244 results about "Traffic risk" patented technology

The invention discloses a perception intelligent driving complex traffic scene dynamic risk prediction method, and relates to the technical field of risk prediction, and the method comprises the steps: obtaining the multi-source traffic dynamic data of a target region in real time; performing space-time alignment processing on the multi-source traffic dynamic data; inputting the space-time coupling feature matrix into a pre-trained depth space-time prediction model to generate a risk thermodynamic map; calculating a dynamic risk index of each traffic sub-region, and generating a risk level distribution sequence; and triggering a self-adaptive early warning response mechanism according to the risk level distribution sequence, and dynamically adjusting operation parameters of the variable information sign and the traffic signal controller. The technical problems of frequent traffic congestion and high accident risk caused by inaccurate traffic risk prediction and difficulty in dynamic adjustment according to the real-time traffic condition in the prior art are solved, and the technical effects of accurately predicting the traffic risk and improving the safety and traffic efficiency in a complex traffic scene are achieved.

The invention relates to the technical field of water traffic risk assessment, particularly provides a traffic risk dynamic assessment model construction system, and solves the problem that the intelligent degree and dynamic performance of traffic risk assessment need to be further improved. According to the system, a first modeling assembly constructs a risk propagation model adopting meta-learning; the second modeling component constructs a multi-subject collaborative risk assessment model; an active intervention component; and generating an active intervention strategy. The traffic safety is improved, and the system can effectively reduce the occurrence rate of traffic accidents through real-time risk perception, accurate evaluation and active intervention; a traffic management dynamic risk map and an intervention strategy are optimized, a scientific decision basis is provided for a traffic management department, and the management efficiency is improved; through multi-channel early warning and path induction, safer and more convenient travel experience can be provided for a driver; the development of intelligent traffic is promoted, and technical support is provided for the realization of an intelligent traffic system.

The invention discloses an urban tunnel intelligent safety operation and maintenance method and system combined with multiple data, particularly relates to the field of intelligent traffic operation and maintenance, and is used for solving the problems of prediction deviation and congestion management caused by tunnel flow fluctuation. In combination with traffic parameters of a multi-point detector, a wave similarity analysis model is utilized to accurately evaluate a propagation path and a target section of fluctuation; meanwhile, by means of a time sequence superposition correction mechanism, a historical fluctuation rule and real-time data are fused, a traffic flow transmission prediction curve after dynamic correction is generated, and balance between physical constraint and prediction precision is achieved; finally, key indexes are output through dynamic updating of the flow prediction model, efficient linkage of signal coordination and induction management and control modules is guided, congestion accumulation and traffic risks caused by fluctuation propagation can be reduced to the maximum extent, the tunnel operation and maintenance efficiency is optimized, and the traffic safety guarantee capacity is enhanced.

The invention provides an urban road traffic risk prediction method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining the multi-source data of each road of a to-be-predicted target city, and carrying out the preprocessing of the multi-source data, and obtaining the standardized data; on the basis of a trained time-space sequence deep learning model, traffic risk prediction is carried out on the standardized data, the traffic risk probability of each road of the target city in the future time period is obtained, and the time-space sequence deep learning model comprises a convolutional neural network CNN model and a long short-term memory network LSTM model which are connected in series. According to the method, the acquired multi-source data is preprocessed, the generalization ability and adaptability of the model are enhanced, traffic risk prediction is performed on the standardized data based on the trained time-space sequence deep learning model, a complex mode can be automatically learned from a large amount of time-space data, the risk prediction accuracy is improved, and convenience is brought to urban traffic management.

The invention relates to the technical field of traffic safety, in particular to a highway traffic safety intelligent early warning system and method based on artificial intelligence. The system is composed of a data acquisition module, a data preprocessing module, an intelligent analysis module, an early warning decision module and an interactive early warning interface, and the data acquisition module collects multi-parameter data through three ways of a sensor, a monitoring system and a meteorological station; the data preprocessing module cleans, standardizes, normalizes and supplements missing values for the collected multi-parameter data, trains the extracted features through the intelligent analysis module by using a feedforward neural network FFNN + self-attention mechanism, constructs a multi-dimensional risk prediction model, automatically identifies the change of a traffic mode, predicts potential dangerous events, and improves the risk prediction efficiency. The interactive response interface displays the early warning information and the multi-parameter data collected in real time. According to the invention, road conditions and traffic risk conditions can be more comprehensively known, powerful data support is provided for decision making of an early warning system, and the early warning accuracy and intelligent level are significantly improved.

