Dead reckoning method, apparatus and electronic device

Abstract

The application discloses a dead reckoning method and device and electronic equipment, comprising: constructing a current state quantity corresponding to a filter according to forward speed information of a target vehicle at a current time, and predicting the current state quantity to obtain a state quantity prediction result of the filter; obtaining a residual error between a current observed vehicle speed value corresponding to the target vehicle and the state quantity prediction result, determining a residual error replacement quantity matched with the residual error, and updating the state quantity prediction result of the filter according to the residual error replacement quantity; and determining a dead reckoning result corresponding to the target vehicle according to the updated state quantity prediction result and steering information of the target vehicle. The technical scheme of the embodiment of the application can improve the reliability of the dead reckoning result.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of fusion positioning technology, and in particular to a dead reckoning method and device and electronic equipment. BACKGROUND

[0002] The odometer is a sensor for measuring the speed and distance of a vehicle, which is usually combined with an inertial measurement unit (IMU) to estimate the position of the vehicle at the next time. However, for the case of vehicle slip or data delay, the vehicle speed monitored by the odometer usually has errors, which can easily lead to deviation of the position estimation result. Although the IMU can provide three-dimensional speed information of the vehicle, the IMU cannot be used alone for a long time due to poor calibration performance.

[0003] In the prior art, when optimizing the dead reckoning method, the measurement error caused by the odometer slip is usually corrected based on the SINS / DR combined navigation system. However, this method does not consider other error conditions of the odometer, such as data delay, which leads to certain limitations of the dead reckoning method. In addition, the existing technology does not correct the deviation of the SINS / DR combined navigation system itself, which leads to low reliability of the dead reckoning result. SUMMARY

[0004] The present application provides a dead reckoning method, device and electronic equipment, which can improve the reliability of the dead reckoning result.

[0005] According to an aspect of the present application, a dead reckoning method is provided, comprising:

[0006] According to the forward speed information of the target vehicle at the current time, a current state quantity corresponding to the filter is constructed, and the current state quantity is predicted to obtain a state quantity prediction result of the filter;

[0007] Obtain the residual error between the current observed vehicle speed value corresponding to the target vehicle and the state quantity prediction result, determine the residual error replacement quantity matched with the residual error, and update the state quantity prediction result of the filter according to the residual error replacement quantity;

[0008] According to the updated state quantity prediction result and the steering information of the target vehicle, determine the dead reckoning result corresponding to the target vehicle.

[0009] According to another aspect of the present application, a dead reckoning device is provided, comprising:

[0010] The state prediction module is configured to construct a current state quantity corresponding to the filter according to forward speed information of the target vehicle at a current time, and predict the current state quantity to obtain a state quantity prediction result of the filter.

[0011] The prediction result updating module is configured to obtain a residual error between a current observed vehicle speed value corresponding to the target vehicle and the state quantity prediction result, determine a residual error replacement quantity matched with the residual error, and update the state quantity prediction result of the filter according to the residual error replacement quantity.

[0012] The calculation result determination module is configured to determine a dead reckoning result corresponding to the target vehicle according to the updated state quantity prediction result and steering information of the target vehicle.

[0013] According to another aspect of the present application, an electronic device is provided, which comprises:

[0014] at least one processor; and

[0015] a memory connected with the at least one processor in communication; wherein,

[0016] the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to execute the dead reckoning method according to any one of the embodiments of the present application.

[0017] According to another aspect of the present application, a computer readable storage medium is provided, which stores computer instructions for enabling a processor to implement the dead reckoning method according to any one of the embodiments of the present application when executed.

[0018] The technical scheme provided by the embodiments of the present application can improve the reliability of the dead reckoning result by constructing a current state quantity corresponding to the filter according to forward speed information of the target vehicle at a current time, predicting the current state quantity to obtain a state quantity prediction result of the filter, obtaining a residual error between a current observed vehicle speed value corresponding to the target vehicle and the state quantity prediction result, determining a residual error replacement quantity matched with the residual error, and updating the state quantity prediction result of the filter according to the residual error replacement quantity, and determining a dead reckoning result corresponding to the target vehicle according to the updated state quantity prediction result and steering information of the target vehicle.

