A method, system, device and storage medium for positioning vehicles in blind spots

Abstract

The present invention discloses a method, system, device and storage medium for positioning vehicles in blind spots, relating to the technical field of vehicle positioning. It includes automatically switching to the autonomous positioning mode when the positioning signal strength is lower than the signal threshold, and simultaneously recording the first signal data and the first angle data; calculating the first trajectory point set according to the first signal data and the first angle data; obtaining the first speed data and the second angle data and calculating the second trajectory point set, and performing time series alignment on the second trajectory point set and the first trajectory point set to generate a deviation sequence; establishing a trajectory deviation analysis model, calculating a correction amount according to the statistical characteristics of the deviation sequence, and if the correction amount exceeds the correction threshold, performing real-time correction in combination with the current heading angle to correct the positioning output. The present invention can ensure that the vehicle can still rely on the internal sensors of the vehicle to achieve continuous positioning when the GNSS signal cannot provide stable support.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle positioning, and particularly to a method, system, device and storage medium for positioning vehicles in monitoring blind areas. Background Art

[0002] With the rapid development of intelligent transportation and autonomous driving technologies, vehicle positioning technology has become a key factor in enhancing road safety and traffic efficiency. Currently, in-vehicle positioning systems typically rely on the Global Navigation Satellite System (GNSS) as the main positioning means, using satellite signals to determine the real-time position of vehicles. With the progress of in-vehicle sensor technology, by integrating information from inertial navigation systems, vehicle speed sensors, steering angle sensors, etc., a variety of vehicle monitoring and positioning methods have been realized, such as the wheel rotation method, landmark matching method, lidar-assisted positioning, etc. These methods improve positioning accuracy through multi-sensor fusion, overcoming problems such as GNSS signal monitoring blind areas, loss or occlusion. Especially in environments where GNSS signals are weak or lost, such as in dense urban high-rise areas, tunnels, and underground parking lots, autonomous positioning methods based on vehicle self-perception have gradually become a powerful means to solve the problem.

[0003] However, the existing technologies still face some challenges. Firstly, in cases where GNSS signals are unstable or completely lost, most of the existing vehicle positioning technologies rely on inertial navigation systems for short-term autonomous positioning. The inertial navigation system is limited by sensor error accumulation and drift problems, and its long-term accuracy is difficult to guarantee. Especially in complex environments, it is unable to correct errors in real time. In addition, existing positioning algorithms often perform poorly in high-dynamic situations, prone to large trajectory deviations and excessive positioning errors. In blind areas (such as tunnels or high-rise building occlusion areas), especially when the signal strength is below the threshold, the existing methods have not effectively solved the problems of maintaining and correcting positioning accuracy, often resulting in the inability to effectively control the positioning error of vehicles, affecting the safety and reliability of the autonomous driving system. Therefore, how to maintain positioning accuracy through effective autonomous positioning methods when the signal monitoring blind area or loss occurs is an urgent problem to be solved in the current technology. Summary of the Invention

[0004] In view of the problems existing in the existing positioning of vehicles in blind areas, the present invention is proposed.

[0005] Therefore, the problem to be solved by the present invention is how to accurately control and correct positioning errors.

[0006] To solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, the present invention provides a method for positioning a vehicle in a monitoring blind area, which includes automatically switching to an autonomous positioning mode when the positioning signal strength is lower than the signal threshold, and simultaneously recording first signal data and first angle data; calculating a first set of trajectory points based on the first signal data and the first angle data; obtaining first speed data and second angle data and calculating a second set of trajectory points, and performing time series alignment on the second set of trajectory points and the first set of trajectory points to generate a deviation sequence; establishing a trajectory deviation analysis model, calculating a correction amount according to the statistical characteristics of the deviation sequence, and if the correction amount exceeds the correction threshold, performing real-time correction in combination with the current heading angle to correct the positioning output.

[0008] As a preferred solution of the method for positioning a vehicle in a monitoring blind area according to the present invention, wherein: the first signal data includes one or more of a pulse signal provided by a vehicle wheel speed sensor and dynamic measurement data provided by an inertial sensor; the first angle data is provided by a vehicle steering angle sensor or a steering wheel angle sensor; the recording frequency of the data is dynamically adjusted according to the real-time motion state.

[0009] As a preferred solution of the method for positioning a vehicle in a monitoring blind area according to the present invention, wherein calculating a first set of trajectory points based on the first signal data and the first angle data includes: based on the first signal data, obtaining signal time series data, and in combination with the first angle data, determining the constraint relationship of trajectory evolution; the determining the constraint relationship of trajectory evolution includes: performing cumulative calculation on the signal time series data in each time period to determine the distance traveled by the vehicle; at the same time, in combination with the deflection relationship defined by the first angle data, determining the position of the trajectory point corresponding to each moment of the vehicle to generate a preliminary first set of trajectory points; associating vehicle structure parameters to correct the preliminary first set of trajectory points, including: obtaining vehicle structure parameters, the vehicle structure parameters affecting the turning radius and motion trajectory of the vehicle; based on the vehicle structure parameters, adjusting the relative positions of the trajectory points in the preliminary first set of trajectory points, and using the vehicle structure parameters to correct the steering path of the vehicle through geometric relationships; the correcting the steering path of the vehicle through geometric relationships using the vehicle structure parameters includes: calculating the steering radius of the front wheels according to the first angle data, the front wheel steering radius determining the size of the vehicle turn; according to the vehicle structure parameters, there is an offset in the motion trajectory of the rear wheels relative to the front wheels, and calculating the correction path of the rear wheels based on the geometric relationship between the front wheel steering radius and the rear wheel steering path to determine the overall turning trajectory of the vehicle; by correcting the trajectory points in the preliminary first set of trajectory points, the first set of trajectory points is obtained.

