Sensor-based non-signal area motion trail navigation method and sensor-based non-signal area motion trail navigation system

Abstract

The invention relates to the technical field of trajectory navigation, in particular to a sensor-based non-signal area motion trajectory navigation method and system. The method comprises the following steps: acquiring inertial motion data, wheel motion data and steering motion data of a vehicle; according to the inertial motion data, the wheel motion data and the steering motion data of the vehicle, determining a current motion state and a credibility coefficient of the vehicle; according to the current motion state of the vehicle and the motion historical data of the vehicle, determining the current environment characteristics of the vehicle in the non-signal area; performing signal feature analysis on the credibility coefficient and the current environment feature to obtain vehicle movement track data of the vehicle including current position, speed, course angle and attitude angle information; and carrying out data verification on the vehicle motion track data to obtain no-signal area motion track navigation data of the vehicle. The invention aims to solve the problem that when a vehicle is located in a non-signal area, accurate navigation of a motion track is difficult to carry out in the non-signal area in the existing navigation method.

Description

TECHNICAL FIELD

[0001] The present 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. BACKGROUND

[0002] When a vehicle travels in a special area such as an underground parking lot, a long tunnel, or under a multi-level viaduct, it may encounter a situation where the satellite navigation signal is interrupted or very weak. The navigation system relying on satellite signals cannot work normally, resulting in loss of vehicle position information, deviation of travel trajectory, and affecting the driving experience. It may also cause safety hazards. The existing navigation method relies on vehicle sensors for navigation.

[0003] However, the zero drift of the inertial measurement unit (especially the gyroscope) caused by long-term vehicle operation exceeds the stable range. When the vehicle enters a signal-free area, the vehicle orientation will continuously and regularly deviate from the actual driving direction, and the heading angle deviation accumulates with the driving time. At the same time, the vehicle travels on unevenly worn road surfaces for a long time, causing slight local wear of the tires of the driving wheels, resulting in a small but continuous difference between the actual circumference of the tires and the preset wheel circumference parameter. In the signal-free area, the external calibration source is lost, and the internal sensor data is completely relied on for dead reckoning. At this time, the small difference in tire circumference will cause regular errors in the estimation of the vehicle's driving distance. In the signal-free area, the core state data representing the current position, speed, and attitude of the vehicle is continuously disturbed by multiple errors, and the errors interact in a complex manner when calculating the vehicle trajectory, making the speed and amplitude of the trajectory drift far exceed a single error source. Further, it is not possible to accurately update the real-time state of the vehicle. Not only does the trajectory calculation result deviate significantly from the actual path, but it is also not possible to effectively determine the precise position and driving direction of the vehicle, thereby failing to provide reliable motion trajectory navigation services at critical moments, resulting in serious trajectory drift and positioning failure of the driver in complex signal-free scenarios such as tunnels or underground parking lots, making it difficult to accurately navigate the motion trajectory in the signal-free area. SUMMARY

[0004] The present application aims to provide a sensor-based motion trajectory navigation method and system in a signal-free area, to solve the problem of existing navigation methods that are difficult to accurately navigate the motion trajectory in a signal-free area when the vehicle is located in the signal-free area.

[0005] To achieve the above-mentioned purpose, the present application adopts the following technical solution: a sensor-based motion trajectory navigation method in a signal-free area, comprising: obtaining inertial motion data, wheel motion data, and steering motion data of the 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, the current motion state including a straight motion state or a jolt motion state; determining a current environmental feature of the vehicle located 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 feature to obtain vehicle motion trajectory data containing current position, speed, heading angle and attitude angle information of the vehicle; performing data verification on the vehicle motion trajectory data to obtain signal-free area motion trajectory navigation data of the vehicle.

[0006] Preferably, in the step of determining the current motion state and the reliability coefficient of the vehicle according to the inertial motion data, the wheel motion data and the steering motion data, the acquisition of the reliability coefficient includes: performing signal feature analysis on the inertial motion data to determine instantaneous disturbance data caused by instantaneous displacement of the vehicle-borne article in the jolt motion state and heading change trend data caused by inherent bias of the inertial measurement unit; determining distance estimation bias coefficients caused by wheel circumference parameters in the straight motion state after data correlation between the wheel motion data and the steering motion data; obtaining the reliability coefficient according to the instantaneous disturbance data, the heading change trend data and the distance estimation bias coefficients.

[0007] Preferably, the step of obtaining the reliability coefficient according to the instantaneous disturbance data, the heading change trend data and the distance estimation bias coefficients includes: respectively correcting the instantaneous disturbance data, the heading change trend data and the distance estimation bias coefficients to obtain corrected instantaneous disturbance data, corrected heading change trend data and corrected distance estimation bias coefficients; performing reliability fusion processing on the corrected instantaneous disturbance data, the corrected heading change trend data and the corrected distance estimation bias coefficients to obtain the reliability coefficient.

[0008] Preferably, the step of respectively correcting the instantaneous disturbance data, the heading change trend data and the distance estimation bias coefficients to obtain corrected instantaneous disturbance data, corrected heading change trend data and corrected distance estimation bias coefficients includes: correcting the instantaneous disturbance data by using yaw rate data in the inertial motion data to obtain corrected instantaneous disturbance data; The speed data in the wheel movement data is used to correct the heading change trend data, to obtain corrected heading change trend data. The acceleration data in the steering movement data is used to correct the distance estimation deviation coefficient, to obtain corrected distance estimation deviation coefficient.

[0009] Preferably, the step of using the speed data in the wheel movement data to correct the heading change trend data, to obtain corrected heading change trend data, comprises: The speed data in the wheel movement data is used to determine the complex motion state of the vehicle; According to the complex motion state and the historical correction coefficient library, a correction parameter of the heading change trend data is determined; The correction parameter of the heading change trend data is used to correct the heading change trend data, to obtain corrected heading change trend data.

[0010] Preferably, in the step of determining the current motion state of the vehicle and the confidence coefficient according to the inertial motion data, the wheel movement data and the steering movement data, the step of determining the current motion state of the vehicle comprises: The short-time micro-perturbation straight-line motion state of the vehicle is determined according to the inertial motion data, the wheel movement data and the steering movement data; The yaw angular velocity component separation processing is performed on the inertial motion data according to the short-time micro-perturbation straight-line motion state, to obtain a zero drift component, a driver correction yaw component and a road micro-perturbation yaw component; The driver correction yaw component and the road micro-perturbation yaw component are removed from the inertial motion data, to obtain a corrected inertial yaw angular velocity; The motion state of the vehicle is identified using the corrected inertial yaw angular velocity, the wheel movement data and the steering movement data, to obtain the current motion state of the vehicle.

[0011] Preferably, the step of performing the yaw angular velocity component separation processing on the inertial motion data according to the short-time micro-perturbation straight-line motion state, to obtain a zero drift component, a driver correction yaw component and a road micro-perturbation yaw component, comprises: The yaw angular velocity component separation processing is performed on the inertial motion data according to the short-time micro-perturbation straight-line motion state, to obtain yaw angular velocity components of different frequencies, a driver micro-correction component and a lateral motion trend component; The yaw angular velocity components of different frequencies are analyzed using the inertial motion data, to obtain a zero drift component; The driver micro-correction component is analyzed using the inertial motion data, to obtain a driver micro-steering component; The inertial motion data is used to perform component analysis on a lateral motion trend component to obtain a road perturbation yawing component.

[0012] Preferably, the step of obtaining the inertial motion data, the wheel motion data and the steering motion data of the vehicle comprises: obtaining inertial motion raw data, wheel motion raw data and steering motion raw data of the vehicle; respectively pre-processing the inertial motion raw data, the wheel motion raw data and the steering motion raw data of the vehicle to obtain pre-processed inertial motion raw data, pre-processed wheel motion raw data and pre-processed steering motion raw data of the vehicle; performing raw data verification on the pre-processed inertial motion raw data, the pre-processed wheel motion raw data and the pre-processed steering motion raw data to obtain the inertial motion data, the wheel motion data and the steering motion data of the vehicle.

