Method and system for analyzing driving state of driving device based on driving route, and medium

Abstract

The invention discloses a driving device driving state analysis method and system based on a driving route and a medium, and relates to the technical field of driving state analysis, and the method comprises the steps: obtaining an actual driving track data set with a unique driving device identifier and a plurality of groups of preset target path data sets; performing target path compliance judgment on the plurality of groups of preset target path data sets to obtain a plurality of compliance target paths meeting compliance conditions; based on the plurality of compliance target paths, performing full path matching analysis on actual track data corresponding to the actual driving track data set in combination with a preset bidirectional matching strategy to obtain a path matching rate of the plurality of compliance target paths; selecting the compliant target path with the highest matching rate as target driving path data, and performing multi-dimensional comprehensive analysis on the driving state of the driving device based on the target driving path data to obtain a driving state evaluation result; the invention provides a driving state analysis method which meets the requirement of a main engine plant for checking the integrity of a test driving route.

Description

Technical Field

[0001] This application relates to the field of driving status analysis technology, and in particular to a method, system and medium for analyzing the driving status of a vehicle based on a driving route. Background Technology

[0002] Most cars today are equipped with an onboard T-Box device with a built-in GPS (Global Positioning System) that automatically reports its location during vehicle startup and driving. Car dealerships (4S stores) typically pre-plan multiple test drive routes for potential customers, but the test drive vehicles may not actually follow these routes, affecting the effectiveness of the OEM's assessment of the number of test drive attempts.

[0003] Existing route matching methods typically rely on real-time matching analysis of shorter paths, primarily focusing on matching the vehicle's current location with the target path during its journey. For example, the route determination method used in invention patent CN109147371A, applied to the field of intelligent public transportation, determines whether the vehicle is traveling towards a target location based on its real-time location. If the vehicle is traveling towards the target location, it is determined that the actual route has not changed, and the currently set route is maintained. If the vehicle is not traveling towards the designated target location, it is determined that the actual route has changed, and a corresponding alarm will be generated.

[0004] The aforementioned route matching method is more often applied to the dynamic matching analysis of the current position and the target path during vehicle travel, and the matching results are used for scenarios such as path replanning and alarm monitoring.

[0005] Therefore, existing methods are mostly used for real-time monitoring and alarms, and do not perform matching analysis on the entire driving route for post-event review and evaluation or assessment mechanisms, which cannot meet the OEM's assessment requirements for the completeness of test drive routes. Summary of the Invention

[0006] To address the shortcomings of existing technologies and to provide a driving state analysis method that meets the OEM's requirements for assessing the completeness of test drive routes, this application provides a driving state analysis method, system, and medium based on driving routes.

[0007] Firstly, the objective of this invention is achieved through the following technical solution: Methods for analyzing the driving status of a vehicle based on its driving route include: Acquire a set of actual driving trajectory data with a unique vehicle identifier and multiple sets of preset target path data; The compliance of multiple sets of preset target path data is judged to obtain multiple compliant target paths that meet the compliance conditions; Based on multiple compliant target paths, and combined with a preset two-way matching strategy, a full path matching analysis is performed on the actual trajectory data corresponding to the actual driving trajectory data set to obtain the path matching rate of multiple compliant target paths; The compliant target path with the highest matching rate is selected as the target driving path data. Based on the target driving path data, the driving status of the vehicle associated with the vehicle identifier is analyzed in multiple dimensions to obtain the driving status evaluation result.

[0008] By adopting the above technical solution, this application mainly analyzes the driving status of a single set of actual driving trajectory data and multiple sets of preset target path data. It does not require additional equipment to be installed on the vehicle; it only needs to report real-time trajectory data. Based on the vehicle identifier, different vehicles can be identified and targeted analysis can be performed. This application's technical solution solves the problem in existing technologies that can only achieve short-range dynamic matching and cannot comprehensively evaluate the entire driving route. It not only improves the accuracy of route matching but also expands the monitoring capabilities for various driving states of the vehicle. Besides vehicles, it can also be applied to scenarios involving real-time alarms and post-event evaluations of the driving status of vehicles such as drones and unmanned boats. Specifically, data acquisition and initialization are first performed to ensure the accuracy and comprehensiveness of the data foundation for subsequent analysis. Then, the set of preset target path data is subjected to target path compliance judgment and inspection. Compliance judgments include the compliance of path points, straight segment spacing, total length, and coverage diameter, etc., to improve the effectiveness of target path screening and avoid mismatches caused by unreasonable paths (such as too many breakpoints or abnormal coverage). To enhance the accuracy of route matching, a two-way matching strategy is employed, employing dual verification through "actual trajectory → target path" and "target path → actual trajectory." This combination reduces misjudgments caused by local noise or instantaneous yaw during the full-path matching analysis process, effectively assessing whether the vehicle is following the predetermined path. It also identifies potential deviations. Finally, the compliant target path with the highest matching rate (which more accurately reflects the vehicle's actual driving situation) is selected as the final route, and multi-dimensional analysis is performed. This multi-dimensional analysis (such as driving time, speed changes, and headroom differences) not only assesses whether the vehicle is following the expected path but also further understands the safety and rationality of its driving behavior, providing data for subsequent improvements. Through rigorous target path compliance checks and the application of the two-way matching strategy, this application is particularly suitable for scenarios requiring high-precision route matching and driving status assessment, such as OEMs' management and evaluation mechanisms for test drive vehicles, effectively improving the efficiency and safety of vehicle management. This achieves the technical objective of "providing a driving status analysis method that meets OEMs' assessment requirements for the integrity of test drive routes."

