101 results about "Traffic reduction" patented technology

The invention provides a high-precision perception-driven intelligent road network collaborative optimization method and system, and relates to the technical field of intelligent traffic, and the method comprises the steps: collecting road sensing data through multi-sensor fusion, and obtaining multi-source perception data; performing dynamic traffic flow modeling according to the multi-source sensing data to obtain a traffic state prediction result; performing optimization decision on the traffic state prediction result to obtain an intelligent optimization decision; performing road network level cooperative control on the intelligent optimization decision to obtain a road network level cooperative control result; and performing real-time dynamic adjustment by using the road network level cooperative control result to obtain a road network scheduling result. According to the method and the device, the technical targets of global collaborative scheduling, improvement of traffic flow prediction precision, optimization of a real-time dynamic scheduling scheme and improvement of the intelligent level of urban traffic management can be realized, and the technical effects of reducing traffic congestion, improving road traffic efficiency, optimizing traffic resource allocation and improving emergency response capability are achieved.

The invention discloses a multi-source data fusion and intelligent analysis method for an intelligent traffic enterprise, and relates to the technical field of intelligent traffic, and the method comprises the following specific steps: S100, collecting video frame data, radar track data and voiceprint data of the traffic enterprise in real time, S200, calculating a quantized video definition score, S300, calculating a radar signal-to-noise ratio, and S500, calculating a video definition score. According to the method, the video, radar and voiceprint data quality can be accurately judged through multi-modal data quality real-time evaluation and credibility quantification, and a cross-modal attention weight matrix is dynamically adjusted according to a quality evaluation result in a complex environment, that is, when the video quality is reduced, the video, radar and voiceprint data quality can be accurately judged; the system automatically increases the radar data weight, ensures that high-quality data source key information is always focused, learns more accurate and comprehensive abnormal characteristics, effectively reduces the traffic accident rate, and guarantees the road traffic safety.

The invention provides a vehicle control method and system and computer equipment, and the method comprises the steps: obtaining multi-modal data of a first target vehicle, determining lamp language features based on the multi-modal data, mapping the lamp language features into lamp language semantic tags through employing a pre-obtained vehicle lamp language coding table, and storing the lamp language semantic tags in a server; and the second target vehicle is controlled according to the lamp language semantic tag, so that the first target vehicle executes or stops a driving intention corresponding to the lamp language semantic tag of the first target vehicle based on a control result of the second target vehicle. According to the method, the multi-modal data is acquired to identify the lamp signal, the problem that a single sensor is easily interfered by the environment to cause misjudgment can be solved, and meanwhile, the driving behavior or driving intention of the vehicle is predicted according to the lamp characteristics, so that a coping strategy is formed in advance, sudden or high-risk situations are effectively avoided, the traffic accident probability is reduced, and the driving safety is improved. And multi-vehicle bidirectional cooperative interaction can be realized in combination with the information of the traffic participants.

The invention relates to a vehicle projection warning method, device, equipment and system, a vehicle, a storage medium and a program product. The method comprises the steps of obtaining video data of an environment where a current vehicle is located; performing target detection processing on the video data to obtain target movement information; wherein the target movement information comprises detected movement trend information and arrival time of each moving target; determining a risk level and motion direction information of the moving target according to the motion trend information and the arrival time of the moving target; and performing projection warning processing outside the vehicle according to the risk level and the movement direction information of the moving target. According to the invention, traffic participants around the vehicle can be reminded to improve attention, driving is controlled, traffic accidents can be reduced, and traffic safety is improved.

The invention discloses a safe driving early warning system based on big data and machine learning, and the system comprises the following steps: S1, data collection: collecting the vehicle position, parking time, vehicle door state, vehicle type and other data in real time through a roadside sensing device, and obtaining environment context information, such as road type, traffic flow, weather condition and other data; s2, data processing and fusion: matching and associating the real-time data acquired in S1 with historical data by using a multi-source data fusion technology, and constructing a complete vehicle behavior data set; s3, establishing a risk assessment model: inputting model related data based on a machine learning algorithm, constructing a door opening risk prediction model, and obtaining a probability value Pdoopen that the model output is a door opening behavior; s4, early warning: when a model early warning value is set to be Pthreshold and a probability value is greater than or equal to the early warning value, early warning about the door opening condition of a front parked vehicle is given to a rear vehicle through a vehicle-road cooperation system; the early warning system can effectively reduce the occurrence of traffic accidents.