[0010] As a preferred solution of the vehicle positioning method in the blind monitoring area of the present invention, the obtaining of the first speed data and the second angle data and the calculation of the second set of trajectory points include: obtaining vehicle attitude change signal data based on the target motion state data of the vehicle, analyzing the vehicle attitude change signal data, and extracting target state parameters for characterizing the vehicle attitude change; the target state parameters include the yaw angle, pitch angle, and roll angle of the vehicle; based on the inertial navigation algorithm, using the target state parameters to calculate the attitude change trajectory of the vehicle, and selecting the angle data corresponding to the moment to be calculated in the attitude change trajectory as the second angle data; at the same time, based on the target motion state data, obtaining vehicle speed signal data, and analyzing the vehicle speed signal data to extract vehicle instantaneous speed data; combining the vehicle acceleration sensor data and the wheel speed data to obtain the first speed data of the vehicle; based on the first speed data and the second angle data, combining the target position state of the vehicle, establishing the displacement evolution of the vehicle, and calculating the motion state of the vehicle in a continuous time segment under the constraint conditions of the displacement evolution; wherein, the motion state includes the target displacement amount and the target direction information per unit time; based on the motion state, using the time series integration calculation method to determine the real-time displacement trajectory of the vehicle, and selecting a plurality of discrete trajectory points in the real-time displacement trajectory to form the second set of trajectory points.

[0011] As a preferred solution of the vehicle positioning method in the blind monitoring area of the present invention, the time series alignment of the second set of trajectory points and the first set of trajectory points to generate a deviation sequence includes: based on the second set of trajectory points and the first set of trajectory points, performing time matching and spatial coordinate transformation to obtain matching trajectory point pairs at the same time index: the time matching includes when the time stamp distributions of the first set of trajectory points and the second set of trajectory points are consistent, performing one-to-one correspondence matching according to the time index; when the time stamp distributions of the first set of trajectory points and the second set of trajectory points are different, using the interpolation method to unify the time stamps; the spatial coordinate transformation includes: when the first set of trajectory points and the second set of trajectory points are represented in the same coordinate system, using the global coordinates of each trajectory point for alignment calculation; when the coordinate systems of the first set of trajectory points and the second set of trajectory points are different, then using the coordinate transformation method for unification; based on the matched trajectory point pairs, calculating the trajectory deviation corresponding to each moment, including position deviation, heading deviation, and cumulative deviation; sorting the calculated trajectory deviation data according to the time index to form a complete deviation sequence.

[0012] As a preferred embodiment of the vehicle positioning method in the blind monitoring area of the present invention, the following steps are included: Based on the trajectory deviation data, a trajectory deviation analysis model is constructed to determine the temporal characteristics of the trajectory deviation sequence, where the trajectory deviation sequence includes the lateral offset information and longitudinal displacement information of the trajectory points; statistical analysis is performed on the trajectory deviation sequence to extract statistical features for correction calculation, and the statistical features include deviation mean, deviation variance, and deviation change rate; according to the statistical features, the lateral offset and longitudinal offset correction amounts are determined.

[0013] As a preferred embodiment of the vehicle positioning method in the blind monitoring area of the present invention, the following steps are included: When the correction amount does not exceed the correction threshold, the current inertial navigation positioning data remains unchanged; when the correction amount exceeds the correction threshold, real-time correction is triggered.

[0014] In a second aspect, the present invention provides a vehicle remote monitoring system, which includes an autonomous positioning mode switching module for automatically switching to the autonomous positioning mode when the positioning signal strength is lower than the signal threshold, and simultaneously recording the first signal data and the first angle data; a first trajectory point calculation module for calculating a first trajectory point set according to the first signal data and the first angle data; a trajectory alignment module for obtaining the first speed data and the second angle data and calculating a second trajectory point set, and performing temporal alignment on the second trajectory point set and the first trajectory point set to generate a deviation sequence; a trajectory deviation analysis module for establishing a trajectory deviation analysis model, calculating a correction amount according to the statistical features of the deviation sequence, and if the correction amount exceeds the correction threshold, performing real-time correction in combination with the current heading angle to correct the positioning output.

[0015] In a third aspect, the present invention provides a computer device, including a memory and a processor, where the memory stores a computer program, and the following steps are included: When the computer program instructions are executed by the processor, the steps of the vehicle positioning method in the blind monitoring area as described in the first aspect of the present invention are implemented.

[0016] In a fourth aspect, the present invention provides a computer-readable storage medium, on which a computer program is stored, and the following steps are included: When the computer program instructions are executed by the processor, the steps of the vehicle positioning method in the blind monitoring area as described in the first aspect of the present invention are implemented.

[0017] The beneficial effects of the present invention are as follows: The present invention can ensure that the vehicle can still achieve continuous positioning relying on internal vehicle sensors (such as wheel speed sensors and steering angle sensors) even when GNSS signals cannot provide stable support. It not only maintains the adaptive ability of the system but also can continue to provide highly reliable trajectory calculation data in various environments (such as tunnels, underground garages, or gaps between high-rise buildings, etc.). By combining the structural parameters of the vehicle with real-time motion data, the movement trajectory of the vehicle is accurately calculated, reducing the influence of external interference on the positioning system. Through association with the vehicle wheelbase parameter, this step effectively compensates for the possible errors during vehicle turning, improving the accuracy of trajectory prediction. Moreover, by integrating the changes in vehicle acceleration and angular velocity into the trajectory calculation, the displacement calculation becomes more accurate. In addition, by comparing the deviation between the actual trajectory and the theoretical trajectory, positioning errors can be identified in real time to ensure high precision in complex dynamic environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0019] Figure 1 It is a flowchart of the vehicle positioning method for monitoring blind areas.

[0020] Figure 2 It is a schematic diagram of the generation of the deviation sequence in the vehicle positioning method for monitoring blind areas.

[0021] Figure 3 It is a structural diagram of the vehicle positioning system for monitoring blind areas. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0022] To make the above objects, features, and advantages of the present invention more obvious and understandable, the following will provide a detailed description of the specific embodiments of the present invention with reference to the drawings in the specification.