[0009] In a preferred example, the target path compliance determination includes the following steps: Based on the preset target path data set, extract the location point sequence of each target path and calculate the number of trajectory points contained in each path; Filter out target paths with more trajectory points than the preset minimum path point count threshold, and remove invalid paths; For each retained target path, the Euclidean distance between any two adjacent trajectory points is calculated iteratively, and abnormal trajectory points are identified and filtered out by combining the preset straight-line distance threshold to obtain the denoised target path; Based on a preset total line length threshold and a preset diameter threshold, the validity and compliance of the denoised target path are determined, and multiple compliant target paths that meet the compliance conditions are obtained.

[0010] By adopting the above technical solutions, the quality of target path data is optimized through path point filtering, straight-line distance denoising, and geometric feature verification. Specifically, fragmented paths are eliminated by using a minimum path point threshold to reduce invalid matches caused by sensor noise or planning defects; abnormal points are removed based on a preset straight-line distance threshold to ensure that the continuity of the path set conforms to the actual road logic and suppress trajectory drift interference; furthermore, abnormal paths that are too long or too short (such as turnaround routes) are excluded through dual verification of the total path length and coverage diameter, thereby improving the baseline reliability of subsequent matching.

[0011] In a preferred embodiment of this application: the step of performing full path matching analysis on the actual trajectory data corresponding to the actual driving trajectory data set based on multiple compliant target paths and in conjunction with a preset two-way matching strategy to obtain the path matching rate of the multiple compliant target paths includes: Based on the coordinate sequences of multiple compliant target paths, a spatial line segment model corresponding to the coordinate sequences is constructed; based on the spatial line segment model, the vertical projection distance from each trajectory point of the actual driving trajectory data set to each line segment is calculated; In the actual driving trajectory data set, trajectory points whose vertical projection distance does not exceed a preset effective distance threshold are selected to generate a candidate matching point set; In the candidate matching point set, the direction consistency of the candidate matching point set is checked based on a preset direction angle threshold: the difference between the direction angle of the trajectory point of the candidate matching point set and the direction angle of the trajectory point of the multiple compliant target paths is calculated. If the direction angle difference is greater than the preset direction angle threshold, it is determined to be a successfully matched line point; the set of matched trajectory points of the entire path is obtained based on all successfully matched line points. Based on the set of matched trajectory points along the entire path and a preset distance decay model, the route matching weight length of the compliant target path and the path matching rate with the actual driving trajectory data set are calculated sequentially to obtain the path matching rates of multiple compliant target paths.

[0012] By adopting the above technical solutions, the spatial line segment model construction and orientation angle consistency verification achieve bidirectional and efficient matching between trajectory points and target paths. Specifically, trajectory points far from the path are filtered based on vertical projection distance to reduce the impact of far-end noise on subsequent path matching. The orientation consistency constraint comparison calculation of orientation angle difference is used to eliminate trajectory points that deviate from the path direction, thereby enhancing the rigor of path orientation matching. Then, based on the preset distance attenuation model, the full path matching rate of the corresponding point set of each compliant target path is comprehensively evaluated, which can reduce the risk of misjudgment and significantly improve the accuracy of path matching.

[0013] In a preferred embodiment, this application further includes: In the actual driving trajectory data set, the actual ground distance d between two adjacent trajectory points is calculated using the ground distance calculation formula. hav ; The preset distance decay model, based on the set of matched trajectory points along the entire path, is calculated using the following formula: Calculate the single trajectory point matching rate Cumulative route matching weight length Where i is the trajectory point identifier; M is the total number of trajectory points in the set of matched trajectory points for the entire path; The formula for calculating the path matching rate is: Among them, L target The path length for the compliance target path.

[0014] By adopting the above technical solution, the matching rate is quantified by the ratio of the weighted sum of distances to the total path length, so as to achieve refined matching evaluation and improve the noise resistance of the distance attenuation model.

[0015] In a preferred embodiment of this application: for each retained target path, the Euclidean distance between any two adjacent trajectory points is calculated iteratively, and abnormal trajectory points are identified and filtered out to obtain the denoised target path, based on a preset straight-line distance threshold, specifically including: For each retained target path, the Euclidean distance between any two adjacent trajectory points in each target path is calculated iteratively; the Euclidean distance between any two adjacent trajectory points is compared with a preset straight-line distance threshold, which includes a maximum straight-line distance and a minimum straight-line distance. If the Euclidean distance between two adjacent trajectory points is greater than the maximum straight-line distance or less than the minimum straight-line distance, the corresponding trajectory point is marked as a drift point and filtered to perform a denoising operation to obtain the denoised target path.

