Sensor-based signal-free area motion trajectory navigation method and system

Abstract

The application relates to the technical field of trajectory navigation, in particular to a sensor-based motion trajectory navigation method and system in a signal-free area. The method comprises the following steps: acquiring inertial motion data, wheel motion data and steering motion data of a vehicle; determining a current motion state and a reliability coefficient of the vehicle according to the inertial motion data, the wheel motion data and the steering motion data of the vehicle; determining current environmental characteristics of the vehicle in the signal-free area according to the current motion state of the vehicle and motion history data of the vehicle; performing signal feature analysis on the reliability coefficient and the current environmental characteristics to obtain vehicle motion trajectory data containing current position, speed, heading angle and attitude angle information of the vehicle; and performing data verification on the vehicle motion trajectory data to obtain motion trajectory navigation data of the vehicle in the signal-free area. The application aims to solve the problem that the existing navigation method is difficult to accurately navigate the motion trajectory in the signal-free area when the vehicle is located in the signal-free area.

Description

Technical Field

[0001] This invention relates to the field of trajectory navigation technology, and specifically to a sensor-based method and system for navigating motion trajectories in signal-free areas. Background Technology

[0002] When vehicles are driving in special areas such as underground parking lots, long tunnels, or under multi-level overpasses, they may encounter situations where satellite navigation signals are interrupted or very weak. Navigation systems that rely on satellite signals cannot work properly, resulting in the loss of vehicle location information, deviations in driving trajectory, affecting the driving experience, and potentially causing safety hazards. Existing navigation methods rely on the vehicle's own sensors for navigation.

[0003] However, prolonged vehicle operation causes the zero-point drift of the inertial measurement unit (especially the gyroscope) to exceed its stable range. When the vehicle enters a no-signal area, its orientation will consistently and regularly deviate from its actual direction of travel, and this deviation accumulates over time. Simultaneously, prolonged travel on unevenly worn surfaces causes slight, localized wear on the drive wheels, resulting in a small but persistent difference between the actual tire circumference and the preset wheel circumference parameters. In areas without satellite signals, without external calibration sources, dead reckoning relies entirely on internal sensor data. In this situation, even small tire circumference differences lead to regular errors in estimating the vehicle's travel distance. Furthermore, in no-signal areas, the core state data representing the vehicle's current position, speed, and attitude are continuously and complexly affected by various errors. These errors interact intricately when calculating the vehicle's trajectory, causing the speed and magnitude of trajectory drift to far exceed that of a single error source. Consequently, it becomes impossible to accurately update the vehicle's real-time status. This not only causes the trajectory calculation results to deviate significantly from the actual path, but also makes it impossible to effectively determine the vehicle's precise position and driving direction. As a result, it cannot provide reliable motion trajectory navigation services at critical moments, leading to severe trajectory drift and positioning failure for drivers in complex no-signal scenarios such as tunnels or underground parking lots, making it difficult to accurately navigate motion trajectories in no-signal areas. Summary of the Invention

[0004] The purpose of this invention is to provide a sensor-based motion trajectory navigation method and system for areas without signal coverage, which solves the problem that existing navigation methods have difficulty in accurately navigating motion trajectories in areas without signal coverage when the vehicle is located.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: a sensor-based method for motion trajectory navigation in signal-free areas, comprising:

[0006] Acquire vehicle inertial motion data, wheel motion data, and steering motion data;

[0007] Based on the vehicle's inertial motion data, wheel motion data, and steering motion data, the vehicle's current motion state and reliability coefficient are determined. The current motion state includes either a straight-line motion state or a bumpy motion state.

[0008] Based on the vehicle's current motion state and historical motion data, determine the current environmental characteristics of the vehicle in the no-signal area;

[0009] Signal feature analysis is performed on the confidence coefficient and current environmental characteristics to obtain vehicle motion trajectory data containing information on the vehicle's current position, speed, heading angle, and attitude angle.

[0010] The vehicle's motion trajectory data is verified to obtain the vehicle's motion trajectory navigation data in areas without signal coverage.

[0011] Preferably, in the step of determining the current motion state and reliability coefficient of the vehicle based on the vehicle's inertial motion data, wheel motion data, and steering motion data, the acquisition of the reliability coefficient includes:

[0012] Signal feature analysis is performed on the inertial motion data to determine the instantaneous disturbance data caused by the instantaneous displacement of the on-board items and the heading change trend data caused by the inherent deviation of the inertial measurement unit when the vehicle is in a bumpy motion state;

[0013] After associating the wheel motion data with the steering motion data, the distance estimation deviation coefficient caused by the wheel circumference parameter in the straight motion state is determined;

[0014] The reliability coefficient is obtained based on the instantaneous disturbance data, the heading change trend data, and the distance estimation deviation coefficient.

[0015] Preferably, the step of obtaining the reliability coefficient based on the instantaneous disturbance data, the heading change trend data, and the distance estimation deviation coefficient includes:

[0016] The instantaneous disturbance data, the heading change trend data, and the distance estimation deviation coefficient are corrected respectively to obtain the corrected instantaneous disturbance data, the corrected heading change trend data, and the corrected distance estimation deviation coefficient.

[0017] The credibility coefficient is obtained by performing credibility fusion processing using the corrected instantaneous disturbance data, the corrected heading change trend data, and the corrected distance estimation deviation coefficient.

[0018] Preferably, the step of correcting the instantaneous disturbance data, the heading change trend data, and the distance estimation deviation coefficient to obtain corrected instantaneous disturbance data, corrected heading change trend data, and corrected distance estimation deviation coefficient includes:

[0019] The instantaneous disturbance data is corrected using the yaw rate data in the inertial motion data to obtain the corrected instantaneous disturbance data.

[0020] By using the speed data from the wheel motion data, the heading change trend data is corrected to obtain the corrected heading change trend data.

[0021] By using the acceleration data in the steering motion data, the distance estimation deviation coefficient is corrected to obtain the corrected distance estimation deviation coefficient.

[0022] Preferably, the steps of using speed data from wheel motion data to correct the heading change trend data to obtain corrected heading change trend data include:

[0023] By utilizing the speed data in the wheel motion data, the complex motion state of the vehicle can be determined;

[0024] Based on the complex motion state and the historical correction coefficient library, determine the correction parameters for the heading change trend data;

[0025] By using correction parameters to correct the heading trend data, the heading trend data is corrected to obtain the corrected heading trend data.

[0026] Preferably, in the step of determining the current motion state and confidence coefficient of the vehicle based on the vehicle's inertial motion data, wheel motion data, and steering motion data, the step of determining the current motion state of the vehicle includes:

[0027] Based on the vehicle's inertial motion data, wheel motion data, and steering motion data, the vehicle's short-term perturbation linear motion state is determined.

[0028] Based on the short-term perturbation linear motion state, the inertial motion data is processed by separating the yaw angular velocity components to obtain the zero-point drift component, the driver correction yaw component, and the road perturbation yaw component.

[0029] The driver-corrected yaw component and the road surface disturbance yaw component are removed from the inertial motion data to obtain the corrected inertial yaw angular velocity.

[0030] Using the corrected inertial yaw rate, wheel motion data, and steering motion data, the vehicle's motion state is identified to obtain the vehicle's current motion state.

[0031] Preferably, the step of separating the yaw rate component from the inertial motion data based on the short-term perturbation linear motion state to obtain the zero-point drift component, the driver-corrected yaw component, and the road surface perturbation yaw component includes:

[0032] Based on the short-term perturbation linear motion state, the inertial motion data is processed by separating the yaw rate components to obtain yaw rate components, driving micro-correction components, and lateral motion trend components at different frequencies.

[0033] Using the inertial motion data, the characteristics of the yaw angular velocity components at different frequencies are analyzed to obtain the zero-point drift component.

[0034] Using the inertial motion data, component analysis is performed on the small driving correction component to obtain the small driving steering component;

[0035] Using the inertial motion data, component analysis is performed on the lateral motion trend component to obtain the road surface micro-disturbance yaw component.

[0036] Preferably, the steps for acquiring vehicle inertial motion data, wheel motion data, and steering motion data include:

[0037] Acquire raw data of vehicle inertial motion, wheel motion, and steering motion;

[0038] The raw data of the vehicle's inertial motion, wheel motion, and steering motion are preprocessed to obtain the preprocessed raw data of the vehicle's inertial motion, wheel motion, and steering motion.