[0019] It should be understood that the content described in this part is not intended to identify key or important features of the embodiments of the present application, nor to limit the scope of the present application. Other features of the present application will become apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0020] In order to make the technical solution in the embodiments of the present application clearer, the drawings needed in the embodiment description will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without any creative effort on the basis of these drawings.

[0021] Figure 1 is a flow chart of a dead reckoning method according to an embodiment of the present application;

[0022] Figure 2 is a flow chart of another dead reckoning method according to an embodiment of the present application;

[0023] Figure 3 is a flow chart of another dead reckoning method according to an embodiment of the present application;

[0024] Figure 4 is a structural schematic diagram of a dead reckoning device according to an embodiment of the present application;

[0025] Figure 5 is a structural schematic diagram of an electronic device implementing a dead reckoning method according to an embodiment of the present application. DETAILED DESCRIPTION

[0026] In order to make the technical solution in the embodiments of the present application clearer, the drawings needed in the embodiment description will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without any creative effort on the basis of these drawings.

[0027] It should be noted that the terms "first", "second", and the like in the description and claims of the present application and the above drawings are used to distinguish similar objects, and do not necessarily indicate a specific order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device including a series of steps or units does not necessarily have to include all the steps or units clearly listed, but can include other steps or units not clearly listed or inherent to the process, method, product or device.

[0028] The method for correcting the measurement error caused by the wheel slip of the odometer in the prior art based on the SINS / DR combined navigation system does not consider other error conditions of the odometer, such as data delay, so that the dead reckoning method has certain limitations and the reliability of the dead reckoning result is low.

[0029] Figure 1 A flowchart of a dead reckoning method provided for the first embodiment of the present application, the embodiment can be applicable to the case of reckoning the driving position of a vehicle, and the method can be executed by a dead reckoning device, which can be realized in the form of hardware and / or software, and can be configured in an electronic device (such as a terminal or a server). As shown in the figure, the method comprises the following steps. Figure 1

[0030] In step 110, the current state quantity corresponding to the filter is constructed according to the forward speed information of the target vehicle at the current time, and the current state quantity is predicted to obtain the state quantity prediction result of the filter.

[0031] In the embodiment, the target vehicle can be a vehicle waiting for dead reckoning. Optionally, the forward speed information of the target vehicle at the current time can be obtained according to the odometer on the target vehicle, and the current state quantity corresponding to the filter can be constructed according to the forward speed information.

[0032] In a specific embodiment, after determining the current state quantity corresponding to the filter, the state quantity of the filter at the next time can be predicted according to the current state quantity and the historical driving information (such as the speed information in the historical driving process and the position information corresponding to different times) of the target vehicle to obtain the state quantity prediction result of the filter.

[0033] In step 120, the residual error between the current observed vehicle speed value corresponding to the target vehicle and the state quantity prediction result is obtained, the residual error replacement quantity matched with the residual error is determined, and the state quantity prediction result of the filter is updated according to the residual error replacement quantity.

[0034] In the embodiment, the current observed vehicle speed value can be the actual vehicle speed value of the target vehicle at the current time. After obtaining the current observed vehicle speed value corresponding to the target vehicle, the difference between the current observed vehicle speed value and the state quantity prediction result can be calculated, and the calculation result can be taken as the residual error between the current observed vehicle speed value and the state quantity prediction result.

[0035] ​In a specific embodiment, after the residual between the current observed vehicle speed value and the state quantity prediction result is obtained, the residual can be optionally linearly or nonlinearly processed to obtain a residual replacement quantity matched with the residual, and the residual replacement quantity and the state quantity prediction result are linearly operated according to a preset operation mode to obtain an updated state quantity prediction result.