[0013] Preferably, the step of performing data verification on the vehicle motion trajectory data to obtain the vehicle motion trajectory navigation data in the signal-free area comprises: performing initial data verification on the vehicle motion trajectory data by using a preset vehicle motion trajectory database to obtain initial vehicle motion trajectory navigation data in the signal-free area and an auxiliary correction coefficient; performing data correction on the initial vehicle motion trajectory navigation data in the signal-free area by using the auxiliary correction coefficient to obtain the vehicle motion trajectory navigation data in the signal-free area.

[0014] The application further provides a sensor-based vehicle motion trajectory navigation system in a signal-free area, which comprises: a data acquisition module configured to obtain inertial motion data, wheel motion data and steering motion data of the vehicle; a state determination module configured to determine a current motion state and a confidence coefficient of the vehicle according to the inertial motion data, the wheel motion data and the steering motion data of the vehicle, wherein the current motion state comprises a straight motion state or a jolt motion state; a feature determination module configured to determine a current environmental feature of the vehicle in the signal-free area according to the current motion state of the vehicle and motion history data of the vehicle; a data analysis module configured to perform signal feature analysis on the confidence coefficient and the current environmental feature to obtain vehicle motion trajectory data containing current position, speed, heading angle and attitude angle information of the vehicle; a data verification module configured to perform data verification on the vehicle motion trajectory data to obtain vehicle motion trajectory navigation data in the signal-free area.

[0015] Compared with the prior art, the sensor-based vehicle motion trajectory navigation method and system in a signal-free area have the following advantages: The present application obtains the inertial motion data, wheel motion data and steering motion data of the vehicle, determines the current motion state and the reliability coefficient of the vehicle, further determines the current environment characteristics in combination with the motion history data, performs signal feature analysis on the reliability coefficient and the environment characteristics to obtain the vehicle motion trajectory data, and finally checks the trajectory data to obtain the motion trajectory navigation data in the signal-free area. By comprehensively utilizing multi-source sensor data and performing fine state recognition, error evaluation and data checking, the present application can significantly improve the navigation accuracy and reliability of the vehicle in the signal-free area, ensure that the driver can accurately identify the driving path in complex scenes such as tunnels and underground parking lots, avoid traffic congestion and safety hazards, and thus provide continuous and accurate navigation services. BRIEF DESCRIPTION OF DRAWINGS

[0016] In order to more clearly illustrate the specific embodiments of the present application, the drawings required to be used in the specific embodiments will be briefly introduced below. In all the drawings, the elements or parts are not necessarily drawn according to the actual proportions.

[0017] Figure 1 A flowchart of a sensor-based motion trajectory navigation method in a signal-free area according to the present application.

[0018] Figure 2 A structural block diagram of a sensor-based motion trajectory navigation system in a signal-free area according to the present application.

[0019] In the figure: 210, data acquisition module; 220, state determination module; 230, feature determination module; 240, data analysis module; 250, data checking module.

[0020] The implementation of the functions and advantages of the present application will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION

[0021] The embodiments of the present application will be disclosed below with reference to the drawings. Many practical details will be described in the following description for the purpose of clear illustration. However, it should be understood that these practical details should not be used to limit the present application. That is, in some embodiments of the present application, these practical details are not necessary. In addition, for the purpose of simplifying the drawings, some conventional structures and components will be shown in the drawings in a simple schematic manner.

[0022] It should be noted that all directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of the present application are only used to explain the relative position relationship, motion condition, etc. between the components in a certain specific posture (as shown in the drawings), and if the specific posture changes, the directional indications will also change accordingly.

[0023] In addition, the descriptions such as "first", "second" and the like in the present application are only for the purpose of description, and do not mean to particularly indicate the order or sequence, nor to limit the present application, which are merely to distinguish the components or operations described by the same technical terms, and cannot be understood as indicating or implying the relative importance of the indicated technical features or implicitly indicating the number of the indicated technical features. Therefore, the features defined with "first", "second" can explicitly or implicitly include at least one of the features. In addition, the technical solutions of various embodiments can be combined with each other, but it must be based on the realization of the ordinary skilled in the art, when the combination of technical solutions appears contradictory or unachievable, it should be considered that the combination of technical solutions does not exist, nor within the protection scope required by the present application.

[0024] The existing vehicle navigation method loses vehicle position information and deviates from the driving track when driving in a signal-free area such as an underground parking lot, a long tunnel or under a multi-level viaduct due to the interruption or weak signal of satellite navigation. Although the prior art attempts to calculate navigation through vehicle sensors, the existing navigation accuracy and reliability are seriously challenged by complex factors such as zero point drift of inertial measurement unit, wheel circumference parameter difference and instantaneous disturbance caused by vehicle bumping, resulting in serious deviation between the trajectory calculation result and the actual path, and the vehicle cannot be provided with accurate motion trajectory navigation.

[0025] In order to further understand the content, characteristics and effects of the present application, the following embodiments are exemplified and described in detail as follows with reference to the accompanying drawings: Please refer to Figure 1 The present application provides a sensor-based motion trajectory navigation method in a signal-free area, comprising the following steps: S100, acquiring inertial motion data, wheel motion data and steering motion data of the vehicle. The inertial motion data refers to the data collected by the inertial measurement unit, including the angular velocity and acceleration of the vehicle in three-dimensional space, etc., which is used to describe the attitude and motion trend of the vehicle. The wheel motion data refers to the data obtained by the wheel speed sensor, such as the wheel speed and driving distance, etc., which is mainly used to estimate the driving distance of the vehicle. The steering motion data refers to the data obtained by the steering angle sensor or steering wheel angle sensor, which is used to reflect the steering angle and steering rate of the vehicle. Specifically, the inertial motion data can be collected in real time by the inertial measurement unit installed on the vehicle, which usually contains a three-axis gyroscope and a three-axis accelerometer, and can provide angular velocity and linear acceleration information of the vehicle. The wheel motion data can be obtained by the wheel speed sensor installed on each wheel, which can measure the wheel speed and further calculate the driving distance of the vehicle. The steering motion data can be obtained by the steering wheel angle sensor or steering angle sensor, which can provide the steering angle of the steering wheel or the actual steering angle of the wheel. The sensor data is periodically read and stored by the central processing unit of the vehicle or a special navigation controller.

[0026] S200, determining the current motion state and the confidence coefficient of the vehicle according to the inertial motion data, the wheel motion data and the steering motion data of the vehicle, wherein the current motion state includes a straight motion state or a bump motion state. The current motion state refers to the motion mode of the vehicle, such as a straight motion state or a bump motion state, which is used for subsequent data processing and error correction. The confidence coefficient is an index to measure the reliability of sensor data or calculation results, and the higher the value is, the more reliable the data is. Specifically, when determining the current motion state of the vehicle, the acceleration and angular velocity in the inertial motion data can be analyzed. For example, when the vehicle is driving straight on a flat road, the accelerometer reading will be relatively stable, and the yaw angular velocity of the gyroscope will be close to zero; when the vehicle passes through a bump road section, the accelerometer reading will show high-frequency and violent fluctuations. By setting corresponding threshold and filtering algorithm, the straight motion state or bump motion state can be identified. When determining the confidence coefficient, the internal consistency of different sensor data can be evaluated. For example, if there is a significant difference between the output of the inertial measurement unit and the output of the wheel speed sensor over a long period of time, the confidence coefficient of the inertial measurement unit may be reduced. The confidence coefficient can be obtained based on statistical methods such as Kalman filtering or particle filtering, and the error model of different sensor data is fused to obtain a comprehensive reliability evaluation.

[0027] S300, determine the current environmental characteristics of the vehicle in the signal-free area according to the current motion state of the vehicle and the motion history data of the vehicle. Specifically, the current environmental characteristics refer to the specific environmental characteristics of the vehicle in the signal-free area, such as road flatness and bend curvature, which will affect the motion mode and sensor data of the vehicle. For example, when the vehicle is in a straight motion state, the current road section can be judged to be a straight road or a gentle bend in combination with the curvature information of the road section in the historical motion data. When the vehicle is in a jolt motion state, the current road section can be judged to be a speed bump, a potholed road, or other jolt areas in combination with the road surface condition information of the road section in the historical motion data. The motion history data can be pre-stored in the navigation system of the vehicle or collected and updated in real time when the vehicle is driving in the signal area. The determination of the environmental characteristics helps to more accurately analyze the signal characteristics subsequently.

