attenuating along with an attitude angle index, and the attitude balance is maintained preferentially when a tracking error is eliminated. The vehicle end execution unit is provided with an environment sensing and composite actuating module, and a hub motor and a control moment gyroscope are used for responding to longitudinal driving and transverse balance instructions respectively, so that dynamic decoupling is achieved. According to the invention, the control conflict between two-wheeled vehicle formation trajectory tracking and attitude stability is effectively solved, and the driving safety of the system is improved.

The invention provides a dynamic traffic scene-oriented unmanned aerial vehicle charging base station site selection and scheduling collaborative optimization method. The method comprises the following steps of: 1, establishing a dynamic traffic flow space-time distribution model and an emergency event probability model based on a GIS (Geographic Information System) platform and traffic monitoring data; step 2, constructing a multi-target dynamic optimization model; 3, solving the multi-target dynamic optimization model by adopting an improved multi-target genetic algorithm; 4, a site selection and scheduling double-layer optimization structure is constructed, and unmanned aerial vehicle task allocation and base station utilization rate balance is realized through an unmanned aerial vehicle charging task scheduling model; and 5, realizing dynamic deployment and scheduling optimization closed loop of the base station through real-time data feedback. The method has remarkable advantages in the aspects of coverage rate, energy consumption, response time delay, system stability and the like. The emergency response speed and the energy utilization efficiency are remarkably improved, the system operation and maintenance cost is reduced, and the method is suitable for scenes such as intelligent traffic and emergency communication.

The present disclosure is related to Intelligent Transport Systems (ITS), and in particular, to service dissemination basic services (SDBS) and / or collective perception service (CPS) of an ITS Station (ITS-S). Implementations of how the SDBS and / or CPS is arranged within the facilities layer of an ITS-S, different conditions for service dissemination messages (SDMs) and / or collective perception message (CPM) dissemination, and format and coding rules of the SDM / CPS generation are provided.

The invention discloses an early warning system and method for judging whether a vehicle track deviates from a predetermined route, and relates to the technical field of intelligent traffic, the method comprises the following steps: collecting and preprocessing vehicle track data in real time, generating an H3 space label based on historical track starting and ending longitudes and latitudes, and constructing a static track pattern library and a Ball-tree space index; a dual-branch anomaly detection model is constructed, a supervised branch captures a track time sequence context through Bi-LSTM to generate a point-level anomaly probability, and an unsupervised branch learns a space-time diagram normal mechanism by using an ST-GAE diagram auto-encoder to reconstruct an error to reflect an anomaly degree; in the online stage, an H3 label matching reference trajectory is generated for the real-time trajectory, and the spatial deviation degree is calculated by means of Ball-tree retrieval; inputting the real-time trajectory data into the model to obtain a supervised anomaly probability and an unsupervised anomaly score, and fusing the supervised anomaly probability and the unsupervised anomaly score into a comprehensive anomaly score according to a preset weight after standardization; and calculating a comprehensive abnormal sub-average value in the sliding window, and triggering graded early warning in combination with continuity verification to realize accurate judgment and early warning of vehicle trajectory deviation.

The invention relates to a road illegal parking real-time monitoring method based on space-time Transform and congestion context, and belongs to the field of intelligent traffic. The method comprises the steps of obtaining a high-precision track through millimeter wave radar radial speed correction, constructing a congestion context in combination with a lane level / local congestion factor and a cumulative static time clock, outputting a parking probability by EWC-XGBoost after spatio-temporal features are fused through a spatio-temporal Transform, dynamically adjusting a residence time threshold value based on DQN, and finally triggering early warning when a direction angle criterion is met. And meanwhile, a 60-second AES encryption track evidence chain is generated. According to the invention, the real-time monitoring capability with a low false alarm rate is realized through a slow motion false alarm resisting design, a full-scene self-adaptive threshold value, continuous online learning and a backtraceable evidence mechanism.

The invention provides a mixed traffic cooperative control method based on multi-agent reinforcement learning, which can be applied to the technical field of intelligent traffic and automatic driving. The method comprises the following steps: inputting first state information of a target intersection and second state information of an adjacent intersection into a signal lamp intelligent body obtained based on public traffic priority target training, and outputting a signal lamp phase control action; inputting the signal lamp information and the automatic driving vehicle information of the automatic driving vehicle at the target intersection into a signal lamp fine-tuning intelligent body obtained based on safety target training, and outputting a signal lamp fine-tuning control action; inputting the signal lamp information, the automatic driving vehicle information and the front vehicle driving information into an automatic driving vehicle intelligent body obtained based on safety efficiency cooperative target training, and outputting an automatic driving vehicle driving action; and under the condition that the first signal lamp is controlled to execute the signal lamp phase control action and the signal lamp fine adjustment control action, the automatic driving vehicle is controlled to execute the driving action of the automatic driving vehicle.