Various embodiments of the present disclosure provide a method for vehicle application. The method which may be performed by a vehicle device comprises: receiving a traffic risk index from a first server. The traffic risk index may indicate a traffic risk based on vehicle-to-everything message data. In accordance with an exemplary embodiment, the method further comprises: determining whether to perform one or more actions, according to the traffic risk index.

The invention discloses an intelligent parking navigation system based on image recognition and the Internet of Things, and relates to the technical field of intelligent parking navigation, the system draws a three-dimensional electronic map of a parking lot, divides the parking lot into monitoring areas, deploys sensors and cameras to collect image data, and transmits the image data to a cloud through the Internet of Things. The intelligent regulation and control model module uses a convolutional neural network CNN to carry out parking space recommendation and path guidance. And the vehicle monitoring module monitors vehicles entering the parking lot in real time, calculates a geometric environment adaptation coefficient and evaluates a traffic risk. And the vehicle guiding analysis module calculates the traffic flow and the parking space idle index, and recommends a proper parking area for the vehicle. The parking space recommendation analysis module evaluates the comfort level of the parking space, ensures the safety of backing and parking, and avoids the risk of traffic jam or traffic accident through strategy adjustment. The system improves the parking efficiency and safety, and optimizes the parking experience.

The invention discloses an intelligent road traffic risk early warning method and system based on vehicle-road cooperation, and relates to the technical field of traffic early warning, and the method comprises the steps: unifying real-time collected road data to a UTM coordinate system through combining time alignment and coordinate conversion, and constructing standardized spatio-temporal data; based on the standardized spatio-temporal data, using a hypergraph neural network to construct a dynamic hypergraph, obtaining node risk features, and generating a comprehensive risk index; according to the risk index feature weight distribution vector, risk root cause probability distribution is obtained through intervention calculation, and a risk level and an early warning type are output; real-time road data are safely aggregated through federal learning, and the dynamic hypergraph is updated in combination with differential privacy. According to the method, the dynamic hypergraph is constructed, the hypergraph neural network is used for capturing the high-order interaction relation between the vehicles, the problem that complex space-time interaction modes between the vehicles are difficult to capture is solved, and the extraction efficiency of node risk features is improved.

The invention discloses a traffic scene risk identification method and device considering dynamic and static information fusion, and a storage medium. The method comprises the steps of 1, obtaining a traffic scene high-precision map and a time sequence track of each vehicle; track points in the time sequence tracks are converted into a high-precision map coordinate system, lane matching is carried out, and a time sequence track high-precision map of each vehicle is obtained; 2, predicting vehicle positions, and extracting dynamic and static characteristics of each vehicle time sequence track; 3, fusing the dynamic and static features of each vehicle to obtain a fused feature; inputting the fusion features of all vehicles into a risk factor prediction model to predict and obtain dynamic and static risk factor prediction results; on the basis of prediction errors between dynamic and static risk factor prediction results and corresponding real results, combined with vehicle position prediction errors, calculating a comprehensive risk value of the traffic scene; the equipment and the storage medium are used for implementing the method. The method provides a new thought for traffic risk identification.

The invention discloses a network abnormal data security early warning evaluation processing system. The system comprises a risk feature acquisition module, a first portrait module, a second portrait module and a distribution association module. The first portrait module can evaluate the abnormal risk of the traffic through a network neural model to obtain a first risk portrait of the encrypted traffic; the second portrait module can obtain the frequency abnormal condition of the distribution detection feature through the global frequency abnormal distribution and the local frequency abnormal distribution of the distribution detection feature, and the distribution association module can obtain the distribution association feature associated with the distribution detection feature with the abnormal frequency. In combination with the deviation degree of the distribution correlation features, evaluating the frequency anomaly degree, and obtaining a second risk portrait of the encrypted traffic corresponding to the distribution detection features; and adjusting the first risk portrait according to the obtained second risk portrait. According to the invention, the monitoring precision of the traffic risk is improved, and the network security is improved.