The invention relates to a traffic accident identification method based on computer vision, which belongs to the traffic accident identification technology and comprises a data acquisition module, a data preprocessing module, a target detection and tracking module, a behavior analysis module, an event identification model and an alarm module. The data acquisition module acquires data from traffic monitoring videos, a vehicle-mounted camera and an unmanned aerial vehicle for aerial photography. According to the invention, traffic monitoring personnel can quickly respond to traffic accidents occurring in real time by quickly identifying the traffic accidents on the road, so that the traffic accidents can be timely responded and pushed to a traffic management center, the police efficiency is improved, and an alarm signal is pushed to a vehicle-mounted system to assist navigation to change a route, reduce traffic jam of an accident road section and improve the traffic safety. And the alarm signal is pushed to a traffic broadcast, and a driver is prompted to carry out route planning in time, so that traffic jam caused by traffic accidents can be relieved, the traffic dispersion efficiency and the traffic rescue efficiency are improved, the labor consumption required by traffic monitoring is reduced, and the working efficiency of traffic management work is improved.

The invention relates to the technical field of expressway service area service resource allocation, in particular to a service area service resource allocation method and system in an expressway scene based on the Internet of Vehicles. Based on wireless communication between the vehicles and the intelligent service area, various service resources such as parking spaces, charging piles / gas stations and rest rooms in the expressway service area can be dynamically managed, the vehicles are distributed to the proper service area through a model algorithm, traffic congestion can be effectively reduced, and the service efficiency is improved. The travel efficiency of the user, the utilization rate of the expressway service area and the economic benefits of various services are improved; by analyzing the spatio-temporal characteristics of the expressway service area, the use scale of various service resources such as parking spaces, charging piles / gas stations and rest rooms in the service area is reasonably measured and calculated, and the service area guiding system is optimally designed, so that various service resource demands in the service area can be accurately identified, the service level of the service area is improved, and the service efficiency is improved. And the construction of an intelligent high-speed service area is promoted.

The invention relates to the technical field of intelligent traffic and data trusted computing, and discloses a traffic flow optimization model based on a trusted data space. According to the model, multi-source traffic data including road monitoring equipment, Internet of Vehicles (V2X), unmanned aerial vehicle images and the like are utilized to construct a trusted data space, and data fusion and optimization calculation are realized. And the non-tampering property of the data is ensured through a block chain technology, and a federated learning algorithm is adopted to carry out data analysis to generate an optimal traffic flow scheduling scheme. The optimization strategy comprises intelligent signal lamp control, dynamic lane allocation and intelligent navigation adjustment so as to improve the road traffic efficiency, reduce traffic congestion and improve the intelligent level of traffic management. The method is suitable for intelligent urban traffic management, and can effectively improve the road operation efficiency and reduce the carbon emission.

The invention relates to the field of urban traffic management and signal control, and discloses an urban mesoscopic credit control unit division method based on breadth search, which comprises the following steps: collecting traffic data in a road network, calculating an optimal signal timing scheme, and finely adjusting a signal period according to a downstream overflow risk; by calculating the coordination benefit of each steering, effective coordination steering is screened out, and a signal coordination trunk line is generated; generating a credit control unit by using a breadth search algorithm (BFS), and reasonably dividing the credit control unit into a single-point credit control unit, a trunk line credit control unit and a regional credit control unit; finally, by calculating the coordination direction and the phase difference, signal coordination and green wave passing are ensured, and a signal control scheme of a road network is optimized. According to the method, the traffic capacity of urban traffic can be improved, traffic congestion is reduced, and the overall traffic fluency is improved.