[0039] The preprocessed raw data of vehicle inertial motion, wheel motion, and steering motion are validated to obtain the vehicle's inertial motion data, wheel motion data, and steering motion data.

[0040] Preferably, the step of verifying the vehicle's motion trajectory data to obtain the vehicle's motion trajectory navigation data in the no-signal area includes:

[0041] Using a pre-set vehicle motion trajectory database, the vehicle motion trajectory data is initially verified to obtain initial navigation data and auxiliary correction coefficients for the vehicle's motion trajectory in areas without signal coverage.

[0042] Using auxiliary correction coefficients, the initial data of the vehicle's motion trajectory navigation in the no-signal area is corrected to obtain the vehicle's motion trajectory navigation data in the no-signal area.

[0043] The present invention also provides a sensor-based motion trajectory navigation system for signal-free areas, the system comprising:

[0044] The data acquisition module is used to acquire the vehicle's inertial motion data, wheel motion data, and steering motion data;

[0045] The state determination module is used to determine the current motion state and reliability coefficient of the vehicle based on the vehicle's inertial motion data, wheel motion data and steering motion data. The current motion state includes a straight motion state or a bumpy motion state.

[0046] The feature determination module is used to determine the current environmental features of the vehicle in the no-signal area based on the vehicle's current motion state and the vehicle's motion history data.

[0047] The data analysis module is used to perform signal feature analysis on the credibility coefficient and current environmental characteristics to obtain vehicle motion trajectory data containing information on the vehicle's current position, speed, heading angle, and attitude angle.

[0048] The data verification module is used to verify the vehicle's motion trajectory data to obtain the vehicle's motion trajectory navigation data in areas without signal coverage.

[0049] Compared with the prior art, the sensor-based motion trajectory navigation method and system for signal-free areas of the present invention have the following advantages:

[0050] This invention acquires vehicle inertial motion data, wheel motion data, and steering motion data to determine the vehicle's current motion state and reliability coefficient. It then combines this with historical motion data to determine the current environmental characteristics. Signal feature analysis is performed on the reliability coefficient and environmental characteristics to obtain vehicle trajectory data. Finally, the trajectory data is verified to obtain navigation data for areas without signal coverage. By comprehensively utilizing multi-source sensor data and performing refined state recognition, error assessment, and data verification, this invention significantly improves the navigation accuracy and reliability of vehicles in areas without signal coverage. This ensures that drivers can accurately identify driving paths in complex scenarios such as tunnels and underground parking lots, avoiding traffic congestion and safety hazards, thereby providing continuous and accurate navigation services. Attached Figure Description

[0051] To more clearly illustrate the specific embodiments of the present invention, the accompanying drawings used in the specific embodiments will be briefly described below. In all the drawings, the elements or parts are not necessarily drawn to scale.

[0052] Figure 1 This is a flowchart of a sensor-based motion trajectory navigation method for signal-free areas according to the present invention.

[0053] Figure 2 This is a structural block diagram of a sensor-based motion trajectory navigation system for signal-free areas according to the present invention.

[0054] In the diagram: 210, Data Acquisition Module; 220, Status Determination Module; 230, Feature Determination Module; 240, Data Analysis Module; 250, Data Verification Module.

[0055] The implementation and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0056] The following drawings disclose several embodiments of the present invention. For clarity, many practical details will be described in the following description. However, it should be understood that these practical details are not intended to limit the invention. That is, in some embodiments of the invention, these practical details are not essential. Furthermore, for the sake of simplicity, some conventional structures and components will be shown in the drawings in a simple schematic manner.

[0057] It should be noted that all directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of the present invention are only used to explain the relative positional relationship and movement of each component in a certain specific posture (as shown in the figure). If the specific posture changes, the directional indication will also change accordingly.

[0058] Furthermore, in this invention, the use of terms such as "first" and "second" is for descriptive purposes only and does not specifically refer to any order or sequence, nor is it intended to limit the invention. They are merely used to distinguish components or operations described using the same technical terms, and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Therefore, a feature defined with "first" or "second" may explicitly or implicitly include at least one of those features. Additionally, the technical solutions of various embodiments can be combined with each other, but only if they are feasible for those skilled in the art. If a combination of technical solutions is contradictory or impossible to implement, such a combination should be considered nonexistent and not within the scope of protection claimed by this invention.

[0059] Existing vehicle navigation methods suffer from lost vehicle position information and deviations in driving trajectories when driving in areas without signal, such as underground parking lots, long tunnels, or under multi-level overpasses, due to interrupted or weak satellite navigation signals. While existing technologies attempt to calculate navigation using the vehicle's own sensors, their accuracy and reliability are severely challenged by complex factors such as zero-point drift of the inertial measurement unit, differences in wheel circumference parameters, and instantaneous disturbances caused by vehicle bumps. This results in significant deviations between the calculated trajectory and the actual path, failing to provide accurate motion trajectory navigation for the vehicle.

[0060] To further understand the content, features, and effects of this invention, the following embodiments are provided, and detailed descriptions are given below in conjunction with the accompanying drawings:

[0061] Please see Figure 1 This invention provides a sensor-based method for navigating motion trajectories in signal-free areas, comprising the following steps:

[0062] S100: Acquire vehicle inertial motion data, wheel motion data, and steering motion data. Inertial motion data refers to data collected by the inertial measurement unit (IMU), including information such as the vehicle's angular velocity and acceleration in three-dimensional space, used to describe the vehicle's attitude and motion trend. Wheel motion data refers to data acquired through wheel speed sensors, such as wheel rotation speed and distance traveled, primarily used to estimate the vehicle's travel distance. Steering motion data refers to data acquired through steering angle sensors or steering wheel angle sensors, used to reflect the vehicle's steering angle and steering rate. Specifically, inertial motion data can be acquired in real time by the IMU installed on the vehicle. The IMU typically includes a three-axis gyroscope and a three-axis accelerometer, providing information on the vehicle's angular velocity and linear acceleration. Wheel motion data can be acquired through wheel speed sensors installed on each wheel, measuring the wheel rotation speed and thus calculating the vehicle's travel distance. Steering motion data can be acquired through steering wheel angle sensors or steering angle sensors, providing the steering wheel rotation angle or the actual steering angle of the wheels. Sensor data is periodically read and stored by the vehicle's central processing unit or a dedicated navigation controller.

[0063] S200. Based on the vehicle's inertial motion data, wheel motion data, and steering motion data, determine the vehicle's current motion state and reliability coefficient. The current motion state includes either a straight-line motion state or a bumpy motion state. The current motion state refers to the vehicle's current motion mode, such as a straight-line motion state or a bumpy motion state, and is used for subsequent data processing and error correction. The reliability coefficient is an indicator that measures the reliability of sensor data or calculation results; a higher value indicates more reliable data. Specifically, when determining the vehicle's current motion state, the acceleration and angular velocity in the inertial motion data can be analyzed. For example, when the vehicle is traveling in a straight line on a flat road, the accelerometer reading will be relatively stable, and the gyroscope's yaw rate will be close to zero; however, when the vehicle travels on a bumpy road, the accelerometer reading will exhibit high-frequency, violent fluctuations. By setting appropriate thresholds and filtering algorithms, the straight-line motion state or the bumpy motion state can be identified. When determining the reliability coefficient, the inherent consistency of data from different sensors can be evaluated. For example, if the output of the inertial measurement unit (IMU) differs significantly from the output of the wheel speed sensor over a long period, the reliability coefficient of the IMU may be reduced. The reliability coefficient can be derived using statistical methods, such as Kalman filtering or particle filtering, by fusing error models from different sensor data to obtain a comprehensive reliability assessment.

[0064] S300. Based on the vehicle's current motion state and historical motion data, determine the current environmental characteristics of the vehicle in the no-signal area. Specifically, the current environmental characteristics refer to the specific environmental features of the vehicle in the no-signal area, such as road surface smoothness and curve curvature, which affect the vehicle's motion mode and sensor data. For example, when the vehicle is moving in a straight line, the curvature information of the road segment in historical motion data can be used to determine whether the current road segment is a straight road or a gentle curve. When the vehicle is moving on bumps, the road surface condition information of the road segment in historical motion data can be used to determine whether the current road segment is a speed bump, a pothole, or other bumpy area. Historical motion data can be pre-stored in the vehicle's navigation system or collected and updated in real time when the vehicle is driving in a signal-enabled area. Determining the environmental characteristics helps to perform more accurate signal feature analysis subsequently.