[0028] S400, signal feature analysis is performed on the credibility 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. The vehicle motion trajectory data is a collection of information including the position, speed, heading angle and attitude angle of the vehicle in the signal-free area. Specifically, signal feature analysis can use various algorithms. For example, when the credibility coefficient is high and the environmental characteristics show a flat straight road, distance calculation can be mainly relied on wheel motion data, and heading angle update can be combined with inertial motion data. When the credibility coefficient is low or the environmental characteristics show a jolt road section, the weight of the inertial motion data can be increased, and a more complex filtering algorithm can be used to suppress noise, so as to more accurately estimate the position, speed, heading angle and attitude angle of the vehicle. For example, state estimation algorithms such as extended Kalman filter or unscented Kalman filter can be used to fuse the vehicle motion trajectory data by taking different sensor data and environmental characteristics as input.

[0029] S500, data verification is performed on the vehicle motion trajectory data to obtain the signal-free area motion trajectory navigation data of the vehicle. The signal-free area motion trajectory navigation data is the final navigation data after verification and optimization, which is used to guide the driving of the vehicle in the signal-free area. Specifically, data verification can be realized by comparing with the preset map information or historical trajectory data. For example, the calculated vehicle trajectory is matched with the digital map of the underground parking lot or the tunnel. If there is a large deviation between the trajectory and the map, the trajectory data can be adjusted and corrected. In addition, the kinematic model of the vehicle can be used to check the consistency of the trajectory data, such as checking whether the speed and acceleration of the vehicle conform to the physical law, and whether the change of the heading angle is consistent with the steering motion data. Through data verification, the accuracy and reliability of the navigation data can be further improved, and the signal-free area motion trajectory navigation data that can be used for navigation is finally generated.

[0030] Specifically, when the vehicle enters the signal-free area, firstly, the inertial motion data, wheel motion data and steering motion data of the vehicle are continuously collected. Then, according to the real-time data, the current motion state of the vehicle (such as straight motion or jounce motion) is identified, and the reliability coefficients of the sensor data are evaluated synchronously. Further, the actual motion environment of the vehicle and the quality of the sensor data can be dynamically reflected, which provides the basis for subsequent error processing. Next, combined with the current motion state of the vehicle and the pre-stored motion history data, the current environmental characteristics of the vehicle in the signal-free area are inferred. For example, if the vehicle is in a straight motion state and the historical data shows that the area is a long straight tunnel, the estimation model will be adjusted. Then, the reliability coefficients and the current environmental characteristics are used as key inputs for signal feature analysis of the sensor data. According to the level of the reliability coefficients and the type of the environmental characteristics, the weight and fusion algorithm of different sensor data are dynamically adjusted. For example, in a jounce section, the weight of the wheel motion data in distance estimation may be reduced, and more reliance on inertial motion data is used for updating the attitude and heading, while advanced filtering algorithms are used to suppress the instantaneous disturbance caused by jounce. Through intelligent fusion, the vehicle motion trajectory data containing the current position, speed, heading angle and attitude angle information can be generated. Finally, the generated vehicle motion trajectory data is strictly checked. Including matching with the pre-set map information, and kinematic consistency check of the trajectory data. Through the check, the cumulative error that may exist in the estimation process can be found and corrected, so that the vehicle signal-free area motion trajectory navigation data with high accuracy and reliability is obtained. Further, it ensures that continuous and accurate navigation services can be provided in the signal-free area.

[0031] In this embodiment, by obtaining the inertial motion data, wheel motion data and steering motion data of the vehicle, the motion information of the vehicle can be comprehensively perceived. Secondly, the current motion state of the vehicle is determined, and the reliability coefficients of the sensor data are synchronously evaluated. Different motion scenarios and changes in sensor data quality can be adaptively responded to, for example, in a bumpy road section, the bumpy motion state can be identified, and the processing strategy for the instantaneous disturbance of the inertial measurement unit is adjusted accordingly, so as to effectively suppress the noise. Furthermore, in combination with the motion history data of the vehicle, the current environmental characteristics of the vehicle located in the signal-free area are determined. The navigation algorithm can be optimized according to the specific environmental conditions (such as straight road, curve, flat road or bumpy road), and the calculation accuracy is further improved. Finally, through signal feature analysis on the reliability coefficients and the current environmental characteristics, the vehicle motion trajectory data containing the current position, speed, heading angle and attitude angle information can be obtained, and through data verification, high-precision signal-free area motion trajectory navigation data can be finally generated. By introducing motion state recognition, reliability evaluation and environmental feature analysis, the present application realizes more refined management of error sources and more intelligent data fusion. For example, when the inertial measurement unit has zero drift, the reliability coefficient will be correspondingly reduced, the weight in the heading angle calculation will be adjusted, and more wheel motion data will be used for auxiliary correction. Therefore, the present application can significantly improve the accuracy and robustness of navigation when facing complex and mutually affecting error sources, and can provide reliable motion trajectory navigation service for the driver in the signal-free area.

[0032] In some embodiments of the present application, in the step of determining the current motion state of the vehicle and the reliability coefficient according to the inertial motion data, wheel motion data and steering motion data of the vehicle, the acquisition of the reliability coefficient comprises: Signal feature analysis is performed on the inertial motion data to determine the instantaneous disturbance data caused by the instantaneous displacement of the vehicle-mounted articles in the bumpy motion state and the heading change trend data caused by the inherent bias of the inertial measurement unit. Specifically, signal feature analysis on the inertial motion data refers to processing the acceleration and angular velocity data output by the inertial measurement unit, such as filtering, frequency spectrum analysis or statistical analysis, to identify and quantify the specific signal features contained therein. In the bumpy motion state, the vehicle will experience significant vertical acceleration and impact, at this time, the instantaneous displacement of the vehicle-mounted articles may cause short-time high-amplitude instantaneous disturbance data in the measurement value. At the same time, due to the manufacturing tolerance and temperature drift of the internal devices of the inertial measurement unit, inherent bias will be generated, which will be manifested as heading change trend data in the heading angle estimation. By in-depth analysis of the inertial motion data, the error components caused by external interference and internal bias can be effectively separated.

[0033] After data association between the wheel motion data and the steering motion data, a distance estimation bias coefficient caused by wheel circumference parameter in straight motion state is determined. Specifically, the wheel motion data is usually provided by wheel speed sensors or odometers for estimating the traveled distance of the vehicle. The steering motion data reflects the steering angle or angular velocity of the vehicle. In straight motion state, the wheel motion data and the steering motion data should theoretically maintain a certain correlation. However, due to factors such as tire wear, tire pressure changes, and load differences, the actual circumference of the wheel may deviate from the nominal value, resulting in errors in the distance estimation based on the wheel speed. By correlating and analyzing the wheel motion data and the steering motion data, such as comparing the expected relationship between the two in straight driving with the actual measured values, this distance estimation bias coefficient caused by the wheel circumference parameter can be identified and quantified.

[0034] A confidence coefficient is obtained according to the instantaneous disturbance data, the heading change trend data, and the distance estimation bias coefficient. The instantaneous disturbance data of this step reflects short-term and sudden measurement uncertainty; the heading change trend data reflects long-term and cumulative direction estimation error; and the distance estimation bias coefficient reflects the systematic error of the odometer. By comprehensively considering error information of different sources and characteristics, a more comprehensive and accurate confidence model can be constructed, and a comprehensive confidence coefficient can be output, which can quantify the overall reliability of the current vehicle motion data.

[0035] Specifically, when the vehicle is driving in a signal-free area, the inertial measurement unit (IMU), wheel speed sensor, and steering angle sensor carried by the vehicle continuously collect data. When the vehicle is driving on a rough road, obvious instantaneous acceleration peaks will be detected in the IMU data, and through signal feature analysis, instantaneous disturbance data caused by vehicle jolting and shaking of on-board objects can be extracted. At the same time, long-term integration of IMU data will show a slow drift of the heading angle, which is identified as the heading change trend data caused by the inherent bias of the inertial measurement unit. When the vehicle enters a straight road section and maintains straight driving, the wheel speed sensor data and the steering angle sensor data are associated. If it is found that the traveled distance calculated based on the wheel speed is slightly mismatched with the straight driving state indicated by the steering angle sensor, the distance estimation bias coefficient caused by the wheel circumference parameter can be calculated. Finally, the quantified instantaneous disturbance data, heading change trend data, and distance estimation bias coefficient are input into a fusion module, and a confidence coefficient reflecting the overall reliability of the current vehicle motion data is obtained by, for example, using a weighted average algorithm based on error variance. For example, when the instantaneous disturbance data is large, the heading change trend is obvious, and the distance estimation bias coefficient is high, the confidence coefficient will decrease accordingly, and vice versa. The confidence coefficient is then used in the subsequent motion trajectory data generation and verification process to optimize the navigation result.

