Navigation method and device based on HAL layer integrated optimization, computer equipment and medium
By integrating the inertial navigation algorithm at the HAL layer, the problem of inaccurate positioning of traditional GPS navigation systems in complex environments is solved, the continuity and accuracy of the navigation system are achieved, and the development and maintenance costs are reduced.
Patent Information
- Application Number
- CN202411580706.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-11-07
- Publication Date
- 2025-02-14
AI Technical Summary
Traditional GPS navigation systems are susceptible to signal occlusion in complex urban environments, resulting in inaccurate positioning or complete failure. The lack of backup navigation mechanisms when GPS is not available, limiting their application.
By integrating the inertial navigation algorithm in the HAL layer, the GPS signal parameters, gyroscope parameters and GPS chip differential identification parameters obtained in real time are generated, and the system HAL layer optimization model is activated when the GPS signal is interrupted, and the navigation correction parameters are calculated. Generate new inertial navigation algorithm data and navigation prediction information.
It realizes the continuity and accuracy of the navigation system when the vehicle is driving on complex road sections, reduces the development and maintenance workload, improves the overall performance of the system, and ensures navigation stability and accuracy.
Smart Images
Figure CN119437276A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the field of intelligent driving technology, and in particular to a navigation method, device, computer equipment and medium based on HAL layer integrated optimization. Background Art
[0002] Traditional GPS navigation systems are easily blocked by signals in complex urban environments, such as urban canyons, tunnels, indoor and underground parking lots, resulting in inaccurate positioning or complete failure. Many navigation systems are overly dependent on GPS signals and lack backup navigation mechanisms when GPS is unavailable, which limits their application in specific environments. Due to the instability of GPS signals, existing navigation systems cannot provide continuous navigation services when the signal is interrupted, affecting the user experience. In the case of weak GPS signals, the accuracy and stability of the navigation system decrease, making it difficult to meet the needs of high-precision navigation. The functions of GPS modules on the market vary, and some modules lack auxiliary functions such as inertial navigation, resulting in complex system integration and poor compatibility. Overcoming the above problems requires excessive navigation resources and low development efficiency. Summary of the invention
[0003] The embodiments of the present application provide a navigation method, apparatus, computer equipment and medium based on HAL layer integrated optimization, aiming to solve the problem of excessive use of navigation resources when a vehicle is traveling on a complex road section.
[0004] In a first aspect, an embodiment of the present application provides a navigation method based on HAL layer integrated optimization, which is applied to a driving navigation system, and the driving navigation system is configured in a vehicle computer of a vehicle to be navigated, and is characterized in that the method includes configuring various parameters in the inertial navigation algorithm raw data acquired in real time into a preset vehicle motion parameter library, and the inertial navigation algorithm raw data includes GPS signal parameters, gyroscope parameters and GPS chip difference identification parameters; if the GPS signal parameters in the vehicle motion parameter library are interrupted in reception, activating a preset system HAL layer optimization model according to the vehicle motion parameter library, and the system HAL layer optimization model is configured in the HAL layer of the driving navigation system; generating a signal corresponding to the inertial navigation algorithm raw data; and The inertial navigation script corresponding to the GPS signal parameter and the vehicle motion data is configured in the system HAL layer optimization model; the gyroscope parameters and the GPS chip difference identification parameters when the GPS signal parameters are interrupted are obtained and marked as the corresponding reference points; the real-time azimuth angle and the vehicle offset of the vehicle to be navigated during driving are calculated based on the reference points to obtain the navigation correction parameters; the gyroscope parameters corresponding to the reference points, the GPS chip difference identification parameters, the navigation correction parameters and the gyroscope parameters before updating are combined to obtain new inertial navigation algorithm data; a new inertial navigation script is generated according to the new inertial navigation algorithm data and the vehicle motion data and is input into the system HAL layer optimization model to obtain the corresponding navigation prediction information.
