Vehicle abnormal position correction method, system and equipment based on road network position information and medium

Abstract

The invention relates to a vehicle abnormal position correction method, system and device based on road network position information and a medium, and the method comprises the steps: collecting and processing road network map information, tracking a vehicle position, and storing the vehicle position as a historical position point set; when the number of the historical position points exceeds a set threshold value, whether the vehicle is in an abnormal deviation state or not is judged according to the scene where the vehicle is located and the relative position relation between the vehicle and the road and road section range; if the vehicle is in the abnormal deviation state, acquiring a latest position point from a historical position point set, and predicting and covering the position of the vehicle; if not, front and back position abnormal state identification is carried out; and when the front and back positions are identified to be abnormal, obtaining the latest position point from the historical position point set, and predicting and covering the position of the vehicle. According to the invention, by fully utilizing and fusing a plurality of data sources, designing an innovative judgment rule and adopting a refined exception handling mode, the accuracy of vehicle position judgment and the fineness of exception handling are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the field of intelligent transportation technology, and in particular to a method, system, device and medium for correcting abnormal position of a vehicle based on road network position information. Background Art

[0002] With the continuous advancement of intelligent transportation systems, accurate monitoring and analysis of vehicle location information has become increasingly critical. Under the framework of digital twin technology, the accurate display of vehicle location and the precise calculation of trajectory indicators play an irreplaceable role in improving traffic management, optimizing traffic planning and ensuring traffic safety. However, given the complexity and variability of actual traffic conditions, vehicle location data is often subject to various interferences and anomalies, such as position deviation, trajectory regression, data flashing, etc., which undoubtedly increases the difficulty of accurate monitoring and trajectory analysis.

[0003] Traditional solutions mostly rely on basic vehicle positioning technology and data analysis methods. These solutions often focus on the processing of real-time data, but fail to make full use of historical data and geographic information, thus limiting the accuracy and comprehensiveness of location judgment. Although some solutions attempt to incorporate road information, their judgment logic is relatively simple and it is difficult to cope with a variety of abnormal conditions. When processing abnormal data, these solutions usually choose to delete it directly or perform simple data corrections. This practice may lead to the loss or misreporting of key location information, which in turn affects the display authenticity of the digital twin platform and the accuracy of trajectory analysis.

[0004] In recent years, although some solutions have adopted artificial intelligence technologies such as deep learning, these models are usually highly complex, not only costly in implementation and maintenance, but also have extremely high requirements for data quality and quantity. This leads to limitations in the actual application of such solutions, such as high consumption of computing resources, long model training cycle, and strict dependence on data quality, making it difficult to widely promote and apply them in actual traffic management. Summary of the invention

[0005] 1. Technical issues to be resolved

[0006] In view of the above-mentioned shortcomings and deficiencies of the prior art, the present invention provides a method, system, device and medium for correcting abnormal position of a vehicle based on road network position information, which solves the technical problems of the prior art in vehicle position monitoring and analysis, such as insufficient data processing, simple anomaly identification, rough anomaly processing and limited application of artificial intelligence.

[0007] (II) Technical solution

[0008] In order to achieve the above object, the main technical solutions adopted by the present invention include:

[0009] In a first aspect, an embodiment of the present invention provides a method for correcting abnormal vehicle position based on road network position information, comprising:

[0010] Collect and pre-process road network map information and store it in the database;

[0011] Track the vehicle position using detection equipment and store it as a set of historical position points of the vehicle;

[0012] When the number of historical position points of a vehicle exceeds a set threshold, the vehicle is judged to be in an abnormal deviation state based on the scene of the vehicle obtained from the road network map information and its relative position relationship with the road and road section range;

[0013] If the vehicle is in an abnormal deviation state, the latest position point is obtained from the historical position point set, and the vehicle position is predicted and covered by calculating the historical longitude speed and latitude speed of the vehicle;

[0014] If the vehicle is not in an abnormal deviation state, the abnormal state of the front and rear positions of the vehicle is identified based on a preset threshold and the acquired vehicle driving state data;

[0015] When the vehicle is identified as having abnormal front and rear positions, the latest position point is obtained from the historical position point set, and the vehicle position is predicted and covered by calculating the longitude speed and latitude speed of the vehicle.

[0016] Optionally, collecting and preprocessing road network map information and storing it in a database includes:

[0017] Extracting road network map information including road information, road section information, lane information and lane centerline information from map data;

[0018] Screen and pre-process the collected road network map information to remove duplicate, invalid or erroneous data;

[0019] Establish a data table structure for storage in the database, and import the pre-processed road network map information into the database according to the data table structure;

[0020] in,

[0021] The road information includes a road number and a road range consisting of a set of longitude and latitude coordinate points, the road range includes a motor vehicle lane, a non-motor vehicle lane and a green belt, and the road range contains at least one road section;

[0022] The road section information includes the road section number, the road number of the road to which it belongs, and the road section range consisting of a set of longitude and latitude coordinate points. The road section range is limited to the motor vehicle lane and contains at least one lane;

[0023] The lane information includes the lane number, the section number of the road section to which it belongs, and the lane range consisting of a set of longitude and latitude coordinate points. Each lane range has a unique corresponding lane centerline.

[0024] The lane centerline information includes the lane centerline number, the lane number of the lane to which it belongs, and a set of longitude and latitude coordinate points from the starting point to the end point of the lane. The lane centerline is composed of more than one line segments connected in sequence.

[0025] Optionally, tracking the vehicle position using a detection device and storing the vehicle position as a historical position point set includes:

[0026] Use detection equipment to continuously track the location of the target vehicle at set time intervals;

[0027] Whenever a new position of the vehicle is detected, the vehicle position data including the longitude of the position point, the latitude of the position point and the detection time of the position point is recorded;

[0028] The vehicle location data is sorted in sequence according to the detection time to form a set of historical location points of the vehicle and stored in the database.

