A control method of inertial navigation system for vehicle testing

Abstract

The invention relates to the technical field of inertial navigation control, and discloses an inertial navigation system control method for vehicle testing. The method comprises: acquiring initial vehicle motion data from an accelerometer and a gyroscope in an inertial measurement unit and statically calibrating the acquired data to obtain calibrated vehicle motion data; in a low GPS signal environment, estimating the position information of the vehicle according to the calibrated vehicle motion data by using high-precision numerical integration; splicing a test vehicle driving trajectory according to the estimated vehicle position information; using pre-stored high-precision map data, correcting the vehicle position by trajectory comparison and analysis to determine whether there is a deviation, and if there is a deviation, performing trajectory correction to generate a control instruction, and operating the vehicle according to the control instruction, so as to improve the positioning accuracy and motion control capability of the intelligent vehicle under complex road conditions.

Description

Technical Field

[0001] The invention relates to the technical field of inertial navigation control, and in particular to an inertial navigation system control method for vehicle testing. Background Art

[0002] As one of the core technologies in vehicle testing, the Inertial Navigation System (INS) is facing increasingly severe challenges and higher requirements in the context of the rapid development of the automotive industry and autonomous driving technology. With the continuous improvement of vehicle performance and the increasing complexity of test scenarios, the traditional inertial navigation system control method has been unable to meet the needs of modern vehicle testing. At present, the field of vehicle testing is undergoing a profound technological innovation. The emergence of emerging technologies such as autonomous driving, new energy vehicles, and intelligent connected vehicles has put forward unprecedented high requirements for the accuracy of vehicle positioning, attitude estimation, and motion state monitoring. At the same time, the test environment has become more diverse and complex, including various complex terrains such as urban canyons, highways, tunnels, and mountainous areas, as well as testing under severe weather conditions. These factors have posed great challenges to the performance and reliability of the inertial navigation system. In addition, the improvement of the degree of vehicle electrification and intelligence requires the inertial navigation system to be more closely integrated and coordinated with other systems of the vehicle. For example, the coordination with advanced driver assistance systems (ADAS), on-board diagnostic systems (OBD), body control systems, etc. requires the inertial navigation system to provide more accurate and real-time data support. Summary of the invention

[0003] In view of this, the present invention proposes an inertial navigation system control method for vehicle testing, which uses artificial intelligence technology to enable the inertial navigation system to better adapt to areas with poor GPS signals, help improve the comprehensiveness and authenticity of the test, meet the growing demand for high-precision vehicle testing, and promote the development of the automotive industry and autonomous driving technology.

[0004] To achieve the above object, the present invention provides an inertial navigation system control method for vehicle testing, comprising the following steps:

[0005] S1: acquiring initial vehicle motion data from an accelerometer and a gyroscope in an inertial measurement unit and performing static calibration on the acquired data to obtain calibrated vehicle motion data, wherein multi-scale Gaussian filtering is an implementation method of the static calibration, and the vehicle motion data includes vehicle speed, acceleration and angular velocity information;

[0006] S2: In a low GPS signal environment, the vehicle position information is estimated using high-precision numerical integration based on the calibrated vehicle motion data, and the test vehicle driving trajectory is formed based on the estimated vehicle position information;

[0007] S3: using the pre-stored high-precision map data, correcting the vehicle position judgment to see if there is a deviation through trajectory comparison analysis, wherein sequence similarity measurement is an implementation method of the trajectory comparison analysis;

[0008] S4: If there is a deviation, trajectory correction is performed to generate control instructions, and the vehicle is operated according to the control instructions, wherein low GPS signal lane-level path planning based on high-precision maps is the implementation method of the trajectory correction.

[0009] As a further improvement method of the present invention:

[0010] Optionally, in step S1, acquiring initial vehicle motion data from an accelerometer and a gyroscope in an inertial measurement unit and statically calibrating the acquired data to obtain calibrated vehicle motion data includes:

[0011] S11: Get initial vehicle motion data from the inertial measurement unit , the vehicle motion data includes acceleration, velocity and angular velocity data collected by an accelerometer and a gyroscope;

[0012] S12: Initial vehicle motion data Apply multi-scale Gaussian kernels for convolution to generate Gaussian pyramids. The convolution calculation formula is:

[0013] ;

[0014] in:

[0015] represents the scale parameter, and the number of scale parameters is equal to the number of pyramid layers;

[0016] t represents the time series index;

[0017] Indicates according to scale Vehicle motion data obtained by filtering at time t;

[0018] Represents the Gaussian kernel function, and the calculation formula is:

[0019] ;

[0020] S13: Perform a differential operation on two adjacent layers of data in the Gaussian pyramid to obtain a Laplacian pyramid. The calculation formula is:

[0021] ;

[0022] in:

[0023] express Differential vehicle motion data at time t under scale;

[0024] S14: Reconstruct the data based on Gaussian pyramid and Laplace pyramid to obtain the calibrated vehicle motion data. The calculation formula is:

[0025] ;

[0026] in:

[0027] Represents an upsampling operation;

[0028] Represents the vehicle motion data after static calibration.