[0005] In a second aspect, an embodiment of the present application further provides a navigation device based on HAL layer integrated optimization, applying a navigation method based on HAL layer integrated optimization according to any one of claims 1 to 5, characterized in that it comprises a configuration unit for configuring various parameters in the inertial navigation algorithm raw data acquired in real time into a preset vehicle motion parameter library; a model activation unit for activating a preset system HAL layer optimization model according to the vehicle motion parameter library if a reception interruption occurs to the GPS signal parameters in the vehicle motion parameter library; a script generation unit for generating an inertial navigation script corresponding to the inertial navigation algorithm raw data and the vehicle motion data and configuring it in the system HAL layer optimization model; a first standard A recording unit is used to obtain the gyroscope parameters and the GPS chip difference identification parameters when the GPS signal parameters are interrupted and mark them as corresponding reference points; a calculation unit is used to calculate the real-time azimuth angle and vehicle offset of the navigation vehicle during driving based on the reference points to obtain navigation correction parameters; an algorithm data generation unit is used to combine the gyroscope parameters corresponding to the reference points, the GPS chip difference identification parameters, the navigation correction parameters and the gyroscope parameters before updating to obtain new inertial navigation algorithm data; a prediction unit is used to generate a new inertial navigation script according to the new inertial navigation algorithm data and the vehicle motion data and input it into the system HAL layer optimization model to obtain corresponding navigation prediction information.
[0006] In a third aspect, an embodiment of the present application further provides a computer device, which includes a memory and a processor, wherein a computer program is stored in the memory, and the above method is implemented when the processor executes the computer program.
[0007] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium, wherein the storage medium stores a computer program, and the computer program includes program instructions, which can implement the above method when executed by a processor.
[0008] The embodiments of the present application provide a navigation method, apparatus, computer equipment and medium based on HAL layer integrated optimization. The method comprises configuring various parameters in the inertial navigation algorithm raw data obtained in real time into a preset vehicle motion parameter library, wherein the inertial navigation algorithm raw data comprises GPS signal parameters, gyroscope parameters and GPS chip difference identification parameters; if the reception of the GPS signal parameters in the vehicle motion parameter library is interrupted, activating the preset system HAL layer optimization model according to the vehicle motion parameter library, and the system HAL layer optimization model is configured in the HAL layer of the driving navigation system; generating an inertial navigation script corresponding to the inertial navigation algorithm raw data and the vehicle motion data and configuring it in the system HAL layer optimization model; obtaining the gyroscope parameters and the GPS chip difference identification parameters when the GPS signal parameters are interrupted and marking them as corresponding reference points; calculating the real-time azimuth angle and the vehicle offset of the vehicle to be navigated during driving based on the reference points to obtain navigation correction parameters; combining the gyroscope parameters corresponding to the reference points, the GPS chip difference identification parameters, the navigation correction parameters and the gyroscope parameters before updating to obtain new inertial navigation algorithm data; generating a new inertial navigation script according to the new inertial navigation algorithm data and the vehicle motion data and inputting it into the system HAL layer optimization model to obtain corresponding navigation prediction information. The above solution realizes the one-time integration of the navigation system and the use of native interfaces when the vehicle is traveling on complex roads, which significantly reduces the development and maintenance workload. Real-time data updates and optimized model predictions improve the overall performance of the system. These technical effects jointly ensure the navigation stability and accuracy of the vehicle in complex environments, and also provide convenience for the long-term operation and maintenance of the system. BRIEF DESCRIPTION OF THE DRAWINGS
[0009] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.
[0010] Figure 1 A flowchart of a navigation method based on HAL layer integrated optimization provided in an embodiment of the present application;
[0011] Figure 2 A schematic diagram of a sub-process of a navigation method based on HAL layer integrated optimization provided in an embodiment of the present application;
[0012] Figure 3 A schematic diagram of another sub-process of the navigation method based on HAL layer integrated optimization provided in an embodiment of the present application;
[0013] Figure 4 A schematic block diagram of a navigation device based on HAL layer integrated optimization provided in an embodiment of the present application;
[0014] Figure 5 A schematic block diagram of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION
[0015] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.
[0016] It should be understood that when used in this specification and the appended claims, the terms "include" and "comprises" indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.
[0017] It should also be understood that the terms used in this application specification are only for the purpose of describing specific embodiments and are not intended to limit the application. As used in this application specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.
[0018] It should be further understood that the term “and / or” used in the specification and appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.
[0019] The embodiments of the present application provide a navigation method, apparatus, computer equipment and medium based on HAL layer integrated optimization.
[0020] The executor of the navigation method based on HAL layer integrated optimization may be the navigation device based on HAL layer integrated optimization provided in the embodiment of the present application, or a computer device integrated with the navigation device based on HAL layer integrated optimization, wherein the navigation device based on HAL layer integrated optimization may be implemented in hardware or software, the computer device may be a terminal or a server, and the terminal may be a smart phone, a tablet computer, a PDA, or a laptop computer, etc.
[0021] The navigation method based on HAL layer integrated optimization is applied to Figure 5 In the computer device 500.