[0036] The embodiment can effectively separate the instantaneous disturbance data and the inherent heading change trend data of the inertial measurement unit in the bumping state by signal feature analysis on the inertial motion data, thereby revealing the uncertainty of the inertial sensor in a complex environment and long-time operation. Meanwhile, by correlating the wheel motion data and the steering motion data, the distance estimation deviation coefficient caused by the wheel circumference parameter can be accurately identified and quantified in the straight-line motion state, thereby avoiding the cumulative error of the odometer caused by the change of tire characteristics in actual use. Since the key error sources are quantified, the subsequent comprehensive and accurate confidence coefficient can be obtained through the credibility fusion processing according to the error data. The confidence coefficient can more truly reflect the reliability of the current vehicle motion data, and provide a more solid foundation for subsequent signal feature analysis and motion trajectory data generation.

[0037] In some embodiments of the application described above, the step of obtaining a confidence coefficient according to the instantaneous disturbance data, the heading change trend data and the distance estimation deviation coefficient comprises: 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 coefficient. Specifically, the correction processing refers to the preprocessing of the original instantaneous disturbance data, heading change trend data and distance estimation deviation coefficient to eliminate or reduce the noise, deviation or error existing therein; the purpose is to improve the accuracy and reliability of these data, and provide high-quality input for subsequent credibility fusion. The correction processing can use various technologies, such as filtering algorithms (such as Kalman filter, extended Kalman filter), statistical methods, machine learning models or compensation methods based on physical models.

[0038] The corrected instantaneous disturbance data, the corrected heading change trend data and the corrected distance estimation deviation coefficient are used for credibility fusion processing to obtain a confidence coefficient. The credibility fusion processing can be understood as comprehensively evaluating and weighting the corrected instantaneous disturbance data, the corrected heading change trend data and the corrected distance estimation deviation coefficient to obtain a unified confidence coefficient. The fusion process fully utilizes the advantages of different data sources, thereby obtaining a more comprehensive vehicle motion state credibility evaluation. The credibility fusion processing can use various fusion algorithms such as evidence theory, fuzzy logic, Bayesian network or weighted average.

[0039] Specifically, when correcting the instantaneous disturbance data, the heading change trend data and the distance estimation bias coefficient, the following methods can be used: For the instantaneous disturbance data, which mainly comes from the instantaneous displacement of the vehicle's cargo when the vehicle is jolted, causing spikes in the acceleration and angular velocity data output by the inertial measurement unit (IMU). To this end, 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, and obtaining more stable corrected instantaneous disturbance data. For the heading change trend data, it may be affected by the inherent bias of the IMU (such as gyroscope drift), causing cumulative errors in the heading angle after long-time integration. To correct this trend bias, the vehicle's heading data in a known straight-line driving state can be used for calibration, or combined with vehicle speed information, a state estimation algorithm such as Kalman filtering can be used to dynamically compensate the heading change trend data, thereby obtaining corrected heading change trend data. For the distance estimation bias coefficient, it mainly comes from the uncertainty of the wheel circumference parameter or factors such as wheel slip. During the correction process, the steering angle information in the steering motion data can be combined with the historical data of the vehicle under different speed and steering conditions to establish a bias model. For example, a model can be trained through machine learning algorithm to predict and compensate the distance estimation bias coefficient according to the current vehicle speed and steering angle, thereby obtaining the corrected distance estimation bias coefficient. After obtaining the corrected instantaneous disturbance data, the corrected heading change trend data and the corrected distance estimation bias coefficient, the D-S evidence theory in the evidence theory can be used for credibility fusion. Specifically, the above three types of corrected data are regarded as different evidence sources, and the corresponding basic probability assignment functions are assigned according to their respective characteristics and reliability. Through the fusion rule of D-S evidence theory, these evidences are combined to finally obtain a more robust credibility coefficient.

[0040] In this embodiment, the instantaneous disturbance data, the heading change trend data and the distance estimation bias coefficient are affected by various factors such as sensor accuracy, environmental noise or vehicle dynamic characteristics when they are originally obtained, resulting in uncertainty or systematic bias in the data. Since the original data is corrected in a targeted manner, the influence of these adverse factors can be effectively removed or weakened, so that the corrected data can more accurately reflect the actual motion characteristics of the vehicle. Furthermore, using the corrected data for credibility fusion processing can ensure that the fusion process is based on more reliable information, thereby avoiding error accumulation and propagation in the original data, and ultimately improving the accuracy and stability of the obtained credibility coefficient.

[0041] In some embodiments of the application described above, the step of correcting the instantaneous disturbance data, the heading change trend data and the distance estimation deviation coefficient respectively to obtain corrected instantaneous disturbance data, corrected heading change trend data and corrected distance estimation deviation coefficient comprises: The yaw rate data in the inertial motion data is used to correct the instantaneous disturbance data to obtain corrected instantaneous disturbance data. Specifically, the instantaneous disturbance data refers to the instantaneous error or noise caused by the instantaneous displacement of the vehicle-mounted articles in the jolt motion state of the vehicle. When correcting the instantaneous disturbance data, the yaw rate data in the inertial motion data is used for correction. The yaw rate data can reflect the rotational motion of the vehicle around the vertical axis. When the vehicle is disturbed instantaneously, the yaw rate will often change accordingly. By analyzing the change trend, the error component in the instantaneous disturbance data can be effectively identified and compensated, so that more accurate corrected instantaneous disturbance data is obtained.

[0042] The speed data in the wheel motion data is used to correct the heading change trend data to obtain corrected heading change trend data. The heading change trend data is the trend error of the change of the vehicle heading with time caused by the inherent deviation of the inertial measurement unit. When correcting the heading change trend data, the speed data in the wheel motion data is used for correction. The speed data in the wheel motion data can provide the actual running speed information of the vehicle. When the vehicle is running straight or turning stably, the speed data is relatively stable, and the deviation in the heading change trend data may be related to the change of speed. By calibrating the heading change trend data in combination with the speed data, the cumulative error caused by the inherent deviation of the inertial measurement unit can be effectively eliminated or weakened, so that the corrected heading change trend data is obtained.

[0043] The acceleration data in the steering motion data is used to correct the distance estimation deviation coefficient to obtain corrected distance estimation deviation coefficient. The distance estimation deviation coefficient specifically refers to the distance estimation error caused by inaccurate wheel circumference parameters or slippage and other factors in the straight motion state. When correcting the distance estimation deviation coefficient, the acceleration data in the steering motion data is used for correction. The acceleration data in the steering motion data can reflect the dynamic characteristics of the vehicle when accelerating or decelerating or turning, which may affect the actual circumference of the wheel or the effective radius in contact with the ground, thereby causing distance estimation deviation. By analyzing the relationship between the acceleration data and the distance estimation deviation coefficient, an effective correction model can be established to accurately correct the distance estimation deviation coefficient, so that the corrected distance estimation deviation coefficient is obtained.