[0029] Optionally, in step S2, estimating the position information of the vehicle using high-precision numerical integration according to the calibrated vehicle motion data includes:

[0030] S21: Acquire calibrated vehicle motion data, including vehicle speed, acceleration, and angular velocity information;

[0031] S22: Obtain the initial position of the test vehicle before the GPS signal disappears , initial time , initial velocity and initial acceleration , and preset the time step parameter h;

[0032] S23: Update the test vehicle position according to the 4th-order Runge-Kutta integral. The calculation process is:

[0033] S23.1: Calculate the first-order slope based on the current round test vehicle speed , the calculation formula is:

[0034] ;

[0035] S23.2: Calculate the second-order slope from the first-order slope , the calculation formula is: ;

[0036] S23.3: Calculate the third-order slope from the second-order slope , the calculation formula is: ;

[0037] S23.4: Calculate the fourth-order slope from the third-order slope , the calculation formula is: ;

[0038] S23.5: Update the test vehicle position based on the calculated slope , the calculation formula is:

[0039] ;

[0040] in:

[0041] Indicates the current location;

[0042] Indicates the current speed;

[0043] Indicates the updated position;

[0044] S23.6: Update the test vehicle speed, calculated as follows:

[0045] ;

[0046] in:

[0047] represents the vehicle speed at time step i+1;

[0048] represents the vehicle speed at time step i;

[0049] represents the vehicle acceleration at time step i;

[0050] S25: Iterate step S23 until the maximum number of iterations is reached, and output the test vehicle position.

[0051] Optionally, in step S3, judging whether there is a deviation by comparing and analyzing the trajectory to correct the vehicle position includes:

[0052] S31: splicing the estimated vehicle positions to obtain a vehicle trajectory position sequence, dividing the vehicle trajectory position sequence into vehicle trajectory position sequence segments of a preset length, each sequence segment consisting of a plurality of continuous vehicle position points;

[0053] S32: for each vehicle position point in the vehicle trajectory position sequence segment, selecting a same-direction road node on a road network within a preset radius as a candidate matching point, and constructing a possible candidate matching path according to the selected candidate matching point;

[0054] S33: Calculate each pair of vehicle trajectory position sequence segments and candidate matching paths similarity;

[0055] in:

[0056] The trajectory vehicle position coordinates representing the nth point;

[0057] Represents the coordinates of the mth candidate matching point;

[0058] The specific calculation process includes:

[0059] S33.1: Build a The cumulative distance matrix D of Indicates the vehicle position To vehicle location and from the candidate matching points To candidate matching point The minimum cumulative distance is calculated as follows:

[0060] ;

[0061] in, Indicates vehicle position and candidate matching points The Euclidean distance of ;

[0062] S33.2: Determine the similarity between the vehicle trajectory position sequence segment and the candidate matching path based on the cumulative distance matrix. The similarity calculation formula is:

[0063] ;

[0064] in:

[0065] represents the exponential function;

[0066] S34: Select the candidate matching path with the largest similarity as the matching result of the corresponding vehicle trajectory position sequence segment, and take the sum of the matching similarities of all vehicle trajectory position sequence segments as the overall similarity of the vehicle trajectory position sequence. If it exceeds the preset threshold, there is no deviation, otherwise there is a deviation.

[0067] Optionally, if there is a deviation in step S4, trajectory correction is performed to generate a control instruction, including:

[0068] S41: matching the estimated vehicle position with lane information in the high-precision map to determine the lane the vehicle is in;

[0069] S42: Performing lane-level obstacle avoidance path planning according to the destination coordinates and the current vehicle position and lane information to generate a lane-level trajectory path;

[0070] S43: Generate a control instruction based on the planned trajectory path, and transmit the control instruction to the execution system of the vehicle to drive according to the trajectory. The control instruction is a steering angle, and the steering angle calculation formula is:

[0071] ;

[0072] in:

[0073] Indicates the vehicle wheelbase;

[0074] Indicates the angle between the vehicle direction and the direction of the next track mark;

[0075] Indicates the foresight distance.