[0016] By adopting the above technical solution, "drift points" in the path are eliminated by comparing the Euclidean distance and straight-line distance thresholds between adjacent trajectory points. By calculating the single trajectory point matching rate and the length of the route matching weight, abnormal points with sudden increases or decreases in the distance between adjacent points can be found (which are easily generated by GPS noise or instantaneous yaw in actual operation). The smoothness of the path is enhanced by calculation, and trajectory segments with geometric continuity that conform to road characteristics are retained. By reducing the number of invalid trajectory points, the computational efficiency and convergence speed of subsequent algorithms are improved.

[0017] In a preferred embodiment of this application: the determination of the validity and compliance of the denoised target path based on a preset total line length threshold and a preset diameter threshold, and the acquisition of multiple compliant target paths that meet the compliance conditions, includes: After performing denoising on all the marked drift points in the denoised target path, we obtain an updated set of multiple preset target path data sets. In the updated set of multiple preset target path data, the total length of the point set of the target path after denoising and the coverage diameter of the area are calculated. The effectiveness of the denoised target path is determined based on preset total line length thresholds and preset diameter thresholds: If the total length of the target path's point set after denoising is greater than the preset total length threshold and the coverage diameter of the area is greater than the preset diameter threshold, then it is marked as a compliant target path that meets the compliance requirements. Otherwise, mark it as an invalid path and remove it.

[0018] By adopting the above technical solution, and using dual thresholds for total length and coverage diameter, the rationality of the target path is ensured. Paths that are too short (e.g., containing only two points) or too dispersed (e.g., coverage diameter far exceeding road width) are eliminated, improving the reliability of compliant paths. Trajectory data from non-road areas (e.g., green belts, parking lots) are filtered using a diameter threshold (e.g., 50 meters) to ensure the geographical rationality of path matching.

[0019] In a preferred embodiment of this application, the step of obtaining the actual driving trajectory data set with a unique vehicle identifier and multiple sets of preset target path data sets includes: The actual driving trajectory data set is obtained based on the vehicle identifier. The actual driving trajectory data set includes the monitoring trajectory parameters of the vehicle during the task time period. The monitoring trajectory parameters include longitude, latitude, direction angle, speed, altitude, driving range, remaining fuel, total mileage and timestamp. Multiple sets of target path data are obtained. Each set of target path data consists of multiple location points arranged in sequence. Each set of target path data includes the path number of the preset target path, the longitude, latitude, direction, speed and altitude of the location points. The driving status assessment results include speed matching degree, direction matching degree, and height matching degree.

[0020] By adopting the above technical solution, multiple sets of target path data are obtained. Each set of data contains multiple location points and their detailed coordinate information (such as longitude, latitude, direction, speed, etc.), ensuring the accuracy and completeness of the target path. Furthermore, this application clarifies that the driving status assessment results should include multiple dimensions such as speed matching degree, direction matching degree, and altitude matching degree, making the assessment results more comprehensive and accurate.

[0021] Secondly, the objective of this invention is achieved through the following technical solution: A vehicle driving status analysis system based on driving route, the system comprising: The data acquisition module is configured to acquire a set of actual driving trajectory data with a unique vehicle identifier and multiple sets of preset target path data. The compliance judgment module is used to judge the compliance of multiple sets of preset target path data to obtain multiple compliant target paths that meet the compliance conditions. The path matching analysis module is used to perform full path matching analysis on the actual trajectory data corresponding to the actual driving trajectory data set based on multiple compliant target paths and in combination with a preset two-way matching strategy, so as to obtain the path matching rate of multiple compliant target paths; The driving status assessment module is used to select the compliant target path with the highest matching rate as the target driving path data, and to perform a multi-dimensional comprehensive analysis of the driving status of the vehicle associated with the vehicle identifier based on the target driving path data, so as to obtain the driving status assessment result.

[0022] By adopting the above technical solutions, the compliance judgment module performs a rigorous compliance check on the target path data, filtering out target paths that meet the conditions and improving the accuracy of subsequent matching analysis. The path matching analysis module employs a two-way matching strategy, combining actual trajectory data and target path data for comprehensive matching analysis, ensuring high accuracy of the matching results. Finally, the driving status assessment module performs a multi-dimensional comprehensive analysis of the vehicle's status based on the target path with the highest matching rate, generating a detailed driving status assessment report.

[0023] Thirdly, the objective of this invention is achieved through the following technical solution: A computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the above-described method for analyzing the driving state of a vehicle based on a driving route.

[0024] Fourthly, the objective of this invention is achieved through the following technical solution: A computer program product includes a computer program / instructions that, when executed by a processor, implement the steps of the vehicle driving state analysis method based on driving route as described above.