The invention discloses a traffic management method and system for an auxiliary sensing robot and electronic equipment, and belongs to the technical field of traffic management and monitoring, and the method comprises the steps: carrying out the deployment decision of the auxiliary sensing robot according to a target region; performing robot deployment on the target area according to the robot deployment scheme; performing real-time traffic data acquisition on the target area according to the plurality of deployment robots to obtain a plurality of traffic monitoring data; traffic safety risk assessment is carried out according to the multiple traffic monitoring data; and sending the plurality of traffic safety risk coefficients to the plurality of deployment robots, and generating a traffic safety early warning signal. The technical problems of low traffic monitoring efficiency, untimely accident early warning and poor traffic safety management effect caused by unreasonable deployment, limited coverage and insufficient real-time traffic risk assessment of an auxiliary sensing robot in the prior art are solved.

The invention discloses a human-vehicle traffic early warning system applied to traffic safety, which relates to the technical field of traffic safety and comprises a data acquisition unit, a data processing unit, an early warning decision unit, a user interaction unit, a safety and compliance unit and a system management and maintenance unit. The data processing unit is used for receiving the trajectory data and processing the trajectory data; the early warning decision-making unit is used for analyzing the processed trajectory data by applying a safe distance technology and a traffic conflict model so as to evaluate and predict traffic risks and generate an early warning decision; and the user interaction unit is used for displaying the generated early warning decision through a visual interface and sending out a corresponding early warning signal. Trajectory data of vehicles and pedestrians can be collected through the data acquisition unit and transmitted to the data processing unit for processing, a solid foundation is provided for subsequent early warning decisions, and timeliness and accuracy of early warning information are ensured.

The invention relates to the technical field of traffic safety, and discloses a traffic risk real-time alarm method and system. The traffic risk real-time alarm method comprises the following steps: determining a traffic event alarm area and a first traffic participant detection frame according to traffic perception information sent by a multi-source perception device in real time; determining a target traffic participant detection frame according to the first traffic participant detection frame; based on the state information of the target traffic participant detection frame at the current time, predicting the position information of the target traffic participant detection frame at the future time; and carrying out traffic risk identification and alarm according to the position information of the target traffic participant detection frame at the current time, the position information of the target traffic participant detection frame at the future time and the traffic event alarm area. Through the scheme of the invention, missed identification and wrong identification of traffic risk events are avoided, the reliability and accuracy of traffic risk identification and alarm are improved, the alarm time delay is reduced, and invalid alarm is avoided.

The invention relates to the technical field of automatic driving risk analysis, in particular to a traffic risk analysis method and analysis device based on a graph neural network, and the method comprises the steps: sequentially carrying out the coding of a graph encoder and the coding of an attention mechanism based on the obtained historical track information of an intelligent agent and the lane line information of a map, and obtaining an attention mechanism; obtaining an attention mechanism coding result; decoding by using a cross attention mechanism based on an attention mechanism coding result; and performing prediction trajectory decoding by using a multi-layer perceptron based on a cross attention mechanism decoding result, performing decoding by using a deconvolution network based on a decoding result, calculating risk matrix loss by using a probability loss function, and performing model training and verification to obtain a risk analysis model based on a graph neural network so as to predict traffic risks. Therefore, the problems that the risk analysis depth of traffic participants is limited and future intentions and potential risks of the traffic participants are difficult to accurately describe by traffic risk analysis methods in related technologies are solved.

The invention discloses a traffic risk factor assessment method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining initial data from historical traffic data, the initial data comprising the feature data of an accident road and the type of an actual risk factor causing a traffic risk; performing abnormal data screening and normalization processing on the initial data to obtain sample data; constructing a feature learning model by using a deep learning algorithm; training the feature learning model by taking the sample data as input to obtain a risk assessment model; a deep learning algorithm is adopted to construct a feature learning model, and a machine learning model capable of identifying different risk features is trained in combination with historical sample data, so that traffic risk factor categories of different intersection road sections can be quickly and accurately evaluated and analyzed, and the development of accident risk prevention work of actual roads can be pertinently guided. And traffic risks can be prevented and controlled in time.