[0029] Optionally, when the number of historical position points of the vehicle exceeds a set threshold, judging whether the vehicle is in an abnormal deviation state according to the scene of the vehicle obtained from the road network map information and the relative position relationship with the road and road section range includes:

[0030] When the number of historical position points of the vehicle exceeds the set threshold, the current vehicle position is obtained;

[0031] Determine whether the current vehicle position is within the road range;

[0032] If the current vehicle position is not within the road range, further judgment is made based on the scene of the vehicle determined by the preset scene label;

[0033] If the vehicle is in a closed road scenario including a highway and a tunnel, the vehicle position will be marked as an abnormal deviation state;

[0034] If the scene is an urban road scene, the vehicle position is marked as normal;

[0035] If the current vehicle position is within the road range, then further determine whether the current vehicle position is within the road section range;

[0036] If the current vehicle position is within the road section, the vehicle position is marked as normal;

[0037] If the current vehicle position is not within the road section, and the vehicle is in a closed road scene including a highway and a tunnel, the vehicle position is marked as normal;

[0038] If the current vehicle position is not within the road section range, and the vehicle is in an urban road scene, then further determine whether the vehicle position at the previous moment was within the road section range;

[0039] If the vehicle position at the last moment was within the road section, the vehicle position will be marked as abnormal deviation;

[0040] If the vehicle position at the last moment is not within the road section range, the vehicle position is marked as normal.

[0041] Optionally, if the vehicle is in an abnormal deviation state, the latest position point is obtained from the historical position point set, and the vehicle position is predicted and covered by calculating the historical longitude speed and latitude speed of the vehicle, including:

[0042] If the vehicle is in an abnormal deviation state, the latest one or more position points are automatically selected from the historical position point set to form a position point subset;

[0043] In the location point subset, adjacent location point pairs are selected in sequence, and the longitude speed and latitude speed between each pair of adjacent location points are calculated respectively according to the location point longitudes, location point latitudes and location point detection times attached to the historical location point set;

[0044] All the calculated longitude velocities are averaged to obtain an average value of the longitude velocities; and all the latitude velocities are averaged to obtain an average value of the latitude velocities;

[0045] The average longitude and latitude speeds are used together with the time difference between the last position of the vehicle and the current position to obtain the predicted longitude and latitude position of the vehicle at the current time, and used to overwrite the original vehicle position at the current time.

[0046] Among them, the predicted longitude and latitude position is:

[0047]

[0048]

[0049] In the formula, lon' n+1 ,lat' n+1 are the predicted longitude and latitude positions, respectively, lon n ,lat n The longitude and latitude of the vehicle’s last position, v lon 、v lat are the average longitude and latitude speeds, respectively, t n+1 is the current moment, t n is the time of the vehicle's last position.

[0050] Optionally, if the vehicle is not in an abnormal deviation state, identifying the abnormal state of the front and rear positions of the vehicle based on a preset threshold and the acquired vehicle driving state data includes:

[0051] According to the current vehicle position and the previous vehicle position, the longitude and latitude of the front and rear positions of the vehicle and the lane centerline heading angle of the lane are obtained, and the vehicle heading angle and the vehicle travel distance are calculated by combining the front and rear position coordinates to form a vehicle travel status data set;

[0052] If the vehicle's travel distance does not exceed the distance threshold, the vehicle is considered stationary;

[0053] If the vehicle travels a distance exceeding the distance threshold, the following abnormal state identification of the front and rear positions of the vehicle is performed based on the vehicle travel status data set:

[0054] When the angle change between the lane centerline heading angle of the vehicle at the current moment and the lane at the previous moment exceeds the preset deviation threshold, it is determined that the vehicle has deviated abnormally;

[0055] When the angle deviation between the heading angle of the centerline of the lane where the vehicle was at the last moment and the heading angle of the vehicle exceeds the preset reversing threshold, it is determined that the vehicle has reversed abnormally;

[0056] When the vehicle travel distance within a preset time interval exceeds a preset flash threshold, it is determined that the vehicle has abnormal flash.

[0057] Optionally, according to the vehicle position at the current moment and the vehicle position at the previous moment, the longitude and latitude of the front and rear positions of the vehicle and the lane centerline heading angle of the lane where the vehicle is located are obtained, and the vehicle heading angle and the vehicle travel distance are calculated in combination with the front and rear position coordinates, and the vehicle driving state data set includes:

[0058] Obtaining the current vehicle position including longitude and latitude, and recording it as the first data item of the vehicle driving status data set;

[0059] According to the road network map information, the lane centerline of the lane where the vehicle is located at the current moment is determined, and the angle between the lane polyline of the lane centerline and the true north direction line is calculated to obtain the lane centerline heading angle, which is recorded as the second data item of the vehicle driving status data set;

[0060] Obtaining the vehicle position at the last moment including longitude and latitude, and recording it as the third data item of the vehicle driving status data set;

[0061] According to the road network map information, determine the lane centerline of the lane where the vehicle is located at the last moment, and calculate the angle between the lane polyline of the lane centerline and the true north direction line to obtain the lane centerline heading angle at the last moment, which is recorded as the fourth data item of the vehicle driving status data set;

[0062] The vehicle heading angle is obtained by calculating the angle between the line connecting the vehicle position at the previous moment and the vehicle position at the current moment and the true north direction line, and recorded as the fifth data item of the vehicle driving state data set;

[0063] The vehicle travel distance is obtained by calculating the straight-line distance between the vehicle position at the previous moment and the vehicle position at the current moment, and is recorded as the sixth data item of the vehicle travel status data set.

[0064] In a second aspect, an embodiment of the present invention provides a vehicle abnormal position correction system based on road network position information, comprising:

[0065] The acquisition and processing module is used to acquire and pre-process the road network map information and store it in the database;

[0066] A location tracking module, used to track the location of the vehicle using a detection device and store it as a set of historical location points of the vehicle;

[0067] The abnormal deviation judgment module is used to judge whether the vehicle is in an abnormal deviation state according to the scene of the vehicle and the relative position relationship with the road and road section range obtained from the road network map information when the number of historical position points of the vehicle exceeds the set threshold;

[0068] The exception handling module is used to obtain the latest position point from the historical position point set if the vehicle is in an abnormal deviation state, and predict and cover the vehicle position by calculating the historical longitude speed and latitude speed of the vehicle;

[0069] A normal processing module, for identifying the abnormal state of the front and rear positions of the vehicle based on a preset threshold and the acquired vehicle driving state data if the vehicle is not in an abnormal deviation state;

[0070] The front and rear position anomaly processing module is used to obtain the latest position point from the historical position point set when the vehicle is identified as having an abnormal front and rear position, and predict and cover the vehicle position by calculating the longitude speed and latitude speed of the vehicle.

[0071] In a third aspect, an embodiment of the present invention provides a vehicle abnormal position correction device based on road network position information, comprising: at least one database; and a memory communicatively connected to the at least one database; wherein the memory stores instructions executable by the at least one database, and the instructions are executed by the at least one database so that the at least one database can execute the vehicle abnormal position correction method based on road network position information as described above.