[0025] In summary, this application includes at least one of the following beneficial technical effects: 1. Acquire actual driving trajectory data and multiple sets of preset target path data, and perform full-path matching analysis to evaluate driving status. First, unique identifiers are used to ensure the accuracy and uniqueness of data sources, thus providing a reliable data foundation for subsequent steps. Second, by judging the compliance of target paths, target paths that meet the standards are selected, improving the accuracy of matching results. Next, a two-way matching strategy is adopted to accurately match the actual trajectory with the target path, which not only improves matching accuracy but also enhances the system's ability to identify abnormal behavior. 2. Spatial line segment model construction and orientation angle consistency verification enable bidirectional and efficient matching between trajectory points and target paths. Attached Figure Description

[0026] Figure 1 This is a flowchart of a vehicle driving state analysis method based on a driving route in one embodiment of this application; Figure 2 This is a flowchart of step S2 in a method for analyzing the driving status of a vehicle based on a driving route according to an embodiment of this application; Figure 3 This is a flowchart of step S3 in a method for analyzing the driving status of a vehicle based on a driving route in one embodiment of this application. Detailed Implementation

[0027] The present application will be further described in detail below with reference to the accompanying drawings.

[0028] In one embodiment, such as Figure 1 As shown, this application discloses a method for analyzing the driving state of a vehicle based on a driving route, specifically including the following steps: S1: Obtain the actual driving trajectory data set with a unique vehicle identifier and multiple sets of preset target path data sets.

[0029] In this embodiment, the unique vehicle identifier is an identity identifier used to uniquely identify the vehicle (such as a vehicle VIN code or a drone serial number), which is used to associate the physical entity of the vehicle with its driving data. The actual driving trajectory data set includes the vehicle's monitored trajectory parameters during the mission period. These parameters include longitude, latitude, heading angle (0°~360°), speed (km / h), altitude (referring to elevation, in meters), range (km), total mileage (km), remaining fuel (L), and timestamp (UTC timestamp, accurate to milliseconds). Data sources include vehicle-mounted T-Boxes, drone flight control systems, and shipborne positioning terminals. Each target path data set consists of multiple location points arranged sequentially. Each target path data set includes the path number of the preset target path (such as ROUTE_001), mission start time, mission end time, GPS time, and the longitude, latitude, direction, speed, and altitude of the location points.

[0030] Specifically, a set of fixed threshold parameters is predefined, including a minimum path point threshold, a preset total line length threshold, a preset diameter threshold, a preset effective distance threshold, a preset direction angle threshold, and a preset straight-line distance threshold. The minimum path point threshold, also known as the minimum location point threshold, is 10 points and can be customized as needed. Similarly, the preset total line length threshold is predefined based on the length of the specific preset target path. The preset diameter threshold is the diameter distance threshold for all points on the line, such as 50 meters. The preset effective distance threshold is also 50 meters. The preset straight-line distance threshold is the straight-line distance threshold between two points on the line, including the maximum straight-line distance (such as 50 meters) and the minimum straight-line distance (such as 0.5 meters). The preset direction angle threshold is 180°.

[0031] The preset target path data set is a pre-planned standardized driving route template, consisting of an ordered sequence of location points, and is applied to scenarios such as 4S store test drive routes, standard logistics delivery routes, and autonomous driving test routes.

[0032] S2: Perform compliance judgment on multiple sets of preset target path data to obtain multiple compliant target paths that meet the compliance conditions.

[0033] Specifically, such as Figure 2 As shown, the compliance determination of the target path includes the following steps: S21: Based on the preset target path data set, extract the location point sequence of each target path and calculate the number of trajectory points contained in each path.

[0034] In this embodiment, the number of trajectory points in the point set of the preset target path data set is traversed and calculated. If it is empty or less than the preset minimum path point threshold, the compliance of the target path data set is set to not pass (i.e., invalid path). Otherwise, step S22 is executed.

[0035] S22: Filter out target paths with trajectory points greater than the preset minimum path point threshold, and remove invalid paths.

[0036] In this embodiment, the minimum number of path points is preset to 10, which is set according to industry standards, such as the reference standard GB / T 39774-2021 "Intelligent Transportation System Vehicle Driving Path Recognition".

[0037] S23: For each retained target path, iteratively calculate the Euclidean distance between any two adjacent trajectory points, and combine it with a preset straight-line distance threshold to determine abnormal trajectory points and filter them to remove noise, thus obtaining the denoised target path.

[0038] In this embodiment, the Euclidean distance (i.e., straight-line distance) between every two trajectory points in each target path is calculated iteratively.

[0039] Specifically, step S23 includes: S231: For each retained target path, iteratively calculate the Euclidean distance between any two adjacent trajectory points in each target path.

[0040] In this embodiment, the Euclidean distance calculation formula is as follows: Convert the latitude and longitude difference to spherical distance, 1 degree ≈ 111.195 kilometers, where i and j are the trajectory point identifiers, for example d 12 lng is the Euclidean distance between trajectory point 1 and trajectory point 2. j , lng i These are the longitude values ​​of points j and i, respectively; lat j lat i are the latitude values ​​of points j and i, respectively.

[0041] S232: Compare the Euclidean distance between any two adjacent trajectory points with a preset straight-line distance threshold, which includes the maximum and minimum straight-line distance values.

[0042] In this embodiment, the maximum straight-line distance, such as 50 meters, can be determined as a drift point based on the speed limit design of the test road in actual use; the minimum straight-line distance, such as 0.5 meters, is used to exclude zero displacement points caused by sensor noise and is determined as noise points.