[0023] In the following description, many specific details are set forth to fully understand the present invention. However, the present invention can also be implemented in other ways different from the description herein. Those skilled in the art can make similar generalizations without departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0024] Secondly, the so-called "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that can be included in at least one implementation of the present invention. The appearances of "in one embodiment" in different places in this specification do not all refer to the same embodiment, nor are they separate or selectively exclusive embodiments from other embodiments.

[0025] Example 1

[0026] Reference Figures 1 to 3 , which is the first embodiment of the present invention. This embodiment provides a method for positioning vehicles in a monitoring blind area. As Figure 1 shown, it includes

[0027] S1: When the positioning signal strength is lower than the signal threshold, automatically switch to the autonomous positioning mode, and record the first signal data and the first angle data at the same time.

[0028] In the embodiment of the present invention, first, the strength of the satellite positioning signal is monitored, and it is judged whether to switch to the autonomous positioning mode by comparing with a preset signal threshold. The specific process is as follows:

[0029] The vehicle's GNSS receiver will receive satellite signals in real time and calculate the strength of the current satellite signal. The signal strength usually refers to the power when the signal arrives at the receiver, and is usually measured in dBm; the quality of the satellite signal is not only measured by the strength, but also includes the stability of the signal. The stability can be measured by the volatility of the signal. If the signal strength continuously falls below the preset threshold and the signal volatility exceeds the set fluctuation range, the signal is considered unstable.

[0030] Exemplarily, a signal stability threshold is set, and combined with the signal volatility. If the signal strength continuously falls below this threshold and the fluctuation exceeds the specified range, it is judged that the current signal is in an unavailable state. When the signal strength is lower than the signal stability threshold and the fluctuation exceeds the set fluctuation range, confirm that the signal is unavailable, and switch the current vehicle positioning to the autonomous positioning mode.

[0031] When the satellite positioning signal of the vehicle cannot provide reliable positioning, automatically switch to the autonomous positioning mode, and use other sensors to provide the vehicle's motion information, including the first signal data and the first angle data. These data are crucial for estimating the position and trajectory of the vehicle.

[0032] The first signal data includes one or more of the pulse signal provided by the vehicle wheel speed sensor and the dynamic measurement data provided by the inertial sensor; the first angle data is provided by the vehicle's steering angle sensor or the steering wheel angle sensor.

[0033] Exemplarily, it is possible to estimate the position of the vehicle relying on in-vehicle sensors (such as wheel speed sensors, IMUs, steering angle sensors, etc.).

[0034] In an embodiment of the present invention, the pulse signal provided by the vehicle wheel speed sensor may be a wheel speed pulse signal, and the first angle data may be steering angle data. Among them, the wheel speed sensor generates a pulse signal through the rotation of the wheel, and the pulse frequency is proportional to the driving speed of the vehicle. Each pulse represents a fixed angle of the wheel rotation. The number of pulses generated per second is recorded, and combined with the tire radius or other parameters, it is converted into the driving speed of the vehicle; the steering angle sensor measures the rotation angle of the vehicle steering wheel, reflects the steering state of the vehicle, and the steering angle is closely related to the change of the vehicle driving path. The angle change data provided by the steering angle sensor is recorded as a time series. Based on the steering angle, the turning radius of the vehicle can be obtained, and then the turning path of the vehicle can be deduced.

[0035] For example, during the driving process of the vehicle, the wheel speed sensor generates 10 pulses per second, and the steering angle recorded by the steering angle sensor is 5°. These data are recorded for subsequent autonomous positioning.

[0036] Optionally, the inertial measurement unit includes an accelerometer and a gyroscope, which are used to measure the acceleration and angular velocity of the vehicle. By integrating these data, the position change of the vehicle can be estimated.

[0037] S2: Calculate a first set of trajectory points according to the first signal data and the first angle data; the first set of trajectory points is generated through the relationship between the signal data and the angle data and is associated with the structural parameters of the vehicle.

[0038] Calculating the first set of trajectory points according to the first signal data and the first angle data includes: based on the first signal data, obtaining signal timing data, and combining the first angle data to determine the constraint relationship of the trajectory evolution.

[0039] In this embodiment, the theoretical trajectory point set of the vehicle is calculated according to the wheel speed pulse signal and the steering angle data of the vehicle. Among them, the theoretical trajectory point is a preliminary trajectory point deduced through the relationship between the signal data and the angle data.

[0040] Specifically, the signal timing data refers to the time series of the pulse signals provided by the wheel speed sensor during the driving process of the vehicle. These pulse signals reflect the number of pulses generated when the vehicle travels one unit of distance. The signal timing data is used to calculate the driving mileage of the vehicle. Among them, the driving distance is the number of pulses corresponding to each section of the vehicle's travel, and the driving distance can be calculated by multiplying the tire radius.

[0041] Determining the constraint relationship of the trajectory evolution includes: performing cumulative calculation on the signal timing data for each time period to determine the distance traveled by the vehicle; at the same time, combining the deflection relationship defined by the first angle data to determine the position of the trajectory point corresponding to each moment, and generating a preliminary first set of trajectory points.

[0042] In this embodiment, the constraint relationship of trajectory evolution refers to how the signal timing data and the angle data jointly act to generate trajectory points during vehicle driving.

[0043] Specifically, by integrating the wheel speed signal in each time period to accumulate the driving distance of the vehicle, the motion state of the vehicle at each moment is deduced by combining the pulse frequency of each time period with the tire radius; combining the steering angle data to determine the driving direction of the vehicle at each moment. The steering angle has a direct relationship with the driving trajectory of the vehicle, and each turn of the vehicle will affect the evolution of the trajectory.

[0044] It should be noted that the vehicle steering angle data represents the current steering angle of the vehicle (usually in degrees or radians), and based on the steering angle of the current time period, the driving direction of the vehicle at the current moment is deduced.

[0045] Combining the signal timing data and the steering angle data, a series of theoretical trajectory points are generated according to the driving distance and steering angle of the vehicle at each moment, and each trajectory point corresponds to the position of the vehicle at a certain moment.