[0076] Optionally, in step S42, performing lane-level obstacle avoidance path planning according to the destination coordinates, the current vehicle position and the lane information to generate a lane-level trajectory path includes:

[0077] S42.1: A road network environment is represented as a road network graph according to a high-precision map, wherein the road network is composed of passable space points and impassable space points, wherein the passable space points represent the coordinates of passable roads in the map, and the impassable space points represent the coordinates of impassable roads or obstacles in the map;

[0078] S42.2: Generate a set of random nodes in the road network graph ,in Represents the i-th random node generated, whose coordinates are , the value range of i is less than N, where N represents the maximum number of random nodes;

[0079] S42.3: Eliminate random nodes at inaccessible spatial point coordinates and only retain nodes at accessible spatial point coordinates to obtain valid nodes;

[0080] S42.4: Connect adjacent valid nodes to form a traversable path diagram. When connecting, ensure that the connecting line does not pass through inaccessible space points. The calculation formula is:

[0081] ;

[0082] in:

[0083] represents an edge set;

[0084] Represents a random node With random nodes The connecting edges between

[0085] is the connection distance threshold;

[0086] Indicates whether the straight line between two points passes through an inaccessible space point;

[0087] S42.5: Add the current vehicle position coordinate point and the destination coordinate point to the traversable path graph and connect them to the nearest valid node;

[0088] S42.6: Use the improved A* algorithm to search the trajectory path of the traversable path graph to obtain the planned path node sequence;

[0089] S42.7: Connect the planned path node sequence as a lane-level trajectory path and return it.

[0090] Optionally, the step of using an improved A* algorithm to search the trajectories of the traversable path graph to obtain a planned path node sequence includes:

[0091] S71: Initialize an open list and a closed list. The open list contains nodes that need to be evaluated and initially contains a start node. The closed list contains nodes that have been evaluated and is initially empty. The actual cost Set to 0 to reduce the heuristic estimate cost of the initial node Assuming the heuristic estimate of the distance from the start node to the target node, the total cost of the initial node is calculated as: ;

[0092] S72: Select from open list The node n with the smallest value is subjected to path extension. The specific process of path extension includes: for each adjacent node n' of the current node n:

[0093] S72.1: Calculation of actual costs ,in Represents a slave node To Node The Euclidean distance of

[0094] S72.2: Calculation slave nodes Heuristic estimated cost to the target node , the heuristic estimation cost calculation formula is:

[0095] ;

[0096] in:

[0097] Representation Node The coordinates of

[0098] Indicates the target node coordinates;

[0099] Represents a judgment node Indicative function of whether the lane is in the same direction as the parent node. The value of the same direction lane is 0, otherwise the value is 1;

[0100] Represents the penalty coefficient for non-codirectional lanes;

[0101] S72: Calculate the total cost, the total cost calculation formula is: ;

[0102] If the node If it is not in the open list, add it to the open list and set its parent node to ;

[0103] S73: Set the current node Move from the open list to the closed list;

[0104] S74: Iterate step S72 until a termination condition is reached, where the current node is the target node or the open list is empty;

[0105] S75: trace back from the target node to the starting node to form a final planned path node sequence.

[0106] In order to solve the above problem, the present invention provides an electronic device, the electronic device comprising:

[0107] A memory storing at least one instruction;

[0108] Communication interface, enabling electronic equipment to communicate; and

[0109] The processor executes the instructions stored in the memory to implement the above-mentioned inertial navigation system control method for vehicle testing.

[0110] In order to solve the above problem, the present invention also provides a computer-readable storage medium, in which at least one instruction is stored. The at least one instruction is executed by a processor in an electronic device to implement the above-mentioned inertial navigation system control method for vehicle testing.

[0111] Compared with the prior art, the present invention proposes an inertial navigation system control method for vehicle testing, which has the following advantages:

[0112] 1) This solution uses multi-scale Gaussian filtering to statically calibrate the accelerometer and gyroscope data in the inertial measurement unit (IMU), which can effectively improve the accuracy and stability of the data and ensure that the calibrated vehicle motion data accurately reflects the actual motion state;

[0113] 2) In low GPS signal environments, this solution uses calibrated vehicle motion data for high-precision numerical integration to estimate the vehicle's position information, and can provide accurate position estimation in weak or no signal conditions;

[0114] 3) This scheme adopts the sequence similarity measurement method to correct the vehicle position through trajectory comparison analysis, which can effectively compare the actual vehicle trajectory with the expected trajectory in the map to determine whether there is a deviation in the position;

[0115] 4) This solution effectively solves the challenges of vehicle positioning and trajectory correction in low GPS signal environments through methods such as static calibration, high-precision numerical integration, trajectory comparison analysis, and lane-level path planning, and improves the positioning accuracy and motion control capabilities of intelligent vehicles under complex road conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0116] Figure 1 A schematic flow chart of an inertial navigation system control method for vehicle testing provided by one embodiment of the present invention;

[0117] Figure 2 A schematic structural diagram of an electronic device for implementing an inertial navigation system control method for vehicle testing provided by an embodiment of the present invention.