[0036] In step 130, a dead reckoning result corresponding to the target vehicle is determined according to the updated state quantity prediction result and steering information of the target vehicle.

[0037] In this step, after the updated state quantity prediction result is obtained, the actual forward speed of the target vehicle can be extracted from the updated state quantity prediction result, and then the position of the target vehicle at the next time is calculated according to the actual forward speed of the target vehicle and the steering information to obtain the dead reckoning result of the target vehicle.

[0038] In this embodiment, by predicting the current state quantity of the filter and updating the state quantity prediction result of the filter according to the residual replacement quantity between the current observed vehicle speed value of the target vehicle and the state quantity prediction result, more stable vehicle state information can be obtained, and the influence of the odometer monitoring error on the dead reckoning result can be avoided. Compared with the method of correcting the measurement error caused by the odometer slip in the prior art based on the SINS / DR combined navigation system, the present embodiment can correct various error conditions of the odometer, thereby improving the expansibility of the dead reckoning method and the reliability of the dead reckoning result.

[0039] The technical scheme provided by the embodiment of the present application can improve the reliability of the dead reckoning result.

[0040] Figure 2 The flowchart of another dead reckoning method provided by the present embodiment can be combined with one or more methods in the above-mentioned embodiments, as shown in Figure 2 The method provided by the present embodiment can further include:

[0041] Step 201, obtaining, by a sensor on the target vehicle, a forward acceleration measurement value and a forward speed measurement value of the target vehicle in a vehicle body coordinate system at a current time.

[0042] The sensor can include an odometer, an IMU, a laser radar, etc., and the present embodiment does not limit the same.

[0043] In one embodiment of the present embodiment, the forward acceleration measurement value of the target vehicle at the current time can be obtained by the IMU on the target vehicle, and the forward speed measurement value of the target vehicle at the current time can be obtained by the odometer. Since the forward acceleration measurement value is measured based on the IMU coordinate system itself, the forward acceleration measurement value can be converted according to the conversion relationship between the IMU coordinate system itself and the vehicle body coordinate system to obtain the forward acceleration measurement value of the target vehicle in the vehicle body coordinate system.

[0044] Step 202, obtaining a preset forward acceleration bias corresponding to the target vehicle at the current time.

[0045] In the present embodiment, since there is a deviation between the forward acceleration measurement value and the actual forward acceleration of the target vehicle, in order to calibrate the forward acceleration measurement value, a preset forward acceleration bias can also be obtained, and the actual forward acceleration of the target vehicle can be obtained according to the preset forward acceleration bias and the forward acceleration measurement value. Specifically, the difference between the forward acceleration measurement value and the forward acceleration bias can be calculated, and the difference value can be taken as the actual forward acceleration of the target vehicle.

[0046] In one specific embodiment, the forward acceleration bias can be determined in real time according to the forward acceleration measurement value obtained by the IMU and the forward speed measurement value obtained by the odometer.

[0047] In one embodiment of the present embodiment, after obtaining the forward acceleration measurement value and the forward speed measurement value of the target vehicle at the current time by the IMU and the odometer respectively, the forward speed estimation value of the target vehicle at the next time can be derived according to the forward acceleration measurement value and the forward speed measurement value, then the forward speed estimation value is compared with the forward speed measurement value obtained by the odometer at the next time, and finally the forward acceleration bias of the target vehicle at the next time is determined according to the comparison result.

[0048] In another embodiment of the present embodiment, the forward speed measurement values of the target vehicle at the current time and the next time can also be obtained by the odometer, then the forward acceleration is calculated according to the two forward speed measurement values, and finally the forward acceleration is compared with the forward acceleration measurement value obtained by the IMU, and the forward acceleration bias is determined according to the comparison result.