[0046] It can be seen that through the cumulative calculation of the signal timing data and combined with the vehicle steering angle data, the system can predict the motion trajectory of the vehicle in weak signal or signal-free intervals. This method avoids the problems of reduced accuracy and failure that may occur in traditional satellite signal-dependent positioning systems.

[0047] Traditional positioning methods, especially under high-precision positioning requirements, usually rely on the intensity and stability of satellite signals. However, in environments such as underground garages and high-rise dense areas, satellite signals may be blocked or interfered with, resulting in a decrease in positioning accuracy. The present invention fills this gap through the data provided by in-vehicle sensors (such as wheel speed sensors and steering angle sensors), ensuring that the positioning system can still operate efficiently in complex environments.

[0048] After generating the preliminary trajectory point set, it is necessary to correct it in combination with the vehicle structure parameters to ensure that the calculation of the trajectory points is consistent with the physical characteristics of the vehicle:

[0049] Correlating the vehicle structure parameters to correct the preliminary first trajectory point set includes: obtaining the vehicle structure parameters, where the vehicle structure parameters affect the turning radius and motion trajectory of the vehicle; adjusting the relative positions of the trajectory points in the preliminary first trajectory point set based on the vehicle structure parameters, and correcting the steering path of the vehicle by using geometric relationships with the vehicle structure parameters.

[0050] Exemplarily, structural parameters such as the turning radius and wheelbase of the vehicle directly affect the geometric shape of the vehicle driving path, especially the turning path of the vehicle.

[0051] The method of correcting the steering path of the vehicle by using the vehicle structure parameters through geometric relationships includes: calculating the steering radius of the front wheels according to the first angle data, where the steering radius of the front wheels determines the turning size of the vehicle; according to the vehicle structure parameters, there is an offset in the movement trajectory of the rear wheels relative to the front wheels, and based on the geometric relationship between the front wheel steering radius and the rear wheel steering path, calculate the corrected path of the rear wheels to determine the overall turning trajectory of the vehicle.

[0052] The turning radius of the vehicle is closely related to structural parameters such as the wheelbase and steering angle of the vehicle. The precise utilization of these parameters helps to more accurately correct the driving path of the vehicle. By calculating the steering radii of the front and rear wheels, the present invention can accurately correct the geometric relationship of the trajectory when the vehicle turns, thereby improving the accuracy of the calculation results.

[0053] In this embodiment, the turning radius of the vehicle is related to parameters such as the steering angle and wheelbase of the vehicle. By using the steering angle data of the front wheels and the wheelbase of the vehicle, the steering radius of the front wheels can be calculated, and then the trajectory points can be corrected.

[0054] Specifically, the following calculation method can be used: the steering radius of the front wheels is equal to the quotient of the wheelbase of the vehicle and the tangent value of the steering angle. That is to say, when the vehicle steers, the steering radius of the front wheels changes with the steering angle and the wheelbase, determining the turning radius and steering trajectory of the vehicle.

[0055] In most vehicles, the movement paths of the front and rear wheels are not exactly the same. The steering path of the rear wheels usually deviates from that of the front wheels, and this deviation is closely related to the wheelbase and steering angle of the vehicle. Based on the relationship between the front wheel steering radius and the wheelbase of the vehicle, the path of the rear wheels can be corrected.

[0056] Specifically, there is an offset in the steering path of the rear wheels relative to that of the front wheels, that is, the offset caused by the difference between the rear wheel steering angle and the front wheel steering angle. By correcting this offset, the steering path of the rear wheels can be accurately deduced, thereby obtaining the overall turning trajectory of the vehicle, and the corrected path more accurately reflects the actual driving trajectory of the vehicle when turning.

[0057] By correcting the trajectory points in the preliminary first trajectory point set, the first trajectory point set is obtained. Through geometric correction of the preliminary trajectory point set and combining with the vehicle structure parameters, the corrected trajectory point set, that is, the first trajectory point set, is obtained as the final driving path of the vehicle.

[0058] S3: Obtain the second angle data, calculate the second trajectory point set, and perform time series alignment on the second trajectory point set and the first trajectory point set to generate a deviation sequence.

[0059] Obtain vehicle attitude change signal data based on the target motion state data of the vehicle, parse the vehicle attitude change signal data, and extract target state parameters for characterizing the vehicle attitude change; the target state parameters include the yaw angle, pitch angle, and roll angle of the vehicle; based on the inertial navigation algorithm, calculate the attitude change trajectory of the vehicle using the target state parameters, and select the angle data corresponding to the moment to be calculated in the attitude change trajectory as the second angle data.

[0060] In this embodiment, based on the target motion state data of the vehicle, collect the vehicle's three-axis acceleration data and three-axis angular velocity data, and perform data parsing on the three-axis acceleration data and three-axis angular velocity data to extract target state parameters for characterizing the vehicle attitude change. Among them, the target state parameters include the yaw angle, pitch angle, and roll angle of the vehicle. These angles represent the orientation changes of the vehicle in three-dimensional space and can reflect the attitude adjustment of the vehicle during driving. For example, the yaw angle describes the rotation of the vehicle around the vertical axis and is usually related to the steering angle of the vehicle. The pitch angle represents the rotation of the vehicle around the transverse axis (usually the front and rear axles of the vehicle) and affects the front and rear tilt of the vehicle. The roll angle describes the rotation of the vehicle around the longitudinal axis (the left and right direction of the vehicle body) and affects the side tilt of the vehicle; based on the inertial navigation algorithm, perform attitude calculation on the inertial measurement data to obtain the real-time attitude change parameters of the vehicle, and select the angle data corresponding to the moment to be calculated in the attitude change trajectory as the second angle data.

[0061] The second angle data usually comes from the vehicle's attitude sensors, especially data related to the vehicle's motion state and steering changes. The vehicle's attitude change signal data comes from the inertial measurement unit (IMU).