[0043] S233: If the Euclidean distance between two adjacent trajectory points is greater than the maximum straight-line distance or less than the minimum straight-line distance, then mark the corresponding trajectory point as a drift point and filter it to perform a denoising operation to obtain the denoised target path.

[0044] In this embodiment, the drift points filtered out because the Euclidean distance is greater than the maximum straight-line distance can be repaired by using linear interpolation.

[0045] S24: Based on the preset total line length threshold and the preset diameter threshold, determine the validity and compliance of the denoised target path, and obtain multiple compliant target paths that meet the compliance conditions.

[0046] In this embodiment, the total length of the compliant target path is: Where N is the number of trajectory points on the compliant target path, and the value of N varies depending on the compliant target path. The preset total route length threshold is compared with the total route length of the compliant target path, and the preset diameter threshold is compared with the diameter (i.e., the coverage diameter) of the area where the set of points on the compliant target path is located.

[0047] The coverage diameter of the target compliant path is obtained by using two trajectory points: the starting trajectory point and the ending trajectory point. The calculation formula is as follows: Among them, D diameter R represents the coverage diameter; R is the Earth's radius, used in the code as 6371 kilometers. The difference in latitude between two points; Δλ represents the dimension between the two points, in radians; Δλ represents the difference in longitude between the two points.

[0048] In this embodiment, step S24 includes: S241: After performing denoising based on all the marked drift points in the denoised target path, an updated set of multiple preset target path data is obtained.

[0049] In this embodiment, the updated multiple sets of preset target path data sets are filtered to remove drift points and noise points in step S23, and then the remaining points are reconnected to form a continuous path.

[0050] S242: In the updated set of multiple preset target path data, calculate the total length of the point set of the target path after denoising and the coverage diameter of the area.

[0051] In this embodiment, the total length of the point set is calculated by summing the Euclidean distances of all adjacent nodes of the denoised target path. The coverage diameter of the area is the longest spherical distance of the path coverage area of ​​the denoised target path.

[0052] S243: Determine the validity of the denoised target path based on a preset total line length threshold and a preset diameter threshold. S2431: If the total length of the point set of the target path after denoising is greater than the preset total length threshold and the coverage diameter of the area is greater than the preset diameter threshold, then it is marked as a compliant target path that meets the compliance conditions.

[0053] S2432: Otherwise, mark it as an invalid path and remove it.

[0054] In this embodiment, a preset total route length threshold is used to determine whether the path meets the minimum length requirement for practical driving; a preset diameter threshold is used to prevent excessive path concentration. Invalid paths are those that do not meet either the preset total route length threshold or the preset diameter threshold and must be eliminated.

[0055] Furthermore, after completing the compliance verification of the target path, the actual driving trajectory data set is obtained. The actual trajectory mileage can be calculated by summing the total mileage of the preceding and following trajectory points. To ensure the accuracy of the mileage calculation, this application also calculates the mileage data using the Haversine formula based on the distance between two actual trajectory points.

[0056] Specifically, the vehicle driving status analysis method based on the driving route also includes: S100: In the actual driving trajectory data set, the actual geodetic distance d between two adjacent trajectory points is calculated using the geodetic distance calculation formula. hav .

[0057] In this embodiment, the Haversine formula is used to calculate the geodetic distance. The calculation of the Haversine formula includes: d hav =R·c; where a and c are intermediate variables; R is the Earth's radius, with a value of 6,371,000 meters; The difference in latitude between the two points; radLat1 is the latitude value of the first trajectory point; radLat2 is the latitude value of the second trajectory point; d hav Δλ represents the actual geodetic distance between the two points; Δλ represents the latitude difference between the two points.

[0058] S200: The preset distance decay model, based on the calculation formula of the set of matched trajectory points along the entire path, includes: Calculate the single trajectory point matching rate Cumulative route matching weight length Where i is the trajectory point identifier; M is the total number of trajectory points in the set of matched trajectory points along the entire path.

[0059] The formula for calculating the path matching rate is: Among them, L targetThe path length for the compliance target path.

[0060] S3: Based on multiple compliant target paths, and combined with a preset two-way matching strategy, perform full path matching analysis on the actual trajectory data corresponding to the actual driving trajectory data set to obtain the path matching rate of multiple compliant target paths.

[0061] In this embodiment, the preset bidirectional matching strategy is to simultaneously perform bidirectional verification of "path matching trajectory" and "trajectory matching path"; forward matching refers to projecting trajectory points onto the path segments of the compliant target path, while reverse matching is to match the path nodes of the compliant target path to the trajectory segments.

[0062] Specifically, such as Figure 3 As shown, step S3 includes: S31: Based on the coordinate sequences of multiple compliant target paths, construct a spatial line segment model of the corresponding coordinate sequence; based on the spatial line segment model, calculate the vertical projection distance from each trajectory point in the actual driving trajectory data set to each line segment.