[0049] Specifically, assuming that the front acceleration measurement value of the target vehicle obtained by the IMU at the current time is 1, the front speed measurement value obtained by the odometer is 0, and the time interval between the next time and the current time is 1s, it can be deduced that the front speed estimation value of the target vehicle at the next time is 1, but the front speed measurement value obtained by the odometer at the next time is 1.2, that is, the difference between the front acceleration measurement value of the target vehicle and the actual acceleration is -0.2, so it can be determined that the corresponding front acceleration bias of the target vehicle at the next time is -0.2.

[0050] The advantage of such setting is that by combining the measurement values obtained by the IMU and the odometer, the corresponding front acceleration bias of the target vehicle can be accurately obtained, and a more real front acceleration can be obtained, thereby improving the reliability of the dead reckoning result.

[0051] In a specific embodiment, the front acceleration measurement value of the Y axis corresponding to the vehicle body coordinate system of the target vehicle at the current time can be obtained by the IMU.

[0052] The advantage of such setting is that only the Y axis acceleration in the six-dimensional measurement information of the IMU is used, and the speed information of the odometer is fused, so that the real-time maintenance of the acceleration bias can be realized, and the dead reckoning process does not depend on the internal parameter calibration of the IMU, thereby the influence of time on the measurement result of the IMU can be eliminated, and the reliability of the dead reckoning result can be improved.

[0053] Step 203, generating the front speed information of the target vehicle at the current time according to the front speed measurement value, the front acceleration measurement value and the front acceleration bias.

[0054] In this step, the actual front acceleration of the target vehicle can be obtained according to the front acceleration measurement value and the front acceleration bias, and then the actual front acceleration, the front speed measurement value and the front acceleration bias are taken as the front speed information of the target vehicle at the current time.

[0055] Step 204, constructing the current state quantity corresponding to the filter according to the front speed information of the target vehicle at the current time.

[0056] In a specific embodiment, assuming that the actual front acceleration of the target vehicle in the vehicle body coordinate system at the current time is a y , the front acceleration bias is b y , the front speed measurement value is v y , and the current state quantity corresponding to the filter is X, then X=(v y , a y , b y ).

[0057] Step 205: Construct the state transition matrix corresponding to the target vehicle according to the preset time interval.

[0058] In this step, assuming the preset time interval is t, the state transition matrix F can be expressed as:

[0059]

[0060] Step 206: Calculate the state variable prediction result of the filter based on the state transition matrix and the current state variable.

[0061] In this step, assume that the predicted state variables of the filter are X', where X' = FX.

[0062] Step 207: Obtain the current vehicle speed value collected by the odometer on the target vehicle at the current moment.

[0063] Step 208: Based on the speed coefficient pre-calibrated for the target vehicle, calibrate the current vehicle speed value to obtain the current observed vehicle speed value corresponding to the target vehicle.

[0064] In this embodiment, since there is a deviation between the vehicle speed value collected by the odometer and the actual vehicle speed value, the current vehicle speed value can be calibrated according to the speed coefficient calibrated on the target vehicle chassis to obtain the actual vehicle speed value of the target vehicle at the current moment (i.e., the currently observed vehicle speed value).

[0065] In one specific embodiment, the current vehicle speed value can be multiplied by the speed coefficient to obtain the current observed vehicle speed value corresponding to the target vehicle.

[0066] Step 209: Obtain the residual between the current observed vehicle speed value and the state quantity prediction result corresponding to the target vehicle.

[0067] Step 210: Input the residual into a pre-constructed neurodynamic model, and calculate at least one feedback quantity corresponding to the residual through the neurodynamic model.

[0068] In this embodiment, the neurodynamic model is an algorithmic model constructed with the goal of improving the stability of a recurrent neural network. The residuals are input into the neurodynamic model, which can process the residuals based on a pre-established iterative algorithm to obtain at least one feedback quantity corresponding to the residuals.

[0069] The advantage of this setup is that by processing the residuals through a neurodynamic model, abnormal residuals can be avoided from affecting filter updates, thereby obtaining more stable vehicle state information and improving the reliability of dead reckoning results.