[0022] Figure 1It is a flowchart of a navigation method based on HAL layer integrated optimization provided in an embodiment of the present application, and the method includes the following steps S110-S170.
[0023] S110, configuring various parameters in the inertial navigation algorithm raw data obtained in real time into a preset vehicle motion parameter library.
[0024] The raw data of the inertial navigation algorithm includes GPS signal parameters, gyroscope parameters and GPS chip difference identification parameters.
[0025] S120: If the reception of the GPS signal parameters in the vehicle motion parameter library is interrupted, activating a preset system HAL layer optimization model according to the vehicle motion parameter library.
[0026] The system HAL layer optimization model is configured in the HAL layer of the driving navigation system.
[0027] S130, generating an inertial navigation script corresponding to the original data of the inertial navigation algorithm and the vehicle motion data and configuring it in the system HAL layer optimization model.
[0028] S140, obtaining gyroscope parameters and GPS chip difference identification parameters when the GPS signal parameters are interrupted and marking them as corresponding reference points.
[0029] S150, calculating the real-time azimuth angle and vehicle offset of the vehicle to be navigated during driving based on the reference point to obtain navigation correction parameters.
[0030] S160, combining the gyroscope parameters corresponding to the reference point, the GPS chip difference identification parameters, the navigation correction parameters and the gyroscope parameters before updating to obtain new inertial navigation algorithm data.
[0031] S170, generating a new inertial navigation script according to the new inertial navigation algorithm data and the vehicle motion data and inputting it into the system HAL layer optimization model to obtain corresponding navigation prediction information.
[0032] In a more specific implementation process, when the system is started, a vehicle motion parameter library is initialized to store GPS signal parameters, gyroscope parameters, GPS chip difference identification parameters, etc. Sensor data such as GPS signal parameters and gyroscope parameters are collected in real time through the HAL layer interface. The collected real-time data is stored in the vehicle motion parameter library to ensure that the data is available at any time. The GPS signal parameters in the vehicle motion parameter library are continuously monitored to detect whether there is a reception interruption. Once the GPS signal reception interruption is detected, the activation mechanism of the HAL layer optimization model is immediately triggered. The preset system HAL layer optimization model is activated to prepare to receive and process inertial navigation data. The inertial navigation algorithm raw data in the vehicle motion parameter library is mapped to the system HAL layer optimization model. Based on the mapped data, the corresponding inertial navigation script is generated, and the script contains instructions and algorithms for calculating navigation correction parameters. At the moment of GPS signal interruption, the gyroscope parameters and GPS chip difference identification parameters are recorded. These parameters are marked as reference points for subsequent navigation correction calculations. The real-time azimuth of the vehicle during driving is calculated using the gyroscope data. The vehicle offset is calculated by comparing the real-time azimuth with the preset trajectory. Integrate azimuth and offset data to generate navigation correction parameters. Combine reference point data (gyroscope parameters, GPS chip difference identification parameters), navigation correction parameters, and gyroscope parameters before update. Generate new inertial navigation algorithm data based on the combined data. Update the inertial navigation script based on the newly generated inertial navigation algorithm data and vehicle motion data. Input the updated script into the system HAL layer optimization model. The model processing script outputs navigation prediction information for use by the navigation system. The implementation process involves multiple links from data acquisition, processing to navigation correction, and each link requires precise data processing and algorithm execution. By integrating these steps at the HAL layer, it is ensured that the navigation system can still provide accurate navigation information when GPS signals are unavailable, while maintaining efficient and stable operation of the system.