[0063] In this embodiment, the coordinate sequence is a sequence of latitude and longitude nodes. The spatial line segment model corresponding to the coordinate sequence discretizes the compliant target path into a sequence of line segments: i = 1, 2, ..., N-1; where LINE i For line segment; P i is the actual trajectory point; i is the trajectory point identifier.

[0064] Calculate the actual trajectory point P i To line segment LINE i vertical projection distance d proj :

[0065] S32: In the actual driving trajectory data set, select trajectory points whose vertical projection distance does not exceed the preset effective distance threshold, and generate a candidate matching point set.

[0066] In this embodiment, trajectory points whose vertical projection distance exceeds a preset effective distance threshold are considered drift points and need to be filtered out again.

[0067] S33: In the candidate matching point set, perform direction consistency verification on the candidate matching point set based on the preset direction angle threshold: S331: Calculate the difference between the direction angle of the trajectory point of the candidate matching point set and the direction angle of the trajectory point of multiple compliant target paths. If the direction angle difference is greater than the preset direction angle threshold, it is determined to be a successfully matched line point; obtain the set of matched trajectory points of the entire path based on all successfully matched line points.

[0068] In this embodiment, the orientation angle of the trajectory points in the candidate matching point set is the orientation angle of the actual trajectory points: the instantaneous heading angle (e.g., P) is calculated using the coordinates of the preceding and following trajectory points. i The azimuth angle is 15° west of south; the azimuth angle of the trajectory points on the compliant target path is the azimuth angle of the target path nodes, calculated based on the coordinates of adjacent nodes. The difference in azimuth angles is the absolute value of the difference. The azimuth angle threshold is set to 180°. A successfully matched line point refers to a trajectory point whose actual trajectory point and the compliant target path segment satisfy the azimuth angle consistency check.

[0069] Specifically, all successfully matched route points and their corresponding trajectory segment data are added to the set of matched trajectory points. The starting point, ending point, distance, distance weight (an important parameter representing the spatial proximity of the trajectory point and the path segment), direction angle magnitude (the absolute difference in direction angle between the actual trajectory point and the target path segment), and direction angle weight (a numerical allocation based on a set direction angle threshold; the weight is set to 1 if the direction angle difference is greater than the preset threshold, otherwise, the weight is set to 0). The set of matched trajectory points undergoes sequence processing, grouping the matching results according to the trajectory point order. Sequence processing refers to grouping and logically reorganizing the matching results according to the trajectory point acquisition time order. Abnormal points that jump across line segments are detected and processed, generating hierarchical matching results. The hierarchical division of the multi-level matching result list includes: Level 1 matching, Level 2 matching, and Level 3 matching. Level 1 matching refers to continuous matching throughout the entire process with the direction angle difference meeting the requirements; Level 2 matching refers to matching with local directional deviations but overall continuity; and Level 3 matching refers to matching only partial segments.

[0070] S34: Based on the set of matched trajectory points along the entire path and the preset distance decay model, calculate the route matching weight length of the compliant target path and the path matching rate with the actual driving trajectory data set in sequence to obtain the path matching rate of multiple compliant target paths.

[0071] S4: Select the compliant target path with the highest matching rate as the target driving path data, and conduct a multi-dimensional comprehensive analysis of the driving status of the vehicle associated with the vehicle identifier based on the target driving path data to obtain the driving status evaluation result.

[0072] In this embodiment, the driving status evaluation results include evaluation results based on dimensions such as speed matching degree, direction matching degree, and height matching degree.

[0073] Specifically, based on data such as the vehicle's identification, longitude, latitude, direction, speed, altitude, remaining fuel, total mileage, and GPS time, analysis data such as driving time, mileage, maximum speed, minimum speed, average speed, number of rapid accelerations, and number of rapid decelerations can be calculated to monitor whether the vehicle's driving status is normal.

[0074] For example, the driving time is the duration from the start timestamp to the end timestamp of the task, obtained by retrieving the GPS timestamp field from the trajectory data. If the actual driving time is less than the preset minimum time (e.g., 1.5 hours), the task is marked as incomplete. The speed matching degree analysis compares the highest speed (the peak instantaneous speed of the actual driving trajectory), the lowest speed (the valley instantaneous speed of the actual driving trajectory), and the average speed (total mileage / driving time). If the highest speed is greater than the preset speed limit (e.g., the speed limit of 60km / h on the test drive section), it is marked as speeding.

[0075] Specifically, the instantaneous acceleration is first calculated using the speed difference between adjacent points, and the number of events where the instantaneous angular velocity exceeds the rapid acceleration threshold and the rapid deceleration threshold is obtained. Then, the number of events is compared with the number of rapid accelerations and rapid decelerations. If the number of rapid accelerations / decelerations is greater than a preset threshold (e.g., ≥15 times), it is marked as aggressive driving behavior.

[0076] In this embodiment, the direction matching degree is determined by comparing the difference between the direction angle of the actual trajectory point and the direction angle of the trajectory point of the target path segment to determine whether the driving direction meets the expectation. The preset direction angle difference threshold is compared with the actual direction angle difference. If it does not exceed the preset direction angle difference threshold, 1 point is assigned; otherwise, 0 points are assigned. Each trajectory point of the actual driving trajectory is scored one by one (different direction angle difference thresholds can be associated with trajectory point identifiers based on different road conditions), and the total score of the entire actual driving trajectory is obtained.