[0065] S400. Perform signal feature analysis on the confidence coefficient and current environmental characteristics to obtain vehicle trajectory data containing information on the vehicle's current position, speed, heading angle, and attitude angle. The vehicle trajectory data is a collection of information including the vehicle's position, speed, heading angle, and attitude angle within a signal-free area. Specifically, signal feature analysis can employ various algorithms. For example, when the confidence coefficient is high and the environmental characteristics indicate a flat, straight road, distance estimation can primarily rely on wheel motion data, combined with inertial motion data for heading angle updates. When the confidence coefficient is low or the environmental characteristics indicate a bumpy road section, the weight of inertial motion data can be increased, and more complex filtering algorithms can be used to suppress noise, thereby more accurately estimating the vehicle's position, speed, heading angle, and attitude angle. For example, extended Kalman filtering or unscented Kalman filtering state estimation algorithms can be used, taking different sensor data and environmental characteristics as input, and fusing them to obtain the vehicle's trajectory data.

[0066] S500: Perform data verification on the vehicle's motion trajectory data to obtain navigation data for the vehicle's motion trajectory in areas without signal coverage. This navigation data is the final navigation data after verification and optimization, used to guide the vehicle's driving in areas without signal coverage. Specifically, data verification can be achieved by comparing it with preset map information or historical trajectory data. For example, the calculated vehicle trajectory can be matched with a digital map of an underground parking lot or tunnel. If there is a significant deviation between the trajectory and the map, the trajectory data can be adjusted and corrected. Furthermore, the consistency of the trajectory data can be checked using the vehicle's kinematic model. For example, it can be checked whether the vehicle's speed and acceleration conform to physical laws, and whether the change in heading angle matches the steering motion data. Through data verification, the accuracy and reliability of the navigation data can be further improved, ultimately generating navigation data for motion trajectory in areas without signal coverage that can be used for navigation.

[0067] Specifically, when a vehicle enters a signalless area, it first continuously collects inertial motion data, wheel motion data, and steering motion data. Then, based on real-time data, the vehicle's current motion state (e.g., straight-line motion or bumpy motion) is identified, and the reliability coefficients of each sensor's data are simultaneously evaluated. This dynamically reflects the actual motion environment of the vehicle and the quality of the sensor data, providing a basis for subsequent error processing. Next, combining the vehicle's current motion state with pre-stored historical motion data, the current environmental characteristics of the vehicle in the signalless area are inferred. For example, if the vehicle is in a straight-line motion and historical data shows that the area is a long, straight tunnel, the inference model will be adjusted. Subsequently, the reliability coefficients and current environmental characteristics are used as key inputs to perform signal feature analysis on the sensor data. Based on the reliability coefficients and the type of environmental characteristics, the weights of different sensor data and the fusion algorithm are dynamically adjusted. For example, on bumpy sections, the weight of wheel motion data in distance estimation may be reduced, and more reliance may be placed on inertial motion data for attitude and heading updates, while advanced filtering algorithms are used to suppress instantaneous disturbances caused by bumps. Through intelligent fusion, vehicle trajectory data containing information on current position, speed, heading angle, and attitude angle can be generated. Finally, the generated vehicle trajectory data undergoes rigorous verification, including matching it with preset map information and performing kinematic consistency checks. Verification identifies and corrects potential accumulated errors from the calculation process, resulting in highly accurate and reliable vehicle trajectory navigation data for areas without signal coverage. This ensures continuously accurate navigation services even in areas without signal coverage.

[0068] In this embodiment, by acquiring the vehicle's inertial motion data, wheel motion data, and steering motion data, the vehicle's motion information can be comprehensively perceived. Secondly, the vehicle's current motion state is determined, and the reliability coefficients of each sensor's data are simultaneously evaluated. It can adaptively respond to different motion scenarios and changes in sensor data quality. For example, on bumpy roads, it can identify the bumpy motion state and adjust the processing strategy for instantaneous disturbances in the inertial measurement unit accordingly, thereby effectively suppressing noise. Furthermore, by combining the vehicle's historical motion data, the current environmental characteristics of the vehicle in a no-signal area are determined. The navigation algorithm can be optimized based on specific environmental conditions (such as straight roads, curves, flat roads, or bumpy roads) to further improve calculation accuracy. Finally, by performing signal feature analysis on the reliability coefficients and current environmental characteristics, vehicle motion trajectory data containing current position, speed, heading angle, and attitude angle information can be obtained. Through data verification, high-precision motion trajectory navigation data for no-signal areas is finally generated. This invention, by introducing motion state recognition, reliability evaluation, and environmental feature analysis, achieves more refined management of error sources and more intelligent data fusion. For example, when the inertial measurement unit (IMU) experiences zero-point drift, its reliability coefficient decreases accordingly, its weight in heading angle calculation is adjusted, and it may rely more on wheel motion data for auxiliary correction. This allows the present invention to significantly improve navigation accuracy and robustness when facing complex and mutually influential error sources, providing drivers with reliable motion trajectory navigation services in areas without signal coverage.

[0069] In some embodiments of this application described above, the step of determining the current motion state and reliability coefficient of the vehicle based on the vehicle's inertial motion data, wheel motion data, and steering motion data includes obtaining the reliability coefficient by:

[0070] Signal feature analysis is performed on the inertial motion data to determine the instantaneous disturbance data caused by the instantaneous displacement of onboard items and the heading trend data caused by the inherent bias of the inertial measurement unit (IMU) during bumpy motion. Specifically, signal feature analysis of the inertial motion data involves processing the acceleration and angular velocity data output by the IMU, such as by using filtering, spectral analysis, or statistical analysis, to identify and quantify the specific signal features contained therein. During bumpy motion, the vehicle experiences significant vertical acceleration and impact. At this time, the instantaneous displacement of onboard items may cause short-term, high-amplitude instantaneous disturbance data in the measured values. Simultaneously, due to manufacturing tolerances and temperature drift of its internal components, the IMU will generate inherent biases that accumulate over time, which manifest as heading trend data in heading angle estimation. Through in-depth analysis of the inertial motion data, the error components caused by external interference and internal biases can be effectively separated.

[0071] By correlating the wheel motion data with the steering motion data, the distance estimation deviation coefficient caused by the wheel circumference parameter under straight-line motion conditions is determined. Specifically, wheel motion data is typically provided by wheel speed sensors or odometers and used to estimate the vehicle's travel distance. Steering motion data reflects the vehicle's steering angle or angular velocity. Theoretically, when the vehicle is in straight-line motion, wheel motion data and steering motion data should maintain a specific correlation. However, due to factors such as tire wear, tire pressure changes, and load differences, the actual wheel circumference may deviate from the nominal value, leading to errors in the travel distance estimated based on wheel speed. By performing correlation analysis on wheel motion data and steering motion data, such as comparing the expected relationship between the two with the actual measured values ​​during straight-line driving, this distance estimation deviation coefficient caused by the wheel circumference parameter can be identified and quantified.

[0072] Based on the instantaneous disturbance data, the heading change trend data, and the distance estimation deviation coefficient, a reliability coefficient is obtained. The instantaneous disturbance data in this step reflects short-term, sudden measurement uncertainties; the heading change trend data reflects long-term, cumulative direction estimation errors; and the distance estimation deviation coefficient reflects the systematic errors of the odometer. By comprehensively considering error information from different sources and with different characteristics, a more comprehensive and accurate reliability model can be constructed, thereby outputting a comprehensive reliability coefficient that quantifies the overall reliability of the current vehicle motion data.

[0073] Specifically, when the vehicle is driving in a signal-free area, its inertial measurement unit (IMU), wheel speed sensors, and steering angle sensors continuously collect data. When the vehicle is traveling on an uneven road surface, significant instantaneous acceleration peaks are detected in the IMU data. Through signal feature analysis, instantaneous disturbance data caused by vehicle bumps and the movement of onboard objects can be extracted. Simultaneously, long-term integration of IMU data reveals a slow drift in the heading angle, identified as a heading change trend caused by inherent biases in the inertial measurement unit. When the vehicle enters a straight section of road and maintains a straight line, the wheel speed sensor data and steering angle sensor data are correlated. If a slight mismatch is found between the distance calculated based on wheel speed and the straight-line driving state indicated by the steering angle sensor, a distance estimation deviation coefficient caused by the wheel circumference parameter can be calculated. Finally, the quantified instantaneous disturbance data, heading change trend data, and distance estimation deviation coefficient are input into a fusion module. For example, a weighted average algorithm based on error variance is used to comprehensively derive a reliability coefficient reflecting the overall reliability of the current vehicle motion data. For example, when instantaneous disturbance data is large, the heading change trend is obvious, and the distance estimation deviation coefficient is high, the reliability coefficient will decrease accordingly, and vice versa. The reliability coefficient is then used in subsequent motion trajectory data generation and verification processes to optimize navigation results.