[0044] Specifically, the vehicle is driving in a signal-free area, its on-board sensors continuously acquire inertial motion data, wheel motion data and steering motion data. When the vehicle passes through a section of bumpy road, the inertial sensor will produce instantaneous disturbance data. At this time, the yaw rate data recorded in the inertial motion data is used to analyze and correct the instantaneous disturbance data in real time. For example, if the yaw rate fluctuates sharply in a short time, it indicates that there is a large instantaneous disturbance. According to the amplitude and frequency of the yaw rate, the corresponding compensation amount is obtained and applied to the instantaneous disturbance data, so as to obtain the corrected instantaneous disturbance data. During the long-time driving of the vehicle, the inertial measurement unit may produce cumulative drift of the heading, forming the heading change trend data. Combined with the speed data in the wheel motion data. For example, when the vehicle is driving straight at a constant speed, if the heading change trend data still shows a slight deflection, according to the stability of the wheel speed, it is judged that the deflection is mainly caused by the inherent deviation of the inertial measurement unit, and the speed data is used as a reference to calibrate the heading change trend data to eliminate or weaken the drift, and obtain the corrected heading change trend data. In addition, when the vehicle is accelerating or decelerating, the wheel may slip slightly or the dynamic change of the circumference parameter, resulting in the generation of distance estimation deviation coefficient. At this time, the acceleration data in the steering motion data is used for correction. For example, when the vehicle accelerates sharply, the acceleration data will increase significantly, according to the size and duration of the acceleration, the possibility and degree of wheel slip are evaluated, and the distance estimation deviation coefficient is corrected accordingly, so as to obtain the corrected distance estimation deviation coefficient. Through specific correction measures, the accuracy of the instantaneous disturbance data, the heading change trend data and the distance estimation deviation coefficient is ensured, and high-quality input is provided for the subsequent credibility fusion processing.

[0045] The embodiment introduces auxiliary sensor data closely related to the characteristics of the data to be corrected, and realizes fine correction of the instantaneous disturbance data, the heading change trend data and the distance estimation deviation coefficient. Specifically, the instantaneous disturbance is usually accompanied by rapid change of the vehicle attitude, and the yaw rate data in the inertial motion data can sensitively capture the instantaneous rotation, so it is used to accurately compensate the instantaneous disturbance data. The cumulative error of the heading change trend is often related to the driving speed and path of the vehicle, and the speed data in the wheel motion data provides a reference for the actual displacement of the vehicle. By correlating and analyzing the speed data with the heading change trend data, the drift error of the inertial measurement unit can be effectively suppressed. In addition, the generation of the distance estimation deviation coefficient is closely related to the actual motion state of the wheel (such as slip, acceleration or deceleration), and the acceleration data in the steering motion data can reflect these dynamic changes, thereby providing a key dynamic reference for the correction of the distance estimation deviation coefficient. By using these sensor data with complementarity and correlation, various error sources can be more accurately identified and eliminated, thereby significantly improving the accuracy of the correction process.

[0046] In some embodiments of the above-mentioned embodiments of the present application, the step of correcting the heading change trend data using the speed data in the wheel motion data includes: The complex motion state of the vehicle is determined using the speed data in the wheel motion data. Specifically, when determining the complex motion state of the vehicle, the speed data in the wheel motion data can be used in combination with the acceleration, angular velocity, and other information of the vehicle to identify the current motion mode of the vehicle through a pre-set threshold judgment, pattern recognition algorithm, or machine learning model. For example, when the speed change rate exceeds the threshold, it can be judged as an acceleration or deceleration state; when the yaw angular velocity is large, it can be judged as a turning state. The complex motion state can be understood as all dynamic changes of the vehicle in the driving process in addition to simple uniform linear motion, such as rapid acceleration, rapid deceleration, rapid turning, braking, or driving on a bumpy road, etc.

[0047] The correction parameter of the heading change trend data is determined according to the complex motion state and the historical correction coefficient library. The historical correction coefficient library is a pre-established database that stores the optimal correction parameters corresponding to the heading change trend data under different complex motion states. This database can be constructed and optimized through a large amount of actual driving data collection, simulation, or expert experience. For example, under a specific type of rapid turning state, the heading change trend data may have a certain specific deviation pattern, and the historical correction coefficient library will store correction parameters for this deviation pattern.

[0048] The heading change trend data is corrected using the correction parameter of the heading change trend data to obtain corrected heading change trend data. After the complex motion state of the vehicle is determined and the corresponding correction parameter is obtained from the historical correction coefficient library, the correction parameter is applied to the heading change trend data. The correction parameter can be a gain coefficient, an offset, a filtering parameter, or other mathematical model parameters, and its purpose is to eliminate or reduce the errors in the heading change trend data caused by the inherent bias of the inertial measurement unit and the complex motion state, so as to obtain more accurate corrected heading change trend data.

[0049] Specifically, when the vehicle is driving in a signal-free area, it enters a continuous S-shaped curve and then accelerates rapidly. First, the speed data in the wheel motion data is continuously obtained. When the vehicle enters the S-shaped curve, the wheel speed data combined with the yaw angular velocity data of the vehicle can be used to identify that the vehicle is currently in a complex motion state of continuous turning. At this time, the historical correction coefficient library is queried, which pre-stores specific correction parameters for the heading change trend data in the continuous turning state, such as dynamic compensation coefficients for gyroscope drift. These parameters are used to correct the currently obtained heading change trend data. Then, when the vehicle exits the curve and accelerates rapidly, the wheel speed data will show a significant increasing trend, according to which it is identified that the vehicle is currently in a complex motion state of rapid acceleration. The historical correction coefficient library is queried again to obtain correction parameters for the rapid acceleration state, such as filtering parameters for suppressing instantaneous acceleration errors. These parameters are applied to the new heading change trend data to obtain corrected heading change trend data. Further, the correction strategy can be adaptively adjusted according to the specific performance of the vehicle in different complex motion states, ensuring that the heading change trend data always maintains high accuracy throughout the driving process, thereby providing a solid data foundation for motion trajectory navigation in signal-free areas.

[0050] In this embodiment, by introducing the recognition of the complex motion state of the vehicle and the application of the historical correction coefficient library, the correction accuracy under complex driving conditions is avoided. Specifically, under different motion states of the vehicle, the inherent deviation of the inertial measurement unit (IMU) and the influence mechanism of external disturbances on the heading change trend data are different. For example, when accelerating or decelerating rapidly, the accelerometer of the IMU may produce instantaneous errors; when turning rapidly, the drift of the gyroscope may be amplified. By using the speed data in the wheel motion data, the 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 verified according to the historical correction coefficient library are found. The correction parameters are optimized for the error characteristics under a specific motion state, which can more accurately compensate for errors in the heading change trend data. Further, adaptive and high-precision correction of the heading change trend data can be achieved, overcoming the limitations of a single fixed correction method in a variable driving environment.

[0051] In some embodiments of the above application, in the step of determining the current motion state of the vehicle and the confidence coefficient based on the inertial motion data, the wheel motion data and the steering motion data of the vehicle, the determination step of the current motion state of the vehicle comprises: According to the inertial motion data, wheel motion data and steering motion data of the vehicle, a short-time perturbation straight-line motion state of the vehicle is determined. Specifically, before determining the current motion state of the vehicle, first, according to the inertial motion data, wheel motion data and steering motion data of the vehicle, it is identified and determined whether the vehicle is in a short-time perturbation straight-line motion state. The short-time perturbation straight-line motion state refers to that the vehicle mainly travels in a straight line in a short time, but may be deviated due to slight external disturbances (such as road unevenness) or slight corrections of the driver (such as slight adjustment of the steering wheel). It provides a basis for subsequent yaw rate component separation and ensures that fine analysis is carried out in a relatively stable motion background.

[0052] According to the short-time perturbation straight-line motion state, the inertial motion data is subjected to yaw rate component separation processing to obtain a zero drift component, a driver correction yaw component and a road perturbation yaw component. This step decomposes the original yaw rate into three main components: a zero drift component, a driver correction yaw component and a road perturbation yaw component. The zero 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 correction yaw component reflects the yaw input applied by the driver to keep the vehicle straight or to make slight steering; and the road perturbation yaw component is caused by external environmental factors such as road unevenness or crosswind. Through separation, the source of the yaw rate can be more clearly understood.

[0053] The driver correction yaw component and the road perturbation yaw component are removed from the inertial motion data to obtain a corrected inertial yaw rate. Specifically, after obtaining the above three components, the driver correction yaw component and the road perturbation yaw component are removed from the original inertial motion data. The purpose of this removal operation is to eliminate the yaw influence of the non-vehicle main motion state caused by the driver's slight operation and road disturbance, so as to obtain a corrected inertial yaw rate which is more pure and can better reflect the motion trend of the vehicle itself. The corrected inertial yaw rate mainly reflects the yaw characteristics of the vehicle in ideal straight-line motion or smooth turning, and excludes external interference.

