182 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 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 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 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 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 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 embodiment of the invention discloses a mixed driving risk identification method and system for a road interleaving area and a computer storage medium, and the method comprises the steps: respectively quantifying the contribution value of each index to a traffic accident and a traffic conflict for each index in a multi-dimensional risk identification index system; sorting the indexes according to the contribution values to obtain a traffic accident contribution value sorting result and a traffic conflict contribution value sorting result; according to the traffic accident contribution value sorting result and the traffic conflict contribution value sorting result, indexes of which the contribution values to the traffic accidents and the traffic conflicts exceed a preset threshold value are selected and combined to form a high-contribution index system; screening the indexes of which the correlation among the indexes is smaller than a threshold value from the high-contribution index system; training a double-risk identification model based on the screened high-contribution index system; and constructing an accident conflict two-dimensional risk matrix so as to detect real-time traffic flow data by using the trained double-risk identification model and the accident conflict two-dimensional risk matrix.

The invention provides an urban low-lying road section emergency supervision Internet of Things large model system and method and a storage medium, and belongs to the field of road monitoring. The emergency supervision management platform is configured to obtain driving data of a low-lying area through the emergency supervision object platform; determining ponding data based on the driving data; determining a traffic risk based on the ponding data; in response to the traffic risk greater than a first threshold, determining a drainage parameter; determining scheduling parameters based on the drainage parameters; and sending a scheduling packet containing the drainage parameters to the unmanned vehicle cluster through the heterogeneous communication network, controlling the unmanned vehicle carrying the drainage device to run to the low-lying area, and controlling the water pumping power of the drainage device to be adjusted to the water pumping power of the drainage parameters. The method can effectively reduce the traffic obstruction and accident risk caused by water accumulation, and improves the emergency response efficiency of urban low-lying road sections during heavy rainfall.

The invention relates to the technical field of intelligent automobiles, in particular to an automatic driving automobile traffic risk pre-judgment and vehicle fault diagnosis system based on ANFIS, which comprises a multi-source data fusion module, a traffic risk pre-judgment module and a vehicle fault identification module, and is characterized in that the multi-source data fusion module improves a Kalman filtering fusion method based on a D-S evidence theory; the multi-source data fusion module is used for effectively fusing multi-source data acquired by a common environment perception sensor to realize data preprocessing, and then the traffic risk pre-judgment module is used for pre-judging and evaluating traffic risks on the basis of taking the fused data as input and outputting a pre-judgment result; and the vehicle fault identification module collects data of the sensor with risks as input data according to the pre-judgment result, and completes fault classification and outputs a result. According to the invention, the perception sensitivity of the self-driving automobile to the external risk is improved, and the efficiency and the safety degree of traffic risk pre-judgment are further improved.

The invention discloses a traffic risk analysis method and system based on multi-source perception data, and relates to the technical field of traffic risk management. The traffic risk analysis method based on the multi-source perception data comprises the following steps of performing noise interference optimization judgment; quantifying the timeliness of traffic risk capture; traffic conflict probability prediction; and carrying out traffic risk decision response analysis. According to the method, whether noise interference optimization is carried out or not is judged through the obtained high-frequency signal amplitude, whether timeliness optimization is carried out or not is judged based on the obtained capture timeliness quantification result, and when the timeliness is qualified, whether interaction parameter step length optimization is carried out or not is judged based on the predicted traffic conflict probability; meanwhile, whether decision response optimization is carried out or not is judged based on the decision response analysis result, and the effect of improving the accuracy of dynamic traffic jam risk assessment in the road transformation area in the urban road transformation process is achieved; the problem that the accuracy of dynamic traffic jam risk assessment is not high in the urban road transformation process in the prior art is solved.

The invention provides a tunnel traffic risk prediction method and system based on multi-source data, and relates to the technical field of risk prediction, and the method comprises the steps: analyzing the influence degree of vehicle feature data on a tunnel state through employing a structure evaluation method based on a toughness theory, and obtaining a feature influence coefficient, dynamically adjusting the feature influence coefficient according to different scenes to obtain a risk weight; calculating a causal association feature of the internal traffic state data and the risk weight through an invariant anomaly detection method; and establishing a risk prediction model, inputting real-time internal traffic state data, and outputting to obtain a prediction result. According to the method, the feature influence coefficient is dynamically adjusted according to different scenes, the model can better adapt to risk changes of the tunnel in different operation states, the accident occurrence probability is reduced, and the passing efficiency and safety of the tunnel are improved.