[0118] In the figure: 1 electronic device, 10 processor, 11 memory, 12 program, 13 communication interface.

[0119] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0120] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0121] The embodiment of the present application provides an inertial navigation system control method for vehicle testing. The execution subject of the inertial navigation system control method for vehicle testing includes but is not limited to at least one of the electronic devices such as a server, a terminal, etc. that can be configured to execute the method provided by the embodiment of the present application. In other words, the inertial navigation system control method for vehicle testing can be executed by software or hardware installed in a terminal device or a server device, and the software can be a blockchain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc.

[0122] S1: Acquire initial vehicle motion data from the accelerometer and gyroscope in the inertial measurement unit and perform static calibration on the acquired data to obtain calibrated vehicle motion data, wherein multi-scale Gaussian filtering is an implementation method of the static calibration, and the vehicle motion data includes vehicle speed, acceleration and angular velocity information.

[0123] In the step S1, initial vehicle motion data is acquired from the accelerometer and gyroscope in the inertial measurement unit and static calibration is performed on the acquired data to obtain calibrated vehicle motion data, including:

[0124] S11: Get initial vehicle motion data from the inertial measurement unit , the vehicle motion data includes acceleration, velocity and angular velocity data collected by an accelerometer and a gyroscope;

[0125] S12: Initial vehicle motion data Apply multi-scale Gaussian kernels for convolution to generate Gaussian pyramids. The convolution calculation formula is:

[0126] ;

[0127] in:

[0128] represents the scale parameter, the number of scale parameters is equal to the number of pyramid layers;

[0129] t represents the time series index;

[0130] Indicates according to scale Vehicle motion data obtained by filtering at time t;

[0131] Represents the Gaussian kernel function, and the calculation formula is:

[0132] ;

[0133] S13: Perform a differential operation on two adjacent layers of data in the Gaussian pyramid to obtain a Laplacian pyramid. The calculation formula is:

[0134] ;

[0135] in:

[0136] express Differential vehicle motion data at time t under scale;

[0137] S14: Reconstruct the data based on Gaussian pyramid and Laplace pyramid to obtain the calibrated vehicle motion data. The calculation formula is:

[0138] ;

[0139] in:

[0140] Represents an upsampling operation;

[0141] Represents the vehicle motion data after static calibration.

[0142] S2: In a low GPS signal environment, the vehicle position information is estimated using high-precision numerical integration based on the calibrated vehicle motion data, and the test vehicle driving trajectory is formed based on the estimated vehicle position information.

[0143] In step S2, the position information of the vehicle is estimated by using high-precision numerical integration according to the calibrated vehicle motion data, including:

[0144] S21: Acquire calibrated vehicle motion data, including vehicle speed, acceleration, and angular velocity information;

[0145] S22: Obtain the initial position of the test vehicle before the GPS signal disappears , initial time , initial velocity and initial acceleration , and preset the time step parameter h;

[0146] S23: Update the test vehicle position according to the 4th-order Runge-Kutta integral. The calculation process is:

[0147] S23.1: Calculate the first-order slope based on the current round test vehicle speed , the calculation formula is:

[0148] ;

[0149] S23.2: Calculate the second-order slope from the first-order slope , the calculation formula is: ;

[0150] S23.3: Calculate the third-order slope from the second-order slope , the calculation formula is: ;

[0151] S23.4: Calculate the fourth-order slope from the third-order slope , the calculation formula is: ;

[0152] S23.5: Update the test vehicle position based on the calculated slope , the calculation formula is:

[0153] ;

[0154] in:

[0155] Indicates the current location;

[0156] Indicates the current speed;

[0157] Indicates the updated position;

[0158] S23.6: Update the test vehicle speed, calculated as follows:

[0159] ;

[0160] in:

[0161] represents the vehicle speed at time step i+1;

[0162] represents the vehicle speed at time step i;

[0163] represents the vehicle acceleration at time step i;

[0164] S25: Iterate step S23 until the maximum number of iterations is reached, and output the test vehicle position.

[0165] S3: Using the pre-stored high-precision map data, correct the vehicle position judgment to see if there is a deviation through trajectory comparison analysis, wherein the sequence similarity measurement is an implementation method of the trajectory comparison analysis.