[0033] Specifically, by integrating the inertial navigation algorithm at the HAL layer, the solution can effectively deal with situations where the GPS signal is interrupted, such as in urban canyons, tunnels, and underground parking lots. Using gyroscopes and other sensor data, the system can maintain navigation functions when the GPS signal is lost. By activating the system HAL layer optimization model, the solution can quickly switch to the inertial navigation mode when the GPS signal is interrupted to ensure the continuity and reliability of the navigation system. Calculating the real-time azimuth and vehicle offset based on the reference point can more accurately reflect the actual position of the vehicle and reduce positioning errors. By introducing navigation correction parameters, the solution can optimize the GPS positioning data and improve the overall positioning accuracy. By configuring the raw data of the inertial navigation algorithm in the vehicle motion parameter library, the solution is convenient for managing different sensor data and optimizing for different vehicles and GPS chips. The solution design allows different inertial navigation algorithms to be adapted by modifying a small number of input and output data structures, providing flexibility in algorithm selection and allowing the optimal algorithm to be selected according to specific needs and environmental conditions. Since the algorithm is integrated at the bottom of the system and is transparent to the upper-level application, it only needs to be integrated once, reducing labor costs and vehicle computing power consumption. The use of native interfaces reduces the learning cost of developers, while ensuring the versatility of the APP and reducing maintenance and usage costs. By updating data at a fixed frequency in a separate thread, the real-time nature of the data is ensured and the system response speed is improved. The system HAL layer optimization model can generate navigation prediction information based on the new inertial navigation algorithm data, providing more accurate navigation guidance for the vehicle. In summary, the navigation method based on HAL layer integrated optimization improves the reliability of the navigation system and can still provide continuous navigation services when the GPS signal is unavailable. The GPS data is corrected by the inertial navigation algorithm to reduce the positioning error. It is easy to adapt to different vehicles and GPS chips, as well as to select different inertial navigation algorithms. One-time integration and native interface use reduce the workload of development and maintenance. Real-time data updates and optimized model predictions improve the overall performance of the system. These technical effects jointly ensure the navigation stability and accuracy of the vehicle in complex environments, and also provide convenience for the long-term operation and maintenance of the system.
[0034] like Figure 2 As shown, in a more specific embodiment, executing method S120 further specifically includes executing steps S121-S125.
[0035] S121. Determine whether the GPS signal parameters corresponding to the current time point are normally configured in the vehicle motion parameter library.
[0036] S122: If the GPS signal parameters are not normally configured in the vehicle motion parameter library, it is determined that the GPS signal parameters are interrupted.
[0037] S123. Send a coordination instruction to the vehicle motion parameter library to activate the system HAL layer optimization model.
[0038] S124. Initialize the system HAL layer optimization model using an inertial navigation algorithm.
[0039] S125. Training the system HAL layer optimization model according to the acquired vehicle motion parameter library and all current data in the vehicle motion parameter library.
[0040] Specifically, in a more specific embodiment, it is checked whether the GPS signal parameters corresponding to the current time point have been normally configured in the vehicle motion parameter library. The vehicle motion parameter library is accessed to retrieve the GPS signal parameter record at the current time point. It is determined whether the parameter record exists and whether the data is complete. If the GPS signal parameters are not normally configured in the vehicle motion parameter library, it is considered that the GPS signal has been interrupted. If the GPS signal parameters at the current time point are not found in the vehicle motion parameter library, or the parameter value is not within the expected range, the interruption determination is triggered. The time point and related status information of the interruption are recorded. Once it is determined that the GPS signal is interrupted, a coordination instruction is sent to the vehicle motion parameter library to activate the system HAL layer optimization model. The instruction is sent to the vehicle motion parameter library management system. After receiving the instruction, the management system starts the activation process of the HAL layer optimization model. The activated system HAL layer optimization model is initialized using the inertial navigation algorithm. The initial parameters and settings of the inertial navigation algorithm are loaded into the HAL layer optimization model. Ensure that the model can start processing data according to the rules of the inertial navigation algorithm. The system HAL layer optimization model is trained according to all current data in the vehicle motion parameter library. Extract all relevant data from the vehicle motion parameter library, including GPS signal parameters (if available), gyroscope parameters, GPS chip difference identification parameters, etc. Use these data as training input to train the HAL layer optimization model to improve the prediction accuracy and adaptability of the model. The training process may include multiple iterations until the model reaches the predetermined performance standard. Through these steps, the system can respond quickly in the event of a GPS signal interruption, activate and initialize the HAL layer optimization model, and train it with the latest vehicle motion data to ensure the continuity and accuracy of the navigation system.
[0041] like Figure 3 As shown, in a more specific embodiment, before executing method S140, it also specifically includes executing steps S141-S143.
[0042] S141, filtering the gyroscope parameters.
[0043] S142: Determine whether the gyroscope parameters meet the preset accuracy value requirements.
[0044] S143: If the gyroscope parameters meet the accuracy requirements, mark the acquired gyroscope parameters.
[0045] Specifically, the gyroscope parameters acquired in real time are filtered to remove noise and abnormal values.