The invention relates to an abnormal road scene image recognition and early warning system, and belongs to the technical field of computer vision and intelligent traffic. The system adopts a distributed architecture with edge computing and cloud collaboration, introduces a differential geometry theory to construct a road scene representation framework, collects data through a camera sensing unit, and sends the data to a cloud server; the edge computing unit executes preprocessing, and the cloud processing unit completes anomaly detection and early warning. The core innovation of the system is that the manifold learning representation module maps high-dimensional features to a Riemannian manifold space; the Riemannian geometric anomaly measurement module defines a multi-dimensional anomaly index; the multi-scale adaptive analysis module realizes comprehensive detection from macroscopic to microscopic; the abnormal scene library is automatically updated; the multi-vehicle cooperation module supports information sharing and joint decision making, the system achieves the effects that the anomaly detection precision is improved by 25-35%, the environmental adaptability is enhanced by 50-70%, the response time is controlled within 250 ms, major road anomalies can be pre-warned 5-30 seconds in advance, the traffic accident risk is reduced by 30-40% potentially, and the traffic efficiency is optimized.

The invention provides an intelligent public security comprehensive management information system and a declaration method, and relates to the technical field of intelligent public security, and the intelligent public security comprehensive management information system comprises a monitoring collection module which is used for setting a two-dimensional coordinate system on a monitoring road surface image and collecting monitoring road surface image data; a monitor is arranged on a monitored road surface, image data are collected, a right turn of a vehicle is analyzed and calculated, a right turn path of the vehicle is analyzed by setting a coordinate system and is compared with coordinates of pedestrians, and when the coordinates of the pedestrians are located on the upper side of the right turn path of the vehicle, the pedestrians are reminded to avoid through a voice broadcast device at the moment; the method can greatly reduce the accident occurrence probability of traffic security, specifically, the conversion speed, the acceleration and the steering angle of the vehicle are analyzed, the vehicle offset point is generated, the vehicle control point is generated, the method is more practical, the output is more accurate, the Bezier curve is used for analyzing the right turning track of the vehicle, and finally the track related formula is output.

The invention relates to the field of traffic signal control, and discloses a PID (Proportion Integration Differentiation)-based dynamic step size signal control timing scheme generation method, which comprises the following steps of: dynamically adjusting signal timing by collecting data such as traffic flow, vehicle speed and queuing length in real time and combining a PID control algorithm; specifically, the system calculates the control deviation and takes the control deviation as the input of a PID algorithm, and adjusts the control deviation of each signal steering according to the weighted synthesis of proportion, integration and differential terms; and finally realizing optimization of a signal control scheme by aggregating the adjustment step length of each steering to a stage and generating a new signal period and a new green signal ratio. The method has high adaptability and stability to various traffic detection data sources, signal timing can be accurately adjusted according to real-time traffic demands, the traffic efficiency of traffic flow is remarkably improved, traffic congestion is reduced, and the intelligent level of urban traffic management is improved.

The invention belongs to the field of intelligent traffic, and relates to a multi-intersection IVCPS cooperative control test case generalization generation method, which comprises the following steps: S1, constructing a mixed traffic flow fundamental diagram model, and deducing the relationship between traffic flow density and flow under different permeability by combining IDM and CACC models; s2, expanding an IVCPS scene body, and designing a multi-intersection road network scene; s3, adopting an upper-layer and lower-layer distributed cooperative control architecture, wherein the upper layer dynamically adjusts signal timing based on traffic pressure information, and the lower layer provides an optimal path and driving speed suggestion for the CAV; s4, constructing a scene parameter extraction module, and extracting a control algorithm, a vehicle model ratio, traffic flow and CAV permeability; S5, constructing a key calculation module, and calculating a parameter key index; and S6, constructing a test case generation module, and generating a combined test case set by adopting an AETG-S algorithm. According to the invention, the traffic efficiency of a multi-intersection road network can be effectively improved, traffic delay and energy consumption are reduced, and a scientific basis is provided for testing and optimization of an intelligent traffic system.

The embodiment of the invention provides an auxiliary driving control method and device, electronic equipment and a vehicle, and relates to the technical field of intelligent driving, environmental perception information of the advancing direction of a target vehicle in the driving process is obtained, and if it is determined that multiple continuous fixed obstacles exist in the advancing direction of the target vehicle based on the environmental perception information, the target vehicle is driven to move. According to the method and the device, if the traffic warning objects are identified, a plurality of continuous fixed obstacles are virtualized into virtual edges, and then auxiliary driving control is carried out based on the virtual edges, so that the plurality of continuous fixed obstacles are virtualized into the virtual edges, and then the auxiliary driving control is carried out by utilizing the virtual edges, so that missing identification of the traffic warning objects can be reduced; in this way, the effect of auxiliary driving control can be improved in a scene facing a plurality of continuous fixed obstacles, and the risk of collision is reduced.