The invention provides an urban rail transit station development intensity multi-objective optimization method in a TOD mode, and relates to the technical field of urban planning and intelligent transportation, and the method comprises the steps: analyzing the spatial relation between various types of land and urban function nodes according to the data after standardization processing, and carrying out the location value evaluation; constructing a development intensity evaluation model according to the location value evaluation, calculating the initial average plot ratio of each type of land, and deducing an initial development intensity parameter in combination with the current situation data; establishing a development intensity optimization target system according to a TOD planning principle, performing iterative optimization on the initial development intensity parameters by adopting a multi-target genetic algorithm, and outputting an optimal solution set; and carrying out comparative analysis on the optimal solution set and a current planning index, and obtaining a plot ratio index in combination with a regional development planning requirement. According to the method, a technical framework of subjective and objective collaborative optimization can be constructed by fusing GIS spatial analysis, an analytic hierarchy process and a non-dominated sorting genetic algorithm.

The invention discloses an LNG (liquefied natural gas) station site selection method and device based on multi-scene dynamic coupling, a computer and a storage medium, belongs to the technical field of LNG infrastructure planning and intelligent transportation, and solves the technical problem that LNG station site selection is difficult to accurately predict under multi-scene dynamic change. The method comprises the following steps: collecting multi-scene site selection data of the LNG filling station; data processing is carried out, and a space-time fusion big data medium table is constructed; constructing a long short-term memory network LSTM and reinforcement learning RL coupling model, and obtaining a real-time demand prediction result; a multi-constraint site selection optimization model is constructed, and an LNG gas station candidate site set is obtained; performing optimization through scene classification and feature modeling and defining a multi-scene layout criterion to obtain an LNG gas station candidate set adaptive to multiple scenes; according to the method, the site selection planning of the LNG filling station is realized by fusing the requirements and constraints of multiple scenes such as high speed, suburban, city and industrial districts, and the method is suitable for the field of administrative planning of the government and site selection planning of gas supply enterprises on the LNG filling station.

The invention discloses a live-action three-dimensional modeling error dynamic compensation method based on multi-source heterogeneous sensor fusion in the field of computer vision and surveying and mapping engineering. The method comprises the following steps: step S101, multi-source data acquisition: synchronously acquiring laser radar point cloud data, RGB-D camera depth images and unmanned aerial vehicle image data; step S102, space-time alignment: carrying out space-time alignment on the multi-sensor data by using an improved joint calibration algorithm, and constructing a unified coordinate system; and step S103, feature extraction and matching: extracting feature points of the data of each sensor, and performing feature matching through a multi-scale descriptor. According to the method, dynamic error compensation and multi-source data deep fusion are carried out, the bottlenecks of a traditional modeling method in precision, adaptability and efficiency are broken through, a high-precision and extensible three-dimensional modeling solution is provided for the fields of intelligent transportation, industrial quality inspection, forestry investigation and the like, and remarkable economic benefits and social benefits are achieved.

The invention relates to the technical field of intelligent traffic and artificial intelligence, and discloses an end-to-end automatic driving method and system based on multi-modal attention fusion, and the method comprises the steps: collecting multi-modal data; generating an initial semantic text and calibrating the initial semantic text to form a calibrated semantic text; performing feature processing on the environment image and the point cloud data acquired by the multi-view camera, performing cross-modal alignment on the calibrated semantic text and the first BEV feature, and performing spatial-temporal feature modeling; generating candidate trajectories, quantifying the collision risk of each candidate trajectory and the dynamic obstacle, screening out low-risk trajectories, and guiding the low-risk trajectory optimization through a rule mask; mapping the optimized track into a control instruction; according to the method, a CLIP cross-modal alignment mechanism is utilized, text features and BEV geometric features are deeply fused, and the recognition accuracy of the key region is improved.

The invention discloses a two-stage cascaded distributed optical fiber sound wave sensing vehicle trajectory reconstruction method and system, belongs to the technical field of intelligent traffic perception, and aims to solve the problem that geometric accuracy and topological integrity are difficult to consider under the conditions of low signal-to-noise ratio and complex road conditions in the conventional DAS vehicle trajectory reconstruction technology. The method comprises the following steps: in the first stage, carrying out nonlinear enhancement on original vibration data and converting the original vibration data into a two-dimensional space-time grayscale image, inputting the two-dimensional space-time grayscale image into a deep learning semantic segmentation network to generate a high-fidelity track mask, extracting a skeleton through morphological processing, and constructing an initial candidate topological graph; and in the second stage, multi-hop neighborhood kinematics constraint pruning is performed on the initial topological graph, false connection edges are eliminated, global optimization matching is performed by using a coupling cost function to repair fractures, then isolated points are recalled through local geometric scores, and finally a vehicle trajectory graph with complete topology is output. According to the method, geometric accuracy and topological integrity can be effectively considered in complex scenes such as speed change, lane change and multi-vehicle intersection, and the robustness of all-weather traffic flow monitoring is improved.