[0072] In a fourth aspect, an embodiment of the present invention provides a computer-readable medium having computer executable instructions stored thereon, and when the executable instructions are executed by a processor, the vehicle abnormal position correction method based on road network position information as described above is implemented.

[0073] (III) Beneficial effects

[0074] The beneficial effects of the present invention are:

[0075] First, the present invention achieves a comprehensive and accurate grasp of the vehicle's location by collecting and preprocessing road network map information and using detection equipment to track the vehicle's location. This not only relies on real-time vehicle location data, but also integrates rich historical data and geographic information, providing a solid foundation for subsequent abnormal judgment and effectively avoiding misjudgments caused by insufficient data utilization.

[0076] Furthermore, when the number of historical location points of a vehicle exceeds a set threshold, the present invention can accurately judge the abnormal deviation state by combining the scene of the vehicle in the road network map information and its relative position relationship with the road and road section range. This refined judgment logic significantly improves the reliability and stability of the system and effectively overcomes the drawback of the overly simple judgment rules in the prior art.

[0077] The present invention also performs well in exception handling. Once it is detected that the vehicle is in an abnormal deviation state or the front and rear positions are abnormal, the latest position point will be immediately obtained from the historical position point set, and the vehicle position will be predicted and covered by calculating the latitude and longitude speed of the vehicle. This processing method not only avoids the information loss and errors caused by directly discarding or simply correcting abnormal data, but also ensures the display effect of the digital twin platform and the accuracy of trajectory indicator calculation, while also improving the roughness of existing exception handling methods.

[0078] It is worth mentioning that, although the present invention has shown a high degree of refinement and intelligence in abnormal judgment and processing, its implementation is relatively simple and practical. This is due to the unique data fusion mechanism and innovative judgment rule design, which enables the present invention to avoid the various limitations brought by complex artificial intelligence models while still maintaining excellent performance.

[0079] Therefore, the present invention proposes to significantly improve the accuracy of vehicle position judgment and the precision of exception handling by making full use of and integrating multiple data sources, designing innovative judgment rules, and adopting refined exception handling methods, thereby providing strong support for the development and application of intelligent transportation systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0080] Figure 1 A schematic flow chart of a method for correcting abnormal vehicle position based on road network position information provided by an embodiment of the present invention;

[0081] Figure 2 A specific flow chart of step S1 of a method for correcting abnormal vehicle position based on road network position information provided by an embodiment of the present invention;

[0082] Figure 3 A specific flow chart of step S2 of a method for correcting abnormal vehicle position based on road network position information provided by an embodiment of the present invention;

[0083] Figure 4 A specific flow chart of step S3 of a method for correcting abnormal vehicle position based on road network position information provided by an embodiment of the present invention;

[0084] Figure 5 A vehicle abnormal deviation state recognition flow chart of a vehicle abnormal position correction method based on road network position information provided by an embodiment of the present invention;

[0085] Figure 6 A specific flow chart of step S4 of a method for correcting abnormal vehicle position based on road network position information provided by an embodiment of the present invention;

[0086] Figure 7 A schematic diagram of abnormal vehicle deviation in a method for correcting abnormal vehicle position based on road network position information provided by an embodiment of the present invention;

[0087] Figure 8 A schematic diagram of an abnormal vehicle reversing method for correcting abnormal vehicle position based on road network position information provided by an embodiment of the present invention;

[0088] Fig. 9 A schematic diagram of abnormal flashing of a vehicle in a method for correcting abnormal vehicle position based on road network position information provided by an embodiment of the present invention;

[0089] Fig.10 A schematic diagram of the overall process of a method for correcting abnormal vehicle position based on road network position information provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0090] In order to better explain the present invention and facilitate understanding, the present invention is described in detail below through specific implementation modes in conjunction with the accompanying drawings.

[0091] Prior to this, in order to facilitate understanding of the technical solution provided by the present invention, some concepts are first introduced below.

[0092] Lane centerline heading angle: The angle between the line connecting the lane centerline from the starting point to the end point and the true north direction is the lane centerline heading angle;

[0093] Vehicle heading angle: The line connecting the front and rear positions of the vehicle is the direction of the vehicle, and the angle between the line and the north direction is the vehicle heading angle;

[0094] Following model: A model in which a vehicle adjusts its own speed and acceleration based on the speed and position data of the vehicle in front and behind, thereby predicting its own position.

[0095] like Figure 1 As shown, an abnormal position correction method for a vehicle based on road network position information proposed in an embodiment of the present invention includes: collecting and preprocessing road network map information and storing it in a database; tracking the vehicle position by using a detection device and storing it as a historical position point set of the vehicle; when the number of historical position points of the vehicle exceeds a set threshold, judging whether the vehicle is in an abnormal deviation state according to the scene in which the vehicle is located and the relative position relationship with the road and section range obtained from the road network map information; if the vehicle is in an abnormal deviation state, obtaining the latest position point from the historical position point set, and predicting and covering the vehicle position by calculating the historical longitude speed and latitude speed of the vehicle; if the vehicle is not in an abnormal deviation state, identifying the abnormal state of the front and rear positions of the vehicle based on a preset threshold and the obtained vehicle driving state data; when the vehicle is identified as having abnormal front and rear positions, obtaining the latest position point from the historical position point set, and predicting and covering the vehicle position by calculating the longitude speed and latitude speed of the vehicle.

[0096] First, the present invention achieves a comprehensive and accurate grasp of the vehicle's location by collecting and preprocessing road network map information and using detection equipment to track the vehicle's location. This not only relies on real-time vehicle location data, but also integrates rich historical data and geographic information, providing a solid foundation for subsequent abnormal judgment and effectively avoiding misjudgments caused by insufficient data utilization.

[0097] Furthermore, when the number of historical location points of a vehicle exceeds a set threshold, the present invention can accurately judge the abnormal deviation state by combining the scene of the vehicle in the road network map information and its relative position relationship with the road and road section range. This refined judgment logic significantly improves the reliability and stability of the system and effectively overcomes the drawback of the overly simple judgment rules in the prior art.