[0074] This embodiment, through signal feature analysis of inertial motion data, effectively separates instantaneous disturbance data under bumpy conditions from the inherent heading change trend data of the inertial measurement unit, thereby revealing the uncertainties of inertial sensors under complex environments and long-term operation. Simultaneously, by correlating wheel motion data and steering motion data, it can accurately identify and quantify the distance estimation deviation coefficient caused by wheel circumference parameters under straight-line motion conditions, avoiding the cumulative error of the odometer due to changes in tire characteristics during actual use. Because key error sources are specifically quantified, subsequent reliability fusion processing based on these error data allows for the generation of a comprehensive and accurate reliability coefficient. The reliability coefficient more realistically reflects the reliability of the current vehicle motion data, providing a more solid foundation for subsequent signal feature analysis and motion trajectory data generation.

[0075] In some embodiments of this application described above, the step of obtaining the reliability coefficient based on the instantaneous disturbance data, the heading change trend data, and the distance estimation deviation coefficient includes:

[0076] The instantaneous disturbance data, the heading change trend data, and the distance estimation deviation coefficient are respectively corrected to obtain corrected instantaneous disturbance data, corrected heading change trend data, and corrected distance estimation deviation coefficients. Specifically, the correction process refers to preprocessing the original instantaneous disturbance data, heading change trend data, and distance estimation deviation coefficients to eliminate or reduce existing noise, bias, or errors; its purpose is to improve the accuracy and reliability of these data, providing high-quality input for subsequent reliability fusion. The correction process can employ various techniques, such as filtering algorithms (e.g., Kalman filtering, extended Kalman filtering), statistical methods, machine learning models, or physics-based compensation methods.

[0077] A credibility coefficient is obtained by performing credibility fusion processing using the corrected instantaneous disturbance data, corrected heading change trend data, and corrected distance estimation deviation coefficient. This credibility fusion processing can be understood as comprehensively evaluating and weighting the corrected instantaneous disturbance data, corrected heading change trend data, and corrected distance estimation deviation coefficient to obtain a unified credibility coefficient. The fusion process fully utilizes the advantages of different data sources, thereby obtaining a more comprehensive credibility assessment of the vehicle motion state. Credibility fusion processing can employ various fusion algorithms, such as evidence theory, fuzzy logic, Bayesian networks, or weighted averaging.

[0078] Specifically, when correcting instantaneous disturbance data, heading trend data, and distance estimation deviation coefficients, the following methods can be used: For instantaneous disturbance data, which mainly originates from the instantaneous displacement of onboard items during vehicle bumps, causing spikes in the acceleration and angular velocity data output by the inertial measurement unit (IMU), digital signal processing methods such as median filtering or moving average filtering can be used to smooth the instantaneous disturbance data, filtering out high-frequency noise and instantaneous spikes to obtain more stable corrected instantaneous disturbance data. For heading trend data, it may be affected by inherent IMU biases (such as gyroscope drift), leading to accumulated errors in the heading angle after long-term integration. To correct this trend bias, heading data under known straight-line driving conditions can be used for calibration, or combined with vehicle speed information, state estimation algorithms such as Kalman filtering can be used to dynamically compensate for the heading trend data, thereby obtaining corrected heading trend data. For the distance estimation deviation coefficient, it mainly originates from uncertainties in wheel circumference parameters or wheel slippage. During the correction process, a deviation model can be established by combining steering angle information from steering motion data with historical vehicle data under different speeds and steering conditions. For example, a model can be trained using machine learning algorithms to predict and compensate for distance estimation deviation coefficients based on the current vehicle speed and steering angle, thus obtaining the corrected distance estimation deviation coefficients. After obtaining the corrected instantaneous disturbance data, corrected heading change trend data, and corrected distance estimation deviation coefficients, credibility fusion can be performed using the DS evidence theory. Specifically, the three types of corrected data are treated as different sources of evidence, and corresponding basic probability assignment functions are assigned based on their respective characteristics and reliability. Through the fusion rules of the DS evidence theory, these pieces of evidence are combined to ultimately obtain a more robust credibility coefficient.

[0079] In this embodiment, the instantaneous disturbance data, heading change trend data, and distance estimation deviation coefficient are affected by various factors such as sensor accuracy, environmental noise, and vehicle dynamic characteristics during their initial acquisition, resulting in uncertainties or systematic biases in the data. Targeted correction processing of the raw data effectively removes or reduces the influence of these adverse factors, allowing the corrected data to more accurately reflect the vehicle's actual motion characteristics. Furthermore, using the corrected data for reliability fusion ensures that the fusion process is based on more reliable information, thereby avoiding the accumulation and propagation of errors in the raw data and ultimately improving the accuracy and stability of the obtained reliability coefficient.

[0080] In some embodiments of this application described above, the steps of correcting the instantaneous disturbance data, the heading change trend data, and the distance estimation deviation coefficient to obtain corrected instantaneous disturbance data, corrected heading change trend data, and corrected distance estimation deviation coefficient include:

[0081] The instantaneous disturbance data is corrected using yaw rate data from inertial motion data to obtain corrected instantaneous disturbance data. Specifically, instantaneous disturbance data refers to the instantaneous error or noise caused by the instantaneous displacement of onboard items when the vehicle is in a bumpy motion state. During the correction process, yaw rate data from the inertial motion data is used for correction. Yaw rate data reflects the vehicle's rotational motion around its vertical axis. When the vehicle experiences an instantaneous disturbance, its yaw rate often changes accordingly. By analyzing the trend of change, error components in the instantaneous disturbance data can be effectively identified and compensated, thereby obtaining more accurate corrected instantaneous disturbance data.

[0082] By utilizing speed data from wheel motion data, the heading trend data is corrected to obtain corrected heading trend data. The heading trend data represents the trend error in vehicle heading over time caused by inherent biases in the inertial measurement unit (IMU). During correction, speed data from the wheel motion data is used. This speed data provides information about the vehicle's actual speed, which is relatively stable during straight-line driving or stable cornering. However, the bias in the heading trend data may be related to speed changes. By calibrating the heading trend data using speed data, the cumulative error caused by inherent biases in the IMU can be effectively eliminated or reduced, resulting in the corrected heading trend data.

[0083] By utilizing acceleration data from steering motion data, the distance estimation deviation coefficient is corrected to obtain the corrected distance estimation deviation coefficient. Specifically, the distance estimation deviation coefficient refers to the distance estimation error caused by inaccurate wheel circumference parameters or slippage during linear motion. During correction, acceleration data from the steering motion data is used. Acceleration data reflects the dynamic characteristics of the vehicle during acceleration, deceleration, or turning. These dynamic characteristics may affect the actual circumference of the wheels or the effective radius of contact with the ground, thus leading to distance estimation deviation. By analyzing the relationship between acceleration data and the distance estimation deviation coefficient, an effective correction model can be established to accurately correct the distance estimation deviation coefficient, thereby obtaining the corrected distance estimation deviation coefficient.

[0084] Specifically, when a vehicle is traveling in a signal-free area, its onboard sensors continuously acquire inertial motion data, wheel motion data, and steering motion data. When the vehicle traverses a bumpy road surface, the inertial sensors generate instantaneous disturbance data. At this time, the yaw rate data recorded in the inertial motion data is used to analyze and correct this instantaneous disturbance data in real time. For example, if the yaw rate fluctuates drastically within a short period, it indicates a large instantaneous disturbance. Based on the amplitude and frequency of the yaw rate, a corresponding compensation amount is derived and applied to the instantaneous disturbance data, thus obtaining corrected instantaneous disturbance data. During long-term vehicle travel, the inertial measurement unit may accumulate heading drift, forming heading trend data. This is combined with speed data from the wheel motion data. For example, when the vehicle is traveling in a straight line at a constant speed, if the heading trend data still shows a slight deflection, based on the stability of the wheel speed, it is determined that the deflection mainly originates from the inherent bias of the inertial measurement unit. Using the speed data as a reference, the heading trend data is calibrated to eliminate or reduce this drift, resulting in corrected heading trend data. Furthermore, during vehicle acceleration or deceleration, slight wheel slippage or dynamic changes in circumference parameters may occur, leading to a distance estimation deviation coefficient. In this case, acceleration data from the steering motion data is used for correction. For example, when the vehicle accelerates rapidly, the acceleration data increases significantly. Based on the magnitude and duration of the acceleration, the likelihood and degree of wheel slippage are assessed, and the distance estimation deviation coefficient is corrected accordingly, resulting in a corrected distance estimation deviation coefficient. Through these specific correction measures, the accuracy of instantaneous disturbance data, heading change trend data, and distance estimation deviation coefficient is ensured, providing high-quality input for subsequent reliability fusion processing.