[0046] Apply digital filtering algorithms, such as low-pass filtering, Kalman filtering, or median filtering, to smooth the gyroscope data. Ensure that the selection of the filtering algorithm is suitable for the characteristics and application scenarios of the vehicle motion data. The accuracy criteria may include the standard deviation, error range, or other quantitative indicators of the parameters. If the gyroscope parameters meet the preset accuracy value requirements, these parameters are marked. Set specific identifiers or tags for qualified gyroscope parameters in the vehicle motion parameter library. The tags can be simple Boolean values, special codes, or additional metadata so that these verified data can be used in subsequent processing. Through these three steps, the system ensures that the gyroscope parameters used when the GPS signal is interrupted are accurate and reliable. This is crucial because the calculation of navigation correction parameters depends on the accuracy of gyroscope data. Ensure that only high-quality data is used for navigation calculations to avoid navigation errors caused by erroneous data. Facilitate the rapid identification and selection of verified data in subsequent steps to improve processing efficiency. After completing these steps, the system can continue to perform step S140, that is, calculate the real-time azimuth and offset of the vehicle based on the reference point to generate navigation correction parameters.
[0047] In a more specific embodiment, it also includes denoising the GPS signal parameters and the gyroscope parameters to obtain new vehicle motion data; converting the new vehicle motion data into a time frame corresponding to the original data of the inertial navigation algorithm; calculating the acceleration and steering angle of the vehicle to be navigated based on the time frame corresponding to the original data of the inertial navigation algorithm; and calculating the real-time azimuth angle and vehicle offset of the vehicle to be navigated based on the acceleration and steering angle of the vehicle to be navigated as navigation correction parameters.
[0048] Specifically, the displacement and velocity of the vehicle are estimated by combining the acceleration data with the integral algorithm. The real-time azimuth of the vehicle during navigation is calculated in combination with the steering angle data. The offset of the vehicle is calculated by comparing the expected position and the actual position of the vehicle. The calculated real-time azimuth and vehicle offset are used as navigation correction parameters. These parameters are integrated into the navigation system to correct navigation errors caused by GPS signal interruption or other factors. It is ensured that the navigation correction parameters can be updated in real time to reflect the latest motion status of the vehicle. Through these steps, the system can maintain high-precision navigation using de-noised and processed gyroscope data when the GPS signal is unreliable or unavailable, thereby improving the robustness and applicability of the navigation system. Acquire an original inertial navigation algorithm raw data stream of a vehicle to be navigated; parse the original inertial navigation algorithm raw data stream to extract the inertial navigation algorithm raw data; perform a quality check on the inertial navigation algorithm raw data to generate inertial navigation algorithm raw data detection information, the inertial navigation algorithm raw data detection information including signal strength detection information; determine whether the GPS signal strength in the signal strength detection information reaches a preset strength standard value; if the GPS signal strength reaches the preset strength standard value, configure the inertial navigation algorithm raw data in a vehicle motion parameter library.
[0049] The main advantage of the above solution is that it reduces the integration workload. Since the algorithm is integrated at the bottom layer of the system (HAL layer), there is no need to repeat the integration work for the upper-layer applications. This means that no matter how many upper-layer applications (APPs) need to use the inertial navigation function, only one integration is required at the system level. Compared with integrating the inertial navigation algorithm separately in each APP, this solution significantly reduces the manpower and time costs. At the same time, since there is no need to repeatedly integrate in each APP, the computing power and performance resource consumption of the vehicle computer are also reduced. Through the bottom-level integration, it can be ensured that all upper-layer applications can seamlessly use the inertial navigation function without worrying about the implementation details of the underlying algorithm, thereby improving the overall development efficiency. The use of native interfaces means that developers do not need to learn additional interfaces or programming paradigms, which reduces the learning cost and usage threshold of developers. Due to the use of standardized native interfaces, the versatility of APPs is guaranteed. This means that APPs can run on different systems and devices without the need for customized development for specific interfaces. The standardization of native interfaces makes the adaptation of algorithms more flexible. When the inertial navigation algorithm needs to be replaced or upgraded, developers only need to modify a small amount of input and output data structures to complete the adaptation, which greatly improves the flexibility and maintainability of the system.
[0050] In summary, this solution integrates the inertial navigation algorithm at the bottom of the system and uses native interfaces, which not only improves the development efficiency and versatility of the APP, but also reduces resource consumption and learning costs, while providing the flexibility of algorithm adaptation, thereby improving the performance of the navigation system and user experience as a whole.