[0098] The present invention also performs well in exception handling. Once it is detected that the vehicle is in an abnormal deviation state or the front and rear positions are abnormal, the latest position point will be immediately obtained from the historical position point set, and the vehicle position will be predicted and covered by calculating the latitude and longitude speed of the vehicle. This processing method not only avoids the information loss and errors caused by directly discarding or simply correcting abnormal data, but also ensures the display effect of the digital twin platform and the accuracy of trajectory indicator calculation, while also improving the roughness of existing exception handling methods.

[0099] It is worth mentioning that, although the present invention has shown a high degree of refinement and intelligence in abnormal judgment and processing, its implementation is relatively simple and practical. This is due to the unique data fusion mechanism and innovative judgment rule design, which enables the present invention to avoid the various limitations brought by complex artificial intelligence models while still maintaining excellent performance.

[0100] Therefore, the present invention proposes to significantly improve the accuracy of vehicle position judgment and the precision of exception handling by making full use of and integrating multiple data sources, designing innovative judgment rules, and adopting refined exception handling methods, thereby providing strong support for the development and application of intelligent transportation systems.

[0101] In order to better understand the above technical solution, exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided to enable a clearer and more thorough understanding of the present invention and to fully convey the scope of the present invention to those skilled in the art.

[0102] Specifically, an embodiment of the present invention provides a method for correcting abnormal vehicle position based on road network position information, comprising:

[0103] S1. Collect and pre-process road network map information and store it in the database.

[0104] Furthermore, if Figure 2 As shown, step S1 includes:

[0105] S11. Extracting road network map information including road information, road section information, lane information and lane centerline information from the map data.

[0106] It is important to understand that the road network map information including road information, road section information, lane information and lane centerline information can be extracted from map data not only from high-precision maps, but also from various sources such as open maps and virtual maps. These map data provide rich road network details, including detailed information on roads, road sections, lanes and lane centerlines, which provide an important foundation for building a comprehensive and accurate road network map. By comprehensively utilizing these diverse map resources, it is possible to obtain the required road network map information more flexibly to adapt to different application scenarios and needs.

[0107] S12. Screening and preprocessing the collected road network map information to remove duplicate, invalid or erroneous data.

[0108] S13. Establish a data table structure for storage in the database, and import the pre-processed road network map information into the database according to the data table structure.

[0109] Specifically, road information includes road number and road range consisting of a set of longitude and latitude coordinate points. The road range includes motor vehicle lanes, non-motor vehicle lanes and green belts, and the road range contains at least one road section; section information includes section number, road number of the road to which it belongs and section range consisting of a set of longitude and latitude coordinate points. The section range is limited to motor vehicle lanes and contains at least one lane; lane information includes lane number, section number of the road to which it belongs and lane range consisting of a set of longitude and latitude coordinate points. Each lane range has a unique corresponding lane centerline; lane centerline information includes lane centerline number, lane number of the lane to which it belongs and a set of longitude and latitude coordinate points from the starting point to the end point of the lane. The lane centerline is composed of more than one line segments connected in sequence.

[0110] S2. Use detection equipment to track the vehicle's position and store it as a set of historical position points of the vehicle.

[0111] Furthermore, if Figure 3 As shown, step S2 includes:

[0112] S21. Utilize the detection equipment to continuously track the position of the target vehicle at set time intervals.

[0113] S22. Whenever a new position of the vehicle is detected, the vehicle position data including the longitude of the position point, the latitude of the position point and the detection time of the position point are recorded.

[0114] S23. Arrange the vehicle location data in sequence according to the detection time to form a historical location point set of the vehicle, and store it in a database.

[0115] In the embodiment of the present invention, the vehicle position is tracked at a time interval Δt using detection equipment such as cameras and radars, and the historical position data of the tracked vehicles is stored in a database. The historical position point set of each vehicle M is P = {P 0 ,...P n}.

[0116] S3. When the number of historical position points of the vehicle exceeds a set threshold, determine whether the vehicle is in an abnormal deviation state based on the scene of the vehicle obtained from the road network map information and its relative position relationship with the road and road section range.

[0117] Furthermore, if Figure 4 As shown, step S3 includes:

[0118] S31. When the number of historical position points of the vehicle exceeds a set threshold, the current vehicle position is obtained.

[0119] S32: Determine whether the current vehicle position is within the road range.

[0120] S33a. If the current vehicle position is not within the road range, further judgment is made based on the scene the vehicle is in. Specifically, the scene the vehicle is in is classified according to the project category. If it is a tunnel, elevated road, expressway, or national highway, it is a closed road scene; if it is a road with an intersection in the city, it is an urban road scene, and the scene label is pre-set as a parameter.

[0121] If the vehicle is in a closed road scene including a highway and a tunnel, the vehicle position is marked as an abnormal deviation state. If the scene is an urban road scene, the vehicle position is marked as a normal state. The scene to which the vehicle belongs is determined based on the object of the project. If it is a tunnel, elevated road, highway, or national highway, it is a closed road scene; if it is a road with an intersection in the city, it is an urban road scene.

[0122] S33b. If the current vehicle position is within the road range, further determine whether the current vehicle position is within the road section range.

[0123] S34. If the vehicle position at the current moment is within the road section range, the vehicle position is marked as normal. If the vehicle position at the current moment is not within the road section range, and the scene where the vehicle is located is a closed road scene including a highway and a tunnel, the vehicle position is marked as normal. If the vehicle position at the current moment is not within the road section range, and the scene where the vehicle is located is an urban road scene, it is further determined whether the vehicle position at the previous moment is within the road section range. If the vehicle position at the previous moment is within the road section range, the vehicle position is marked as abnormal deviation state. If the vehicle position at the previous moment is not within the road section range, the vehicle position is marked as normal.

[0124] In one embodiment, reference Figure 5 , the identification of abnormal vehicle deviation state specifically includes the following steps:

[0125] Determine the current vehicle position P n+1 Is it within the road range?

[0126] If the current vehicle position P n+1 If the vehicle is not within the road range, in closed road scenarios such as highways and tunnels, the vehicle position is marked as abnormal deviation.

[0127] If the current vehicle position P n+1 Not within the road range, in urban road scenarios, to avoid abnormal road conditions such as left turns and entering tunnels, the vehicle position is marked as normal.

[0128] If the current vehicle position P n+1Within the road range, determine the current vehicle position P n+1 Whether it is within the road section range.

[0129] Determine the position point P n+1 Whether it is within the road section range.

[0130] If the current vehicle position P n+1 Within the road section, the vehicle position is marked as normal.

