Vehicle electronic fence judgment method and system

Abstract

The invention relates to the technical field of vehicle monitoring, and particularly discloses a vehicle electronic fence judgment method and system, and the method comprises the steps: S1, obtaining the geographic position information data of a vehicle, and processing the geographic position information data to obtain standardized vehicle position input data; acquiring a preset electronic fence boundary; s2, inputting the vehicle position input data into a preset double-engine space calculation model to obtain a preliminary fence judgment result; s3, performing intersection detection on the vehicle track obtained by the geographic position information data and the fence boundary polygon, and correcting a judgment deviation caused by a positioning error or numerical value jitter; s4, performing spatio-temporal joint index acceleration on the judgment; s5, adaptively adjusting and judging the radius of the buffer area by a preset dynamic buffer area optimization module according to the motion characteristics of the vehicle; and S6, generating fence access event alarm information. By adopting the technical scheme of the invention, high real-time performance, robustness and dynamic adaptive capability of the judgment process can be realized on the premise of ensuring the space judgment precision.

Description

Technical Field

[0001] This invention relates to the field of vehicle monitoring technology, and in particular to a method and system for determining vehicle electronic fences. Background Technology

[0002] In the field of vehicle monitoring, electronic fence technology is widely used to limit the range of vehicle movement. Current mainstream solutions are mostly based on the ray-casting method, which simplifies the Earth's surface into a Cartesian plane for determining points and polygons. However, this type of solution faces the following three technical bottlenecks in practical applications: First, spatial model distortion leads to judgment errors. Existing solutions simplify the Earth's curved surface into a plane for processing. In high-latitude or cross-time zone scenarios, the boundary judgment error caused by planar projection may exceed 200 meters, which can easily lead to false alarms or missed alarms in fence determination.

[0003] Second, the lack of dynamic adaptability leads to a high false alarm rate. Existing technologies typically use a fixed buffer distance, which cannot adapt to the real-time movement of the vehicle. Especially when the vehicle speed exceeds 80 km / h, due to factors such as GPS sampling delay, this static buffering scheme is prone to a high false alarm rate.

[0004] Third, the index structure is complex to maintain, making it difficult to support high-concurrency real-time updates. To improve judgment efficiency, existing technologies attempt to optimize retrieval by constructing endpoint sorting and interval index structures, which has some effect in small to medium-sized fence scenarios. However, when facing large-scale dynamic management scenarios with tens of thousands of fences, such solutions require maintaining complex index structures to support CRUD operations. Engineering practice and testing show that the update and maintenance cost of this type of index structure is high, and a single update may take more than 5ms; when facing high-concurrency fence real-time refresh requirements, the overall performance and responsiveness of the system will be significantly affected, making it difficult to meet the requirements of low-latency applications.

[0005] Therefore, there is an urgent need for a vehicle electronic fence determination method and system that integrates high-precision spatial computing, real-time dynamic optimization, and efficient indexing. Summary of the Invention

[0006] One of the objectives of this invention is to provide a vehicle electronic fence determination method that can achieve high real-time performance, robustness, and dynamic adaptability in the determination process while ensuring spatial determination accuracy.

[0007] To solve the above-mentioned technical problems, this application provides the following technical solution: A method for determining a vehicle's electronic fence includes the following steps: S1. Real-time acquisition of vehicle location information data, time synchronization and format processing to obtain standardized vehicle location input data; Obtain the preset electronic fence boundary; S2. Input the vehicle location data into the preset dual-engine spatial calculation model, and determine the spatial relationship between the vehicle location and the electronic fence boundary through WebMercator projection or spherical ray determination algorithm to obtain the preliminary fence determination result. S3. Input the preliminary fence determination result into the preset robust intersection determination module, perform intersection detection on the vehicle trajectory obtained from the geographic location information data and the fence boundary polygon, and use a robust algorithm to correct the determination deviation caused by positioning error or numerical jitter. S4. Accelerate the decision-making process by using a spatiotemporal joint index; S5. Obtain the vehicle's motion characteristics, and based on the vehicle's motion characteristics, the radius of the judgment buffer is adaptively adjusted by the preset dynamic buffer optimization module. S6. Based on the results of the robust intersection determination module and the dynamic buffer optimization module, generate alarm information for fence entry and exit events.

[0008] Furthermore, the geographic location information data includes latitude and longitude, and speed; Motion characteristics include velocity, acceleration, rate of change of direction, and historical trajectory.