The invention discloses a traffic risk prediction method based on spatio-temporal feature fusion and dynamic attention, and the method comprises the steps: firstly introducing a spatio-temporal feature fusion module based on a cross-attention mechanism, effectively capturing a cooperative relation between semantic information and motion information, and dynamically adjusting the weight of each piece of modal information according to a traffic scene; therefore, the alignment effect and the expression capability of the cross-modal features are improved. And a dynamic object attention module is further designed, and only key traffic participants with relatively high risk in the current frame are concerned, so that the response timeliness and the judgment precision of the model in an actual scene are enhanced. Experimental results on a DAD data set show that the method provided by the invention is remarkably superior to the existing mainstream method in two key indexes of average precision and average early warning time, AP exceeds 90%, mTTA reaches 4.45 seconds or above, and the application potential and engineering practical value of the method in a complex traffic environment are fully verified.

The invention discloses a mountainous area highway traffic risk short-time prediction method and system, relates to the technical field of intelligent traffic, and solves the problems that a traditional traffic risk prediction method is single in data dimension and inaccurate in scene division. According to the technical scheme, the method is characterized in that a multi-source heterogeneous data set including historical accident data, road linear data, real-time traffic flow data and meteorological data is constructed; performing clustering analysis on the multi-source heterogeneous data set by adopting a clustering algorithm, determining core clustering features, and dynamically correcting a membership function in combination with road linear data to divide scenes to obtain classified scenes; determining a risk level of the classification scene through a pre-constructed gradient boosting tree model and a long short-term memory network hybrid model; and triggering a grading early warning signal according to the risk grade, and issuing early warning information and a dynamic management and control measure corresponding to the risk grade based on the grading early warning signal. The effects of improving the risk analysis data coverage rate, being more accurate in scene division and improving the risk prediction precision are achieved.

The invention discloses a meteorological early warning information fusion method and system based on a road network, and the method comprises the steps: obtaining the topology and meteorological characteristics of the road network, carrying out the clustering to form an aggregation unit, and setting a sharing threshold value; obtaining multi-source early warning data according to units and standardizing the data; a comprehensive result is obtained through duplicate removal, sorting and conflict resolution of a scheduling mechanism; a weather system movement parameter is extracted to calculate a differentiated early warning advance; determining a risk level and a confidence interval by combining real-time traffic state fuzzy reasoning; inquiring emergency resources and linking the traffic control system to push early warning; historical data closed-loop feedback optimization model parameters are collected; and applying the optimization model to realize road network meteorological early warning. According to the invention, the space-time accuracy and response efficiency of meteorological early warning are significantly improved, and the road network traffic risk in severe weather is reduced.

The invention provides a ship monitoring and early warning system and method, and belongs to the field of ship traffic transportation safety. A ship monitoring system collects ship navigation information and uploads the ship navigation information to a vehicle-road cooperative transportation management platform; the vehicle-road cooperative operation and management platform is used for judging whether the ship approaches a preset risk area fence or not according to the navigation information, if yes, monitoring the navigation attitude of the ship based on the risk monitoring equipment to obtain a navigation attitude monitoring result, analyzing the driving behavior of the ship according to the navigation information to obtain a driving behavior analysis result, and sending the driving behavior analysis result to the vehicle-road cooperative operation and management platform; and if the navigation attitude monitoring result and / or the driving behavior analysis result represent that the ship has the traffic risk, navigation guidance is performed on the ship and an early warning is sent to a vehicle in the risk area fence. The navigation attitude and the driving behavior of the ship are respectively monitored based on the risk monitoring equipment and the ship monitoring system, the ship does not need to deploy too many complex equipment, the influence of visibility is avoided, and enough accident early warning processing buffer time is provided.

The invention relates to a traffic risk deduction method and system based on a dynamic knowledge graph. The method comprises the following steps: establishing a communication interface and collecting heterogeneous data streams; unifying all data in the heterogeneous data stream to the same space-time reference and fusing the data; based on the fused data, constructing a dynamic knowledge graph, and performing incremental updating on the dynamic knowledge graph by using continuously inflowing new data; inputting the dynamic knowledge graph into a trained time sequence diagram neural network model and a pre-established causal rule base to respectively obtain a prediction result and a reasoning result; carrying out fusion, verification and deduction on the prediction result and the reasoning result to obtain a deduction result; and based on the deduction result, generating structured early warning information, and directionally sending the structured early warning information to the screened target. Compared with the prior art, the method has the advantage of high prediction precision.