[0077] Height matching assessment determines whether the driving height meets requirements by comparing the height difference between the actual trajectory points and the corresponding points on the target path. This is based on a preset driving height threshold.

[0078] For example, assume the height difference threshold is Δh threshold The high-weight model is as follows: Where β is the attenuation coefficient, with a value of 10; Δh is the height difference; height matching score = W height ×100.

[0079] The multi-dimensional comprehensive evaluation is based on setting weight coefficients for parameters in each dimension, and then performing a weighted comprehensive calculation to obtain the final multi-dimensional comprehensive evaluation result.

[0080] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0081] In one embodiment, a vehicle driving status analysis system based on driving route is provided, which corresponds to the vehicle driving status analysis method based on driving route in the above embodiments.

[0082] The vehicle driving status analysis system based on driving routes includes a data acquisition module, a compliance judgment module, a route matching analysis module, and a driving status evaluation module. Detailed descriptions of each functional module are as follows: The data acquisition module is configured to acquire a set of actual driving trajectory data with a unique vehicle identifier and multiple sets of preset target path data. The compliance assessment module is used to assess the compliance of multiple sets of preset target path data to obtain multiple compliant target paths that meet the compliance conditions. The path matching analysis module performs full path matching analysis on the actual trajectory data corresponding to the actual driving trajectory data set based on multiple compliant target paths and a preset two-way matching strategy, obtaining the path matching rate of multiple compliant target paths. The driving status evaluation module selects the compliant target path with the highest matching rate as the target driving path data, and performs a multi-dimensional comprehensive analysis of the driving status of the vehicle associated with the vehicle identifier based on the target driving path data, obtaining the driving status evaluation result. Specific limitations of the driving route-based vehicle driving status analysis system can be found in the limitations of the driving route-based vehicle driving status analysis method above, and will not be repeated here. Each module in the above-mentioned driving route-based vehicle driving status analysis system can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in the computer device, or stored in the memory of the computer device as software, so that the processor can call and execute the operations corresponding to each module.

[0083] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor: S1: Obtain a set of actual driving trajectory data with a unique vehicle identifier and a set of multiple preset target path data sets; S2: Perform compliance judgment on multiple sets of preset target path data to obtain multiple compliant target paths that meet the compliance conditions; S3: Based on multiple compliant target paths, and combined with a preset two-way matching strategy, perform full path matching analysis on the actual trajectory data corresponding to the actual driving trajectory data set to obtain the path matching rate of multiple compliant target paths; S4: Select the compliant target path with the highest matching rate as the target driving path data, and conduct a multi-dimensional comprehensive analysis of the driving status of the vehicle associated with the vehicle identifier based on the target driving path data to obtain the driving status evaluation result.

[0084] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM), etc.

[0085] In one embodiment, particularly according to an embodiment of the invention, the process described above with reference to the flowchart can be implemented as a computer software program. For example, embodiments of the invention include a computer program product comprising a computer program / instructions that, when executed by a processor, implement the steps of the described route-based vehicle driving state analysis method. In such embodiments, the computer program can be downloaded and installed from a network via a communication module, and / or installed from a removable medium. When the computer program is executed by a central processing unit (CPU), it performs the various functions defined in this invention.

[0086] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0087] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for analyzing the driving state of a vehicle based on its driving route, characterized in that, include: Acquire a set of actual driving trajectory data with a unique vehicle identifier and multiple sets of preset target path data; The compliance of multiple sets of preset target path data is judged to obtain multiple compliant target paths that meet the compliance conditions; Based on multiple compliant target paths, and combined with a preset two-way matching strategy, a full path matching analysis is performed on the actual trajectory data corresponding to the actual driving trajectory data set to obtain the path matching rate of multiple compliant target paths; The compliant target path with the highest matching rate is selected as the target driving path data. Based on the target driving path data, the driving status of the vehicle associated with the vehicle identifier is analyzed in multiple dimensions to obtain the driving status evaluation result.

2. The method for analyzing the driving status of a vehicle based on its driving route according to claim 1, characterized in that, The compliance determination of the target path includes the following steps: Based on the preset target path data set, extract the location point sequence of each target path and calculate the number of trajectory points contained in each path; Filter out target paths with more trajectory points than the preset minimum path point count threshold, and remove invalid paths; For each retained target path, the Euclidean distance between any two adjacent trajectory points is calculated iteratively, and abnormal trajectory points are identified and filtered out by combining the preset straight-line distance threshold to obtain the denoised target path; Based on a preset total line length threshold and a preset diameter threshold, the validity and compliance of the denoised target path are determined, and multiple compliant target paths that meet the compliance conditions are obtained.