The application relates to the field of data processing, and in particular to a method and device for reconstructing an expressway in an interchange area, equipment and a medium. The method comprises the following steps: the application accurately determines the interchange type by comprehensively considering the traffic engineering diagram of the current expressway, historical accident data, location information and reconstruction reasons, deeply analyzes the structural influence of the reconstruction on the existing road network, combines the current turning traffic volume and the long-term predicted traffic volume, scientifically formulates the geometric layout optimization scheme and the road technical index, and finally generates a targeted reconstruction plan, which effectively improves the foresight, rationality and safety of the reconstruction project, ensures that the expressway can better adapt to future traffic demand after reconstruction, optimizes the road network structure, and reduces the risk of traffic accidents, so that the effectiveness of the reconstruction is improved through comprehensive and detailed data analysis and design optimization.

The application belongs to the technical field of transportation, and particularly relates to a traffic prediction method based on multi-graph gated convolution and Conv-LSTM. The DTWN similarity value filling module is used to calculate the similarity between time series, and the average value of K data with high similarity is taken to fill in the missing data, so as to ensure the integrity of the traffic data. The traffic data is decomposed into approximate coefficients and detail coefficients through DWT. The approximate coefficients reflect the long-term trend of traffic flow, and the detail coefficients contain short-term fluctuation information. Different models are combined to process the data obtained through DWT. The detail coefficients are processed through the multi-graph gated dilated convolution MGDCN to capture the short-term fluctuation, and the approximate coefficients are processed through the Conv-LSTM to capture the long-term trend. Compared with the existing traffic prediction model, the application can fully fill in the missing data, capture the short-term fluctuation and long-term trend of traffic, effectively reduce the error in traffic prediction, and improve the accuracy of traffic prediction.

The present invention discloses a method, system and medium for speed limit inheritance at the starting point of lanes in a high-precision map. The method includes the following steps: obtaining road network data and lane self-limited speed data; constructing a set of road topological relationships for the entering road, the road inside the intersection and the exiting road according to the road network data; constructing a set of lane topological relationships for the entering lane and the exiting lane according to the road network data and the set of road topological relationships; processing speed limit inheritance at the starting point of the lane according to the set of road topological relationships, the set of lane topological relationships and the lane self-limited speed data; selecting roads with unprocessed speed limit inheritance in the road network data for legal speed limit association; enabling big data speed limit inheritance at the starting point of all lanes in the high-precision map, providing more powerful support for safer driving of autonomous driving lanes, and at the same time greatly reducing the occurrence of traffic accidents and the workload of manual operations.

A processor-implemented method for managing bus traffic of a system-on-chip (SoC) includes receiving a mixed signal in a computing device including the SoC. One or more polarity changes in a slope of a waveform corresponding to the mixed signal are detected. One or more of an analog-to-digital converter (ADC) or a portion of SoC bus traffic associated with ADC operation is selectively disabled between one or more polarity changes in a slope of a waveform.

A processor-implemented method for managing bus traffic of a system-on-a-chip (SoC) includes receiving a mixed signal in a computing device including the SoC. One or more polarity changes of a slope of a waveform corresponding to the mixed signal are detected. One or more of an analog-to-digital converter (ADC) or a portion of SoC bus traffic related to ADC operation are selectively disabled between one or more polarity changes in the slope of the waveform.

The invention discloses a maintenance management system applied to an expressway, and relates to the technical field of data analysis, and the system comprises the steps: collecting the road state data of the expressway, creating an anomaly detection auto-encoder, marking the abnormal road state data, and building an expressway pavement state trend chart; based on the expressway pavement state trend chart, establishing a pavement degradation prediction model, and predicting a future expressway pavement damage probability distribution diagram; fitting and analyzing the fitness of each executable maintenance scheme based on known maintenance scheme types and the expressway pavement damage probability distribution map, screening out an executable maintenance scheme set of expressway pavement damage at unit time nodes in the future, and taking influence factors during the execution of the maintenance schemes as influence variables; and establishing a dynamic planning state transition equation, and generating an optimal highway maintenance scheme. The method has the advantages that the maintenance cost is reduced, the emergency response speed is increased, and traffic interference is reduced.