[0009] Furthermore, in step S2, when the vehicle's real-time latitude absolute value | When |≤60°, the projection engine is activated to project and map the vehicle's latitude and longitude data to the plane coordinate system via Web Mercator; the plane coordinates are compared with the polygon of the preset electronic fence to determine the spatial relationship between the vehicle and the electronic fence, so as to obtain a preliminary determination result of whether the vehicle is located inside the fence.

[0010] Furthermore, in step S2, when the projection engine is enabled, adaptive error compensation is also employed, with the following formula: ; in, The average radius of the Earth; The latitude of the vehicle; For dynamic projection scaling levels, Characterizing latitudinal projection deformation; through Perform dynamic precision control.

[0011] Furthermore, in step S2, when | At |>60, the spherical engine is activated, and the spherical ray determination algorithm maps the vehicle's current position to point P in the spherical coordinate system. , The boundary of the electronic fence is discretized into a closed spherical polygon formed by connecting several vertices. Rays are emitted from point P in any direction on the sphere, and the number of intersections between these rays and the boundary line segments of the fence is calculated. If the number of intersections is odd, the vehicle is determined to be inside the fence; If the number of intersections is even, the vehicle is determined to be outside the fence.

[0012] Furthermore, in step S2, when the spherical engine is enabled, the great circle equation is used when calculating the intersection of the ray and the boundary: ; in, , The latitudes of the ray's origin and boundary endpoints. Difference in longitude; The spherical ray determination algorithm uses the vehicle pose point P( , Starting from point Q, launch along a great circle trajectory to point Q at the same latitude and longitude of 180° East. The ray equation is expressed as: (180°), ； in, It represents the flatness.

[0013] Furthermore, in step S3, continuous positioning data of a vehicle driving near the boundary of the electronic fence is selected as a sample to obtain a trajectory point sequence. The robust intersection determination module performs the following optimization steps: S301. Apply a weighted moving average filter to the latitude and longitude coordinates of continuous trajectory points, with the weights set as follows: The calculation formula is: ; S302. Calculate the shortest distance between the corrected trajectory segment and the boundary line segment. When the distance is less than 5 meters and the vehicle speed is less than 2 km / h, it is determined to be a false boundary crossing caused by positioning jitter.

[0014] Furthermore, in step S4, the spatiotemporal joint index acceleration module includes a quadtree coarse screening structure in the spatial dimension and a trajectory window incremental update mechanism in the temporal dimension. A quadtree coarse screening structure is used to divide the polygonal boundary of the electronic fence into boundary line segment units with a length ≤100m, and index them into grid buckets according to their geographic coordinates. The bucket size is set as follows: ; in The maximum buffer radius for vehicle positioning error; When new vehicle location data is received, the set of fence segments adjacent to the current vehicle location is filtered out using a quadtree spatial index structure, and the spherical ray determination calculation in step S2 is performed only on the candidate set. The trajectory window incremental update mechanism is used to maintain the sliding time window by combining the time series characteristics of continuous vehicle positioning. The system only performs judgment calculations on newly added trajectory points within the window, and caches and reuses historical trajectory results that have already been judged outside the window.

[0015] Furthermore, in step S5, the dynamic buffer optimization module optimizes the real-time velocity v, acceleration a, and turning radius. Generate dynamic buffer band width: ; in, For response time, This is the steering coefficient; When a vehicle enters this dynamic buffer zone, i.e., the vehicle's position is ≤ [distance from the fence boundary] When this occurs, the linkage control strategy is triggered: The positioning sampling frequency was increased from 1Hz to 5Hz; the dual-engine decision-switching latitude threshold in step S2 was tightened to | | is 55°.

[0016] The second objective of this invention is to provide a vehicle electronic fence determination system using the above-described method.