[0070] Step 211: Determine the residual substitution amount that matches the residual based on each of the feedback quantities.

[0071] In this embodiment, optionally, each feedback quantity can be linearly processed to obtain the residual substitution quantity.

[0072] In one embodiment of this example, determining a residual substitute that matches the residual based on each of the feedback quantities includes: integrating each of the feedback quantities over time to obtain an integral result corresponding to each of the feedback quantities; and summing the integral results corresponding to each of the feedback quantities to obtain a residual substitute that matches the residual.

[0073] In one specific embodiment, the residual is input into the neurodynamic model, and the feedback quantity output by the neurodynamic model is... in:

[0074]

[0075] In this embodiment, A is the passive decay rate, representing the self-decay rate of the residual substitution ε, which is usually set to 1; B and D are symmetric values, representing the upper and lower boundary values ​​of the input corresponding to the neurodynamic model, respectively; S i S represents the input value of the neurodynamic model at the current moment. i Including stimulating input values and inhibitory input values

[0076] After obtaining the above feedback quantities, each feedback quantity can be integrated over time, and the integration results corresponding to each feedback quantity can be accumulated to obtain the residual substitution quantity ε that matches the residual.

[0077] Step 212: Update the state variable prediction results of the filter based on the residual substitution amount and the preset Kalman gain.

[0078] In this step, assuming the preset Kalman gain is K, the state variable prediction result X' of the filter can be updated according to the following formula:

[0079] X″=X′+Kε

[0080] Where X” represents the predicted state variables after the filter update.

[0081] Step 213: Based on the updated state quantity prediction results and the steering information of the target vehicle, determine the dead reckoning result corresponding to the target vehicle.

[0082] The technical solution provided in this invention uses sensors on the target vehicle to acquire the forward acceleration and forward velocity measurements of the target vehicle in the vehicle coordinate system at the current moment, obtains a preset forward acceleration offset corresponding to the target vehicle at the current moment, generates forward velocity information based on the forward velocity, forward acceleration, and forward acceleration offset, constructs the current state variable corresponding to the filter based on the forward velocity information, constructs a state transition matrix based on a preset time interval, calculates the state variable prediction result of the filter based on the state transition matrix and the current state variable, and obtains the current vehicle speed collected by the odometer at the current moment. The method involves calibrating the current vehicle speed value based on the pre-calibrated speed coefficient of the target vehicle to obtain the current observed vehicle speed value corresponding to the target vehicle. The residual between the current observed vehicle speed value and the state quantity prediction result is then obtained and input into a neurodynamic model. At least one feedback quantity is calculated through the neurodynamic model, and a residual substitution quantity is determined based on each feedback quantity. The state quantity prediction result of the filter is updated based on the residual substitution quantity and a preset Kalman gain. Finally, the dead reckoning result corresponding to the target vehicle is determined based on the updated state quantity prediction result and the steering information of the target vehicle. This technique can improve the reliability of dead reckoning results.

[0083] Figure 3 This is a flowchart of another dead reckoning method provided in this embodiment. In this embodiment, the technical solution can be combined with one or more methods from the above embodiments, such as... Figure 3 As shown, the method provided in this embodiment may further include:

[0084] Step 301: Based on the forward speed information of the target vehicle at the current moment, construct the current state quantity corresponding to the filter, and predict the current state quantity to obtain the state quantity prediction result of the filter.

[0085] Step 302: Obtain the original covariance matrix of the filter at the current time.

[0086] In this embodiment, optionally, if it is the first time dead reckoning is performed on the target vehicle, the original covariance matrix can be calculated based on the current state variables; if it is not the first time dead reckoning is performed on the target vehicle, the covariance matrix generated in the previous dead reckoning process can be obtained as the original covariance matrix of the filter at the current time.