This invention relates to the field of road management, specifically to a data-driven road traffic risk control system and method, comprising: a road monitoring module, a vehicle management module, an accident analysis module, a traffic focusing module, and a dispatching and allocation module. The road monitoring module is used to establish a road network model; the vehicle management module is used to plan the optimal driving route; the accident analysis module is used to determine the type of accident; the traffic focusing module is used to optimize patrol routes; and the dispatching and allocation module is used to build an exchange service platform for command and dispatch. This invention can improve traffic operation efficiency, reduce accident risks, build an intelligent traffic management system, enhance the perception and management level of urban traffic, improve the accuracy of traffic accident target detection, improve traffic infrastructure construction, enhance road network safety, optimize the allocation of human and material resources, and reduce traffic management costs.

The invention provides a user flow and city flow reduction method and device and a readable storage medium, and the method comprises the steps: carrying out the information analysis of NetFlow flow and AAA flow, and obtaining a NetFlow analysis result and an AAA analysis result; performing flow calculation according to the NetFlow analysis result to obtain a NetFlow flow calculation result, and performing flow calculation according to the AAA analysis result to obtain an AAA flow calculation result; carrying out city matching according to the NetFlow flow calculation result and the AAA flow calculation result, and carrying out city flow reduction to obtain NetFlow city reduction flow; and according to the NetFlow flow calculation result and the AAA flow calculation result, carrying out user internet protocol IP address matching, and carrying out user flow reduction to obtain NetFlow user reduction flow. The method can effectively reduce the flow reduction error.

The invention discloses a port ship traffic organization optimization and regulation and control method, which belongs to the technical field of waterway traffic optimization and intelligent management and control, and comprises the following steps: collecting port data, constructing an information physical system framework, and dynamically managing a port by using the port data through the information physical system framework. The method comprises the following steps of: extracting and quantizing, performing uniform feature fusion, obtaining a judgment result by utilizing an event triggering mechanism, updating a system state, predicting the updated system state by utilizing a model prediction control algorithm to obtain a system step length, predicting the system step length in real time by utilizing a self-adaptive vision field mechanism to obtain a real-time step length, and calculating the real-time step length according to the real-time step length. And solving by utilizing a multiplier alternating direction method according to the judgment result and the real-time step length to obtain a port ship traffic scheduling control strategy. According to the invention, high-efficiency ship traffic scheduling and resource optimization can be realized in a complex and changeable port environment, the port transportation efficiency is obviously improved, traffic congestion is reduced, and the self-adaptive capability of the system is enhanced.

The application discloses a port ship traffic organization optimization and regulation method, and belongs to the technical field of waterway traffic optimization and intelligent management and control. The method comprises the following steps: collecting port data, constructing an information physical system framework, using the information physical system framework to realize dynamic management of the port, uniformly fusing features after extraction and quantization, using an event triggering mechanism to obtain a judgment result, updating a system state, using a model prediction control algorithm to predict the system step length, using an adaptive horizon mechanism to obtain a real-time step length, using a multiplier alternating direction method to solve according to the judgment result and the real-time step length, and obtaining a port ship traffic scheduling control strategy. The application can realize efficient ship traffic scheduling and resource optimization in a complex and changeable port environment, significantly improve port transportation efficiency, reduce traffic congestion, and enhance the adaptive ability of the system.

The application provides a kind of underground mine truck scheduling method and device based on state machine and reinforcement learning, it is related to underground mine transportation system scheduling optimization technical field.The method comprises: obtaining the scene model of underground mine transportation system, constructs topological structure model, constructs the underground mine truck fleet operation problem according to topological structure model and conflict type, and then constructs the fleet scheduling decision model based on state machine and reinforcement learning;Wherein, each truck is regarded as the agent of fleet scheduling decision model, and the state machine is equipped for the agent;With the goal of maximizing transportation efficiency, reducing waiting time and reducing conflict, the fleet scheduling decision model is solved, and the scheduling result of mine truck in the underground transportation working face with bidirectional single lane driving characteristics is obtained.The application realizes the intelligent scheduling of underground mine truck fleet by combining state machine and reinforcement learning technology, to improve transportation efficiency, reduce traffic conflict and reduce energy consumption.