[0085] This embodiment achieves refined correction of instantaneous disturbance data, heading trend data, and distance estimation deviation coefficients by introducing auxiliary sensor data closely related to the characteristics of the data to be corrected. Specifically, instantaneous disturbances are usually accompanied by rapid changes in vehicle attitude, and yaw rate data in inertial motion data can sensitively capture this instantaneous rotation, thus being used to accurately compensate for instantaneous disturbance data. The cumulative error of heading trend is often related to the vehicle's speed and path; speed data in wheel motion data provides a reference for the vehicle's actual displacement. By correlating this with heading trend data, drift error of the inertial measurement unit can be effectively suppressed. Furthermore, the generation of distance estimation deviation coefficients is closely related to the actual motion state of the wheels (such as slippage, acceleration, or deceleration). Acceleration data in steering motion data can reflect these dynamic changes, thus providing a crucial dynamic reference for correcting distance estimation deviation coefficients. By utilizing these complementary and correlated sensor data, various error sources can be more accurately identified and eliminated, thereby significantly improving the accuracy of the correction process.

[0086] In some embodiments of this application described above, the step of correcting the heading change trend data using speed data from wheel motion data to obtain corrected heading change trend data includes:

[0087] By utilizing speed data from wheel motion data, the complex motion state of a vehicle can be determined. Specifically, when determining the complex motion state of a vehicle, speed data from wheel motion data can be combined with information such as vehicle acceleration and angular velocity. Pre-set threshold judgments, pattern recognition algorithms, or machine learning models can be used to identify the vehicle's current motion mode. For example, when the rate of change of speed exceeds a threshold, it can be judged as an acceleration or deceleration state; when the yaw rate is large, it can be judged as a turning state. Complex motion states can be understood as all dynamic changes of the vehicle during driving, other than simple uniform linear motion, such as rapid acceleration, rapid deceleration, sharp turns, braking, or driving on bumpy roads.

[0088] Based on the complex motion states and the historical correction coefficient database, correction parameters for the heading change trend data are determined. The historical correction coefficient database is a pre-established database that stores the optimal correction parameters for the heading change trend data under different complex motion states. This database can be constructed and optimized through extensive real-world driving data collection, simulation, or expert experience. For example, in certain types of sharp turns, the heading change trend data may exhibit a specific deviation pattern; the historical correction coefficient database will store correction parameters for this deviation pattern.

[0089] The heading trend data is corrected using correction parameters, resulting in corrected heading trend data. This step involves determining the vehicle's complex motion state and retrieving the corresponding correction parameters from the historical correction coefficient database. These correction parameters are then applied to the heading trend data. Correction parameters can be gain coefficients, offsets, filter parameters, or other mathematical model parameters. Their purpose is to eliminate or reduce errors in the heading trend data caused by inherent biases of the inertial measurement unit and complex motion states, thereby obtaining more accurate corrected heading trend data.

[0090] Specifically, when a vehicle is driving in an area without a signal, it enters a series of S-shaped curves and then accelerates rapidly. First, the vehicle continuously acquires speed data from its wheel motion data. When the vehicle enters the S-curve, the wheel speed data, combined with the vehicle's yaw rate data, is used to identify that the vehicle is currently in a complex motion state of continuous turning. At this point, a historical correction coefficient database is queried. This database contains specific correction parameters for heading change trend data under continuous turning conditions, such as dynamic compensation coefficients for gyroscope drift. These parameters are used to correct the currently acquired heading change trend data. Next, when the vehicle exits the curve and accelerates rapidly, the wheel speed data shows a significant increasing trend, thus identifying that the vehicle is currently in a complex motion state of rapid acceleration. The historical correction coefficient database is queried again to obtain correction parameters for the rapid acceleration state, such as filtering parameters to suppress instantaneous acceleration errors. These parameters are applied to new heading change trend data to obtain corrected heading change trend data. Furthermore, it can adaptively adjust the correction strategy based on the vehicle's specific performance under different complex motion states, ensuring that the heading change trend data remains highly accurate throughout the entire driving process, thus providing a solid data foundation for motion trajectory navigation in areas without signal coverage.

[0091] In this embodiment, by introducing the identification of complex vehicle motion states and applying a historical correction coefficient database, insufficient correction accuracy under complex driving conditions is avoided. Specifically, the inherent bias of the inertial measurement unit (IMU) and the influence mechanism of external disturbances on the heading change trend data differ under different vehicle motion states. For example, during rapid acceleration or deceleration, the IMU's accelerometer may produce instantaneous errors; during sharp turns, gyroscope drift may be amplified. By utilizing the speed data in the wheel motion data, the current complex motion state of the vehicle can be accurately identified, such as straight-line acceleration, high-speed turning, or low-speed braking. After identifying the specific complex motion state, the optimal correction parameters that match the motion state are found according to the pre-established historical correction coefficient database. The correction parameters are optimized for the error characteristics under specific motion states, enabling more accurate compensation for errors in the heading change trend data. This allows for adaptive and high-precision correction of the heading change trend data, overcoming the limitations of a single fixed correction method in variable driving environments.

[0092] In some embodiments of this application described above, the step of determining the current motion state and reliability coefficient of the vehicle based on the vehicle's inertial motion data, wheel motion data, and steering motion data includes the following steps:

[0093] Based on the vehicle's inertial motion data, wheel motion data, and steering motion data, the vehicle's short-term perturbation linear motion state is determined. Specifically, before determining the vehicle's current motion state, the system first identifies and determines whether the vehicle is in a short-term perturbation linear motion state based on the vehicle's inertial motion data, wheel motion data, and steering motion data. A short-term perturbation linear motion state refers to a situation where the vehicle primarily travels in a straight line for a short period, but may experience yaw due to minor external disturbances (such as uneven road surfaces) or minor driver corrections (such as slight steering wheel adjustments). This provides a foundation for subsequent yaw angular velocity component separation, ensuring refined analysis within a relatively stable motion context.

[0094] Based on the short-term perturbation linear motion state, the inertial motion data undergoes yaw rate component separation processing to obtain the zero-point drift component, driver-corrected yaw component, and road perturbation yaw component. This step decomposes the original yaw rate into three main components: the zero-point drift component, the driver-corrected yaw component, and the road perturbation yaw component. The zero-point drift component is usually caused by the inherent error of the inertial measurement unit itself and may exist even when the vehicle is stationary; the driver-corrected yaw component reflects the yaw input applied by the driver to maintain straight-line driving or to perform minor steering operations; the road perturbation yaw component is the vehicle yaw caused by external environmental factors such as uneven road surfaces or crosswinds. Separation allows for a clearer understanding of the source of the yaw rate.

[0095] The driver-corrected yaw component and the road surface disturbance yaw component are removed from the inertial motion data to obtain the corrected inertial yaw rate. Specifically, after obtaining the above three components, the driver-corrected yaw component and the road surface disturbance yaw component are removed from the original inertial motion data. The purpose of this removal operation is to eliminate the yaw effect caused by minor driver operations and road surface disturbances that are not part of the vehicle's main motion state, thereby obtaining a purer corrected inertial yaw rate that better reflects the vehicle's own motion trend. The corrected inertial yaw rate mainly reflects the yaw characteristics of the vehicle during ideal straight-line motion or smooth turning, eliminating external interference.

[0096] Using the corrected inertial yaw rate, wheel motion data, and steering motion data, the vehicle's motion state is identified to obtain its current motion state. This step, by comprehensively analyzing these corrected and more accurate data, can more reliably distinguish whether the vehicle is in a straight-line motion state or a bumpy motion state, thus obtaining the vehicle's current motion state. For example, when the corrected inertial yaw rate is close to zero, and the wheel motion data and steering motion data also indicate straight-line travel, the vehicle is identified as being in a straight-line motion state; conversely, if there are significant yaw or bumpy characteristics, it is identified as being in a bumpy motion state.