3. The method for analyzing the driving status of a vehicle based on a driving route according to claim 1 or 2, characterized in that, The method involves performing a full path matching analysis on the actual trajectory data corresponding to the actual driving trajectory data set based on multiple compliant target paths, combined with a preset two-way matching strategy, to obtain the path matching rate of the multiple compliant target paths, including: Based on the coordinate sequences of multiple compliant target paths, a spatial line segment model corresponding to the coordinate sequences is constructed; based on the spatial line segment model, the vertical projection distance from each trajectory point of the actual driving trajectory data set to each line segment is calculated; In the actual driving trajectory data set, trajectory points whose vertical projection distance does not exceed a preset effective distance threshold are selected to generate a candidate matching point set; In the candidate matching point set, a direction consistency check is performed on the candidate matching point set based on a preset direction angle threshold: Calculate the difference between the direction angle of the trajectory points in the candidate matching point set and the direction angle of the trajectory points in the multiple compliant target paths. If the direction angle difference is greater than the preset direction angle threshold, it is determined to be a successfully matched line point. Based on all successfully matched line points, obtain the set of matched trajectory points for the entire path. Based on the set of matched trajectory points along the entire path and a preset distance decay model, the route matching weight length of the compliant target path and the path matching rate with the actual driving trajectory data set are calculated sequentially to obtain the path matching rates of multiple compliant target paths.

4. The method for analyzing the driving status of a vehicle based on its driving route according to claim 3, characterized in that, The method also includes: In the actual driving trajectory data set, the actual ground distance d between two adjacent trajectory points is calculated using the ground distance calculation formula. hav ; The preset distance decay model, based on the set of matched trajectory points along the entire path, is calculated using the following formula: Calculate the single trajectory point matching rate Cumulative route matching weight length Where i is the trajectory point identifier; M is the total number of trajectory points in the set of matched trajectory points for the entire path; The formula for calculating the path matching rate is: Among them, L target The path length for the compliance target path.

5. The method for analyzing the driving state of a vehicle based on a driving route according to claim 2, characterized in that, For each retained target path, the Euclidean distance between any two adjacent trajectory points is calculated iteratively, and abnormal trajectory points are identified and filtered out using a preset straight-line distance threshold to obtain the denoised target path. Specifically, this includes: For each retained target path, iteratively calculate the Euclidean distance between any two adjacent trajectory points in each target path; The distance between any two adjacent trajectory points is compared with a preset straight-line distance threshold, which includes a maximum straight-line distance and a minimum straight-line distance. If the Euclidean distance between two adjacent trajectory points is greater than the maximum straight-line distance or less than the minimum straight-line distance, the corresponding trajectory point is marked as a drift point and filtered to perform a denoising operation to obtain the denoised target path.

6. The method for analyzing the driving state of a vehicle based on a driving route according to claim 5, characterized in that, The validity and compliance of the denoised target path are determined based on a preset total line length threshold and a preset diameter threshold, resulting in multiple compliant target paths that meet the compliance conditions, including: After performing denoising on all the marked drift points in the denoised target path, we obtain an updated set of multiple preset target path data sets. In the updated set of multiple preset target path data, the total length of the point set of the target path after denoising and the coverage diameter of the area are calculated. The effectiveness of the denoised target path is determined based on preset total line length thresholds and preset diameter thresholds: If the total length of the target path's point set after denoising is greater than the preset total length threshold and the coverage diameter of the area is greater than the preset diameter threshold, then it is marked as a compliant target path that meets the compliance requirements. Otherwise, mark it as an invalid path and remove it.

7. The method for analyzing the driving state of a vehicle based on a driving route according to claim 1, characterized in that, The acquisition of the actual driving trajectory data set with a unique vehicle identifier and multiple sets of preset target path data sets includes: The actual driving trajectory data set is obtained based on the vehicle identifier. The actual driving trajectory data set includes the monitoring trajectory parameters of the vehicle during the task time period. The monitoring trajectory parameters include longitude, latitude, direction angle, speed, altitude, driving range, remaining fuel, total mileage and timestamp. Multiple sets of target path data are obtained. Each set of target path data consists of multiple location points arranged in sequence. Each set of target path data includes the path number of the preset target path, the longitude, latitude, direction, speed and altitude of the location points. The driving status assessment results include speed matching degree, direction matching degree, and height matching degree.

8. A vehicle driving status analysis system based on driving route, characterized in that, The system includes: The data acquisition module is configured to acquire a set of actual driving trajectory data with a unique vehicle identifier and multiple sets of preset target path data. The compliance judgment module is used to judge the compliance of multiple sets of preset target path data to obtain multiple compliant target paths that meet the compliance conditions. The path matching analysis module is used to perform full path matching analysis on the actual trajectory data corresponding to the actual driving trajectory data set based on multiple compliant target paths and in combination with a preset two-way matching strategy, so as to obtain the path matching rate of multiple compliant target paths; The driving status assessment module is used to select the compliant target path with the highest matching rate as the target driving path data, and to perform a multi-dimensional comprehensive analysis of the driving status of the vehicle associated with the vehicle identifier based on the target driving path data, so as to obtain the driving status assessment result.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the vehicle driving state analysis method based on driving route as described in any one of claims 1 to 7.

10. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instruction is executed by the processor, it implements the steps of the vehicle driving state analysis method based on driving route as described in any one of claims 1 to 7.

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