[0017] This solution employs a dual-engine spatial computation model, utilizing a spherical ray criterion algorithm to perform preliminary calculations of the spatial relationship between the vehicle's position and the electronic fence boundary, obtaining a basic determination of whether the vehicle has crossed the boundary. Subsequently, the robust intersection determination module optimizes the determination results based on the preliminary results through a collinearity fault-tolerance mechanism, correcting deviations caused by positioning errors or numerical jitter, thereby improving determination accuracy and stability. Building upon this, the spatiotemporal joint index acceleration module performs spatial pre-screening and temporal incremental calculations on the determination objects through a quadtree spatial coarse screening and trajectory window incremental update mechanism, further enhancing the system's computational efficiency in large-scale fence and high-frequency positioning scenarios. The dynamic buffer optimization module adaptively adjusts the determination buffer range based on the vehicle's real-time motion characteristics, enabling early prediction of vehicle boundary crossing trends, and enhances the system's responsiveness to complex dynamic scenarios by adjusting parameters such as positioning sampling frequency and determination threshold. Finally, the robust determination results and dynamic buffer information are comprehensively processed to generate fence entry / exit event alarms and trigger preset control strategies, including remote speed limits, driver prompts, and vehicle status reporting, achieving real-time safety management of vehicles. Through the synergistic effect of the above steps, this embodiment can achieve high real-time performance, robustness, and dynamic adaptive capability in the determination process while ensuring spatial determination accuracy. Attached Figure Description

[0018] Figure 1 This is a flowchart of an embodiment of a vehicle electronic fence determination method. Detailed Implementation

[0019] The following detailed description illustrates the specific implementation method: Example 1 like Figure 1 As shown, a vehicle electronic fence determination method according to this embodiment includes the following steps: S1. Data Acquisition: The vehicle positioning module acquires the vehicle's geographical location information in real time through the vehicle-to-everything (V2X) CAN bus. The geographical location information includes latitude, longitude, speed, and other data. The module also performs time synchronization and format processing on the geographical location information to obtain standardized vehicle location input data.

[0020] Obtain the preset electronic fence boundary.

[0021] S2. Input the vehicle location data into the preset dual-engine spatial calculation model, and determine the spatial relationship between the vehicle location and the electronic fence boundary through WebMercator projection or spherical ray determination algorithm to obtain the preliminary fence determination result.

[0022] Specifically, when the vehicle's real-time latitude absolute value| When |≤60°, the projection engine is activated to project and map the vehicle's latitude and longitude data to a planar coordinate system via Web Mercator; the planar coordinates are compared with the polygon of the preset electronic fence to determine the point-to-polygon relationship, and the spatial relationship of the vehicle relative to the electronic fence is determined by the ray method or equivalent algorithm to obtain a preliminary determination result of whether the vehicle is located inside the fence.

[0023] In this embodiment, when the projection engine is enabled, adaptive error compensation is also introduced, with the following formula:

[0024] in, The average radius of the Earth is 6,378,137 m (the semi-major axis of WGS84) in this embodiment; The latitude of the vehicle; This represents the dynamic projection scaling level, which is 18-22 in this embodiment; The compensation coefficient is at the micrometer level to meet automotive-grade positioning accuracy requirements. Characterizing latitudinal projection distortion: at the equator ( =0°) Minimum deformation, at the pole ( =90°) The deformation is the greatest, and the cosine function can accurately quantify the influence of latitude on projection distortion.

[0025] pass Dynamic precision control is implemented; for every increase of one zoom level, the error compensation precision doubles. In this embodiment, the actual value range is: zoom=18 (urban road network) → Compensation amount ≈ 0.8m zoom=22 (elevated positioning) → compensation ≈ 0.05m The maximum deformation of the Web Mercator projection over a 60° latitude zone can be reduced from 4.5% to 0.02%, demonstrating good compensation.

[0026] When | At time 60, the spherical engine is activated, and the spherical ray determination algorithm maps the vehicle's current position to point P in the spherical coordinate system. , The electronic fence boundary is discretized into a closed spherical polygon formed by connecting several vertices. Rays are emitted from point P in any direction on the sphere (usually the positive direction of the meridian), and the number of intersections between these rays and the fence boundary segments is calculated. If the number of intersections is odd, the vehicle is determined to be inside the fence; If the number of intersections is even, the vehicle is determined to be outside the fence.

[0027] Meanwhile, to improve the accuracy of the judgment, the algorithm uses the great circle equation when calculating the intersection point of the ray and the boundary:

[0028] in, , The latitudes of the ray's origin and boundary endpoints. This is due to the difference in longitude.

[0029] By calculating the intersection of the spherical rays, the system can obtain the spatial topological relationship between the vehicle position and the electronic fence boundary, thus achieving a preliminary fence determination result.

[0030] In the spherical ray determination algorithm of this embodiment, the vehicle pose point P( , Starting from point Q, launch along a great circle trajectory to point Q at the same latitude and longitude of 180° East. The ray equation is expressed as: (180°),

[0031] in, The flatness is 1 / 298.257223563 in this embodiment.