[0097] Specifically, when a vehicle travels at a relatively stable speed on a highway, due to slight road surface undulations and minor steering wheel adjustments made by the driver to maintain lane position, the yaw rate data measured by its inertial measurement unit (IMU) will contain multiple components, including zero-point drift, driver correction, and road surface disturbances. First, based on the vehicle's inertial motion data, wheel motion data, and steering motion data, it is determined that the vehicle is currently in a short-term perturbation linear motion state. Next, the yaw rate data measured by the IMU undergoes component separation processing. For example, high-pass filtering or Kalman filtering methods can be used to distinguish the high-frequency driver-corrected yaw component and road surface disturbance yaw component from the low-frequency zero-point drift component. Assuming the separated zero-point drift component is 0.01 degrees / second, the driver-corrected yaw component fluctuates between -0.05 and 0.05 degrees / second, and the road surface disturbance yaw component fluctuates between -0.03 and 0.03 degrees / second, the driver-corrected yaw component and the road surface disturbance yaw component are then removed from the original yaw rate. For example, if the original yaw rate is 0.1 degrees / second, after removing the above components, the corrected inertial yaw rate might be 0.01 degrees / second (close to zero), more accurately reflecting that the vehicle is actually moving in a straight line. Finally, using the near-zero corrected inertial yaw rate, combined with wheel speed data (e.g., the left and right wheel speeds are basically the same) and steering angle data (e.g., the steering wheel angle is close to zero), it is possible to accurately identify that the vehicle is currently in a straight-line motion state, rather than misjudging it as a slight turn or bump. Accurate motion state recognition helps subsequent navigation algorithms to make more accurate position and heading estimations in areas without signal coverage.

[0098] This embodiment performs refined yaw rate component separation processing on the vehicle's inertial motion data to avoid potential accuracy deficiencies in identifying the vehicle's current motion state under complex driving environments. Specifically, after determining the vehicle's short-term perturbation linear motion state, the original yaw rate is decomposed into a zero-point drift component, a driver-corrected yaw component, and a road perturbation yaw component. This allows for the identification and quantification of yaw effects from different sources. By removing the driver-corrected yaw component and the road perturbation yaw component, noise and interference introduced by minor driver operations and external road disturbances can be effectively filtered out, resulting in a purer and more accurate corrected inertial yaw rate that better reflects the vehicle's actual motion trend. The corrected inertial yaw rate more accurately represents the vehicle's true motion state, avoiding misjudgments caused by external interference. Finally, this corrected data is combined with wheel motion data and steering motion data for motion state identification, making the judgment of the vehicle's current motion state more precise.

[0099] In some embodiments of this application described above, the step of performing yaw rate component separation processing on the inertial motion data based on the short-term perturbation linear motion state to obtain the zero-point drift component, the driver-corrected yaw component, and the road surface perturbation yaw component includes:

[0100] Based on the short-term perturbation linear motion state, the inertial motion data undergoes yaw rate component separation processing to obtain yaw rate components of different frequencies, driving micro-correction components, and lateral motion trend components. Specifically, when the vehicle is in a short-term perturbation linear motion state, the yaw rate in the inertial motion data contains multiple pieces of information. First, the inertial motion data undergoes preliminary yaw rate component separation processing. The purpose is to decompose the original yaw rate signal into several preliminary components, namely, yaw rate components of different frequencies, driving micro-correction components, and lateral motion trend components. Among them, the yaw rate components of different frequencies can be understood as decomposing the original signal into different frequency ranges through frequency domain analysis (such as Fourier transform or wavelet analysis) to distinguish slow-changing, medium-changing, and fast-changing signals; the driving micro-correction component refers to the yaw change caused by the driver's subtle steering operation to maintain vehicle stability during straight-line driving; the lateral motion trend component mainly reflects the lateral displacement or attitude change trend of the vehicle under the influence of external factors such as uneven road surface or crosswinds.

[0101] Using the inertial motion data, the characteristics of the yaw angular velocity components at different frequencies are analyzed to obtain the zero-point drift component. The zero-point drift component is a slowly varying low-frequency signal; its characteristic analysis can include filtering, mean calculation, or trend fitting of the low-frequency signal to separate it from other frequency components. The aim is to accurately quantify and eliminate the inherent systematic errors of the inertial measurement unit (IMU).

[0102] Using the inertial motion data, component analysis is performed on the minute driving correction components to obtain the minute driving steering components. These minute steering components refer to the yaw rate generated by the driver's subtle steering wheel adjustments to correct slight deviations from the vehicle's heading while maintaining straight-line travel. Component analysis, such as through threshold judgment, pattern recognition, or correlation analysis with steering motion data, can distinguish them from other noise or disturbances. The aim is to accurately identify the driver's true intentions and avoid misinterpreting them as unintended vehicle movements.

[0103] Using the inertial motion data, component analysis is performed on the lateral motion trend component to obtain the road surface disturbance yaw component. The road surface disturbance yaw component refers to the minute, instantaneous changes in vehicle attitude or heading caused by factors such as road surface unevenness, potholes, or changes in lateral slope during vehicle operation. Component analysis of this component, such as through high-pass filtering, instantaneous peak detection, or correlation analysis with vehicle bump conditions, can effectively separate it from other smooth motions or driver operations. The aim is to accurately capture and quantify the impact of road surface factors on vehicle heading.

[0104] Specifically, a vehicle is traveling in an almost straight line in a no-signal area, but the road surface is slightly uneven, and the driver makes subtle steering wheel adjustments to maintain heading. In this situation, the yaw rate data collected by the onboard inertial measurement unit (IMU) will contain multiple information, including zero-point drift, driver corrections, and road surface disturbances. First, based on the short-term linear motion state with minor disturbances, the inertial motion data undergoes preliminary yaw rate component separation processing. For example, a multi-stage filter bank can be used to process the raw yaw rate signal, decomposing it into yaw rate components of different frequencies: low frequency (e.g., 0-0.1Hz), mid frequency (e.g., 0.1-1Hz), and high frequency (e.g., above 1Hz). Simultaneously, by analyzing the correlation between steering wheel angle sensor data and yaw rate, the minor driver correction component is preliminarily identified; by analyzing the vehicle's vertical acceleration data, the lateral motion trend component is preliminarily identified. Further, the characteristics of the low-frequency yaw rate component are analyzed using the inertial motion data. Since zero-point drift typically manifests as a slowly changing DC or extremely low-frequency signal, the zero-point drift component can be accurately obtained by long-term averaging or trend fitting of this low-frequency component. Next, inertial motion data is used to analyze the initially identified minor driving correction components. For example, a threshold can be set; when the rate of change of yaw angle exceeds this threshold for a short period and is highly consistent with the trend of steering wheel angle change, it is identified as a minor driving steering component. Finally, inertial motion data is used to analyze the initially identified lateral motion trend components. For example, a high-pass filter can be used to filter out low-frequency components, and combined with the instantaneous peak detection of the vehicle's vertical acceleration, the instantaneous yaw change caused by road bumps can be identified as a road surface disturbance yaw component. Through this refined separation process, the zero-point drift component, minor driving steering component, and road surface disturbance yaw component can be accurately extracted from the complex inertial motion data, providing clean and reliable yaw angle rate data for subsequent motion state recognition and trajectory navigation.

[0105] This embodiment first decomposes the yaw rate signal in inertial motion data into yaw rate components of different frequencies, driver micro-correction components, and lateral motion trend components, achieving preliminary decoupling of the original complex signal. This makes it possible to perform targeted characteristic analysis and component analysis on each component. Specifically, by analyzing the characteristics of components of different frequencies, the low-frequency and slowly varying zero-point drift component can be effectively identified and extracted, thus avoiding confusion with high-frequency signals such as road surface disturbances. At the same time, more detailed component analysis of the driver micro-correction component and the lateral motion trend component can accurately distinguish between the driver's subtle steering operations and instantaneous disturbances caused by the road surface. Through multi-level and refined separation processing, a cleaner and more accurate input is provided for subsequent removal of the driver-corrected yaw rate component and the road surface disturbance yaw rate component, thereby ensuring the accuracy of the corrected inertial yaw rate.