[0051] Figure 4 1 is a schematic block diagram of a navigation device based on HAL layer integrated optimization provided by an embodiment of the present application. As shown in the figure, corresponding to the above navigation method based on HAL layer integrated optimization, the present application also provides a navigation device 100 based on HAL layer integrated optimization. The navigation device based on HAL layer integrated optimization includes a unit for executing the above navigation method based on HAL layer integrated optimization, and the device can be configured in a desktop computer, tablet computer, laptop computer, etc. For details, please refer to Figure 4 The navigation device 100 based on HAL layer integrated optimization includes a configuration unit 110, which is used to configure various parameters in the inertial navigation algorithm raw data obtained in real time into a preset vehicle motion parameter library; a model activation unit 120, which is used to activate the preset system HAL layer optimization model according to the vehicle motion parameter library if the reception of the GPS signal parameters in the vehicle motion parameter library is interrupted; a script generation unit 130, which is used to generate an inertial navigation script corresponding to the inertial navigation algorithm raw data and the vehicle motion data and configure it in the system HAL layer optimization model; a first marking unit 140, which is used to obtain the gyroscope when the GPS signal parameters are interrupted The gyroscope parameters and GPS chip difference identification parameters corresponding to the reference point are combined, and the gyroscope parameters are marked as corresponding reference points; a calculation unit 150 is used to calculate the real-time azimuth angle and vehicle offset of the navigation vehicle during driving based on the reference point to obtain navigation correction parameters; an algorithm data generation unit 160 is used to combine the gyroscope parameters corresponding to the reference point, the GPS chip difference identification parameters, the navigation correction parameters and the gyroscope parameters before updating to obtain new inertial navigation algorithm data; a prediction unit 170 is used to generate a new inertial navigation script according to the new inertial navigation algorithm data and vehicle motion data, and input it into the system HAL layer optimization model to obtain corresponding navigation prediction information. And, a first judgment unit is used to judge whether the GPS signal parameters corresponding to the current time point are normally configured in the vehicle motion parameter library; a judgment unit is used to judge whether the GPS signal parameters are interrupted if the GPS signal parameters are not normally configured in the vehicle motion parameter library; an activation unit is used to send a coordination instruction to the vehicle motion parameter library to activate the system HAL layer optimization model; an initialization unit is used to initialize the system HAL layer optimization model using an inertial navigation algorithm; a training unit is used to train the system HAL layer optimization model according to the acquired vehicle motion parameter library and all current data in the vehicle motion parameter library; a filtering unit is used to filter the gyroscope parameters; a second judgment unit is used to judge whether the gyroscope parameters meet the preset accuracy value requirements; a second marking unit is used to mark the acquired gyroscope parameters if the gyroscope parameters meet the accuracy value requirements.
[0052] It should be noted that those skilled in the art can clearly understand that the specific implementation process of the above-mentioned navigation device and each unit based on HAL layer integrated optimization can refer to the corresponding description in the aforementioned method embodiment, and for the convenience and brevity of description, it will not be repeated here.
[0053] The above navigation device based on HAL layer integrated optimization can be implemented in the form of a computer program, which can be used in Figure 5 Runs on the computer device shown.
[0054] See also Figure 5 , which shows a schematic block diagram of a computer device provided in an embodiment of the present application. The computer device 500 can be a terminal or a server, wherein the terminal can be an electronic device with communication functions such as a smart phone, a tablet computer, a laptop computer, a desktop computer, a personal digital assistant, and a wearable device. The server can be an independent server or a server cluster composed of multiple servers.
[0055] The computer device 500 includes a processor 502 , a memory, and a network interface 505 connected via a system bus 501 , wherein the memory may include a non-volatile storage medium 503 and an internal memory 504 .
[0056] The non-volatile storage medium 503 can store an operating system 5031 and a computer program 5032. The computer program 5032 includes program instructions, and when the program instructions are executed, the processor 502 can execute a navigation method based on HAL layer integrated optimization.
[0057] The processor 502 is used to provide computing and control capabilities to support the operation of the entire computer device 500 .
[0058] The internal memory 504 provides an environment for the operation of the computer program 5032 in the non-volatile storage medium 503. When the computer program 5032 is executed by the processor 502, the processor 502 can execute a navigation method based on HAL layer integrated optimization.
[0059] The network interface 505 is used to communicate with other devices over the network. Figure 5 The structure shown in the figure is merely a block diagram of a portion of the structure related to the solution of the present application, and does not constitute a limitation on the computer device 500 to which the solution of the present application is applied. The specific computer device 500 may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.
[0060] It should be understood that in the embodiment of the present application, the processor 502 may be a central processing unit (CPU), and the processor 502 may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among them, the general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.
[0061] It can be understood by those skilled in the art that all or part of the processes in the method for implementing the above embodiment can be completed by instructing the relevant hardware through a computer program. The computer program includes program instructions, and the computer program can be stored in a storage medium, which is a computer-readable storage medium. The program instructions are executed by at least one processor in the computer system to implement the process steps of the embodiment of the above method.