[0062] Furthermore, based on the inertial navigation algorithm, performing attitude calculation on the inertial measurement data to obtain the real-time attitude change parameters of the vehicle includes:

[0063] Use filtering methods (such as Kalman filtering, low-pass filtering) to preprocess the signal data to remove high-frequency noise and interference;

[0064] Based on the attitude angular velocity data, the quaternion method or the direction cosine matrix (DCM) can be used for attitude analysis to avoid singularities in Euler angle calculations and establish an attitude evolution equation; optionally, during the attitude evolution process, constraint conditions such as driving stability limitations can also be considered, and the yaw angle, pitch angle, and roll angle of the vehicle can be calculated.

[0065] At the same time, based on the target motion state data, obtain the vehicle speed signal data, and perform parsing on the vehicle speed signal data to extract the vehicle angular velocity data to obtain the first speed data of the vehicle.

[0066] Obtaining the target state parameters of the vehicle mainly relies on the inertial navigation algorithm. By using the acceleration data and angular velocity data obtained by the IMU sensor, the instantaneous attitude of the vehicle is calculated. The original data provided by the acceleration sensor can be used to estimate the instantaneous speed of the vehicle after filtering and denoising; the angular velocity sensor is used to measure the rotation rate of the vehicle around each axis. Combining with the acceleration data, the attitude change of the vehicle can be deduced.

[0067] Based on the first speed data and the second angle data, combined with the target position state of the vehicle, establish the displacement evolution of the vehicle, and calculate the motion state of the vehicle within a continuous time segment under the constraint conditions of the displacement evolution; wherein, the motion state includes the target displacement amount and the target direction information per unit time; based on the motion state, use the time series integration calculation method to determine the real-time displacement trajectory of the vehicle, and select a plurality of discrete trajectory points in the real-time displacement trajectory to form the second trajectory point set.

[0068] It should be noted that the displacement evolution of the vehicle is based on the target motion state data of the vehicle, and combines the first speed data and the second angle data to calculate the displacement of the vehicle at a certain moment. In this process, the speed data (including instantaneous speed and acceleration data) and the attitude change angle data are used to estimate the displacement amount and the driving direction of the vehicle.

[0069] In the embodiment of the present invention, data such as the vehicle's three-axis acceleration and angular velocity data are obtained to calculate the displacement per unit time. For example, assuming that the speed of the vehicle is known, the displacement of the vehicle can be approximately determined within a small time step; and under the influence of the yaw angle, the motion direction of the vehicle changes, and the lateral and longitudinal direction displacements of the vehicle under the action of the yaw angle can be obtained.

[0070] Optionally, the motion of the vehicle is not only affected by the longitudinal speed but also by the lateral angle. Assuming that the vehicle has a certain lateral acceleration (such as the centrifugal force caused by a road bend), the motion trajectory of the vehicle needs to be corrected according to this. Using the roll angle and pitch angle of the vehicle, the motion trajectory of the vehicle body on an uneven road or slope can be further adjusted. Usually, the changes in these angles are obtained through the acceleration sensor and are used to fine-tune the driving trajectory of the vehicle.

[0071] Within each time segment, update the position and direction of the vehicle using the current speed and angle data of the vehicle. Using the displacement evolution of the vehicle, the target displacement amount and the target direction information at each moment can be calculated by updating. For example, based on the current position and the calculated lateral and longitudinal direction displacements, a new position after a time segment can be obtained.

[0072] To accurately obtain the displacement trajectory of a vehicle, a time series integration calculation method can be adopted. This is a method that calculates the motion state of the vehicle in different time segments through step-by-step integration:

[0073] For each time segment, calculate the displacement and direction of the vehicle, and update the position of the vehicle according to the motion state. The update process is as follows: Calculate the speed and angle data within each time segment; Based on the current speed and angle data, update the motion state of the vehicle within this time segment; Use the aforementioned displacement evolution to calculate the displacement and update the current position of the vehicle; By accumulating the displacements of each time segment, gradually calculate the motion trajectory of the vehicle; According to the calculated real-time displacement trajectory, select several discrete trajectory points in the trajectory.

[0074] It should be noted that when establishing the displacement evolution, certain constraint conditions need to be considered to ensure that the calculation results are consistent with the actual situation. Common constraint conditions include: The acceleration and speed of the vehicle have physical maximum values, and these limitations need to be considered in the model; The steering angle of the vehicle cannot exceed a certain maximum value, and the attitude angles such as the yaw angle and pitch angle of the vehicle are also subject to certain limitations; Or environmental factors such as road surface friction and slope will affect the motion state of the vehicle and need to be incorporated into the model as constraint conditions. Through these constraint conditions, it can be ensured that the established displacement evolution can be effectively applied in different driving environments.

[0075] Align the second trajectory point set with the first trajectory point set in time series, as Figure 2 shown, generating a deviation sequence includes the following steps:

[0076] Based on the second trajectory point set and the first trajectory point set, perform time matching and spatial coordinate transformation to obtain matching trajectory point pairs at the same time index.

[0077] It should be noted that the purpose of time series alignment is to arrange trajectory data from different sources (the second trajectory point set and the first trajectory point set) in chronological order for further deviation analysis. This process can be divided into two parts: time matching and spatial coordinate transformation.

[0078] Time matching is the core part of time series alignment. By using timestamps, the points in the trajectory point set are corresponded, so that the trajectory points of each timestamp can be matched with the trajectory points in another set.

[0079] Among them, time matching includes when the timestamp distributions of the first trajectory point set and the second trajectory point set are consistent, performing one-to-one corresponding matching according to the time index; when the timestamp distributions of the first trajectory point set and the second trajectory point set are different, using interpolation methods to unify the timestamps;

[0080] In this embodiment, the time matching is mainly divided into the following two cases:

[0081] Matching when the timestamp distributions are the same:

[0082] If the timestamp distributions of the first set of trajectory points and the second set of trajectory points are the same (i.e., their timestamp values strictly correspond), then matching can be performed in a one-to-one manner. Each trajectory point is directly mapped to the trajectory point with the same timestamp in the other set according to its timestamp. For example, assume that the timestamps of the second set of trajectory points are , and the timestamps of the first set of trajectory points are also , then the trajectory point at is matched with the trajectory point at , and so on.