[0131] If the current vehicle position P n+1 If the vehicle is not within the road section and is in a closed road scenario such as a highway or tunnel, the vehicle position is marked as normal to avoid situations such as vehicles driving in the emergency lane on closed roads.

[0132] If the location point is not within the road section and is in an urban road scene, determine the vehicle location point P at the previous moment n Whether it is within the road section range.

[0133] Determine the vehicle position point P at the previous moment n Whether it is within the road section range.

[0134] If the vehicle position at the last moment is within the road section, the vehicle position is marked as abnormal deviation.

[0135] If the vehicle position point at the previous moment is also not within the road section range, the vehicle position is marked as normal.

[0136] S4. If the vehicle is in an abnormal deviation state, the latest position point is obtained from the historical position point set, and the vehicle position is predicted and covered by calculating the historical longitude speed and latitude speed of the vehicle.

[0137] Furthermore, if Figure 6 As shown, step S4 includes:

[0138] S41. If the vehicle is in an abnormal deviation state, the latest one or more position points are automatically selected from the historical position point set to form a position point subset.

[0139] S42. In the location point subset, select adjacent location point pairs in turn, and calculate the longitude speed and latitude speed between each pair of adjacent location points according to the location point longitudes, location point latitudes and location point detection times in the historical location point set.

[0140] S43, averaging all the calculated longitude velocities to obtain an average value of the longitude velocities; and averaging all the latitude velocities to obtain an average value of the latitude velocities.

[0141] S44. Using the obtained average longitude speed and average latitude speed, combined with the time difference between the vehicle's last position and the current time, the predicted longitude and latitude position of the vehicle at the current time is obtained, and used to cover the original vehicle position at the current time.

[0142] In another embodiment, the vehicle's latest latitude and longitude position p' is predicted n+1 The description includes the following steps:

[0143] Get the latest m position points from the historical vehicle position point set P to form a set PM, where m can be 5.

[0144] Calculate the adjacent position points PM in the set PM in sequence i With PM i+1 Longitude speed v i,lon With latitude speed v i,lat , that is: v i,lon =(lon i+1 -lon i ) / (t i+1 -t i ),v i,lat =(lat i+1 -lat i ) / (t i+1 -t i ).

[0145] Calculate the average longitude speed of m locations The average latitude velocity

[0146] Calculate the latest latitude and longitude position of the vehicle p' n+1 ,Right now t n+1 is the current moment, t n is the time of the vehicle's last position. n ,lat n The longitude and latitude of the vehicle’s last position respectively.

[0147] S5a: If the vehicle is not in an abnormal deviation state, the abnormal state of the front and rear positions of the vehicle is identified based on a preset threshold and the acquired vehicle driving state data.

[0148] Furthermore, if Figure 7 As shown, step S5a includes:

[0149] S5a1. According to the vehicle position at the current moment and the vehicle position at the previous moment, obtain the longitude and latitude of the front and rear positions of the vehicle, and the lane centerline heading angle of the lane where the vehicle is located, and calculate the vehicle heading angle and the vehicle travel distance in combination with the front and rear position coordinates to form a vehicle travel status data set.

[0150] Furthermore, step S5a1 includes: obtaining the vehicle position at the current moment including longitude and latitude, and recording it as the first data item of the vehicle driving status data set; determining the lane centerline of the lane where the vehicle position is located at the current moment according to the road network map information, and calculating the angle between the lane zigzag line of the lane centerline and the true north direction line to obtain the lane centerline heading angle, and recording it as the second data item of the vehicle driving status data set; obtaining the vehicle position at the previous moment including longitude and latitude, and recording it as the third data item of the vehicle driving status data set; determining the lane centerline of the lane at the previous moment according to the road network map information. The lane centerline of the lane where the vehicle is located is obtained by calculating the angle between the lane fold line of the lane centerline and the true north line, and the heading angle of the lane centerline at the previous moment is obtained, which is recorded as the fourth data item of the vehicle driving status data set; the vehicle heading angle is obtained by calculating the angle between the line connecting the vehicle position at the previous moment and the vehicle position at the current moment and the true north line, and the fifth data item of the vehicle driving status data set; the vehicle driving distance is obtained by calculating the straight-line distance between the vehicle position at the previous moment and the vehicle position at the current moment, and the sixth data item of the vehicle driving status data set.

[0151] In a specific embodiment, each data item is represented as:

[0152] 1) The vehicle’s current position at time t is point P n+1 The coordinates (lon n+1 ,lat n+1 ), including longitude and latitude.

[0153] 2) The vehicle’s current position at time t is point P n+1 The lane centerline heading angle angle n+1 The lane centerline heading angle is the angle between the lane fold line and the true north direction, which is calculated from the lane centerline (CenterLine) information of the road network map information.

[0154] 3) The vehicle’s position point P at the last moment t-Δt n The coordinates (lon n ,lat n ), including longitude and latitude.

[0155] 4) The vehicle’s position point P at the last moment t-Δt n The lane centerline heading angle angle n .

[0156] 5) Vehicle heading angle θ n,n+1 P is the vehicle's last position point n and the current position point P n+1 Connection P n Pn+1 The angle with true north.

[0157] 6) Vehicle driving distance dis n,n+1 P is the vehicle's last position point n and the current position point P n+1 Connection P n P n+1 Length.

[0158] S5a2, if the vehicle's travel distance does not exceed the distance threshold, the vehicle is deemed to be stationary. If the vehicle's travel distance exceeds the distance threshold, the following abnormal state identification of the front and rear positions of the vehicle is performed based on the vehicle's travel status data set: When the angle change between the lane centerline heading angle of the lane where the vehicle is currently located and the previous moment exceeds the preset deviation threshold, the vehicle is determined to have an abnormal deviation. When the angle deviation between the lane centerline heading angle of the vehicle at the previous moment and the vehicle heading angle exceeds the preset reversing threshold, the vehicle is determined to have an abnormal reversing. When the vehicle's travel distance within a preset time interval exceeds the preset flashing threshold, the vehicle is determined to have an abnormal flashing.

[0159] In another embodiment, abnormal vehicle deviation recognition: the vehicle deviates to the opposite lane or other lanes in a short period of time, and the lane where the vehicle is located changes dramatically, as shown in the schematic diagram Figure 7 The change in the heading angle between the centerline of the lane where the vehicle is located at the last moment and the current moment exceeds the threshold value θ change , can be 120°, that is, |angle n -angle n+1 |≥θ change .