[0106] In some embodiments of this application, the steps of acquiring vehicle inertial motion data, wheel motion data, and steering motion data include:

[0107] This step involves acquiring raw data on the vehicle's inertial motion, wheel motion, and steering motion. Specifically, this means directly collecting the raw data generated during vehicle operation using the onboard sensor system. Inertial motion data can be acquired by the inertial measurement unit (IMU), including the raw outputs from the accelerometer and gyroscope; wheel motion data can be acquired by wheel speed sensors, reflecting wheel rotation speed or mileage information; and steering motion data can be acquired by steering angle sensors, reflecting the vehicle's steering angle or steering rate. This raw data forms the basis for subsequent motion trajectory navigation.

[0108] The raw data of the vehicle's inertial motion, wheel motion, and steering motion are preprocessed to obtain preprocessed raw data for these three parameters. Specifically, the purpose of preprocessing is to eliminate noise and errors in the raw data, and to standardize the data format and synchronize the time. For example, preprocessing may include low-pass filtering of the raw inertial motion data to remove high-frequency noise, unit conversion of the raw wheel motion data to standardize speed or distance units, and timestamp alignment of all sensor data to ensure data synchronization. Preprocessing improves data quality and consistency, laying the foundation for subsequent data verification and motion state determination.

[0109] The preprocessed raw data of vehicle inertial motion, wheel motion, and steering motion are validated to obtain the vehicle's inertial motion data, wheel motion data, and steering motion data. This raw data validation step aims to identify and correct outliers, missing values, or inconsistencies in the data. Specifically, the validation process may include range checks, such as determining whether sensor readings exceed reasonable physical ranges; consistency checks, such as comparing the logical relationships between different sensor data; and redundancy checks, such as using multi-sensor fusion technology to verify data reliability. Through raw data validation, it can be ensured that the final inertial motion data, wheel motion data, and steering motion data used for navigation are accurate, reliable, and complete.

[0110] This embodiment refines the three steps of raw data acquisition, preprocessing, and data verification to ensure high accuracy and reliability of the inertial motion, wheel motion, and steering motion data input into subsequent navigation algorithms. First, directly acquiring the raw data ensures information integrity. Second, the preprocessing step effectively filters out noise and errors, standardizes the data format, and provides standardized input for subsequent processing. Finally, the data verification step further eliminates abnormal and inconsistent data, thereby avoiding navigation errors caused by data quality issues. This provides a solid data foundation for vehicle trajectory navigation in areas without signal coverage.

[0111] In some embodiments of this application described above, the step of verifying the vehicle motion trajectory data to obtain vehicle motion trajectory navigation data in areas without signal coverage includes:

[0112] Using a pre-set vehicle trajectory database, initial data verification is performed on the vehicle trajectory data to obtain initial navigation data and auxiliary correction coefficients for vehicle movement trajectories in signal-free areas. The pre-set vehicle trajectory database can be understood as a collection of vehicle trajectory information pre-stored for specific geographical areas or road types. This database may contain historical vehicle driving data, high-precision map data, road topology information, and typical trajectory patterns under different motion states. Its purpose is to provide a reliable reference benchmark for vehicle trajectory data to facilitate initial verification in signal-free areas. The database can be constructed by collecting, processing, and modeling a large amount of vehicle driving data in different environments; for example, it may contain typical driving trajectories for specific tunnels, underground parking lots, or mountain roads. Initial data verification refers to the process of comparing and analyzing the currently acquired vehicle trajectory data with the reference trajectories in the pre-set vehicle trajectory database. This allows for a preliminary assessment of the rationality and deviation of the current trajectory data and the identification of potential error sources. Specifically, this can be achieved by determining indicators such as positional deviation, heading angle deviation, or speed deviation between the current trajectory and the matching trajectory in the database. This allows us to obtain initial navigation data for the vehicle's movement trajectory in signal-free areas. This data represents trajectory information that has been preliminarily verified but not yet fully corrected. Simultaneously, auxiliary correction coefficients are generated. These coefficients reflect the degree of deviation between the current trajectory data and the reference trajectory, and the direction of correction. The auxiliary correction coefficients are a set of parameters used to correct errors in the vehicle's trajectory data. These coefficients can be dynamically generated based on the results of the initial data verification; for example, they may include position offset, heading angle correction, speed scaling factor, or attitude angle correction. Their purpose is to provide a precise basis for subsequent data correction.

[0113] By using auxiliary correction coefficients, the initial navigation data for a vehicle's motion trajectory in a no-signal area is corrected to obtain the vehicle's no-signal area motion trajectory navigation data. Specifically, data correction refers to the process of finely refining the initial navigation data for a vehicle's motion trajectory in a no-signal area using the aforementioned auxiliary correction coefficients. By applying the auxiliary correction coefficients to the initial navigation data, deviations found in the initial calibration can be effectively eliminated or reduced, thereby obtaining more accurate and reliable vehicle no-signal area motion trajectory navigation data.

[0114] Specifically, when a vehicle is entering a long underground tunnel, GPS signals are completely lost within the tunnel. Before entering the tunnel, the vehicle's navigation system has loaded a pre-set vehicle trajectory database for that tunnel, containing typical driving trajectory data for different lanes and speeds within the tunnel. Once inside the tunnel, the vehicle's inertial motion data, wheel motion data, and steering motion data are continuously acquired and analyzed using signal characteristics to obtain vehicle trajectory data containing information on the vehicle's current position, speed, heading angle, and attitude angle. This trajectory data is then sent to a data verification module. First, the pre-set vehicle trajectory database is used to perform initial data verification on the current vehicle trajectory data. For example, the vehicle's real-time position, speed, and heading angle are compared with the reference trajectory for the corresponding tunnel segment in the database. If a persistent deviation is found between the vehicle's heading angle and the reference heading angle in the database (e.g., the vehicle actually veers to the left, while the database indicates straight-line driving), or if the vehicle's estimated position deviates laterally from the lane centerline in the database, these deviations are identified. Based on these deviations, initial navigation data for the vehicle's motion trajectory in no-signal areas is obtained, and auxiliary correction coefficients are generated. For example, the auxiliary correction coefficients may include a continuous rightward heading angle correction and a rightward position offset correction. Next, the initial navigation data for the vehicle's motion trajectory in no-signal areas is corrected using the auxiliary correction coefficients. Specifically, the calculated heading angle correction and position offset correction are superimposed on the initial navigation data in real time, thereby correcting the vehicle's current heading angle and position estimate. For example, if the initial data estimates a heading angle of 0 degrees (straight line), but the auxiliary correction coefficient indicates a 5-degree rightward correction, the corrected heading angle will become 5 degrees. Through this process, even in long-term no-signal tunnel environments, the vehicle's motion trajectory data can be continuously verified and corrected based on external references, ensuring that the vehicle can accurately travel along the preset trajectory or actual lane, avoiding navigation drift caused by accumulated sensor errors, and ultimately obtaining high-precision navigation data for the vehicle's motion trajectory in no-signal areas.

[0115] This embodiment introduces a preset vehicle trajectory database, providing a relatively reliable external reference benchmark for vehicle trajectory data in signal-free areas. When a vehicle enters a signal-free area, its trajectory data is first initially verified against this database. This allows for the identification of trajectory deviations caused by factors such as inertial sensor drift, wheel slippage, or steering errors. Through comparison, initial navigation data for the vehicle's trajectory in signal-free areas is obtained, and an auxiliary correction coefficient is generated simultaneously. This coefficient precisely quantifies the difference between the current trajectory and the preset trajectory. Subsequently, this auxiliary correction coefficient is used to correct the initial navigation data, thereby bringing the trajectory data, which may have accumulated errors, back to a range more consistent with the actual environment or historical experience. Due to the mechanism based on external reference and dynamic correction, the vehicle's navigation data in signal-free areas can maintain high accuracy and stability.

[0116] For a sensor-based motion trajectory method in a signal-free region based on any of the above embodiments, please refer to [link to relevant documentation]. Figure 2 The present invention also provides a sensor-based motion trajectory navigation system for signal-free areas, which includes a data acquisition module 210, a state determination module 220, a feature determination module 230, a data analysis module 240, and a data verification module 250.

[0117] The data acquisition module 210 is used to acquire the vehicle's inertial motion data, wheel motion data, and steering motion data.

[0118] The state determination module 220 is used to determine the current motion state and reliability coefficient of the vehicle based on the vehicle's inertial motion data, wheel motion data and steering motion data. The current motion state includes a straight motion state or a bumpy motion state.

[0119] The feature determination module 230 is used to determine the current environmental features of the vehicle in the no-signal area based on the vehicle's current motion state and the vehicle's motion history data.

[0120] The data analysis module 240 is used to perform signal feature analysis on the credibility coefficient and current environmental characteristics to obtain vehicle motion trajectory data containing information on the vehicle's current position, speed, heading angle, and attitude angle.