[0083] Interpolation matching when the timestamp distributions are different:

[0084] If there are differences in the timestamp distributions (for example, the timestamps of the second set of trajectory points are not synchronized with those of the first set of trajectory points, and the intervals are different), then interpolation methods need to be used to unify the timestamps. This is usually accomplished through linear interpolation or other advanced interpolation algorithms (such as spline interpolation, Lagrange interpolation, etc.). The purpose of interpolation is to solve the problem of timestamp mismatches caused by inconsistent sensor sampling frequencies or external factors. For example, assume that the timestamps of the first set of trajectory points are , while the timestamps of the second set of trajectory points are . Since has no corresponding timestamp, using the linear interpolation method, a new trajectory point is inserted between and , and the trajectory point at the moment of is calculated, thereby achieving timestamp alignment.

[0085] Spatial coordinate transformation refers to aligning the coordinates of the trajectory points in two trajectory sets when the coordinate systems are different. The key to this step is to use an appropriate transformation method according to the coordinate system differences of the trajectory points and map them to the same coordinate system for matching.

[0086] Among them, the spatial coordinate transformation includes: when the first set of trajectory points and the second set of trajectory points are represented in the same coordinate system, the global coordinates of each trajectory point are used for alignment calculation; when there are differences in the coordinate systems of the first set of trajectory points and the second set of trajectory points, then coordinate transformation methods are used for unification.

[0087] In this embodiment, if the first set of trajectory points and the second set of trajectory points are represented in the same coordinate system (for example, both use the global coordinate system or the local coordinate system), the alignment calculation can be directly performed through the global coordinates. At this time, only the coordinates of each trajectory point need to be directly used for matching to ensure that the trajectory points under the same time index have the same geographical location.

[0088] When there are differences in the coordinate systems of the first set of trajectory points and the second set of trajectory points, a coordinate transformation method needs to be used for unification. Common coordinate transformation methods include: Translation transformation: If the two coordinate systems only differ in position, they can be aligned through translation transformation. For example, the origin of one set of trajectory points can be translated to the origin position of the other set of trajectory points. Rotation transformation: If there is a rotational difference between the two coordinate systems, a rotation matrix is required for coordinate transformation. The rotation matrix can rotate the points of one coordinate system to another coordinate system. Affine transformation: The affine transformation can simultaneously include operations such as translation, rotation, and scaling, and is applicable to more complex coordinate system transformations, etc.

[0089] Based on the matched trajectory point pairs, calculate the corresponding trajectory deviations at each moment, including position deviation, heading deviation, and cumulative deviation; sort the calculated trajectory deviation data according to the time index to form a complete deviation sequence.

[0090] In the embodiment of the present invention, the position deviation refers to the difference in position between the second trajectory point and the first trajectory point at each timestamp. For each matched trajectory point pair, the position deviation is calculated through the Euclidean distance; the heading deviation refers to the difference in direction between the second trajectory point and the first trajectory point. The heading is usually determined by the movement direction or steering angle of the vehicle, so the heading deviation can be obtained by calculating the difference in the heading angles of the two. The cumulative deviation is the cumulative sum of the position deviations, reflecting the overall deviation between the two trajectories. Usually, the cumulative deviation gradually increases over time. Therefore, the positioning accuracy during long-term operation can be evaluated through the cumulative deviation.

[0091] S4: Establish a trajectory deviation analysis model, calculate the correction amount according to the statistical characteristics of the deviation sequence. If the correction amount exceeds the correction threshold, perform real-time correction in combination with the current heading angle to correct the positioning output.

[0092] In an inertial navigation-based vehicle positioning system, the accuracy of the trajectory is crucial. To ensure that the system can provide accurate positioning information, it is necessary to continuously analyze and correct the deviation. Specifically, during the trajectory generation process, due to the influence of various factors, there is often a certain error between the actual trajectory of the vehicle and the theoretical trajectory. To reduce the impact of these errors on the positioning accuracy, it is necessary to perform real-time analysis of the trajectory deviation and perform dynamic correction according to the analysis results.

[0093] Based on the above trajectory deviation data, a trajectory deviation analysis model is constructed to determine the temporal characteristics of the trajectory deviation sequence, where the trajectory deviation sequence includes the lateral offset information and longitudinal displacement information of the trajectory points; statistical analysis is performed on the trajectory deviation sequence to extract statistical features for correction calculation, and the statistical features include deviation mean, deviation variance, and deviation change rate; according to the statistical features, the lateral offset and longitudinal offset correction amounts are determined.

[0094] Among them, the lateral offset refers to the lateral error between the actual driving path of the vehicle and the predetermined trajectory, usually caused by steering angle, wheel speed error, or external environmental factors (such as road conditions); the longitudinal displacement refers to the deviation of the vehicle along the driving direction.

[0095] In the embodiment of the present invention, in order to better perform deviation correction, it is first necessary to perform statistical analysis on the trajectory deviation data, including deviation mean, deviation variance, and deviation change rate.

[0096] Among them, the deviation mean represents the average value of the trajectory deviation within a certain time range, reflecting the deviation level of the overall vehicle trajectory from the target trajectory; the deviation variance measures the volatility of the trajectory deviation, reflecting the change range of the trajectory deviation of the vehicle within a period of time; the deviation change rate represents the change speed of the deviation per unit time, used to judge the change trend of the trajectory error.

[0097] By comprehensively considering multiple statistical features such as deviation mean, variance, and change rate, the trajectory deviation of the vehicle can be more accurately identified and corrected, avoiding the deviation caused by a single statistical feature.

[0098] Based on the deviation characteristics obtained from the statistical analysis, a correction amount calculation method can be further established. The correction amount reflects the cumulative effect caused by the trajectory deviation and is used to dynamically adjust the output of the inertial navigation system.