[0166] In step S3, the vehicle position is corrected by trajectory comparison analysis to determine whether there is a deviation, including:

[0167] S31: splicing the estimated vehicle positions to obtain a vehicle trajectory position sequence, dividing the vehicle trajectory position sequence into vehicle trajectory position sequence segments of a preset length, each sequence segment consisting of a plurality of continuous vehicle position points;

[0168] S32: for each vehicle position point in the vehicle trajectory position sequence segment, selecting a same-direction road node on a road network within a preset radius as a candidate matching point, and constructing a possible candidate matching path according to the selected candidate matching point;

[0169] S33: Calculate each pair of vehicle trajectory position sequence segments and candidate matching paths similarity;

[0170] in:

[0171] The trajectory vehicle position coordinates representing the nth point;

[0172] Represents the coordinates of the mth candidate matching point;

[0173] The specific calculation process includes:

[0174] S33.1: Build a The cumulative distance matrix D of Indicates the vehicle position To vehicle location and from the candidate matching points To candidate matching point The minimum cumulative distance is calculated as follows:

[0175] ;

[0176] in, Indicates vehicle position and candidate matching points The Euclidean distance of ;

[0177] S33.2: Determine the similarity between the vehicle trajectory position sequence segment and the candidate matching path based on the cumulative distance matrix. The similarity calculation formula is:

[0178] ;

[0179] in:

[0180] represents the exponential function;

[0181] S34: Select the candidate matching path with the largest similarity as the matching result of the corresponding vehicle trajectory position sequence segment, and take the sum of the matching similarities of all vehicle trajectory position sequence segments as the overall similarity of the vehicle trajectory position sequence. If it exceeds the preset threshold, there is no deviation, otherwise there is a deviation.

[0182] S4: Construct an environmental assessment model for ecologically sensitive areas, and use the environmental assessment model for ecologically sensitive areas to receive key indicator data and conduct environmental impact assessment.

[0183] If there is a deviation in step S4, trajectory correction is performed to generate a control instruction, including:

[0184] S41: matching the estimated vehicle position with lane information in the high-precision map to determine the lane the vehicle is in;

[0185] S42: Performing lane-level obstacle avoidance path planning according to the destination coordinates and the current vehicle position and lane information to generate a lane-level trajectory path;

[0186] S43: Generate a control instruction based on the planned trajectory path, and transmit the control instruction to the execution system of the vehicle to drive according to the trajectory. The control instruction is a steering angle, and the steering angle calculation formula is:

[0187] ;

[0188] in:

[0189] Indicates the vehicle wheelbase;

[0190] Indicates the angle between the vehicle direction and the direction of the next track mark;

[0191] Indicates the foresight distance.

[0192] In the step S42, lane-level obstacle avoidance path planning is performed according to the destination coordinates, the current vehicle position and the lane information to generate a lane-level trajectory path, including:

[0193] S42.1: A road network environment is represented as a road network graph according to a high-precision map, wherein the road network is composed of passable space points and impassable space points, wherein the passable space points represent the coordinates of passable roads in the map, and the impassable space points represent the coordinates of impassable roads or obstacles in the map;

[0194] S42.2: Generate a set of random nodes in the road network graph ,in Represents the i-th random node generated, whose coordinates are , the value range of i is less than N, where N represents the maximum number of random nodes;

[0195] S42.3: Eliminate random nodes at inaccessible spatial point coordinates and only retain nodes at accessible spatial point coordinates to obtain valid nodes;

[0196] S42.4: Connect adjacent valid nodes to form a traversable path diagram. When connecting, ensure that the connecting line does not pass through inaccessible space points. The calculation formula is:

[0197] ;

[0198] in:

[0199] represents an edge set;

[0200] Represents a random node With random nodes The connecting edges between

[0201] is the connection distance threshold;

[0202] Indicates whether the straight line between two points passes through an inaccessible space point;

[0203] S42.5: Add the current vehicle position coordinate point and the destination coordinate point to the traversable path graph and connect them to the nearest valid node;

[0204] S42.6: Use the improved A* algorithm to search the trajectory path of the traversable path graph to obtain the planned path node sequence;

[0205] S42.7: Connect the planned path node sequence as a lane-level trajectory path and return it.

[0206] The method of using the improved A* algorithm to search the trajectories of the traversable path graph to obtain a planned path node sequence includes:

[0207] S71: Initialize an open list and a closed list. The open list contains nodes that need to be evaluated and initially contains a start node. The closed list contains nodes that have been evaluated and is initially empty. The actual cost Set to 0 to reduce the heuristic estimate cost of the initial node Assuming the heuristic estimate of the distance from the start node to the target node, the total cost of the initial node is calculated as: ;

[0208] S72: Select from open list The node n with the smallest value is subjected to path extension. The specific process of path extension includes: for each adjacent node n' of the current node n:

[0209] S72.1: Calculation of actual costs ,in Represents a slave node To Node The Euclidean distance of

