141 results about "Traffic reduction" patented technology

The invention discloses an expressway digital twinborn dynamic construction method, which relates to the technical field of traffic, acquires and preprocesses multi-dimensional data of an expressway system, and constructs a traffic flow model of an expressway based on a deep learning technology and in combination with the multi-dimensional data of the expressway system. The method comprises the steps of mapping multi-dimensional data, collected in real time, of an expressway system to a traffic flow model, dynamically updating a traffic state in the traffic flow model, performing short-term traffic flow prediction by using the traffic flow model, simulating influences when different traffic events occur, and analyzing existing risks. According to the method, the valuable traffic information is extracted from the mass data by capturing and analyzing the traffic flow data in real time and utilizing the machine learning and data mining technology, so that the digital twinborn body can accurately predict the future traffic flow and provide a decision-making basis, the occurrence of traffic congestion and accidents is reduced, and the traffic flow prediction efficiency is improved. And the traffic capacity and the safety of the expressway are improved.

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-intersection signal cooperative control method based on deep reinforcement learning, and the method comprises the steps: firstly constructing a comprehensive reward function which comprises waiting time, queuing length and the influence of adjacent intersections, and constructing a mathematical model of a multi-intersection signal control system based on the reward function, so as to achieve the maximization of a comprehensive reward; and secondly, designing a deep Q network cooperative control algorithm for explicit strategy adjustment, and optimizing and adjusting adjustable strategy function parameters by introducing an explicit strategy adjustment function and utilizing a stochastic gradient descent method. And finally, dynamically adjusting the signal control strategy of each intersection in the road network in a complex and changeable traffic environment by designing a parameter sharing mechanism, thereby realizing collaborative optimization control of multiple intersections. According to the invention, the cooperative control efficiency of a multi-intersection signal system is obviously improved, traffic delay is reduced, and the traffic capacity of a road network is improved.

The invention discloses an intelligent traffic data processing method and system based on a 5G communication system, and relates to the technical field of traffic data processing, and the method comprises the steps: collecting data based on a 5G communication network; preprocessing the collected traffic data through an edge computing node; real-time prediction and risk assessment are carried out on traffic flow based on the cloud platform; sending real-time traffic information and optimization suggestions to a traffic management center and related vehicles; when an emergency occurs, a rapid response mechanism is triggered through a 5G network, and traffic resources are scheduled. The system collects, processes and analyzes traffic data in real time through the characteristics of high bandwidth and low delay of the 5G communication network and the distributed processing capability of edge calculation, achieves the real-time prediction and management of traffic flow and accident risks, and can improve the road utilization efficiency, reduce traffic accidents and improve the traffic safety through the intelligent decision and optimization control strategy. And the emergency response capability is improved, so that a more intelligent solution is provided for urban traffic management.

The invention relates to an intersection lane division and signal timing cooperative control method in an automatic driving environment, and belongs to the field of intelligent traffic. According to the method, lane function division schemes in corresponding directions are published by using lane function adjustment marks, signal timing schemes in corresponding directions are published by using lane-level signal lamps, and personalized suggestion schemes are pushed to network-connected automatic driving vehicles by using a network-connected communication technology, so that lane-level and vehicle-level refined intelligent management and control are realized, and the intelligent management and control efficiency is improved. The intersection traffic efficiency is improved, and intersection road traffic resources are utilized to the maximum extent. According to the invention, the problem of insufficient management refinement of the plane intersection can be overcome, the traffic capacity of the intersection is improved, traffic congestion is reduced, and the utilization rate of road traffic resources is improved.

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 the technical field of vehicles, and discloses an expressway free policy-based driving route optimization method and system, an electronic device, a storage medium and a vehicle, and the method comprises the steps: responding to a driving route set by a user, identifying whether the driving route comprises an expressway section, responding to the obtained current time, and determining whether the driving route comprises the expressway section; judging whether the current time is in a high-speed free policy period or not, namely comparing the current time with a policy database; on the basis of the driving route and in combination with the expressway section information, predicting a route end point and predicted time for arriving at each toll station; and based on the prediction result, optimizing the driving route and reminding the user, including judging whether the user may miss the expressway free policy or not, and dynamically planning the driving route according to the judgment result and generating corresponding reminding information. According to the invention, free policies of the expressway can be enjoyed to the greatest extent, the expressway congestion is effectively reduced, and the occurrence of traffic accidents is reduced.

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 relates to the field of traffic transportation, and discloses a traffic transportation scheduling method based on visual identification, comprising the following steps: installing camera devices at a plurality of traffic nodes for collecting traffic flow image data in real time; preprocessing the image data, wherein the preprocessing comprises image denoising, resolution adjustment and lane area division; vehicle detection and classification are carried out on the image data through a deep learning model, and the position, the speed, the driving direction and the vehicle type of the vehicle are identified; performing cross-frame tracking on the detected vehicles by using a multi-target tracking algorithm to generate a real-time trajectory of each vehicle; and predicting the future traffic flow based on the long and short-term memory network model, and generating a flow change trend in a future period of time. Through visual identification and real-time flow prediction, the system can identify traffic jam points in advance and make an intelligent scheduling decision, thereby effectively reducing traffic jam and improving vehicle passing efficiency.