[0099] The calculation of the correction amount is as follows: Based on the trajectory deviation analysis model, the lateral offset correction amount and longitudinal offset correction amount at the current moment are calculated respectively, used to characterize the cumulative deviation of the output of the inertial navigation system; when the lateral offset correction amount and longitudinal offset correction amount do not exceed the corresponding correction thresholds, the current inertial navigation positioning data remains unchanged; when one or more of the lateral offset correction amount and longitudinal offset correction amount exceed the corresponding correction thresholds, real-time correction is triggered.

[0100] The correction amount is essentially a correction for the current deviation of the vehicle.

[0101] It should be noted that lateral offset correction usually has a higher priority because it directly affects the driving stability of the vehicle.

[0102] Further, the correction threshold is usually set through statistical analysis of historical trajectory data, and the correction threshold should be adjusted according to the following factors: If the system has a high tolerance for errors, a higher correction threshold can be set. Conversely, if the system requires high-precision positioning, a lower correction threshold should be set. Different application scenarios have different requirements for the correction threshold. For example, in the case of high-speed driving, even a small deviation may accumulate rapidly, so a lower correction threshold is required; while in the case of low-speed driving or parking, a larger deviation can be tolerated.

[0103] Once the correction amount exceeds the preset correction threshold, real-time correction is performed in combination with the current heading angle to correct the positioning output. For example, if the vehicle has a lateral offset (e.g., the vehicle deviates from the predetermined lane), the role of the heading angle is to determine the angular difference between the actual turning direction of the vehicle and the predetermined trajectory. When the heading angle of the vehicle is inconsistent with the predetermined heading, the correction of the lateral offset will be adjusted according to the current heading of the vehicle.

[0104] Exemplarily, if the vehicle deviates from the lane and the deviation angle between the current heading angle and the direction of the lane center line is large, then the correction strategy will focus on adjusting the offset position of the vehicle and correcting it towards the center line of the lane. At this time, the correction amount needs to be adjusted according to the change of the heading angle for the offset direction and correction amplitude.

[0105] For longitudinal offset, especially when the vehicle has a large offset along the road direction, the heading angle will affect the driving path of the vehicle. In the longitudinal offset correction, if the heading angle of the vehicle changes significantly (such as a sharp turn), the correction amount will be dynamically adjusted according to this change.

[0106] To ensure the smoothness and effectiveness of the correction process, a dynamic sliding window model is used to process the trajectory data:

[0107] In the correction process, the sliding window model pays special attention to the correction of lateral offset. Lateral offset is usually caused by steering angle error or vehicle dynamics characteristics. When the correction amount exceeds the correction threshold, position correction is performed according to the real-time data of the inertial navigation system (INS), and the corrected position information is updated to the vehicle's real-time positioning system to ensure the accuracy of the position output.

[0108] Further, this embodiment also provides a vehicle remote monitoring system, as Figure 3 shown, including:

[0109] An autonomous positioning mode switching module 100, configured to automatically switch to the autonomous positioning mode when the positioning signal strength is lower than the signal threshold, and record the first signal data and the first angle data at the same time;

[0110] The first trajectory point calculation module 200 is configured to calculate a first set of trajectory points according to the first signal data and the first angle data;

[0111] The trajectory alignment module 300 is configured to obtain the first speed data and the second angle data, calculate a second set of trajectory points, and perform temporal alignment on the second set of trajectory points and the first set of trajectory points to generate a deviation sequence;

[0112] The trajectory deviation analysis module 400 is configured to establish a trajectory deviation analysis model, calculate a correction amount according to the statistical characteristics of the deviation sequence, and if the correction amount exceeds a correction threshold, perform real-time correction in combination with the current heading angle to correct the positioning output.

[0113] This embodiment further provides a computer device applicable to the situation of the monitoring blind area vehicle positioning method, including a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the monitoring blind area vehicle positioning method as proposed in the above embodiment.

[0114] This computer device may be a terminal. The computer device includes a processor, a memory, a communication interface, a display screen, and an input device connected through a system bus. Among them, the processor of this computer device is used to provide computing and control capabilities. The memory of this computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of this computer device is used to communicate with an external terminal in a wired or wireless manner. The wireless manner can be achieved through WIFI, a carrier network, NFC (Near Field Communication), or other technologies. The display screen of this computer device may be a liquid crystal display screen or an electronic ink display screen. The input device of this computer device may be a touch layer covering the display screen, or a button, a trackball, or a touchpad provided on the housing of the computer device, or an external keyboard, a touchpad, or a mouse, etc.

[0115] This embodiment further provides a storage medium, on which a computer program is stored, and when the program is executed by a processor, it implements the monitoring blind area vehicle positioning method as proposed in the above embodiment.

[0116] In summary, the present invention can ensure that the vehicle can still achieve continuous positioning relying on internal vehicle sensors (such as wheel speed sensors and steering angle sensors) when GNSS signals cannot provide stable support. It not only maintains the adaptive ability of the system but also continues to provide highly reliable dead reckoning data in various environments (such as tunnels, underground garages, or gaps between high-rise buildings, etc.). By combining the structural parameters of the vehicle with real-time motion data, the movement trajectory of the vehicle is accurately calculated, reducing the influence of external interference on the positioning system. Through the association with the vehicle wheelbase parameter, this step effectively compensates for the errors that may occur during vehicle turning, improving the accuracy of trajectory prediction. Moreover, by integrating the changes in vehicle acceleration and angular velocity into the trajectory calculation, the displacement calculation becomes more accurate. In addition, by comparing the deviation between the actual trajectory and the theoretical trajectory, positioning errors can be identified in real time to ensure high precision in complex dynamic environments.

[0117] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered within the scope of the claims of the present invention.