[0054] The current motion state of the vehicle is identified by using the corrected inertial yaw rate, wheel motion data and steering motion data. The current motion state of the vehicle is obtained. This step can more reliably distinguish whether the vehicle is in a straight-line motion state or a jolt motion state by comprehensively analyzing these corrected and more accurate data, so as to obtain the current motion state of the vehicle. 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 a straight-line motion state; on the contrary, if there are significant yaw or jolt characteristics, it is identified as a jolt motion state.

[0055] Specifically, when the vehicle is driving on the highway at a relatively stable speed, the yaw rate data measured by the inertial measurement unit (IMU) of the vehicle will contain various components such as zero drift, driver correction and road perturbation due to the slight ups and downs of the road and the slight steering wheel adjustment made by the driver to maintain the lane. First, according to the inertial motion data, wheel motion data and steering motion data of the vehicle, it is determined that the vehicle is currently in a short-time perturbation straight-line motion state. Then, the yaw rate data measured by the IMU is processed for component separation. For example, high-frequency driver correction yaw component and road perturbation yaw component can be distinguished from low-frequency zero drift component by methods such as high-pass filtering or Kalman filtering. Assuming that the separated zero drift component is 0.01 degrees / second, the driver correction yaw component fluctuates between -0.05 and 0.05 degrees / second, and the road perturbation yaw component fluctuates between -0.03 and 0.03 degrees / second. Subsequently, the driver correction yaw component and the road perturbation yaw component are 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 obtained may be 0.01 degrees / second (close to zero), which more accurately reflects that the vehicle is actually in straight-line motion. Finally, using the corrected inertial yaw rate close to zero, combined with the wheel speed data (e.g., the left and right wheel speeds are basically the same) and the steering angle data (e.g., the steering wheel angle is close to zero), the vehicle can be accurately identified as being in a straight-line motion state, rather than being misjudged as a slight turn or a bump. Through accurate motion state recognition, it is helpful for the subsequent navigation algorithm to more accurately estimate the position and heading in a signal-free area.

[0056] The embodiment avoids the possible lack of accuracy in identifying the current motion state of the vehicle in complex driving environments by performing fine yaw rate component separation processing on the inertial motion data of the vehicle. Specifically, after determining the short-time perturbation straight-line motion state of the vehicle, the original yaw rate is decomposed into a zero drift component, a driver correction yaw component and a road perturbation yaw component. Different sources of yaw influence can be identified and quantified. By removing the driver correction yaw component and the road perturbation yaw component, noise and interference introduced by the driver's small operation and external road disturbance can be effectively filtered out, thereby obtaining a corrected inertial yaw rate that is more pure and better reflects the actual motion trend of the vehicle. The corrected inertial yaw rate can more accurately represent the true motion state of the vehicle, avoiding misjudgment due to external interference. Finally, using this corrected data in combination with the wheel motion data and the steering motion data for motion state recognition makes the judgment of the current motion state of the vehicle more accurate.

[0057] In some embodiments of the above application, the step of separating the yaw rate component from the inertial motion data according to the short-time perturbation straight-line motion state to obtain the zero-point drift component, the driver's corrected yaw component, and the road perturbation yaw component includes: According to the short-time perturbation straight-line motion state, the yaw rate component is separated from the inertial motion data to obtain yaw rate components of different frequencies, a driver's slight correction component, and a lateral motion trend component. Specifically, when the vehicle is in a short-time perturbation straight-line motion state, the yaw rate in the inertial motion data contains multiple information. First, the yaw rate component is separated from the inertial motion data, and the purpose is to decompose the original yaw rate signal into several preliminary components, i.e., yaw rate components of different frequencies, a driver's slight correction component, and a lateral motion trend component. Among them, the yaw rate component of different frequencies can be understood as decomposing the original signal into different frequency intervals through frequency domain analysis (such as Fourier transform or wavelet analysis) to distinguish slow, medium and fast signals; the driver's slight correction component refers to the yaw change caused by the fine steering operation of the driver to maintain the stability of the vehicle during straight-line driving; the lateral motion trend component mainly reflects the trend of lateral displacement or attitude change of the vehicle under the influence of external factors such as road unevenness or crosswind.

[0058] Using the inertial motion data, the yaw rate component of different frequencies is analyzed to obtain the zero-point drift component. Among them, the zero-point drift component is a slowly changing low-frequency signal, and its characteristic analysis can include filtering, mean calculation or trend fitting of low-frequency signals, so as to separate it from other frequency components. The purpose is to accurately quantify and eliminate the systematic error inherent in the inertial measurement unit (IMU).

[0059] Using the inertial motion data, the driver's slight correction component is analyzed to obtain the driver's slight steering component. Among them, the driver's slight steering component refers to the yaw rate generated by the fine steering wheel adjustment of the driver to correct the slight deviation of the vehicle from the heading. By analyzing the component, such as through threshold judgment, pattern recognition or correlation analysis with the steering motion data, it can be distinguished from other noise or disturbances. The purpose is to accurately identify the true intention of the driver and avoid misjudging it as an unintended motion of the vehicle.

[0060] The inertial motion data is used to perform component analysis on the lateral motion trend component to obtain a road perturbation yaw component. The road perturbation yaw component refers to a small instantaneous change in the vehicle attitude or heading caused by factors such as road unevenness, potholes, or lateral slope changes during vehicle driving. Component analysis, such as high-pass filtering, instantaneous peak detection, or correlation analysis with the vehicle jounce state, can effectively separate it from other smooth motion or driver operation. The purpose is to accurately capture and quantify the influence of road factors on vehicle heading.

[0061] Specifically, a vehicle is driving in an approximately straight line in a signal-free area, but there is slight unevenness in the road, and the driver makes slight steering adjustments to maintain the heading. At this time, the yaw rate data collected by the on-board inertial measurement unit (IMU) will contain multiple information such as zero drift, driver correction, and road perturbation. First, according to the short-time perturbation straight-line motion state, the inertial motion data is preliminarily processed for yaw rate component separation. For example, a multi-stage filter bank can be used to process the original yaw rate signal, decomposing the signal into yaw rate components of different frequencies in the low frequency band (e.g. 0-0.1 Hz), medium frequency band (e.g. 0.1-1 Hz) and high frequency band (e.g. above 1 Hz). At the same time, by analyzing the correlation between the steering angle sensor data and the yaw rate, the driver's small correction component is preliminarily identified; by analyzing the vehicle vertical acceleration data, the lateral motion trend component is preliminarily identified. Further, the inertial motion data is used to analyze the characteristics of the low-frequency yaw rate component. Since zero drift usually appears as a slowly changing direct current or very low frequency signal, long-term averaging or trend fitting of this low-frequency component can accurately obtain the zero drift component. Then, the inertial motion data is used to analyze the small driving correction component preliminarily identified. For example, a threshold can be set, and when the yaw rate change rate exceeds the threshold in a short time and is highly consistent with the steering angle change trend, it is identified as a small steering component. Finally, the inertial motion data is used to analyze the lateral motion trend component preliminarily identified. For example, a high-pass filter can be used to filter out low-frequency components, and combined with instantaneous peak detection of the vehicle vertical acceleration, instantaneous yaw changes caused by road bumps are identified as road perturbation yaw components. Through the above fine separation process, the zero drift component, the small driving steering component, and the road perturbation yaw component can be accurately separated from the complex inertial motion data, providing pure and reliable yaw rate data for subsequent motion state recognition and trajectory navigation.

[0062] The embodiment realizes preliminary decoupling of the original complex signal by first decomposing the yaw rate signal in the inertial motion data into yaw rate components of different frequencies, driving minor correction components, and lateral motion trend components. This makes it possible to subsequently analyze the characteristics and components of each component. Specifically, through the characteristic analysis of different frequency components, the low-frequency and slowly changing zero drift component can be effectively identified and extracted, thereby avoiding confusion with high-frequency signals such as road perturbations. Meanwhile, more detailed component analysis of the driving minor correction component and the lateral motion trend component can accurately distinguish the fine steering operation of the driver from the instantaneous disturbance caused by the road. Through multi-level and fine separation processing, a more pure and accurate input is provided for subsequent removal of the driver correction yaw component and the road perturbation yaw component, thereby ensuring the accuracy of the corrected inertial yaw rate.