[0032] S3. Input the preliminary fence determination result into the preset robust intersection determination module, perform intersection detection on the vehicle trajectory obtained from the geographic location information data and the fence boundary polygon, and use a robust algorithm to correct the determination deviation caused by positioning error or numerical jitter, thereby improving the accuracy and stability of spatial determination.

[0033] Specifically, continuous positioning data of a vehicle driving near the boundary of an electronic fence (3-5 meters in this embodiment) is selected as a sample, with a sampling frequency of 1Hz, to obtain the following trajectory point sequence:

[0034] The boundary of the electronic fence is a closed rectangular polygon composed of the following four vertices: A(120.384600, 31.215400), B(120.384900, 31.215400), C(120.384900, 31.215600), and D(120.384600, 31.215600).

[0035] Because the vehicle was traveling at low speed near the boundary, the positioning points at times t2 and t3 were affected by GPS errors (approximately ±3m). During the initial ray determination stage, the system incorrectly identified t3 as an "outside-the-fence point," thus generating a false "boundary crossing event." To resolve this issue, the robust intersection determination module performs the following optimization steps: S301. Trajectory Smoothing Process: A weighted moving average filter is applied to the latitude and longitude coordinates of continuous trajectory points, with the weights set to... The calculation formula is:

[0036] After smoothing, the corrected coordinates at time t3 are (120.384715, 31.215491), and the distance from the boundary is corrected from -2.8 meters (out of bounds) to +0.9 meters (within the fence).

[0037] S302. Intersection detection and threshold judgment: Calculate the shortest distance between the corrected trajectory segment and the boundary line segment. When the distance is less than 5 meters and the vehicle speed is less than 2 km / h, it is judged as a false boundary crossing caused by positioning jitter. In this sample, the distance between the trajectory segment t2→t3 and the boundary is 1.2 meters, and the vehicle speed is 0.8 km / h. Therefore, the system judges it as "not crossing the boundary" and eliminates the false alarm.

[0038] Through the robust optimization process described above, the system can maintain high accuracy in boundary violation detection even in the presence of positioning errors, noise, or data jitter, significantly improving the reliability and engineering usability of spatial determination.

[0039] In this embodiment, the robust intersection determination module optimizes the spherical ray determination results obtained in step S2. Based on reusing the intersection calculation data from the geodesic ray method, the robustness of the ray intersection determination rules is enhanced: counting is only performed when a ray crosses the upper boundary of the fence from top to bottom, ignoring the lower boundary intersection; and for cases where the vehicle position point and boundary point are collinear, a distance threshold is set to trigger sub-pixel-level relocation to reduce errors caused by numerical jitter. Through these optimizations, the stability of spatial relationship determination can be improved without recalculation.

[0040] S4. To further improve the real-time performance and computational efficiency of large-scale electronic fence determination, this embodiment sets up a spatiotemporal joint index acceleration module. This module includes a quadtree coarse screening structure in the spatial dimension and a trajectory window incremental update mechanism in the temporal dimension. Specifically, Quadritree coarse screening structure: The electronic fence boundary polygon is divided into boundary line segment units with a length ≤100m, and indexed into grid buckets according to their geographic coordinates. The bucket size is set as follows:

[0041] in This is the maximum buffer radius for vehicle positioning errors.

[0042] When new vehicle location data is received, the system first uses a quadtree spatial index structure to quickly filter out the set of fence segments that are close to the vehicle's current location. The S2 spherical ray determination calculation is performed only on this candidate set, thereby significantly reducing the number of calculation objects and improving the determination response speed.

[0043] The trajectory window incremental update mechanism, combined with the time-series characteristics of continuous vehicle positioning, allows the system to maintain a sliding time window. The system only performs judgment calculations on newly added trajectory points within the window, while cached and reused historical trajectory results already judged outside the window. When a vehicle enters the same area multiple times within a short period, the fence judgment status is updated incrementally to avoid duplicate calculations.

[0044] S5. Dynamic buffer optimization processing to obtain the vehicle's motion characteristics, including speed, acceleration, rate of change of direction, and historical trajectory.

[0045] Based on the vehicle's motion characteristics, the dynamic buffer optimization module adaptively adjusts the radius of the judgment buffer to achieve early prediction and dynamic adjustment of the warning range for boundary crossing trends.