The invention relates to the technical field of intelligent traffic, and discloses a vehicle trajectory prediction method and system based on time-space diagram attention fusion. The method comprises the steps of collecting historical trajectory data of a vehicle; the bidirectional long-short-term memory network is used as an encoder to carry out bidirectional encoding on the displacement sequence, and time sequence characteristics of the vehicle are obtained; introducing a graph attention network, regarding the vehicle as a node of a graph, constructing a sparse edge index based on a central position, and obtaining spatial features of the vehicle through multi-layer graph attention convolution and based on time sequence features of the vehicle; after the time sequence features and the spatial features of the vehicle are fused, the fused features are input into an encoder for global attention calculation; and carrying out decoding, injection modal embedding and position coding on the output characteristics of the encoder to generate multi-modal future trajectory prediction. According to the method, the vehicle trajectory can be predicted more accurately through cooperative combination of time and space processing, especially in a complex traffic scene.

The invention discloses a vehicle-road collaborative perception and fusion estimation vehicle stability control method for a beyond-visual-range scene, and belongs to the technical field of crossing of intelligent traffic, vehicle control engineering and an automobile safety technology. The method comprises the following steps: acquiring vehicle dynamic data through a vehicle end sensor, estimating a dynamic pavement adhesion coefficient based on a seven-degree-of-freedom model and unscented Kalman filtering, acquiring a pavement image by using a roadside camera, identifying a pavement state by using a MobileNetV3 + model, and mapping the pavement state into a visual pavement adhesion coefficient; a discrete time Kalman filter is adopted to fuse dynamics and visual estimation results, and the fusion weight is dynamically adjusted according to the confidence coefficient; and transmitting the fused road adhesion coefficient to a vehicle end controller, and designing an upper-layer controller and a lower-layer controller based on sliding mode control and road adhesion coefficient identification, thereby realizing stability control of the vehicle under complex road conditions. The accuracy and foresight of road adhesion coefficient estimation can be effectively improved, and the handling stability of the vehicle under the limit working condition is enhanced.

The invention relates to the technical field of intelligent traffic, in particular to a precise traffic accident early warning system based on multi-sensor fusion. Comprising a multi-modal data acquisition unit; the intelligent central processing unit is used for generating differentiated early warning instructions adaptive to different traffic participant types based on the three-dimensional risk decoupling result and the collision risk prediction result; a grading differentiation early warning execution unit; and an emergency linkage unit. According to the method, a meta-task sampling strategy is constructed through causal effect value grading and intersection topological feature clustering, edge lightweight adaptation is achieved in combination with gradient compression, parameter self-evolution is achieved through sliding window monitoring and knowledge distillation, and edge computing power and early warning precision are balanced. According to the invention, the multi-modal graded differentiated early warning terminals adapted to different traffic participants and a 5G edge low-delay public security and emergency linkage mechanism are matched, the early warning pertinence and the disposal practicability are quickly responded and improved, and the problem of insufficient edge adaptation and precision balance in the prior art is solved.

The invention discloses an environment perception method and system based on multiple laser radars and edge computing, and belongs to the field of intelligent traffic and vehicle infrastructure collaboration.The method comprises the steps that firstly, space-time alignment and edge preprocessing are conducted on collected point cloud data, multi-source voxel point cloud is generated, coarse semantic segmentation is conducted, and then the multi-source voxel point cloud is obtained; a target registration network is used for estimating a posture and fusing point clouds, reverse denoising and dense reconstruction are carried out through a diffusion model, a static background model is constructed based on a sliding time window, dynamic point clouds and a passable area graph are output through voxel difference and aerial view analysis, and finally three-dimensional target detection and cross-radar fusion and tracking are carried out in combination with the passable area. The edge side is responsible for data compression and abnormal rollback, incremental information is only issued to the vehicle end, a transmission strategy is adjusted in a self-adaptive mode, and real-time performance and reliability are guaranteed.

The invention discloses a road crack development prediction method and system based on time sequence images and environmental factors, and belongs to the technical field of road maintenance monitoring and intelligent traffic. The method comprises the following steps: acquiring a road crack multi-time sequence image of a predicted road section and multi-source potential energy field environmental variables such as traffic load, environmental climate and pavement materials in a corresponding time period; calculating a comprehensive feature vector based on the image and the environment quantity; constructing a crack evolution potential energy field according to the comprehensive feature vector, and mapping the potential energy field features into a dynamic heterogeneous graph structure; and carrying out adaptive learning and time sequence modeling on the structure through a dynamic heterogeneous graph architecture search model (PE-DHGAS) driven by potential energy, and outputting road crack development prediction information and images. According to the method, the precision and stability of road crack evolution trend prediction can be improved, the prediction result can be used for risk assessment and maintenance decision making, and scientific and prospective support is provided for a road maintenance department.