The present invention relates to the technical field of vehicle monitoring methods, and specifically to a vehicle monitoring method and system, comprising the following steps: using a vehicle monitoring system to collect real-time vehicle data including speed, acceleration, and steering angle. In the present invention, by comprehensively utilizing a vehicle monitoring system, a machine learning algorithm, computer vision technology, cloud computing and big data analysis technology, a geographic information system, and a wireless sensor network, all-round monitoring of the real-time driving status of a vehicle, identification and verification of abnormal driving behavior, prediction and analysis of traffic flow, planning of intelligent navigation routes, and sharing of information between vehicles and cooperative driving functions are realized. By collecting vehicle data in real time and identifying abnormal driving behavior, timely discovering and warning of traffic risks, traffic flow prediction and intelligent route planning help drivers avoid congestion points and potential accident risk points, saving driving time, and sharing of information between vehicles and cooperative driving improve driving efficiency and safety.

The invention provides a main vehicle operation control method based on road environment traffic risk field identification. Firstly, a vehicle-mounted platform of a main vehicle is used for collecting motion tracks and motion data of the main vehicle and vehicles with abnormal driving behaviors around the main vehicle in real time, and an abnormal driving behavior feature set is formed; secondly, the driving risk field is used for representing the driving risk of the vehicle with the abnormal driving behavior, and the real-time risk type is judged; thirdly, the risk type and the risk level of the abnormal driving behavior are divided through the interaction risk field potential. And finally, according to the identified abnormal driving behavior type and the risk type and the risk level thereof, the vehicle control system enters different working modes to control longitudinal and transverse movement of the main vehicle, so that the driving risk is avoided, and the driving safety is guaranteed. Potential risks caused by abnormal driving behaviors can be avoided in the driving process of the vehicle, the probability of occurrence of traffic accidents is reduced, and the driving safety of the vehicle is guaranteed.

The invention is suitable for the field of driving safety management, and provides an intelligent driving safety auxiliary management method and system, and the system comprises a road condition data collection module, a traffic risk assessment module, a multi-perception risk identification module, a warning information transmission module, and a secondary state analysis module. According to the scheme, the driving track of the truck is predicted, and the view blind area of the truck in the driving track can be judged. Through the accurate identification, the system can better monitor the safety condition around the truck, especially potential risks in a view blind area, and matches the positions and behavior modes of other road participants. Through the coincidence detection function, the system can effectively identify the existence of other road participants in the view blind area of the truck and evaluate the potential collision risk. Through the method, the system can find possible collision danger in advance, early warning information is generated in time, and accidents are avoided.

The invention provides a traffic risk intelligent monitoring and early warning method, system and device and a medium, and belongs to the technical field of intelligent traffic. The method comprises the following steps: collecting real-time traffic data, environmental meteorological data and accident report data as source data; standardizing the source data, and storing the standardized source data into a database according to different data structures in a classified manner; classifying the source data according to information recorded by the source data by utilizing a target monitoring model and an object comparison technology; the event type of the source data comprises a weather event, a vehicle event, a facility event and an environment event; based on the source data of different event types, respectively calculating the score of each event; calculating a real-time comprehensive risk index based on the score of each event; and determining a risk level based on the real-time comprehensive risk index, adding a risk identifier at an early warning position in a monitoring platform map according to the risk level, and dividing a road section management area for the early warning position.

The invention discloses a traffic operation risk real-time identification method based on a low-altitude unmanned aerial vehicle, relates to the technical field of unmanned aerial vehicle traffic monitoring, and solves the technical problem that unmanned aerial vehicle resources are wasted or the coverage of data acquisition is insufficient due to unreasonable arrangement of unmanned aerial vehicles in the prior art, so that the comprehensive identification efficiency of traffic risks is reduced. Comprising the steps that road section basic data of all road sections in a to-be-monitored area are acquired, and the road section basic data comprise road section types, historical accident data and historical traffic flow data; setting a road section danger score of each road section based on the road section basic data; setting an unmanned aerial vehicle distribution scheme based on the road section risk score; acquiring monitoring video data of the unmanned aerial vehicle, and analyzing based on the monitoring video data to obtain an accident risk score of each traffic subject of the road section; giving an alarm based on the accident risk score; by performing targeted analysis on different road sections and setting the number of the unmanned aerial vehicles used for monitoring the different road sections, the comprehensive efficiency of traffic risk identification is improved.