[0046] Specifically, the dynamic buffer optimization module optimizes the real-time speed v (m / s) and acceleration a (m / s²) obtained from the CAN bus. 2 ), turning radius (m), generating dynamic buffer band width:

[0047] in, For response time, This is the steering coefficient.

[0048] When a vehicle enters this dynamic buffer zone, i.e., the vehicle's position is ≤ [distance from the fence boundary] At that time, the linkage control strategy is triggered: the positioning sampling frequency is increased from 1Hz to 5Hz; the dual-engine determination of the latitude threshold in step S2 is also triggered. | at 60°, tighten to | | is 55°; S6. Fence alarm and control output: Based on the results of the robust intersection judgment module and the dynamic buffer optimization module, generate fence entry and exit event alarm information, and trigger corresponding control commands according to preset strategies. Control commands include but are not limited to remote speed limit, driver prompt or vehicle status reporting, etc. Finally, output the processing results to the vehicle management platform or driving terminal.

[0049] This solution uses a dual-engine spatial calculation model in step S2, employing a spherical ray criterion algorithm to perform preliminary calculations of the spatial relationship between the vehicle's position and the electronic fence boundary, obtaining a basic determination result of whether the vehicle has crossed the boundary. Subsequently, the robust intersection determination module in step S3 optimizes the determination result based on the preliminary result through a collinearity fault-tolerance mechanism, correcting deviations caused by positioning errors or numerical jitter, thereby improving the determination accuracy and stability. Building on this, the spatiotemporal joint index acceleration module in step S4 performs spatial pre-screening and temporal incremental calculation of the determination object through a quadtree spatial coarse screening and trajectory window incremental update mechanism, further improving the system's computational efficiency in large-scale fence and high-frequency positioning scenarios. The dynamic buffer optimization module in step S5 adaptively adjusts the determination buffer range based on the vehicle's real-time motion characteristics, enabling early prediction of vehicle boundary crossing trends, and enhances the system's responsiveness to complex dynamic scenarios by adjusting parameters such as positioning sampling frequency and determination threshold. Finally, step S6 integrates the robust judgment result with the dynamic buffer information to generate a fence entry / exit event alarm and trigger a preset control strategy, including remote speed limiting, driver prompts, and vehicle status reporting, thereby achieving real-time safety management of the vehicle. Through the synergistic effect of the above steps, this embodiment can achieve high real-time performance, robustness, and dynamic adaptability in the judgment process while ensuring spatial judgment accuracy.

[0050] This embodiment also provides a vehicle electronic fence determination system that uses the above method.

[0051] The above are merely embodiments of the present invention. The invention is not limited to the fields covered by these embodiments. Commonly known structures and characteristics in the solutions are not described in detail here. Those skilled in the art are aware of all common technical knowledge in the field prior to the application date or priority date, are able to access all existing technologies in that field, and have the ability to apply conventional experimental methods prior to that date. Those skilled in the art can, under the guidance of this application, improve and implement this solution in combination with their own capabilities. Some typical known structures or methods should not be obstacles for those skilled in the art to implement this application. It should be noted that those skilled in the art can make several modifications and improvements without departing from the structure of the present invention. These should also be considered within the scope of protection of the present invention, and will not affect the effectiveness of the implementation of the present invention or the practicality of the patent. The scope of protection claimed in this application should be determined by the content of its claims, and the specific embodiments described in the specification can be used to interpret the content of the claims.

Claims

1. A method for determining vehicle electronic fences, characterized in that, Includes the following steps: S1. Real-time acquisition of vehicle location information data, time synchronization and format processing to obtain standardized vehicle location input data; Obtain the preset electronic fence boundary; S2. Input the vehicle position data into the preset dual-engine spatial calculation model, and determine the spatial relationship between the vehicle position and the electronic fence boundary through Web Mercator projection or spherical ray determination algorithm to obtain the preliminary fence determination result. S3. Input the preliminary fence determination result into the preset robust intersection determination module, perform intersection detection on the vehicle trajectory obtained from the geographic location information data and the fence boundary polygon, and use a robust algorithm to correct the determination deviation caused by positioning error or numerical jitter. S4. Accelerate the decision-making process by using a spatiotemporal joint index; S5. Obtain the vehicle's motion characteristics, and based on the vehicle's motion characteristics, the radius of the judgment buffer is adaptively adjusted by the preset dynamic buffer optimization module. S6. Based on the results of the robust intersection determination module and the dynamic buffer optimization module, generate alarm information for fence entry and exit events.