[0160] Abnormal vehicle reverse detection: A vehicle appears behind the vehicle’s previous position in a short period of time, as shown in the diagram below. Figure 8 The vehicle's lane centerline heading angle at the last moment is shown as n The vehicle heading angle θ n,n+1 The angle deviation exceeds the threshold value θ back , can be 90°, that is, |angle n -θ 01 |>θ back .

[0161] Abnormal vehicle flash recognition: A vehicle appears in front of the vehicle at a distance from its previous position in a short period of time, as shown in the diagram below. Fig. 9 As shown. The vehicle's forward distance exceeds the threshold value dis t0 , Δt can be taken as 20m when the time interval is 500ms, that is, dis n,n+1 ≥dis t0 .

[0162] S5b, when the vehicle is identified as having abnormal front and rear positions, the latest position point is obtained from the historical position point set, and the vehicle position is predicted and covered by calculating the longitude speed and latitude speed of the vehicle. It should be noted that the process of step S5b is consistent with step S4.

[0163] In addition, an embodiment of the present invention provides a vehicle abnormal position correction system based on road network position information, comprising:

[0164] The collection and processing module is used to collect and pre-process road network map information and store it in the database.

[0165] The location tracking module is used to track the vehicle location using detection equipment and store it as a set of historical location points of the vehicle.

[0166] The abnormal deviation judgment module is used to judge whether the vehicle is in an abnormal deviation state based on the vehicle scene and the relative position relationship with the road and road section range obtained from the road network map information when the number of historical position points of the vehicle exceeds the set threshold.

[0167] The exception handling module is used to obtain the latest position point from the historical position point set if the vehicle is in an abnormal deviation state, and predict and cover the vehicle position by calculating the historical longitude speed and latitude speed of the vehicle.

[0168] The normal processing module is used to identify the abnormal state of the front and rear positions of the vehicle based on a preset threshold and the acquired vehicle driving state data if the vehicle is not in an abnormal deviation state.

[0169] The front and rear position anomaly processing module is used to obtain the latest position point from the historical position point set when the vehicle is identified as having an abnormal front and rear position, and predict and cover the vehicle position by calculating the longitude speed and latitude speed of the vehicle.

[0170] Furthermore, an embodiment of the present invention provides a vehicle abnormal position correction device based on road network position information, comprising: at least one database; and a memory communicatively connected to the at least one database; wherein the memory stores instructions executable by the at least one database, and the instructions are executed by the at least one database so that the at least one database can execute the vehicle abnormal position correction method based on road network position information as described above.

[0171] The embodiment of the present invention introduces an innovative device, which is specifically used to correct abnormal vehicle positions based on road network position information. The core components of this device include at least one database and a memory connected to these databases.

[0172] The function of the database is mainly to store, manage and process data related to the road network location information, which is the basis for executing the abnormal position correction of the vehicle. The memory is responsible for storing a series of instructions that can be executed by the database.

[0173] When these instructions are executed by the database, the device can automatically run the aforementioned vehicle abnormal position correction method based on road network position information. This method can effectively identify and correct abnormal data of vehicle position, thereby improving the accuracy and reliability of position information.

[0174] In short, this device realizes automatic detection and correction of abnormal vehicle positions through the collaborative work of database and memory, providing strong support for various applications that rely on precise location information.

[0175] Furthermore, an embodiment of the present invention provides a computer-readable medium having computer executable instructions stored thereon, and when the executable instructions are executed by a processor, the vehicle abnormal position correction method based on road network position information as described above is implemented.

[0176] When these instructions are executed by the processor, the aforementioned vehicle abnormal position correction method based on road network position information can be implemented. Through this computer-readable medium, the correction method can be conveniently deployed on various computing devices. As long as the device is equipped with a corresponding processor and can read and execute the instructions on this medium, it can automatically perform the correction operation of the abnormal position of the vehicle.

[0177] This computer-readable medium not only facilitates the dissemination and application of the method, but also greatly improves the flexibility and universality of the correction operation, allowing more devices and systems to benefit from this advanced correction technology.

[0178] In summary, the embodiments of the present invention provide a method, system, device and medium for correcting abnormal vehicle position based on road network position information. Fig.10 , the overall process is:

[0179] (1) Collect and obtain the road network map information such as roads, sections, lanes, etc. required for vehicle abnormal position correction. After screening and preprocessing, these data will be stored in the database for subsequent use.

[0180] (2) Cameras, radars and other detection equipment track the vehicle position at time intervals Δt, and the historical position data of the tracked vehicles is stored in the database. The historical position point set of each vehicle M is P = {P 0 ,...P n}. When the vehicle obtains the latest real-time position P at the current moment n+1 When , if the number of historical position points of vehicle M meets the point requirement, go to step (3), otherwise go to step (8).

[0181] (3) Calculate P based on the scene where the vehicle is located, including urban roads and closed roads. n+1 The relative position relationship with the road range and section range is used to calculate the vehicle's latest position P n+1 Abnormal deviation state identification.

[0182] (4) If the vehicle is identified as abnormal deviation state, go to step (7); if the vehicle is in normal state, according to the vehicle's latest position P n+1 The position P at the previous moment n , obtain the longitude, latitude and lane centerline heading angle of the vehicle's front and rear positions, and calculate the vehicle heading angle and vehicle travel distance in combination with the front and rear coordinates to form a vehicle driving status data set N.

[0183] (5) If the vehicle's travel distance does not exceed the distance threshold, the vehicle is considered stationary and the process proceeds to step (8). If the vehicle's travel distance exceeds the threshold, the abnormal state of the vehicle's front and rear positions is identified based on the data set N calculated in step (4), including abnormal deviation of the vehicle, abnormal reverse movement of the vehicle, and abnormal flashing of the vehicle.

[0184] (6) If the vehicle is identified as being in an abnormal front and rear position state, proceed to step (7); if the vehicle is in a normal state, proceed to step (8).

[0185] (7) Based on the latest k positions of the historical position point set P, calculate the historical longitude and latitude speed of the vehicle and predict the latest longitude and latitude position P of the vehicle n ' +1 , which covers the latest vehicle position P n+1 .

[0186] (8) The vehicle's latest position P n+1 Add the historical location point set P, and update P to P 0 ,…,P n ,P n+1 , and complete the update in the database. Among them, when the vehicle position is marked as abnormal deviation state or abnormal front and rear position state, the vehicle's latest position P n+1 The vehicle position point P predicted in step (7) n ' +1 ; The vehicle position is normal, the vehicle's latest position P n+1 The vehicle position point is directly obtained by radar, video and other detection equipment in step (2).