The invention discloses a synergy control method for a confluence area based on Rainbow DQN and a driving risk field, relates to the technical field of traffic safety, and aims to solve the problems that the overall traffic quality of mixed traffic flow is reduced and the confluence risk is increased due to the fact that a control logic is not matched with a real-time risk in an existing signal control method for the confluence area of an expressway. According to the invention, by considering the CAV-containing mixed traffic flow of the main line of the confluence area, the main line traffic flow information and the ramp queuing information, the state space and the action space of the cooperative control agent are designed in a refined manner, and a reward function considering the traffic efficiency, the traffic safety and the cooperative interaction is provided; therefore, the main line speed limit value can be dynamically controlled, the ramp queuing vehicles are dynamically coordinated, the main line CAV vehicle driving track is induced, and the passing efficiency and the traffic safety are effectively improved. The overall traffic quality of the mixed traffic flow is ensured, and the confluence risk is reduced.

The invention relates to the technical field of risk management and control of expressways and urban expressways, in particular to a real-time prediction method for asphalt pavement friction coefficients in ice and snow weather. Comprising friction coefficient prediction models of an iced road surface, a snowy road surface and a snowy ablation road surface. The influence factor for determining the friction coefficient of the iced road surface is the thickness of the ice layer, and a good exponential function relationship exists between the thickness of the ice layer and the friction coefficient. The friction coefficient of the snowy road surface is obviously influenced by relative sliding in the snow layer, and the thickness of the snow layer is the key for determining sliding between the inner layers of the snow layer. The accumulated snow melting road surface, the ice layer and the snow layer have negative effects on the anti-sliding performance of the road surface, and a good exponential function relation exists between the total ice and snow thickness of the accumulated snow melting road surface and the friction coefficient. According to the method, a friction system prediction equation is established by adopting a nonlinear regression method, real-time prediction can be carried out for different road surface states, and friction coefficient parameters are provided for traffic risk management and control of expressways in severe weather in winter and urban expressways in ice and snow weather.

The invention provides a traffic risk processing method and system based on a spatial intelligent scene, and relates to the technical field of traffic management. The method comprises the following steps: a cloud sends an inspection strategy to inspection equipment, the inspection equipment acquires multi-source heterogeneous data of a to-be-detected area according to the inspection strategy, constructs a digital twin initial model of the to-be-detected area according to the multi-source heterogeneous data, and uploads the digital twin initial model and the multi-source heterogeneous data to the cloud; and the cloud constructs a digital twin target model of the to-be-detected area according to the digital twin initial model and the multi-source heterogeneous data, determines a traffic risk level of the to-be-detected area according to the digital twin target model of the to-be-detected area, and issues a risk processing strategy matched with the traffic risk level to the inspection equipment and the risk processing equipment. The invention aims to improve the problems that the traffic condition monitoring is not comprehensive, the risk early warning is lagged, the emergency disposal efficiency is low, and the overall traffic safety level is influenced in the existing method.

The invention belongs to the field of expressway traffic data analysis, and relates to an ETC system-based expressway traffic data analysis method, which comprises the following steps of: acquiring original vehicle passing data streams which are acquired by all ETC gantries in a preset time period of a monitored road section and comprise vehicle identifiers, passing timestamps, portal position codes and vehicle type codes; analyzing the original vehicle passing data flow to generate vehicle speed individual dynamic features and vehicle type group static features, constructing a multi-dimensional risk data set with road segment identifiers and time windows as indexes, and inputting the multi-dimensional risk data set into a pre-trained deep learning model, and outputting an accident potential probability value result, executing early warning state judgment based on a probability gradient judgment rule, generating an accident risk thermodynamic diagram of the monitored road section according to the early warning state, and feeding back the accident risk thermodynamic diagram, thereby effectively solving the problems of insufficient prospective prediction and low regional accident potential assessment precision in the prior art. And the timeliness and the accuracy of traffic risk assessment are obviously improved.

The invention provides a flow bandwidth intelligent distribution optimization method and system, and relates to the technical field of flow bandwidth intelligent distribution optimization, and the method comprises the steps: constructing an information transmission network, obtaining a node information transmission path, analyzing flow change data, and obtaining a path risk node; obtaining path intersection node information, generating intersection risk nodes, obtaining risk feature data, and constructing a multi-level traffic risk network; calculating a first-level abnormal coefficient and a second-level abnormal coefficient, comparing the first-level abnormal coefficient and the second-level abnormal coefficient with a first-level preset threshold value and a second-level preset threshold value respectively, and then performing traffic abnormality judgment to obtain a traffic abnormality judgment result; according to the method and the system, the problems that in the prior art, flow management is lack of flexibility, intelligence and comprehensive evaluation are solved, and the like.