2. The vehicle electronic fence determination method according to claim 1, characterized in that: The geographic location information data includes latitude and longitude, and speed; Motion characteristics include velocity, acceleration, rate of change of direction, and historical trajectory.

3. The vehicle electronic fence determination method according to claim 2, characterized in that: In step S2, when the vehicle's real-time latitude absolute value | When |≤60°, the projection engine is activated to project and map the vehicle's latitude and longitude data to the plane coordinate system via Web Mercator; the plane coordinates are compared with the polygon of the preset electronic fence to determine the spatial relationship between the vehicle and the electronic fence, so as to obtain a preliminary determination result of whether the vehicle is located inside the fence.

4. The vehicle electronic fence determination method according to claim 3, characterized in that: In step S2, when the projection engine is enabled, adaptive error compensation is also used, and the formula is: ； in, The average radius of the Earth; The latitude of the vehicle; For dynamic projection scaling levels, Characterizing latitudinal projection deformation; through Perform dynamic precision control.

5. The vehicle electronic fence determination method according to claim 4, characterized in that: In step S2, when | At |>60, the spherical engine is activated, and the spherical ray determination algorithm maps the vehicle's current position to point P in the spherical coordinate system. , The boundary of the electronic fence is discretized into a closed spherical polygon formed by connecting several vertices. Rays are emitted from point P in any direction on the sphere, and the number of intersections between these rays and the boundary line segments of the fence is calculated. If the number of intersections is odd, the vehicle is determined to be inside the fence; If the number of intersections is even, the vehicle is determined to be outside the fence.

6. The vehicle electronic fence determination method according to claim 5, characterized in that: In step S2, when the spherical engine is enabled, the great circle equation is used when calculating the intersection of the ray and the boundary: ； in, , The latitudes of the ray's origin and boundary endpoints. Difference in longitude; The spherical ray determination algorithm uses the vehicle pose point P( , Starting from point Q, launch along a great circle trajectory to point Q at the same latitude and longitude of 180° East. The ray equation is expressed as: (180°), ； in, It represents the flatness.

7. The vehicle electronic fence determination method according to claim 6, characterized in that: In step S3, continuous positioning data of a vehicle driving near the boundary of the electronic fence is selected as a sample to obtain a trajectory point sequence. The robust intersection determination module then performs the following optimization steps: S301. Apply a weighted moving average filter to the latitude and longitude coordinates of continuous trajectory points, with the weights set as follows: The calculation formula is: ； S302. Calculate the shortest distance between the corrected trajectory segment and the boundary line segment. When the distance is less than 5 meters and the vehicle speed is less than 2 km / h, it is determined to be a false boundary crossing caused by positioning jitter.

8. The vehicle electronic fence determination method according to claim 7, characterized in that: In step S4, the spatiotemporal joint index acceleration module includes a quadtree coarse screening structure in the spatial dimension and a trajectory window incremental update mechanism in the temporal dimension. A quadtree coarse screening structure is used to divide the polygonal boundary of the electronic fence into boundary line segment units with a length ≤100m, and index them into grid buckets according to their geographic coordinates. The bucket size is set as follows: ； in The maximum buffer radius for vehicle positioning error; When new vehicle location data is received, the set of fence segments adjacent to the current vehicle location is filtered out using a quadtree spatial index structure, and the spherical ray determination calculation in step S2 is performed only on the candidate set. The trajectory window incremental update mechanism is used to maintain the sliding time window by combining the time series characteristics of continuous vehicle positioning. The system only performs judgment calculations on newly added trajectory points within the window, and caches and reuses historical trajectory results that have already been judged outside the window.

9. The vehicle electronic fence determination method according to claim 8, characterized in that: In step S5, the dynamic buffer optimization module optimizes the real-time velocity v, acceleration a, and turning radius. Generate dynamic buffer band width: ； in, For response time, This is the steering coefficient; When a vehicle enters this dynamic buffer zone, i.e., the vehicle's position is ≤ [distance from the fence boundary] At that time, the linkage control strategy is triggered: The positioning sampling frequency was increased from 1Hz to 5Hz; the dual-engine decision-switching latitude threshold in step S2 was tightened to | | is 55°.

10. A vehicle electronic fence determination system, characterized in that, Use the method described in any one of claims 1-9.

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