[0187] Therefore, the present invention aims to provide a more comprehensive, accurate and efficient vehicle abnormal position correction solution to overcome the shortcomings of the prior art. The solution fully integrates the real-time data, historical data and geographic information of the vehicle position, designs detailed rule scenarios, and thus achieves a more comprehensive and accurate vehicle position judgment, effectively avoiding misjudgment caused by insufficient data utilization.

[0188] The present invention uses innovative, geometric-based judgment rules that can accurately identify a variety of complex abnormal situations, including vehicle deviation, reversal, flashing, etc. This method not only improves the reliability and stability of the system, but also overcomes the defect that the existing judgment rules are too simple.

[0189] In addition, a unique data fusion mechanism is used to effectively combine the real-time data and historical data of vehicle location with geographic information such as road shape and heading, providing a solid foundation for accurately judging vehicle location and abnormal situations. This mechanism can make detailed judgments based on the characteristics of different road types and vehicle driving history, further enhancing its practicality and accuracy.

[0190] It is worth mentioning that the solution also uses the vehicle position prediction model to accurately process abnormal data, avoiding the loss and errors of information that may be caused by direct abandonment or simple correction. This not only ensures the display effect of the digital twin platform and the accuracy of trajectory indicator calculation, but also effectively improves the problem of rough existing abnormal processing.

[0191] Although the solution of the present invention is powerful, it still maintains the characteristics of simplicity and practicality. It avoids the use of complex artificial intelligence models, thereby reducing the limitations of application difficulty, promotion difficulty, high computing resource requirements, long training time and stringent requirements on data quality.

[0192] In general, the present invention provides an efficient, accurate and practical solution for correcting abnormal vehicle positions through innovative judgment rules, detailed judgment scenarios and unique data fusion mechanism.

[0193] Since the system / device described in the above embodiments of the present invention is a system / device used to implement the method of the above embodiments of the present invention, a person skilled in the art can understand the specific structure and deformation of the system / device based on the method described in the above embodiments of the present invention, and thus will not be described in detail here. All systems / devices used in the method of the above embodiments of the present invention belong to the scope of protection of the present invention.

[0194] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0195] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems) and computer program products according to embodiments of the present invention. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions.

[0196] It should be noted that in the claims, any reference numerals placed between brackets shall not be construed as limiting the claims. The word "comprising" does not exclude the presence of components or steps not listed in the claims. The word "a" or "an" preceding a component does not exclude the presence of a plurality of such components. The invention may be implemented by means of hardware comprising several different components and by means of a suitably programmed computer. In the claims enumerating several means, several of these means may be embodied by the same hardware. The use of the words first, second, third, etc., is for convenience of expression only and does not indicate any order. These words may be understood as part of the component name.

[0197] In addition, it should be noted that, in the description of this specification, the description of the terms "one embodiment", "some embodiments", "embodiment", "example", "specific example" or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, unless they are contradictory.

[0198] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments after knowing the basic creative concept. Therefore, the claims should be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present invention.

[0199] Obviously, those skilled in the art can make various modifications and variations to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention should also include these modifications and variations.

Claims

1. A method for correcting abnormal vehicle position based on road network position information, characterized in that: include: Collect and pre-process road network map information and store it in the database; Track the vehicle position using detection equipment and store it as a set of historical position points of the vehicle; When the number of historical position points of a vehicle exceeds a set threshold, the vehicle is judged to be in an abnormal deviation state based on the scene of the vehicle obtained from the road network map information and its relative position relationship with the road and road section range; If the vehicle is in an abnormal deviation state, the latest position point is obtained from the historical position point set, and the vehicle position is predicted and covered by calculating the historical longitude speed and latitude speed of the vehicle; If the vehicle is not in an abnormal deviation state, the abnormal state of the front and rear positions of the vehicle is identified based on a preset threshold and the acquired vehicle driving state data; When the vehicle is identified as having abnormal front and rear positions, the latest position point is obtained from the historical position point set, and the vehicle position is predicted and covered by calculating the longitude speed and latitude speed of the vehicle.

2. The vehicle abnormal position correction method based on road network position information according to claim 1, characterized in that: Collect and pre-process road network map information and store it in the database including: Extracting road network map information including road information, road section information, lane information and lane centerline information from map data; Screen and pre-process the collected road network map information to remove duplicate, invalid or erroneous data; Establish a data table structure for storage in the database, and import the pre-processed road network map information into the database according to the data table structure; in, The road information includes a road number and a road range consisting of a set of longitude and latitude coordinate points, the road range includes a motor vehicle lane, a non-motor vehicle lane and a green belt, and the road range contains at least one road section; The road section information includes the road section number, the road number of the road to which it belongs, and the road section range consisting of a set of longitude and latitude coordinate points. The road section range is limited to the motor vehicle lane and contains at least one lane; The lane information includes the lane number, the section number of the road section to which it belongs, and the lane range consisting of a set of longitude and latitude coordinate points. Each lane range has a unique corresponding lane centerline. The lane centerline information includes the lane centerline number, the lane number of the lane to which it belongs, and a set of longitude and latitude coordinate points from the starting point to the end point of the lane. The lane centerline is composed of more than one line segments connected in sequence.

3. The vehicle abnormal position correction method based on road network position information according to claim 1, characterized in that: The vehicle position is tracked using detection equipment and stored as a set of historical position points of the vehicle, including: Use detection equipment to continuously track the location of the target vehicle at set time intervals; Whenever a new position of the vehicle is detected, the vehicle position data including the longitude of the position point, the latitude of the position point and the detection time of the position point is recorded; The vehicle location data is sorted in sequence according to the detection time to form a set of historical location points of the vehicle and stored in the database.