[0063] In some embodiments of the present application, the step of obtaining inertial motion data, wheel motion data, and steering motion data of the vehicle comprises: Obtaining inertial motion raw data, wheel motion raw data, and steering motion raw data of the vehicle. This step refers to directly collecting the raw data generated by the vehicle during driving through the on-board sensor system. Specifically, the inertial motion raw data can be obtained by an inertial measurement unit (IMU), including the raw output of an accelerometer and a gyroscope; the wheel motion raw data can be obtained by a wheel speed sensor, reflecting the speed or mileage information of the wheel; and the steering motion raw data can be obtained by a steering angle sensor, reflecting the steering angle or steering rate of the vehicle. These raw data are the basis for subsequent motion trajectory navigation.

[0064] Preprocessing the inertial motion raw data, wheel motion raw data, and steering motion raw data of the vehicle respectively to obtain preprocessed inertial motion raw data, wheel motion raw data, and steering motion raw data of the vehicle. Specifically, the purpose of preprocessing is to eliminate noise and errors in the raw data, and to unify the data format and time synchronization. For example, preprocessing can include low-pass filtering of the inertial motion raw data to remove high-frequency noise, unit conversion of the wheel motion raw data to unify the speed or distance unit, and time stamp alignment of all sensor data to ensure data synchronization. Through preprocessing, the quality and consistency of the data can be improved, laying a foundation for subsequent data verification and motion state determination.

[0065] The raw data of the pre-processed inertial motion, wheel motion and steering motion of the vehicle are checked to obtain the inertial motion data, wheel motion data and steering motion data of the vehicle. The raw data checking in this step aims to identify and correct abnormal values, missing values or inconsistencies in the data. Specifically, the checking process can include range checking of the data, such as judging whether the sensor readings are beyond the reasonable physical range; consistency checking, such as comparing the logical correlation between different sensor data; and redundancy checking, such as verifying the reliability of the data using multi-sensor fusion technology. Through raw data checking, it can be ensured that the inertial motion data, wheel motion data and steering motion data used for navigation are accurate, reliable and complete.

[0066] The embodiment refines the three steps of raw data acquisition, preprocessing and data checking, ensuring that the inertial motion data, wheel motion data and steering motion data input into the subsequent navigation algorithm have high precision and high reliability. First, direct acquisition of raw data ensures the integrity of information; second, the preprocessing step effectively filters out noise and errors, unifies the data format and provides standardized input for subsequent processing; finally, the data checking step further eliminates abnormal and inconsistent data, thereby avoiding navigation errors caused by data quality problems. It provides a solid data foundation for the vehicle to navigate the motion trajectory in the signal-free area.

[0067] In some embodiments of the present application, the step of data checking the vehicle motion trajectory data to obtain the vehicle motion trajectory data in the signal-free area includes: The vehicle motion trajectory database is used to perform data initial verification on the vehicle motion trajectory data, to obtain initial data of the vehicle motion trajectory in the signal-free area and an auxiliary correction coefficient. The vehicle motion trajectory database can be understood as a set of vehicle motion trajectory information related to a specific geographical area or road type stored in advance. The database can include historical vehicle driving data, high-precision map data, road topology information, and typical trajectory patterns in different motion states. The purpose is to provide a reliable reference benchmark for vehicle motion trajectory data for initial verification in the signal-free area. The database can be constructed by collecting, processing and modeling a large amount of vehicle driving data in different environments, for example, it can include typical driving trajectories in specific tunnels, underground parking lots or mountain roads. Data initial verification refers to the process of comparing and analyzing the current acquired vehicle motion trajectory data with the reference trajectory in the preset vehicle motion trajectory database. Further, the rationality and deviation of the current trajectory data are preliminarily evaluated, and potential error sources are identified. Specifically, it can be determined by the position deviation, heading angle deviation or speed deviation between the current trajectory and the matching trajectory in the database. Thus, the initial data of the vehicle motion trajectory in the signal-free area can be obtained, which is the trajectory information that has been preliminarily verified but not completely corrected, and the auxiliary correction coefficient is generated, which reflects the deviation degree and correction direction between the current trajectory data and the reference trajectory. The auxiliary correction coefficient is a parameter set used to correct the error of the vehicle motion trajectory data. The coefficient can be dynamically generated according to the results of data initial verification, for example, it can include position offset, heading angle correction, speed ratio factor or attitude angle correction. The purpose is to provide accurate correction basis for subsequent data correction.

[0068] The auxiliary correction coefficient is used to correct the initial data of the vehicle motion trajectory in the signal-free area, to obtain the vehicle motion trajectory navigation data in the signal-free area. Specifically, data correction refers to the process of using the above-mentioned auxiliary correction coefficient to finely correct the initial data of the vehicle motion trajectory in the signal-free area. By applying the auxiliary correction coefficient to the initial data, the deviation found in the initial verification can be effectively eliminated or reduced, so as to obtain more accurate and reliable vehicle motion trajectory navigation data in the signal-free area.

[0069] Specifically, the vehicle is entering a long distance underground tunnel, in which GPS signal is completely lost. Before the vehicle enters the tunnel, its navigation system has loaded a preset vehicle motion trajectory database of the tunnel, which contains typical driving trajectory data of different lanes and different speeds in the tunnel. When the vehicle enters the tunnel, its inertial motion data, wheel motion data and steering motion data are continuously acquired and analyzed by signal characteristics to obtain vehicle motion trajectory data containing current position, speed, heading angle and attitude angle information of the vehicle. Subsequently, the vehicle motion trajectory data is sent to the data verification module. First, the preset vehicle motion trajectory database is used to perform initial data verification on the current vehicle motion trajectory data. For example, the real-time position, speed and heading angle of the current vehicle are compared with the reference trajectory of the corresponding tunnel segment in the database. If it is found that there is a persistent deviation between the heading angle of the current vehicle and the reference heading angle in the database (for example, the vehicle actually deviates to the left, while the database shows that it should be straight driving), or the estimated position of the vehicle deviates from the center line of the lane in the database, these deviations will be identified. Based on these deviations, the initial data of the vehicle motion trajectory navigation in the signal-free area is obtained, and auxiliary correction coefficients are generated. For example, the auxiliary correction coefficients may include a continuous right heading angle correction amount and a right position offset correction amount. Then, the initial data of the vehicle motion trajectory navigation in the signal-free area is corrected using the auxiliary correction coefficients. Specifically, the calculated heading angle correction amount and position offset correction amount are superimposed on the initial navigation data in real time to correct the current heading angle and position estimation of the vehicle. For example, if the initial data estimates the heading angle to be 0 degrees (straight), but the auxiliary correction coefficients indicate that it should be corrected to the right by 5 degrees, then the corrected heading angle will be 5 degrees. Through the above process, even in a tunnel environment with no signal for a long time, the motion trajectory data of the vehicle can be continuously and externally referenced to verify and correct, so that the vehicle can accurately drive along the preset trajectory or the actual lane, avoid navigation drift caused by sensor cumulative error, and ultimately obtain high-precision vehicle motion trajectory navigation data in the signal-free area.

[0070] The preset vehicle motion trajectory database is introduced to provide an external relatively reliable reference for the vehicle motion trajectory data in the signal-free area. When the vehicle enters the signal-free area, the motion trajectory data is first subjected to initial data checking with the database. Then, the trajectory deviation caused by factors such as inertial sensor drift, wheel slip or steering error can be identified. Through comparison, the initial data of the vehicle motion trajectory navigation in the signal-free area can be obtained, and the auxiliary correction coefficient can be generated synchronously, which accurately quantifies the difference between the current trajectory and the preset trajectory. Subsequently, the initial data of the navigation is subjected to data correction by using the auxiliary correction coefficient, so as to pull the trajectory data with possible accumulated error 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 navigation data of the vehicle in the signal-free area can maintain high accuracy and stability.

[0071] Based on any one of the above sensor-based signal-free area motion trajectory methods, please refer to Figure 2 The application also provides a sensor-based signal-free area motion trajectory navigation system, which comprises a data acquisition module 210, a state determination module 220, a feature determination module 230, a data analysis module 240 and a data checking module 250.