Claims

1. A method for positioning a vehicle in a monitoring blind area, characterized in that: include: When the positioning signal strength is lower than the signal threshold, it automatically switches to the autonomous positioning mode and records the first signal data and the first angle data at the same time; Calculate a first trajectory point set according to the first signal data and the first angle data; Acquire first speed data and second angle data and calculate a second trajectory point set, and perform time sequence alignment between the second trajectory point set and the first trajectory point set to generate a deviation sequence; Establishing a trajectory deviation analysis model, calculating a correction value according to the statistical characteristics of the deviation sequence, and if the correction value exceeds a correction threshold, performing real-time correction in combination with the current heading angle to correct the positioning output; Calculating the first trajectory point set according to the first signal data and the first angle data includes: based on the first signal data, obtaining signal timing data, and determining the constraint relationship of trajectory evolution in combination with the first angle data; determining the constraint relationship of trajectory evolution includes: accumulating the signal timing data for each time period to determine the distance traveled by the vehicle; at the same time, determining the trajectory point position corresponding to the vehicle at each time in combination with the deflection relationship defined by the first angle data, and generating a preliminary first trajectory point set; correlating the vehicle structure parameters to modify the preliminary first trajectory point set, including: obtaining the vehicle structure parameters, the vehicle structure parameters affecting the vehicle turning radius and motion trajectory; based on The vehicle structural parameters adjust the relative positions of the trajectory points in the preliminary first trajectory point set, and use the vehicle structural parameters through geometric relationships to correct the vehicle's steering path; the method of correcting the vehicle's steering path through geometric relationships using the vehicle structural parameters includes: calculating the steering radius of the front wheels based on the first angle data, and the front wheel steering radius determines the vehicle's turning size; according to the vehicle structural parameters, the movement trajectory of the rear wheels is offset relative to the front wheels, and the corrected path of the rear wheels is calculated based on the geometric relationship between the front wheel steering radius and the rear wheel steering path to determine the overall turning trajectory of the vehicle; the first trajectory point set is obtained by correcting the trajectory points in the preliminary first trajectory point set.

2. The method for positioning a vehicle in a monitoring blind area according to claim 1, characterized in that: The first signal data includes one or more of a pulse signal provided by a vehicle wheel speed sensor and dynamic measurement data provided by an inertial sensor; The first angle data is provided by a steering angle sensor or a steering wheel angle sensor of the vehicle.

3. The method for positioning a vehicle in a monitoring blind area as claimed in claim 2, characterized in that: The obtaining of the first speed data and the second angle data and calculating the second trajectory point set comprises: Acquire vehicle posture change signal data based on the target motion state data of the vehicle, analyze the vehicle posture change signal data, and extract target state parameters for characterizing the vehicle posture change; the target state parameters include the yaw angle, pitch angle and roll angle of the vehicle; calculate the posture change trajectory of the vehicle based on the inertial navigation algorithm using the target state parameters, and select the angle data corresponding to the time to be calculated in the posture change trajectory as the second angle data; at the same time, acquire vehicle speed signal data based on the target motion state data, analyze the vehicle speed signal data, and extract the vehicle instantaneous speed data; combine the vehicle acceleration sensor data and the wheel speed data to obtain the first speed data of the vehicle; Based on the first speed data and the second angle data, combined with the target position state of the vehicle, the displacement evolution of the vehicle is established, and under the constraint of the displacement evolution, the motion state of the vehicle in a continuous time segment is calculated; wherein the motion state includes the target displacement amount and target direction information per unit time; based on the motion state, the real-time displacement trajectory of the vehicle is determined using a time series integral calculation method, and a plurality of discrete trajectory points are selected from the real-time displacement trajectory to form the second trajectory point set.

4. The method for positioning a vehicle in a monitoring blind area according to claim 1, characterized in that: Performing time alignment on the second trajectory point set and the first trajectory point set to generate a deviation sequence includes: Based on the second trajectory point set and the first trajectory point set, time matching and spatial coordinate transformation are performed to obtain matching trajectory point pairs under the same time index: The time matching includes performing one-to-one matching according to time index when the timestamp distributions of the first trajectory point set and the second trajectory point set are consistent; and unifying the timestamps by using an interpolation method when the timestamp distributions of the first trajectory point set and the second trajectory point set are different; The spatial coordinate transformation includes: when the first trajectory point set and the second trajectory point set are represented in the same coordinate system, using the global coordinates of each trajectory point to perform alignment calculation; when there is a difference between the coordinate systems of the first trajectory point set and the second trajectory point set, using a coordinate conversion method to unify them; Based on the matched trajectory point pairs, the trajectory deviation corresponding to each moment is calculated; the calculated trajectory deviation data is sorted by time index to form a complete deviation sequence.

5. The method for positioning a vehicle in a monitoring blind area according to claim 1, characterized in that: Based on the trajectory deviation data, a trajectory deviation analysis model is constructed to determine the time series characteristics of the trajectory deviation sequence, wherein the trajectory deviation sequence includes the lateral offset information and the longitudinal displacement information of the trajectory point; the trajectory deviation sequence is statistically analyzed to extract statistical features for correction calculation, wherein the statistical features include the deviation mean, the deviation variance, and the deviation change rate; and the lateral offset and longitudinal offset correction amounts are determined according to the statistical features.

6. The method for locating a vehicle in a monitoring blind area according to claim 5, characterized in that: When the correction value does not exceed the correction threshold, the current inertial navigation positioning data is maintained unchanged; when the correction value exceeds the correction threshold, real-time correction is triggered.

7. A vehicle remote monitoring system, based on the monitoring blind spot vehicle positioning method according to any one of claims 1 to 6, characterized in that: Also includes: An autonomous positioning mode switching module, used to automatically switch to the autonomous positioning mode when the positioning signal strength is lower than the signal threshold, and simultaneously record the first signal data and the first angle data; A first trajectory point calculation module, used for calculating a first trajectory point set according to the first signal data and the first angle data; A trajectory alignment module, used to obtain the first speed data and the second angle data and calculate a second trajectory point set, and perform time sequence alignment on the second trajectory point set and the first trajectory point set to generate a deviation sequence; The trajectory deviation analysis module is used to establish a trajectory deviation analysis model, calculate the correction amount according to the statistical characteristics of the deviation sequence, and if the correction amount exceeds the correction threshold, perform real-time correction in combination with the current heading angle to correct the positioning output.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method for positioning a vehicle in a monitored blind spot are implemented as described in any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for positioning a vehicle in a monitored blind spot are implemented.

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