The invention provides a traffic flow intelligent sensing scheduling method and system under a special meteorological condition, and relates to the technical field of traffic management, and the method comprises the steps: carrying out the conjoint analysis of a historical traffic data set and a historical special meteorological data set, and constructing a meteorological-traffic correlation model; connecting a meteorological system to obtain real-time special meteorological data; inputting the real-time special meteorological data into the meteorological-traffic association model to obtain predicted traffic data of the next time period, and generating an initial traffic flow scheduling scheme; constructing a traffic accident thermodynamic diagram through traffic simulation; and optimizing the initial traffic flow scheduling scheme according to the traffic accident thermodynamic diagram to obtain a traffic flow scheduling optimization scheme. Through the traffic flow scheduling method and device, the technical problem of poor traffic flow scheduling efficiency caused by incomplete risk identification under the special meteorological condition in the prior art can be solved, the traffic risk under the special meteorological condition is comprehensively identified through joint analysis of the traffic flow data and the special meteorological data, and the traffic flow scheduling efficiency is improved.

The invention discloses a highway tunnel traffic risk early warning method and system based on a Bayesian network, and belongs to the field of highway tunnel safety, and the method comprises the steps: collecting traffic information at a plurality of moments, the traffic information comprising a plurality of influence parameters and a traffic accident result, and the plurality of influence parameters comprising a road parameter, an environment parameter and a traffic flow parameter; using a random forest algorithm to screen a plurality of influence parameters from all traffic information, wherein the correlation degree with the traffic accident is greater than a preset threshold value, and taking the influence parameters as model training parameters; training a Bayesian model based on the model training parameters to obtain a traffic risk early warning model; and carrying out judgment and early warning on traffic risks by using the traffic risk early warning model. According to the invention, a plurality of parameter training early warning models with the maximum association degree with the occurrence of traffic accidents can be screened, and the accuracy of traffic risk early warning is improved.

The invention discloses a highway intelligent risk monitoring method and system based on multi-modal information, and the method comprises the steps: collecting multi-modal traffic data, constructing a road network holographic portrait through a road network holographic portrait risk prediction model, positioning a potential risk point through a dual time-space mask anomaly detection model, and carrying out the detection of the potential risk point. Lane-level risk assessment is completed by means of a lane-level risk dynamic assessment algorithm, and risk information is output after multi-dimensional fusion analysis is carried out through a traffic risk intelligent research and judgment platform. All the models and algorithms are operated cooperatively, data integration, feature mining, anomaly recognition, risk assessment and fusion research and judgment are achieved step by step, a whole-process monitoring system from data collection to result output is constructed, refined and dynamic risk monitoring from the global road network to the local lane is achieved, the risk recognition accuracy and monitoring comprehensiveness are effectively improved, and the risk monitoring efficiency is improved. And technical support is provided for safe operation of the expressway.

The invention provides an automatic driving intention recognition method and system, and provides a deep learning model fusing Transform and BiLSTM, which can effectively capture key time features and distinguish the relevance between a driving style and a driving intention, further can accurately recognize and predict the driving intention of a human driver, and improves the driving intention recognition accuracy. Therefore, the self-driving automobile is helped to understand the intention of the surrounding vehicles so as to make auxiliary decisions, thereby improving the road traffic efficiency and reducing the traffic risk.

The present application discloses a traffic risk value determining method and apparatus, an electronic device, and a storage medium. In the method, by means of real-time vehicle information of vehicles, a first real-time risk value of another vehicle to a target vehicle can be determined; by means of the real-time vehicle information of each vehicle and real-time environmental information, a second real-time risk value of a traffic accident-prone area can be determined; when the first real-time risk value or the second real-time risk value increases, a target real-time risk value increases accordingly. The driving behaviors of adjacent vehicles are analyzed, and whether a certain section of road has safety risks can also be determined, thereby improving the accuracy of risk analysis, effectively avoiding accidents and ensuring the safety of users.