4. The vehicle abnormal position correction method based on road network position information as claimed in claim 2, characterized in that: When the number of historical position points of a vehicle exceeds a set threshold, judging whether the vehicle is in an abnormal deviation state based on the scene of the vehicle obtained from the road network map information and its relative position relationship with the road and road section range includes: When the number of historical position points of the vehicle exceeds the set threshold, the current vehicle position is obtained; Determine whether the current vehicle position is within the road range; If the current vehicle position is not within the road range, further judgment is made based on the scene of the vehicle determined by the preset scene label; If the vehicle is in a closed road scenario including a highway and a tunnel, the vehicle position will be marked as an abnormal deviation state; If the scene is an urban road scene, the vehicle position is marked as normal; If the current vehicle position is within the road range, then further determine whether the current vehicle position is within the road section range; If the current vehicle position is within the road section, the vehicle position is marked as normal; If the current vehicle position is not within the road section, and the vehicle is in a closed road scene including a highway and a tunnel, the vehicle position is marked as normal; If the current vehicle position is not within the road section range, and the vehicle is in an urban road scene, then further determine whether the vehicle position at the previous moment was within the road section range; If the vehicle position at the last moment was within the road section, the vehicle position will be marked as abnormal deviation; If the vehicle position at the last moment is not within the road section range, the vehicle position is marked as normal.

5. The vehicle abnormal position correction method based on road network position information according to claim 1, characterized in that: If the vehicle is in an abnormal deviation state, the latest position point is obtained from the historical position point set. By calculating the historical longitude and latitude speed of the vehicle, the vehicle position is predicted and covered, including: If the vehicle is in an abnormal deviation state, the latest one or more position points are automatically selected from the historical position point set to form a position point subset; In the location point subset, adjacent location point pairs are selected in sequence, and the longitude speed and latitude speed between each pair of adjacent location points are calculated respectively according to the location point longitudes, location point latitudes and location point detection times attached to the historical location point set; All the calculated longitude velocities are averaged to obtain an average value of the longitude velocities; and all the latitude velocities are averaged to obtain an average value of the latitude velocities; The average longitude and latitude speeds are used together with the time difference between the last position of the vehicle and the current position to obtain the predicted longitude and latitude position of the vehicle at the current time, and used to overwrite the original vehicle position at the current time. Among them, the predicted longitude and latitude position is: In the formula, lon' n+1 ,lat' n+1 are the predicted longitude and latitude positions, respectively, lon n ,lat n The longitude and latitude of the vehicle’s last position, are the average longitude and latitude speeds, respectively, t n+1 is the current moment, t n is the time of the vehicle's last position.

6. The vehicle abnormal position correction method based on road network position information according to claim 1, characterized in that: If the vehicle is not in an abnormal deviation state, the abnormal state identification of the front and rear positions of the vehicle based on the preset threshold and the acquired vehicle driving state data includes: According to the current vehicle position and the previous vehicle position, the longitude and latitude of the front and rear positions of the vehicle and the lane centerline heading angle of the lane are obtained, and the vehicle heading angle and the vehicle travel distance are calculated by combining the front and rear position coordinates to form a vehicle travel status data set; If the vehicle's travel distance does not exceed the distance threshold, the vehicle is considered stationary; If the vehicle travels a distance exceeding the distance threshold, the following abnormal state identification of the front and rear positions of the vehicle is performed based on the vehicle travel status data set: When the angle change between the lane centerline heading angle of the vehicle at the current moment and the lane at the previous moment exceeds the preset deviation threshold, it is determined that the vehicle has deviated abnormally; When the angle deviation between the heading angle of the centerline of the lane where the vehicle was at the last moment and the heading angle of the vehicle exceeds the preset reversing threshold, it is determined that the vehicle has reversed abnormally; When the vehicle travel distance within a preset time interval exceeds a preset flash threshold, it is determined that the vehicle has abnormal flash.

7. The vehicle abnormal position correction method based on road network position information as claimed in claim 6, characterized in that: According to the current vehicle position and the previous vehicle position, the longitude and latitude of the front and rear positions of the vehicle, and the lane centerline heading angle of the lane where the vehicle is located are obtained. The vehicle heading angle and the vehicle travel distance are calculated by combining the front and rear position coordinates. The vehicle driving status data set includes: Obtaining the current vehicle position including longitude and latitude, and recording it as the first data item of the vehicle driving status data set; According to the road network map information, the lane centerline of the lane where the vehicle is located at the current moment is determined, and the angle between the lane polyline of the lane centerline and the true north direction line is calculated to obtain the lane centerline heading angle, which is recorded as the second data item of the vehicle driving status data set; Obtaining the vehicle position at the last moment including longitude and latitude, and recording it as the third data item of the vehicle driving status data set; According to the road network map information, determine the lane centerline of the lane where the vehicle is located at the last moment, and calculate the angle between the lane polyline of the lane centerline and the true north direction line to obtain the lane centerline heading angle at the last moment, which is recorded as the fourth data item of the vehicle driving status data set; The vehicle heading angle is obtained by calculating the angle between the line connecting the vehicle position at the previous moment and the vehicle position at the current moment and the true north direction line, and recorded as the fifth data item of the vehicle driving state data set; The vehicle travel distance is obtained by calculating the straight-line distance between the vehicle position at the previous moment and the vehicle position at the current moment, and is recorded as the sixth data item of the vehicle travel status data set.

8. A vehicle abnormal position correction system based on road network position information, characterized in that: include: The acquisition and processing module is used to acquire and pre-process the road network map information and store it in the database; A location tracking module, used to track the location of the vehicle using a detection device and store it as a set of historical location points of the vehicle; The abnormal deviation judgment module is used to judge whether the vehicle is in an abnormal deviation state according to the scene of the vehicle and the relative position relationship with the road and road section range obtained from the road network map information when the number of historical position points of the vehicle exceeds the set threshold; The exception handling module is used to obtain the latest position point from the historical position point set if the vehicle is in an abnormal deviation state, and predict and cover the vehicle position by calculating the historical longitude speed and latitude speed of the vehicle; A normal processing module, for identifying the abnormal state of the front and rear positions of the vehicle based on a preset threshold and the acquired vehicle driving state data if the vehicle is not in an abnormal deviation state; The front and rear position anomaly processing module is used to obtain the latest position point from the historical position point set when the vehicle is identified as having an abnormal front and rear position, and predict and cover the vehicle position by calculating the longitude speed and latitude speed of the vehicle.

9. A vehicle abnormal position correction device based on road network position information, characterized in that: include: at least one database; And a memory connected to at least one database in communication; wherein the memory stores instructions that can be executed by at least one database, and the instructions are executed by at least one database so that at least one database can execute the vehicle abnormal position correction method based on road network position information as described above.

10. A computer-readable medium having computer-executable instructions stored thereon, characterized in that: When the executable instructions are executed by the processor, the vehicle abnormal position correction method based on the road network position information as described above is implemented.

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