[0210] S72.2: Calculation slave nodes Heuristic estimated cost to the target node , the heuristic estimation cost calculation formula is:

[0211] ;

[0212] in:

[0213] Representation Node The coordinates of

[0214] Indicates the target node coordinates;

[0215] Represents a judgment node Indicative function of whether the lane is in the same direction as the parent node. The value of the same direction lane is 0, otherwise the value is 1;

[0216] Represents the penalty coefficient for non-codirectional lanes;

[0217] S72: Calculate the total cost, the total cost calculation formula is: ;

[0218] If the node If it is not in the open list, add it to the open list and set its parent node to ;

[0219] S73: Set the current node Move from the open list to the closed list;

[0220] S74: Iterate step S72 until a termination condition is reached, where the current node is the target node or the open list is empty;

[0221] S75: trace back from the target node to the starting node to form a final planned path node sequence.

[0222] like Figure 2 FIG. 1 is a schematic diagram of the structure of an electronic device for implementing an inertial navigation system control method for vehicle testing provided by an embodiment of the present invention.

[0223] The electronic device 1 may include a processor 10 , a memory 11 , a communication interface 13 and a bus, and may also include a computer program stored in the memory 11 and executable on the processor 10 , such as a program 12 .

[0224] The memory 11 includes at least one type of readable storage medium, and the readable storage medium includes a flash memory, a mobile hard disk, a multimedia card, a card-type memory (e.g., SD or DX memory, etc.), a magnetic memory, a disk, an optical disk, etc. In some embodiments, the memory 11 may be an internal storage unit of the electronic device 1, such as a mobile hard disk of the electronic device 1. In other embodiments, the memory 11 may also be an external storage device of the electronic device 1, such as a plug-in mobile hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device 1. Further, the memory 11 may also include both an internal storage unit of the electronic device 1 and an external storage device. The memory 11 may not only be used to store application software and various types of data installed in the electronic device 1, such as the code of the program 12, etc., but also be used to temporarily store data that has been output or is to be output.

[0225] The processor 10 may be composed of an integrated circuit in some embodiments, for example, a single packaged integrated circuit, or a plurality of packaged integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and a combination of various control chips, etc. The processor 10 is the control core (Control Unit) of the electronic device, and uses various interfaces and lines to connect various components of the entire electronic device, and executes or executes programs or modules (such as programs 12 for implementing inertial navigation system control) stored in the memory 11, and calls data stored in the memory 11 to execute various functions of the electronic device 1 and process data.

[0226] The communication interface 13 may include a wired interface and / or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which is generally used to establish a communication connection between the electronic device 1 and other electronic devices, and to achieve connection and communication between internal components of the electronic device.

[0227] The bus may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. The bus is configured to realize connection and communication between the memory 11 and at least one processor 10, etc.

[0228] Figure 2 Only an electronic device with components is shown, and those skilled in the art will understand that Figure 2 The structure shown does not constitute a limitation on the electronic device 1, and may include fewer or more components than shown in the figure, or combine certain components, or arrange the components differently.

[0229] For example, although not shown, the electronic device 1 may also include a power source (such as a battery) for supplying power to various components. Preferably, the power source may be logically connected to the at least one processor 10 through a power management device, so that the power management device can realize functions such as charging management, discharging management, and power consumption management. The power source may also include one or more DC or AC power sources, recharging devices, power failure detection circuits, power converters or inverters, power status indicators, and other arbitrary components. The electronic device 1 may also include a variety of sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be repeated here.

[0230] Optionally, the electronic device 1 may further include a user interface, which may be a display, an input unit (such as a keyboard), or a standard wired interface or a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, and an OLED (Organic Light-Emitting Diode) touch device. The display may also be appropriately referred to as a display screen or a display unit, which is used to display information processed in the electronic device 1 and to display a visual user interface.

[0231] It should be understood that the embodiment is for illustration only and the scope of the patent application is not limited to this structure.

[0232] The program 12 stored in the memory 11 in the electronic device 1 is a combination of multiple instructions. When running in the processor 10, it can achieve:

[0233] Acquire initial vehicle motion data from an accelerometer and a gyroscope in an inertial measurement unit and statically calibrate the acquired data to obtain calibrated vehicle motion data;

[0234] In a low GPS signal environment, the vehicle's position information is estimated using high-precision numerical integration based on the calibrated vehicle motion data, and the test vehicle's driving trajectory is formed based on the estimated vehicle position information;

[0235] Using the pre-stored high-precision map data, the vehicle position is corrected through trajectory comparison and analysis to determine whether there is any deviation;

[0236] If there is a deviation, the trajectory is corrected to generate a control instruction, and the vehicle is operated according to the control instruction.