[0072] The data acquisition module 210 is used to acquire the inertial motion data, wheel motion data and steering motion data of the vehicle.

[0073] The state determination module 220 is used to determine the current motion state and the reliability coefficient of the vehicle according to the inertial motion data, wheel motion data and steering motion data of the vehicle, wherein the current motion state comprises a straight motion state or a jolt motion state.

[0074] The feature determination module 230 is used to determine the current environmental feature of the vehicle in the signal-free area according to the current motion state of the vehicle and the motion history data of the vehicle.

[0075] The data analysis module 240 is used to perform signal feature analysis on the reliability coefficient and the current environmental feature to obtain the vehicle motion trajectory data containing the current position, speed, heading angle and attitude angle information of the vehicle. The data checking module 250 is used to perform data checking on the vehicle motion trajectory data to obtain the signal-free area motion trajectory navigation data of the vehicle.

[0076] In this embodiment, the motion information of the vehicle is collected by the data acquisition module 210 to provide a basis for subsequent processing. The state determination module 220 and the feature determination module 230 intelligently identify the real-time motion state of the vehicle and the environmental features, providing key context information for data analysis. The data analysis module 240 combines the confidence coefficient and the environmental features to perform deep fusion and analysis of the sensor data, generating high-precision vehicle motion trajectory data. Finally, the data verification module 250 strictly verifies the trajectory data to ensure that the output navigation data is accurate and reliable. In this way, the problem of low navigation accuracy and poor reliability caused by sensor error accumulation and environmental complexity is effectively avoided, and continuous and accurate trajectory navigation is provided for the vehicle in the signal-free area.

[0077] The above embodiments are only used to illustrate the technical solutions of the present application, but not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application, and they should be covered in the scope of the present application.

Claims

1. A sensor-based navigation method for motion trajectory in a signal-free zone, characterized in that, 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, wherein the current motion state comprises a straight motion state or a jounce motion state; determining a current environmental feature of the vehicle in a 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 feature to obtain vehicle motion trajectory data containing current position, speed, heading angle and attitude angle information of the vehicle; performing data verification on the vehicle motion trajectory data to obtain signal-free area motion trajectory navigation data of the vehicle.

2. The sensor-based signal-free zone motion trajectory navigation method of claim 1, wherein, In the step of 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, the acquisition of the reliability coefficient comprises the following steps: performing signal feature analysis on the inertial motion data to determine instantaneous disturbance data caused by instantaneous displacement of an on-board article in the jounce motion state of the vehicle and heading change trend data caused by inherent bias of an inertial measurement unit; determining a distance estimation bias coefficient caused by a wheel circumference parameter in the straight motion state after data correlation between the wheel motion data and the steering motion data; obtaining the reliability coefficient according to the instantaneous disturbance data, the heading change trend data and the distance estimation bias coefficient.

3. The sensor-based signal-free zone motion trajectory navigation method of claim 2, wherein, The step of obtaining the reliability coefficient according to the instantaneous disturbance data, the heading change trend data and the distance estimation bias coefficient comprises the following steps: respectively correcting the instantaneous disturbance data, the heading change trend data and the distance estimation bias coefficient to obtain corrected instantaneous disturbance data, corrected heading change trend data and corrected distance estimation bias coefficient; performing reliability fusion processing by using the corrected instantaneous disturbance data, the corrected heading change trend data and the corrected distance estimation bias coefficient to obtain the reliability coefficient.

4. The sensor-based signal-free zone motion trajectory navigation method of claim 3, wherein, The step of respectively correcting the instantaneous disturbance data, the heading change trend data and the distance estimation bias coefficient to obtain corrected instantaneous disturbance data, corrected heading change trend data and corrected distance estimation bias coefficient comprises the following steps: correcting the instantaneous disturbance data by using yaw rate data in the inertial motion data to obtain corrected instantaneous disturbance data; correcting the heading change trend data by using speed data in the wheel motion data to obtain corrected heading change trend data; correcting the distance estimation bias coefficient by using acceleration data in the steering motion data to obtain corrected distance estimation bias coefficient.

5. The sensor-based signal-free zone motion trajectory navigation method of claim 4, wherein, The step of correcting the heading change trend data by using speed data in the wheel motion data to obtain corrected heading change trend data comprises the following steps: determining a complex motion state of the vehicle by using the speed data in the wheel motion data; determining a correction parameter of the heading change trend data according to the complex motion state and a historical correction coefficient library; The heading change trend data is corrected by using a correction parameter of the heading change trend data to obtain corrected heading change trend data.

6. The sensor-based signal-free zone motion trajectory navigation method of claim 1, wherein, In the step of determining the current motion state of the vehicle and the confidence coefficient according to the inertial motion data, the wheel motion data and the steering motion data of the vehicle, the step of determining the current motion state of the vehicle comprises: According to the short-time micro-perturbation straight-line motion state, the inertial motion data is separated into a yaw angular velocity component to obtain a zero-point drift component, a driver correction yaw component and a road micro-perturbation yaw component. According to the short-time micro-perturbation straight-line motion state, the inertial motion data is separated into a yaw angular velocity component to obtain a zero-point drift component, a driver correction yaw component and a road micro-perturbation yaw component. The driver correction yaw component and the road micro-perturbation yaw component are removed from the inertial motion data to obtain a corrected inertial yaw angular velocity. The motion state of the vehicle is identified by using the corrected inertial yaw angular velocity, the wheel motion data and the steering motion data to obtain the current motion state of the vehicle.

7. The sensor-based signal-free zone motion trajectory navigation method of claim 6, wherein, According to the short-time micro-perturbation straight-line motion state, the inertial motion data is separated into a yaw angular velocity component to obtain a zero-point drift component, a driver correction yaw component and a road micro-perturbation yaw component. According to the short-time micro-perturbation straight-line motion state, the inertial motion data is separated into a yaw angular velocity component to obtain a zero-point drift component, a driver correction yaw component and a road micro-perturbation yaw component. According to the short-time micro-perturbation straight-line motion state, the inertial motion data is separated into a yaw angular velocity component to obtain a zero-point drift component, a driver correction yaw component and a road micro-perturbation yaw component. The step of obtaining the inertial motion data, the wheel motion data and the steering motion data of the vehicle comprises: The inertial motion original data, the wheel motion original data and the steering motion original data of the vehicle are obtained.

8. The sensor-based signal-free area motion trajectory navigation method of claim 1, wherein, The inertial motion original data, the wheel motion original data and the steering motion original data of the vehicle are preprocessed respectively to obtain preprocessed inertial motion original data, preprocessed wheel motion original data and preprocessed steering motion original data of the vehicle. The preprocessed inertial motion original data, the preprocessed wheel motion original data and the preprocessed steering motion original data of the vehicle are verified to obtain the inertial motion data, the wheel motion data and the steering motion data of the vehicle. The step of verifying the vehicle motion trajectory data to obtain the vehicle motion trajectory navigation data in the signal-free area comprises: The vehicle motion trajectory data is initially verified by using a preset vehicle motion trajectory database to obtain vehicle motion trajectory navigation initial data in the signal-free area and an auxiliary correction coefficient; 9. The sensor-based signal-free area motion trajectory navigation method of claim 1, wherein, The vehicle motion trajectory navigation initial data in the signal-free area is corrected by using the auxiliary correction coefficient to obtain the vehicle motion trajectory navigation data in the signal-free area. The system comprises: a data acquisition module for acquiring the inertial motion data, the wheel motion data and the steering motion data of the vehicle; 10. A sensor-based signal-free zone motion trajectory navigation system, characterized by, ​ ​ a state determining module configured to determine a current motion state and a confidence coefficient of the vehicle according to inertial motion data, wheel motion data and steering motion data of the vehicle, the current motion state including a straight motion state or a jounce motion state; a feature determining module configured to determine a current environmental feature of the vehicle located in the signal-free area according to the current motion state of the vehicle and motion history data of the vehicle; a data analyzing module configured to perform signal feature analysis on the confidence coefficient and the current environmental feature to obtain vehicle motion trajectory data containing current position, speed, heading angle and attitude angle information of the vehicle; a data verifying module configured to perform data verification on the vehicle motion trajectory data to obtain signal-free area motion trajectory navigation data of the vehicle.

Images