In order to improve passenger satisfaction with a transportation service and to provide an operation plan creation means capable of reducing operating costs for a transportation service provider, an operation plan creation device comprises: a transportation leg network generation unit that generates transportation leg network information that defines each transportation leg of the transportation service; a cancellation determination unit that generates transportation leg cancellation possibility information that indicates whether or not cancellation of each transportation leg of the transportation service is possible; a travel route candidate determination unit that generates travel route candidate information that indicates a travel route candidate for each travel plan in passenger travel plan information; a people flow prediction unit that generates people flow prediction information that indicates a predicted people flow in the transportation service; and an operation plan generation unit that generates new operation plan information in which certain transportation legs of the transportation service are eliminated.

The application provides an implementation method for detecting vehicle left turn, which comprises the following steps: monitoring the motion state of the vehicle by using a six-axis inertial measurement unit (IMU); performing data processing and six-axis fusion algorithm processing on the collected three-axis acceleration and three-axis angular velocity data to obtain three-axis Euler angles of the vehicle; performing integral and angle change calculation on the stored yaw angle and Z-axis angular velocity data to obtain the angle change when the vehicle turns; comparing the real-time calculated Z-axis angle change value with a preset left turn angle threshold value, and if the threshold value is exceeded, it is judged that the vehicle is left turning; introducing a time window mechanism; after the vehicle starts left turning, the Z-axis angle change of the vehicle in each data sampling period is continuously monitored, and when the Z-axis angular velocity decreases and tends to zero, it is judged that the left turning action is completed. Through accurate identification of the left turning action of the vehicle, the application reduces the occurrence of traffic accidents and provides more intelligent decision support for the traffic management system.

The invention provides a control method and device and a vehicle. The control method can be applied to the field of intelligent driving. The method comprises the following steps: acquiring information of a hybrid vehicle queue, wherein the hybrid vehicle queue comprises intelligent network connection vehicles and manual driving vehicles; establishing a dynamic model of the hybrid vehicle queue; decoupling the dynamic model to obtain a differential state equation and a nominal state equation; determining a reachable set of the nominal state equation according to the reachable set of the differential state equation; and controlling the intelligent networked vehicle according to the reachable set of the nominal state equation. The embodiment of the invention can be applied to an intelligent vehicle or an electric vehicle, the energy consumption of the intelligent network connection vehicle in the driving process of the hybrid vehicle queue is reduced, the energy consumption in traffic is reduced, the economy of the intelligent network connection vehicle is further improved, and a powerful supporting technology is provided for building a green and energy-saving intelligent traffic system.

The invention relates to the technical field of traffic monitoring and management, and particularly discloses a traffic flow abnormity intelligent identification and early warning method and system based on deep learning. According to the method, after traffic monitoring video data is obtained and processed, information such as vehicle tracks is extracted to generate traffic state feature data, a traffic feature combination matrix and dominant feature parameters are determined, a traffic flow dynamic feature sequence is generated, traffic flow states are classified, the traffic abnormal degree is determined, and a corresponding management and control strategy is generated. According to the invention, accurate identification and timely early warning of traffic flow anomalies can be realized, the traffic management efficiency is improved, and the probability of occurrence of traffic accidents is reduced.

This invention relates to the field of vehicle control technology, and more specifically to a multi-vehicle system model for non-signal-controlled intersections. Through third-order linear transformation, the nonlinear system model can be converted into a third-order linear state-space model. This simplifies the model while meeting the requirements of vehicle motion control. Considering parameter mismatch and bounded acceleration, it more realistically reflects the longitudinal dynamics data of vehicles during actual driving, making it suitable for more realistic vehicle and traffic conditions. This provides more accurate data for vehicle control systems to control vehicles. By analyzing the vehicle terminal dynamics data through a multi-vehicle control system, it is possible to better control the current vehicle to maintain a certain and appropriate distance from surrounding vehicles to ensure safe driving. Simultaneously, it can alleviate traffic congestion, reduce traffic accidents, and increase traffic capacity.