[0237] Specifically, the specific implementation method of the processor 10 for the above instructions can refer to Figure 1 to Figure 2 The description of the relevant steps in the corresponding embodiments will not be repeated here.

[0238] It should be noted that the serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages and disadvantages of the embodiments. And the terms "including", "comprising" or any other variants thereof in this article are intended to cover non-exclusive inclusion, so that a process, device, article or method including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, device, article or method. In the absence of further restrictions, an element defined by the sentence "including a ..." does not exclude the presence of other identical elements in the process, device, article or method including the element.

[0239] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes a number of instructions for a terminal device (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present invention.

[0240] The above are only preferred embodiments of the present invention, and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for controlling an inertial navigation system for vehicle testing, characterized in that: The method comprises: S1: acquiring initial vehicle motion data from an accelerometer and a gyroscope in an inertial measurement unit and performing static calibration on the acquired data to obtain calibrated vehicle motion data, wherein multi-scale Gaussian filtering is an implementation method of the static calibration, and the vehicle motion data includes vehicle speed, acceleration and angular velocity information; S2: In a low GPS signal environment, the vehicle position information is estimated using high-precision numerical integration based on the calibrated vehicle motion data, and the test vehicle driving trajectory is formed based on the estimated vehicle position information; The vehicle position information is estimated using high-precision numerical integration based on the calibrated vehicle motion data, including: S21: Acquire calibrated vehicle motion data, including vehicle speed, acceleration, and angular velocity information; S22: Obtain the initial position of the test vehicle before the GPS signal disappears , initial time , initial velocity and initial acceleration , and preset the time step parameter h; S23: Update the test vehicle position according to the 4th-order Runge-Kutta integral. The calculation process is: S23.1: Calculate the first-order slope based on the current round test vehicle speed , the calculation formula is: ； S23.2: Calculate the second-order slope from the first-order slope , the calculation formula is: ; S23.3: Calculate the third-order slope from the second-order slope , the calculation formula is: ; S23.4: Calculate the fourth-order slope from the third-order slope , the calculation formula is: ; S23.5: Update the test vehicle position based on the calculated slope , the calculation formula is: ； in: Indicates the current location; Indicates the current speed; Indicates the updated position; S23.6: Update the test vehicle speed, calculated as follows: ； in: represents the vehicle speed at time step i+1; represents the vehicle speed at time step i; represents the vehicle acceleration at time step i; S24: iteratively execute step S23 until the maximum number of iterations is reached, and output the position of the test vehicle; S3: using the pre-stored high-precision map data, correcting the vehicle position judgment to see if there is a deviation through trajectory comparison analysis, wherein sequence similarity measurement is an implementation method of the trajectory comparison analysis; The vehicle position is corrected by trajectory comparison and analysis to determine whether there is any deviation, including: S31: splicing the estimated vehicle positions to obtain a vehicle trajectory position sequence, dividing the vehicle trajectory position sequence into vehicle trajectory position sequence segments of a preset length, each sequence segment consisting of a plurality of continuous vehicle position points; S32: for each vehicle position point in the vehicle trajectory position sequence segment, selecting a same-direction road node on a road network within a preset radius as a candidate matching point, and constructing a possible candidate matching path according to the selected candidate matching point; S33: Calculate each pair of vehicle trajectory position sequence segments and candidate matching paths similarity; in: The trajectory vehicle position coordinates representing the nth point; Represents the coordinates of the mth candidate matching point; The specific calculation process includes: S33.1: Build a The cumulative distance matrix D of Indicates the vehicle position To vehicle location and from the candidate matching points To candidate matching point The minimum cumulative distance is calculated as follows: ； in, Indicates vehicle position and candidate matching points The Euclidean distance of ; S33.2: Determine the similarity between the vehicle trajectory position sequence segment and the candidate matching path based on the cumulative distance matrix. The similarity calculation formula is: ； in: represents the exponential function; S34: Select the candidate matching path with the largest similarity as the matching result of the corresponding vehicle trajectory position sequence segment, and take the sum of the matching similarities of all vehicle trajectory position sequence segments as the overall similarity of the vehicle trajectory position sequence. If it exceeds a preset threshold, there is no deviation, otherwise there is a deviation; S4: If there is a deviation, trajectory correction is performed to generate control instructions, and the vehicle is operated according to the control instructions, wherein low GPS signal lane-level path planning based on high-precision maps is the implementation method of the trajectory correction.