[0087] In a specific embodiment, if this is the first dead reckoning of the target vehicle, the covariance corresponding to the current state variable can be calculated, and an original covariance matrix can be constructed based on the covariance corresponding to the current state variable. The diagonal of the original covariance matrix represents the covariance corresponding to the current state variable.

[0088] Step 303: Based on the state transition matrix and the preset noise matrix corresponding to the filter, predict the original covariance matrix to obtain the covariance matrix prediction result.

[0089] In this step, assuming the original covariance matrix is ​​P and the preset noise matrix corresponding to the filter is Q, the original covariance matrix P can be predicted according to the following formula:

[0090] P' = FPF T +Q

[0091] Where P' is the predicted result of the covariance matrix.

[0092] Step 304: Obtain the residual between the current observed vehicle speed value and the state quantity prediction result corresponding to the target vehicle.

[0093] Step 305: Input the residual into a pre-constructed neurodynamic model, and calculate at least one feedback quantity corresponding to the residual through the neurodynamic model.

[0094] Step 306: Determine the residual substitution amount that matches the residual based on each of the feedback quantities.

[0095] Step 307: Obtain the Kalman gain based on the covariance matrix prediction result, the preset observation matrix corresponding to the filter, and the preset observation noise matrix.

[0096] In this step, assuming the preset observation matrix corresponding to the filter is H and the preset observation noise matrix is ​​R, the Kalman gain K can be calculated according to the following formula:

[0097] K = P'H T (HP'H T +R) -1

[0098] Step 308: Update the state variable prediction results of the filter based on the residual substitution amount and Kalman gain.

[0099] Step 309: Update the covariance matrix prediction result based on the Kalman gain, the preset identity matrix, and the preset observation matrix corresponding to the filter, and use the updated covariance matrix prediction result as the original covariance matrix corresponding to the filter at the next time step.

[0100] In this embodiment, assuming the preset identity matrix is ​​I, the covariance matrix prediction result P' can be updated according to the following formula:

[0101] P”=(I-KH)P'

[0102] Where P” is the updated covariance matrix prediction result, and I is a 3*3 identity matrix.

[0103] After obtaining the updated covariance matrix prediction result, the updated covariance matrix prediction result can be used as the original covariance matrix of the filter at the next time step, so as to determine the dead reckoning result of the target vehicle at the next time step.

[0104] Step 310: Based on the updated state quantity prediction results and the steering information of the target vehicle, determine the dead reckoning result corresponding to the target vehicle.

[0105] The technical solution provided by this invention constructs the current state quantity corresponding to the filter based on the forward speed information of the target vehicle at the current moment, predicts the current state quantity to obtain the state quantity prediction result, obtains the original covariance matrix corresponding to the filter at the current moment, predicts the original covariance matrix to obtain the covariance matrix prediction result, obtains the residual between the current observed vehicle speed value corresponding to the target vehicle and the state quantity prediction result, inputs the residual into a neurodynamic model, calculates at least one feedback quantity through the neurodynamic model, determines the residual substitution quantity based on each feedback quantity, obtains the Kalman gain based on the covariance matrix prediction result, a preset observation matrix, and a preset observation noise matrix, updates the state quantity prediction result of the filter based on the residual substitution quantity and the Kalman gain, updates the covariance matrix prediction result based on the Kalman gain, a preset identity matrix, and the preset observation matrix corresponding to the filter, and uses the updated covariance matrix prediction result as the original covariance matrix corresponding to the filter at the next moment. Based on the updated state quantity prediction result and the steering information of the target vehicle, the dead reckoning result corresponding to the target vehicle is determined. This technical means can improve the reliability of dead reckoning results.

[0106] Figure 4 This is a schematic diagram of a dead reckoning device provided in an embodiment of the present invention. The dead reckoning device includes: a state prediction module 410, a prediction result update module 420, and a reckoning result determination module 430.