[0121] The data verification module 250 is used to verify the vehicle motion trajectory data to obtain the vehicle's motion trajectory navigation data in areas without signal coverage.

[0122] In this embodiment, the data acquisition module 210 collects vehicle motion information, providing a foundation for subsequent processing. The state determination module 220 and feature determination module 230 intelligently identify the vehicle's real-time motion state and environmental characteristics, providing key contextual information for data analysis. The data analysis module 240 combines the reliability coefficient and environmental characteristics to perform deep fusion and analysis of the sensor data, generating high-precision vehicle motion trajectory data. Finally, the data verification module 250 rigorously verifies the trajectory data to ensure the accuracy and reliability of the output navigation data. This effectively avoids low navigation accuracy and poor reliability caused by the accumulation of sensor errors and environmental complexity, enabling continuous and accurate trajectory navigation for the vehicle in areas without signal coverage.

[0123] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention 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 or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered within the scope of the present invention specification.

Claims

1. A sensor-based method for navigating motion trajectories in signal-free areas, characterized in that, include: Acquire vehicle inertial motion data, wheel motion data, and steering motion data; Based on the vehicle's inertial motion data, wheel motion data, and steering motion data, the vehicle's current motion state and reliability coefficient are determined. The current motion state includes either a straight-line motion state or a bumpy motion state. Based on the vehicle's current motion state and historical motion data, determine the current environmental characteristics of the vehicle in the no-signal area; Signal feature analysis is performed on the confidence coefficient and current environmental characteristics to obtain vehicle motion trajectory data containing information on the vehicle's current position, speed, heading angle, and attitude angle. The vehicle motion trajectory data is verified to obtain the vehicle's motion trajectory navigation data in areas without signal coverage. In the step of determining the current motion state and reliability coefficient of the vehicle based on the vehicle's inertial motion data, wheel motion data, and steering motion data, the acquisition of the reliability coefficient includes: Signal feature analysis is performed on the inertial motion data to determine the instantaneous disturbance data caused by the instantaneous displacement of the on-board items and the heading change trend data caused by the inherent deviation of the inertial measurement unit when the vehicle is in a bumpy motion state; After associating the wheel motion data with the steering motion data, the distance estimation deviation coefficient caused by the wheel circumference parameter in the straight motion state is determined; The reliability coefficient is obtained based on the instantaneous disturbance data, the heading change trend data, and the distance estimation deviation coefficient.

2. The sensor-based motion trajectory navigation method for signal-free areas according to claim 1, characterized in that, The steps for obtaining the reliability coefficient based on the instantaneous disturbance data, the heading change trend data, and the distance estimation deviation coefficient include: The instantaneous disturbance data, the heading change trend data, and the distance estimation deviation coefficient are corrected respectively to obtain the corrected instantaneous disturbance data, the corrected heading change trend data, and the corrected distance estimation deviation coefficient. Using the corrected instantaneous disturbance data, the corrected heading change trend data, and the corrected distance estimation bias The difference coefficients are subjected to credibility fusion processing to obtain the credibility coefficients.

3. The sensor-based motion trajectory navigation method for signal-free areas according to claim 2, characterized in that, The steps of correcting the instantaneous disturbance data, the heading change trend data, and the distance estimation deviation coefficient to obtain the corrected instantaneous disturbance data, the corrected heading change trend data, and the corrected distance estimation deviation coefficient include: The instantaneous disturbance data is corrected using the yaw rate data in the inertial motion data to obtain the corrected instantaneous disturbance data. By using the speed data from the wheel motion data, the heading change trend data is corrected to obtain the corrected heading change trend data. By using the acceleration data in the steering motion data, the distance estimation deviation coefficient is corrected to obtain the corrected distance estimation deviation coefficient.

4. The sensor-based motion trajectory navigation method for signal-free areas according to claim 3, characterized in that, The steps for correcting the heading change trend data using speed data from wheel motion data to obtain the corrected heading change trend data include: By utilizing the speed data in the wheel motion data, the complex motion state of the vehicle can be determined; Based on the complex motion state and the historical correction coefficient library, determine the correction parameters for the heading change trend data; By using correction parameters to correct the heading trend data, the heading trend data is corrected to obtain the corrected heading trend data.

5. The sensor-based motion trajectory navigation method for signal-free areas according to claim 1, characterized in that, The step of determining the current motion state and reliability coefficient of the vehicle based on its inertial motion data, wheel motion data, and steering motion data includes the following steps: Based on the vehicle's inertial motion data, wheel motion data, and steering motion data, the vehicle's short-term perturbation linear motion state is determined. Based on the short-term perturbation linear motion state, the inertial motion data is processed by separating the yaw angular velocity components to obtain the zero-point drift component, the driver correction yaw component, and the road perturbation yaw component. The driver-corrected yaw component and the road surface disturbance yaw component are removed from the inertial motion data to obtain the corrected inertial yaw angular velocity. Using the corrected inertial yaw rate, wheel motion data, and steering motion data, the vehicle's motion state is identified to obtain the vehicle's current motion state.

6. The sensor-based motion trajectory navigation method for signal-free areas according to claim 5, characterized in that, The steps of separating the yaw rate component from the inertial motion data based on the short-term perturbation linear motion state to obtain the zero-point drift component, the driver-corrected yaw component, and the road perturbation yaw component include: Based on the short-term perturbation linear motion state, the inertial motion data is processed by separating the yaw rate components to obtain yaw rate components, driving micro-correction components, and lateral motion trend components at different frequencies. Using the inertial motion data, the characteristics of the yaw angular velocity components at different frequencies are analyzed to obtain the zero-point drift component. Using the inertial motion data, component analysis is performed on the small driving correction component to obtain the small driving steering component; Using the inertial motion data, component analysis is performed on the lateral motion trend component to obtain the road surface micro-disturbance yaw component.

7. The sensor-based motion trajectory navigation method for signal-free areas according to claim 1, characterized in that, The steps for acquiring vehicle inertial motion data, wheel motion data, and steering motion data include: Acquire raw data of vehicle inertial motion, wheel motion, and steering motion; The raw data of the vehicle's inertial motion, wheel motion, and steering motion are preprocessed to obtain the preprocessed raw data of the vehicle's inertial motion, wheel motion, and steering motion. The preprocessed raw data of vehicle inertial motion, wheel motion, and steering motion are validated to obtain the vehicle's inertial motion data, wheel motion data, and steering motion data.

8. The sensor-based motion trajectory navigation method for signal-free areas according to claim 1, characterized in that, The steps for verifying the vehicle's motion trajectory data to obtain navigation data for the vehicle's motion trajectory in areas without signal coverage include: Using a pre-set vehicle motion trajectory database, the vehicle motion trajectory data is initially verified to obtain initial navigation data and auxiliary correction coefficients for the vehicle's motion trajectory in areas without signal coverage. Using auxiliary correction coefficients, the initial data of the vehicle's motion trajectory navigation in the no-signal area is corrected to obtain the vehicle's motion trajectory navigation data in the no-signal area.

9. A sensor-based motion trajectory navigation system for signal-free areas, characterized in that, The system includes: The data acquisition module is used to acquire the vehicle's inertial motion data, wheel motion data, and steering motion data; The state determination module is used to determine the current motion state and reliability coefficient of the vehicle based on the vehicle's inertial motion data, wheel motion data and steering motion data. The current motion state includes a straight motion state or a bumpy motion state. It is also used to perform signal feature analysis on the inertial motion data to determine the instantaneous disturbance data caused by the instantaneous displacement of the on-board items and the heading change trend data caused by the inherent deviation of the inertial measurement unit when the vehicle is in a bumpy motion state; After associating the wheel motion data with the steering motion data, the distance estimation deviation coefficient caused by the wheel circumference parameter in the straight motion state is determined; The reliability coefficient is obtained based on the instantaneous disturbance data, the heading change trend data, and the distance estimation deviation coefficient; The feature determination module is used to determine the current environmental features of the vehicle in the no-signal area based on the vehicle's current motion state and the vehicle's motion history data. The data analysis module is used to perform signal feature analysis on the credibility coefficient and current environmental characteristics to obtain vehicle motion trajectory data containing information on the vehicle's current position, speed, heading angle, and attitude angle. The data verification module is used to verify the vehicle's motion trajectory data to obtain the vehicle's motion trajectory navigation data in areas without signal coverage.

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