2. The inertial navigation system control method for vehicle testing according to claim 1, characterized in that: In the step S1, initial vehicle motion data is acquired from the accelerometer and gyroscope in the inertial measurement unit and static calibration is performed on the acquired data to obtain calibrated vehicle motion data, including: S11: Get initial vehicle motion data from the inertial measurement unit , the vehicle motion data includes acceleration, velocity and angular velocity data collected by an accelerometer and a gyroscope; S12: Initial vehicle motion data Apply multi-scale Gaussian kernels for convolution to generate Gaussian pyramids. The convolution calculation formula is: ； in: represents the scale parameter, the number of scale parameters is equal to the number of pyramid layers; t represents the time series index; Indicates according to scale Vehicle motion data obtained by filtering at time t; Represents the Gaussian kernel function, and the calculation formula is: ； S13: Perform a differential operation on two adjacent layers of data in the Gaussian pyramid to obtain a Laplacian pyramid. The calculation formula is: ； in: express Differential vehicle motion data at time t under scale; S14: Reconstruct the data based on Gaussian pyramid and Laplace pyramid to obtain the calibrated vehicle motion data. The calculation formula is: ； in: Represents an upsampling operation; Represents the vehicle motion data after static calibration.

3. The inertial navigation system control method for vehicle testing according to claim 1, characterized in that: If there is a deviation in step S4, trajectory correction is performed to generate a control instruction, including: S41: matching the estimated vehicle position with lane information in the high-precision map to determine the lane the vehicle is in; S42: Performing lane-level obstacle avoidance path planning according to the destination coordinates and the current vehicle position and lane information to generate a lane-level trajectory path; S43: Generate a control instruction based on the planned trajectory path, and transmit the control instruction to the execution system of the vehicle to drive according to the trajectory. The control instruction is a steering angle, and the steering angle calculation formula is: ； in: Indicates the vehicle wheelbase; Indicates the angle between the vehicle direction and the direction of the next track mark; Indicates the foresight distance.

4. The inertial navigation system control method for vehicle testing according to claim 3, characterized in that: In the step S42, lane-level obstacle avoidance path planning is performed according to the destination coordinates, the current vehicle position and the lane information to generate a lane-level trajectory path, including: S42.1: Representing a road network environment as a road network graph according to a high-precision map, wherein the road network graph is composed of passable space points and impassable space points, wherein the passable space points represent the coordinates of passable roads in the map, and the impassable space points represent the coordinates of impassable roads or obstacles in the map; S42.2: Generate a set of random nodes in the road network graph ,in Represents the i-th random node generated, whose coordinates are , the value range of i is less than N, where N represents the maximum number of random nodes; S42.3: Eliminate random nodes at inaccessible spatial point coordinates and only retain nodes at accessible spatial point coordinates to obtain valid nodes; S42.4: Connect adjacent valid nodes to form a traversable path diagram. When connecting, ensure that the connecting line does not pass through inaccessible space points. The calculation formula is: ； in: represents an edge set; Represents a random node With random nodes The connecting edges between is the connection distance threshold; Indicates whether the straight line between two points passes through an inaccessible space point; S42.5: Add the current vehicle position coordinate point and the destination coordinate point to the traversable path graph and connect them to the nearest valid node; S42.6: Use the improved A* algorithm to search the trajectory path of the traversable path graph to obtain the planned path node sequence; S42.7: Connect the planned path node sequence as a lane-level trajectory path and return it.

5. The inertial navigation system control method for vehicle testing according to claim 4, characterized in that: The method of using the improved A* algorithm to search the trajectories of the traversable path graph to obtain a planned path node sequence includes: S71: Initialize an open list and a closed list. The open list contains nodes that need to be evaluated and initially contains a start node. The closed list contains nodes that have been evaluated and is initially empty. The actual cost Set to 0 to reduce the heuristic estimate cost of the initial node Assuming the heuristic estimate of the distance from the start node to the target node, the total cost of the initial node is calculated as: ; S72: Select from open list The node n with the smallest value is subjected to path extension. The specific process of path extension includes: for each adjacent node n' of the current node n: S72.1: Calculation of actual costs ,in Represents a slave node To Node The Euclidean distance of S72.2: Calculation slave nodes Heuristic estimated cost to the target node , the heuristic estimation cost calculation formula is: ； in: Representation Node The coordinates of Indicates the target node coordinates; Represents a judgment node Indicative function of whether the lane is in the same direction as the parent node. The value of the same direction lane is 0, otherwise the value is 1; Represents the penalty coefficient for non-codirectional lanes; S72: Calculate the total cost. The total cost calculation formula is: ; If the node If it is not in the open list, add it to the open list and set its parent node to ; S73: Set the current node Move from the open list to the closed list; S74: Iterate step S72 until a termination condition is reached, where the current node is the target node or the open list is empty; S75: trace back from the target node to the starting node to form a final planned path node sequence.

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