The invention relates to the technical field of intelligent driving, in particular to a traffic risk analysis method and device based on scene understanding, equipment and a medium, and the method comprises the steps: obtaining the lane line information of a current road, the future trajectory of a to-be-analyzed vehicle, and the vehicle distribution information; based on a deep learning traffic scene understanding model of a graph neural network, traffic scene feature information is extracted according to the lane line information, the future trajectory of the to-be-analyzed vehicle and the vehicle distribution information; and based on the traffic scene feature information, obtaining traffic risks generated by all vehicles on the current road at each moment, and obtaining a risk analysis result according to the traffic risks at each moment. Therefore, the lane line and vehicle trajectory features are extracted through the graph neural network, the risk distribution in different road scenes is dynamically analyzed in combination with the deep learning model, the collision risk position and time are accurately predicted, the problems of inaccurate risk assessment and the like of automatic driving in a complex dynamic traffic environment are solved, and accurate space-time risk early warning is realized.

The invention, which relates to the technical field of logistics supply management, discloses a production-marketing docking and cold-chain logistics integrated intelligent scheduling method comprising the following steps: S10, constructing a unified scheduling data model, and fusing multi-temperature-zone attributes, cargo dynamic energy consumption, a vehicle real-time state, an order temperature-sensitive characteristic and a delivery emergency degree to form a standardized decision data set; s20, executing multi-objective collaborative optimization, coupling the road surface type energy consumption coefficient with the order urgency score, and realizing accurate trade-off among the transportation cost, the energy consumption and the timeliness; s30, integrating meteorological and geological data, dynamically predicting path traffic risks caused by rainfall and soft soil texture, and correcting traffic parameters in real time; and S40, through cooperative sensing and image recognition of a vehicle-mounted sensor, the vehicle body pollution state is automatically judged, delayed use is executed on the limited vehicle, and standby transport capacity is started. According to the invention, through data fusion, dynamic optimization, risk early warning and closed-loop management and control, the problems of data islands, rigid path planning, slow environment response and out-of-control vehicle state are solved.

The invention relates to the technical field of tunnel safety management, in particular to a tunnel pavement thrown object early warning method based on multi-modal perception, which comprises the following steps: acquiring multi-modal data of a target tunnel pavement; performing feature extraction on the visible light image to obtain feature information of the thrown object; inputting the three-dimensional point cloud data into an LSTM network, and obtaining the spatial displacement of the thrown object in a short period; constructing an ST-GNN network, and inputting the short-term spatial displacement of the thrown object into the ST-GNN network to obtain an enhanced spatial-temporal feature vector; inputting the visible light image and the heat radiation image into a Transform network to obtain long-term distribution heat map features; obtaining a spatio-temporal distribution prediction result of the thrown object in combination with the enhanced spatio-temporal feature vector and the long-term distribution heat map feature; and obtaining a tunnel traffic risk early warning result according to the multi-modal data and the spatio-temporal distribution prediction result of the thrown objects. According to the invention, timely and effective early warning information can be provided.

The invention relates to the technical field of road early warning, and discloses an intelligent traffic risk early warning device and method.The intelligent traffic risk early warning device comprises a vehicle type recognition module, a vehicle meeting conflict judgment module and a conflict isolation protection module, the vehicle type recognition module recognizes and collects vehicle images through a YOLOv7 method based on fusion CBAM enhancement, outputs various kinds of information of vehicles passing through a curve, and sends the information to the collision isolation protection module; the collision isolation and protection module is used for receiving a collision signal and outputting the collision signal to the meeting collision judgment module, establishing a summarized data set of meeting actual conditions and executing a meeting safety control method, and the collision isolation and protection module is used for controlling the lifting of a plurality of flexible warning columns arranged at the entrances of the curves on the two sides based on the meeting collision judgment module. According to the device, the minimum vehicle turning radius is dynamically calculated in combination with the vehicle meeting conflict judgment module and compared with the same type of a vehicle meeting critical model, and signal early warning dynamic isolation dual protection is matched, so that the problems that a traditional convex mirror depends on subjective judgment of a driver, and an existing early warning device is manually operated and poor in environmental adaptability are solved; and the safety of vehicle meeting at the mountain road curve is improved.

The application provides a vehicle control method and a vehicle, and belongs to the technical field of vehicle control. The vehicle control method comprises the following steps: obtaining a traffic risk level of a road section in front of the vehicle and current state information of the vehicle, and the vehicle is provided with an active suspension; determining a target response strategy based on the traffic risk level and the current state information; and controlling the vehicle to execute the target response strategy, wherein the target response strategy comprises a height adjustment strategy of the active suspension. Thus, different traffic risk levels are divided according to the road conditions of the road section in front of the vehicle, and different target response strategies are provided in combination with the current state information of the vehicle, for example, direct traffic, traffic after height adjustment, or detouring in the case that there is still a risk of scratching after adjustment. Therefore, the vehicle intelligently and objectively controls the corresponding target response strategy based on different traffic risk levels and the current state of the vehicle, timely and effectively prevents scratching during vehicle driving, and improves vehicle safety.