The invention relates to the technical field of traffic management, and provides a traffic safety auxiliary management method, the method is applied to persuasion robots, at least one persuasion robot is arranged on each supervision road section, traffic information is obtained by obtaining traffic conditions on the supervision road sections, and the traffic information is confirmed to be violation information or accident information; according to the violation information or the accident information, the persuasion robot executes different working modes to perform persuasion warning work; the system can be used in traffic accidents or other emergencies, meets the traffic demands under different conditions, can provide corresponding assistance and persuasion work under various conditions, and reduces the conditions of traffic jam and traffic violation.

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 a tunnel light control method and device, electronic equipment and a computer storage medium, and belongs to the technical field of intelligent control, and the tunnel light control method comprises the steps: obtaining the traffic flow data at a tunnel entrance, the vehicle speed of a vehicle, and the brightness value outside the tunnel; based on the traffic flow data, the vehicle speed of the vehicle and the brightness value outside the tunnel, determining a light brightness initial value inside the tunnel and a reduction coefficient of the light brightness inside the tunnel, wherein the reduction coefficient is used for adjusting the light brightness at different positions in the tunnel step by step; and adjusting the light brightness initial value based on the reduction coefficient to obtain the target light brightness of each position in the tunnel. According to the invention, the light brightness at different positions in the tunnel can be intelligently adjusted under different traffic flows and vehicle speeds according to actual conditions, the light brightness at different positions in the tunnel is ensured to be appropriate, and the risk of traffic accidents is reduced while energy is saved.

The invention discloses a traffic signal optimization method and system based on artificial intelligence, and the method comprises the steps: collecting vehicle microscopic behavior data, and obtaining a driving intention prediction result; according to the driving intention prediction result, constructing a dynamic vehicle motion track; and completing the optimization of the intersection traffic signal by using the constructed dynamic vehicle motion trail. The traffic signal response speed and accuracy can be effectively improved, the traffic jam is reduced, and the traffic efficiency is improved. By adopting the convolutional neural network and the long and short term memory network, the system can automatically extract and analyze the driving behavior characteristics and mine the driving intention information, thereby optimizing the duration and phase sequence of the signal lamp and ensuring the smoothness of the traffic flow. Besides, the dynamic planning method based on the Markov process model enables the signal optimization decision to be more scientific and reasonable, can adapt to the change of traffic flow in real time, and further improves the intelligent level of traffic management.

The invention discloses an intelligent traffic management method and system based on data analysis, and belongs to the traffic field, the intelligent traffic management method based on data analysis comprises the following steps: obtaining road data, and obtaining speed limit information of a road; the method comprises the following steps of: dividing the collected average vehicle speed in a road by the set speed limit information of the road to obtain a division value, and judging that the congestion condition is better and the division value is smaller when the division value is larger, and has the beneficial effects that the congestion condition is judged according to the average vehicle speed and the speed limit information in the road, and the priority arrangement of the congestion condition is carried out in a database table, so that the congestion condition is more accurate. And finally, the signal lamp duration of the intersection is adjusted based on the priority, so that the road traffic efficiency is improved, and traffic congestion is reduced. When it is found that the road traffic efficiency cannot be effectively increased by adjusting the duration time of the signal lamp, whether the road is abnormal or not can be judged and fed back to a map service provider in time, vehicles driving into the road are reduced, and traffic jam is reduced.

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 discloses a driving fatigue relieving system based on a brain-computer interface, and the system comprises an electroencephalogram signal collection module which is used for collecting the electroencephalogram data of a driver in real time; the electroencephalogram signal analysis module is connected to the electroencephalogram signal acquisition module and is used for processing and analyzing the electroencephalogram data received from the electroencephalogram signal acquisition module so as to judge the mental state of the driver; and the execution application module is connected to the electroencephalogram signal analysis module and is used for executing a corresponding auxiliary strategy based on an analysis result of the electroencephalogram signal analysis module. According to the driving fatigue relieving system based on the brain-computer interface, a direct interaction control channel between the brain of the driver and the external equipment is constructed, so that the mental state of the driver is monitored and intervened in real time, and a new technical means is provided for improving driving safety and reducing traffic accident risks.

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 invention discloses an AGV traffic control method and device, terminal equipment and a storage medium, and the method comprises the steps: obtaining the entrance point state information of each entrance point of each traffic control region in each preset time period, calculating to obtain an average queuing intensity index and an entrance point traffic operation efficiency index of the entrance point position of each traffic control area; taking the traffic control area when the traffic operation efficiency index of the entrance point is smaller than a preset traffic operation efficiency index threshold as a first target traffic control area, and increasing the number of the entrance points; and taking the traffic control area when the average queuing intensity index is smaller than a preset queuing intensity index threshold as a second target traffic control area, and scheduling the to-be-scheduled AGV to other traffic control areas except the second target traffic control area until no queuing AGV exists in the second target traffic control area. According to the invention, the AGV congestion condition in the traffic control area can be 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 invention relates to an intelligent traffic signal lamp control system, which comprises an inductive control module, a self-adaptive control module and an intersection blockage identification module, the induction control module adopts single-point control induction control and is used for analyzing vehicle feedback data information at the intersection. According to the intelligent traffic signal lamp control system, based on real-time traffic flow information, a sensor, a camera or other data sources are used for detecting traffic flow conditions in all directions of an intersection. The data can be transmitted to a road condition information management database for analysis and processing. According to traffic flow in different directions and actual road conditions, the system automatically adjusts traffic light time in each direction, and traffic pressure is reduced.

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.