[0062] Therefore, the present application also provides a storage medium. The storage medium can be a computer-readable storage medium. The storage medium stores a computer program, wherein the computer program includes program instructions. When the program instructions are executed by the processor, the processor executes the following steps: configuring the various parameters in the inertial navigation algorithm raw data acquired in real time to a preset vehicle motion parameter library, wherein the inertial navigation algorithm raw data includes GPS signal parameters, gyroscope parameters, and GPS chip difference identification parameters; if the reception of the GPS signal parameters in the vehicle motion parameter library is interrupted, activating the preset system HAL layer optimization model according to the vehicle motion parameter library, and the system HAL layer optimization model is configured in the HAL layer of the driving navigation system; generating an inertial navigation script corresponding to the inertial navigation algorithm raw data and the vehicle motion data and configuring it in the system HAL layer optimization model; obtain the gyroscope parameters and the GPS chip difference identification parameters when the GPS signal parameters are interrupted and mark them as corresponding reference points; calculate the real-time azimuth angle and vehicle offset of the navigation vehicle during driving based on the reference points to obtain navigation correction parameters; combine the gyroscope parameters corresponding to the reference points, the GPS chip difference identification parameters, the navigation correction parameters and the gyroscope parameters before updating to obtain new inertial navigation algorithm data; generate a new inertial navigation script according to the new inertial navigation algorithm data and the vehicle motion data and input it into the system HAL layer optimization model to obtain corresponding navigation prediction information.
[0063] The storage medium may be a USB flash drive, a mobile hard disk, a read-only memory (ROM), a magnetic disk, or an optical disk, etc., which are computer-readable storage media that can store program codes.
[0064] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.
[0065] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of each unit is only a logical function division, and there may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed.
[0066] The steps in the method of the embodiment of the present application can be adjusted in order, combined and deleted according to actual needs. The units in the device of the embodiment of the present application can be combined, divided and deleted according to actual needs. In addition, the functional units in the various embodiments of the present application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.
[0067] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, terminal, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application.
[0068] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in the present application, and these modifications or replacements should be included in the protection scope of the present application. Therefore, the protection scope of the present application shall be based on the protection scope of the claims.
Claims
1. A navigation method based on HAL layer integrated optimization, applied to a driving navigation system, wherein the driving navigation system is configured in a vehicle computer of a vehicle to be navigated, characterized in that: The method comprises: Configuring various parameters in the inertial navigation algorithm raw data acquired in real time into a preset vehicle motion parameter library, wherein the inertial navigation algorithm raw data includes GPS signal parameters, gyroscope parameters, and GPS chip difference identification parameters; If the reception of the GPS signal parameters in the vehicle motion parameter library is interrupted, activating a preset system HAL layer optimization model according to the vehicle motion parameter library, wherein the system HAL layer optimization model is configured in the HAL layer of the driving navigation system; Generate an inertial navigation script corresponding to the inertial navigation algorithm raw data and the vehicle motion data and configure it in the system HAL layer optimization model; Obtain the gyroscope parameters and the GPS chip difference identification parameters when the GPS signal parameters are interrupted and mark them as corresponding reference points; Calculating the real-time azimuth and vehicle offset of the vehicle to be navigated during driving based on the reference point to obtain navigation correction parameters; Combining the gyroscope parameters corresponding to the reference point, the GPS chip difference identification parameters, the navigation correction parameters and the gyroscope parameters before updating to obtain new inertial navigation algorithm data; A new inertial navigation script is generated according to the new inertial navigation algorithm data and the vehicle motion data and is input into the system HAL layer optimization model to obtain corresponding navigation prediction information.
2. The navigation method based on HAL layer integrated optimization according to claim 1, characterized in that: If the GPS signal parameters in the vehicle motion parameter library are interrupted, activating a preset system HAL layer optimization model according to the vehicle motion parameter library includes: Determine whether the GPS signal parameters corresponding to the current time point are normally configured in the vehicle motion parameter library; If the GPS signal parameters are not normally configured in the vehicle motion parameter library, determining that the GPS signal parameters are interrupted; Sending a coordination instruction to the vehicle motion parameter library to activate the system HAL layer optimization model; Initializing the system HAL layer optimization model using an inertial navigation algorithm; The system HAL layer optimization model is trained according to the acquired vehicle motion parameter library and all current data in the vehicle motion parameter library.