The invention provides an intelligent road management and maintenance system and method based on multi-model fusion, and relates to the technical field of road maintenance and intelligent traffic. The method comprises the following steps: acquiring road condition data and positioning data through a multi-source data acquisition module; the disease recognition module is combined with a neural network model to analyze the multi-modal data, so that the disease detection and recognition precision is improved; the big data processing early warning module mines a disease rule through a big data model, and realizes disease early warning and predictive maintenance; the digital twin modeling module is used for constructing a road digital twin model and realizing real-time monitoring and simulation analysis; and the decision scheduling module realizes automatic maintenance scheduling based on the output of the modules. Through multi-model deep fusion, the problems of high manual dependence, low precision, lack of predictability and scheduling lag in traditional road management and maintenance are solved, the road maintenance efficiency, quality and intelligent level are remarkably improved, and the maintenance cost is reduced.

The invention relates to the technical field of intelligent traffic, in particular to a traffic abnormal event dynamic detection method and system based on trajectory analysis, and the method comprises the steps: receiving original space-time trajectory data; preprocessing the original spatio-temporal trajectory data, and mapping the original spatio-temporal trajectory data to a road section sequence of a road network through a map matching algorithm; constructing a behavior model based on the track sequence after map matching, learning the track sequence of the normal behavior mode on the road section, and establishing an observation emission model and a state transition matrix; in the online stage, the log-likelihood value of an observation sequence and / or the similarity between the observation sequence and a normal behavior pattern cluster are / is calculated for a real-time track in a set sliding window, and when the log-likelihood value and / or the similarity exceed a set threshold value, the track is marked as abnormal; performing time-space aggregation on abnormal trajectories of the same road section or intersection in unit time according to single vehicle abnormality judgment, and triggering group abnormal event alarm when aggregation data exceed a preset value.

The invention relates to the technical field of intelligent traffic and automatic driving, and discloses a driving takeover condition identification method and system based on multivariate situation awareness, which is used for solving the problem that a fixed safety threshold value is not representative any more during driving takeover condition identification, and comprises the following steps: obtaining a current human-vehicle-ring safety situation index; the method comprises the following steps: acquiring safety influence parameters, evaluating to obtain a multi-dimensional situation threshold correction index, judging whether multi-stage safety threshold adjustment is needed or not, if so, adjusting an original multi-stage safety threshold to obtain an actual multi-stage safety threshold, and generating a takeover instruction. And the response behavior data of the driver to the takeover instruction is obtained, the actual multi-level safety threshold is adjusted again according to the response behavior data, the multi-level safety threshold after feedback adjustment is obtained, and the safety of intelligent traffic in a complex environment is effectively improved.

The invention relates to the technical field of intelligent traffic, and discloses a device for intelligent automatic control of cleaning of a hydraulic supporting rod. The device comprises a closed disc, the interior of the closed disc is movably connected with a movable sliding block and a reset spring, the lower part of the closed disc is movably connected with a pollution discharge track, theinterior of the pollution discharge track is movably connected with a pollution control sliding block, the right side of the closed disc is movably connected with a transmission wheel, the upper partof a transmission wheel valve is movably connected with a cleaning wheel, and the left side of the cleaning wheel is movably connected with a supporting base. The cleaning wheel is controlled to startrotating to drive the movable sliding block to rightwards move, the movable sliding block resets under the elasticity effect of a movable spring, meanwhile, the lower part of the movable sliding block is movably connected with a pollution removal brush, dust and the like on the outer side of the hydraulic rod is removed through motion of the pollution removal brush, thus the effect of cleaning the outer side of the hydraulic rod is achieved, and the working efficiency is improved.

The invention discloses a vehicle collaborative decision-making system and method based on real-time traffic situation prediction, and relates to the technical field of intelligent traffic. According to the invention, multi-source traffic data are fused through a vehicle-road-cloud cooperative sensing system, and a real-time dynamic traffic scene map is constructed; based on the improved LSTM neural network, traffic situation prediction is carried out by introducing space-time map convolution and a double attention mechanism; the prediction result is converted into risk field and opportunity field data; a hierarchical optimization strategy of centralized planning and distributed execution is adopted, a global optimization instruction is generated based on a genetic algorithm, each vehicle carries out local adjustment according to risk field and opportunity field data inquired in real time, a final control instruction is generated, and multi-vehicle cooperative control with both safety and efficiency is achieved. According to the method, the problems of incomplete perception, inaccurate prediction, delayed control response and the like in a complex traffic environment are effectively solved, and the overall efficiency and safety of regional traffic are remarkably improved.