3. The navigation method based on HAL layer integrated optimization according to claim 1, characterized in that: Before obtaining the gyroscope parameters and the GPS chip difference identification parameters when the GPS signal parameters are interrupted and marking them as corresponding reference points, the method includes: Performing filtering on the gyroscope parameters; Determining whether the gyroscope parameters meet the preset accuracy value requirements; If the gyroscope parameters meet the accuracy value requirement, the acquired gyroscope parameters are marked.
4. The navigation method based on HAL layer integrated optimization according to claim 1, characterized in that: The method of calculating the real-time azimuth and vehicle offset of the vehicle to be navigated during driving based on the reference point to obtain navigation correction parameters includes: De-noising the GPS signal parameters and the gyroscope parameters to obtain new vehicle motion data; Converting new vehicle motion data into a time frame corresponding to the raw data of the inertial navigation algorithm; Calculating the acceleration and steering angle of the vehicle to be navigated based on the time frame corresponding to the raw data of the inertial navigation algorithm; The real-time azimuth angle of the vehicle to be navigated and the vehicle offset are calculated based on the acceleration of the vehicle to be navigated and the steering angle as the navigation correction parameters.
5. The navigation method based on HAL layer integrated optimization according to claim 1, characterized in that: The configuration of various parameters in the raw data of the inertial navigation algorithm obtained in real time into a preset vehicle motion parameter library includes: Obtaining an original inertial navigation algorithm raw data stream of the vehicle to be navigated; Parsing the original inertial navigation algorithm raw data stream to extract the inertial navigation algorithm raw data; Performing a quality check on the inertial navigation algorithm raw data to generate inertial navigation algorithm raw data detection information, wherein the inertial navigation algorithm raw data detection information includes signal strength detection information; Determine whether the GPS signal strength in the signal strength detection information reaches a preset strength standard value; If the GPS signal strength reaches a preset strength standard value, the inertial navigation algorithm raw data is configured in the vehicle motion parameter library.
6. A navigation device based on HAL layer integrated optimization, using the navigation method based on HAL layer integrated optimization according to any one of claims 1 to 5, characterized in that: include: A configuration unit, used to configure various parameters in the raw data of the inertial navigation algorithm acquired in real time into a preset vehicle motion parameter library; A model activation unit, configured to activate a preset system HAL layer optimization model according to the vehicle motion parameter library if reception of the GPS signal parameters in the vehicle motion parameter library is interrupted; A script generation unit, used to generate an inertial navigation script corresponding to the inertial navigation algorithm raw data and the vehicle motion data and configure it in the system HAL layer optimization model; A first marking unit, used to obtain the gyroscope parameters and the GPS chip difference identification parameters when the GPS signal parameters are interrupted and mark them as corresponding reference points; A calculation unit, used to calculate the real-time azimuth and vehicle offset of the vehicle to be navigated during driving based on the reference point, and obtain navigation correction parameters; An algorithm data generating unit, used for combining the gyroscope parameters corresponding to the reference point, the GPS chip difference identification parameters, the navigation correction parameters and the gyroscope parameters before updating to obtain new inertial navigation algorithm data; A prediction unit is used to generate a new inertial navigation script according to the new inertial navigation algorithm data and the vehicle motion data and input it into the system HAL layer optimization model to obtain corresponding navigation prediction information.
7. The navigation device based on HAL layer integrated optimization according to claim 6, characterized in that: The model activation unit comprises: A first judgment unit, used to judge whether the GPS signal parameters corresponding to the current time point are normally configured in the vehicle motion parameter library; a determination unit, configured to determine that an interruption occurs to the GPS signal parameters if the GPS signal parameters are not normally configured in the vehicle motion parameter library; An activation unit, used for sending a coordination instruction to the vehicle motion parameter library to activate the system HAL layer optimization model; An initialization unit, used to initialize the system HAL layer optimization model using an inertial navigation algorithm; A training unit is used to train the system HAL layer optimization model according to the acquired vehicle motion parameter library and all current data in the vehicle motion parameter library.
8. The navigation device based on HAL layer integrated optimization according to claim 6, characterized in that: The marking unit comprises: A filtering unit, used for filtering the gyroscope parameters; A second judgment unit, used to judge whether the gyroscope parameters meet the preset accuracy value requirements; The second marking unit is used to mark the acquired gyroscope parameters if the gyroscope parameters meet the accuracy value requirement.
9. A computer device, characterized in that: The computer device includes a memory and a processor, the memory stores a computer program, and the processor implements the method according to any one of claims 1 to 5 when executing the computer program.
10. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by a processor, the method according to any one of claims 1 to 5 can be implemented.