[0107] The state prediction module 410 is used to construct the current state quantity corresponding to the filter based on the forward speed information of the target vehicle at the current moment, and to predict the current state quantity to obtain the state quantity prediction result of the filter.

[0108] The prediction result update module 420 is used to obtain the residual between the current observed vehicle speed value corresponding to the target vehicle and the state quantity prediction result, determine the residual replacement amount that matches the residual, and update the state quantity prediction result of the filter according to the residual replacement amount.

[0109] The calculation result determination module 430 is used to determine the dead reckoning result corresponding to the target vehicle based on the updated state quantity prediction result and the steering information of the target vehicle.

[0110] The technical solution provided by this invention improves the reliability of dead reckoning by constructing the current state quantity of a filter based on the forward speed information of the target vehicle at the current moment, predicting the current state quantity to obtain the state quantity prediction result of the filter, obtaining the residual between the current observed vehicle speed value and the state quantity prediction result, determining the residual replacement quantity that matches the residual, updating the state quantity prediction result of the filter based on the residual replacement quantity, and determining the dead reckoning result corresponding to the target vehicle based on the updated state quantity prediction result and the steering information of the target vehicle.

[0111] Based on the above embodiments, the state prediction module 410 includes:

[0112] The acceleration acquisition unit is used to acquire the forward acceleration measurement value and forward velocity measurement value of the target vehicle in the vehicle coordinate system at the current moment through the sensors on the target vehicle;

[0113] The offset acquisition unit is used to acquire the preset forward acceleration offset of the target vehicle at the current moment.

[0114] The speed information determination unit is used to generate the forward speed information of the target vehicle at the current moment based on the forward speed measurement value, the forward acceleration measurement value, and the forward acceleration offset.

[0115] The state transition matrix construction unit is used to construct the state transition matrix corresponding to the target vehicle according to a preset time interval.

[0116] The state quantity prediction unit is used to calculate the state quantity prediction result of the filter based on the state transition matrix and the current state quantity.

[0117] The covariance matrix acquisition unit is used to acquire the original covariance matrix of the filter at the current time.

[0118] The covariance matrix prediction unit is used to predict the original covariance matrix based on the state transition matrix and the preset noise matrix corresponding to the filter, so as to obtain the covariance matrix prediction result.

[0119] The prediction result update module 420 includes:

[0120] The current vehicle speed value acquisition unit is used to acquire the current vehicle speed value collected by the odometer on the target vehicle at the current moment;

[0121] The vehicle speed calibration unit is used to calibrate the current vehicle speed value according to the speed coefficient pre-calibrated for the target vehicle, so as to obtain the current observed vehicle speed value.

[0122] The residual input unit is used to input the residual into a pre-constructed neurodynamic model, and through the neurodynamic model, calculate at least one feedback quantity corresponding to the residual;

[0123] The residual substitution determination unit is used to determine the residual substitution amount that matches the residual based on each of the feedback amounts;

[0124] The filter update unit is used to update the state variable prediction results of the filter based on the residual substitution amount and the preset Kalman gain.

[0125] The feedback quantity integration unit is used to integrate each of the feedback quantities with respect to time to obtain the integration result corresponding to each of the feedback quantities.

[0126] The integral result accumulation unit is used to accumulate the integral results corresponding to each of the feedback quantities to obtain the residual substitution quantity that matches the residual.

[0127] The Kalman gain acquisition unit is used to obtain the Kalman gain based on the covariance matrix prediction result, the preset observation matrix corresponding to the filter, and the preset observation noise matrix.

[0128] The covariance matrix update unit is used to update the covariance matrix prediction result based on the Kalman gain, the preset identity matrix, and the preset observation matrix corresponding to the filter, and to use the updated covariance matrix prediction result as the original covariance matrix corresponding to the filter at the next time step.

[0129] The above-described apparatus can execute the methods provided in all the foregoing embodiments of the present invention, and has the corresponding functional modules and beneficial effects for executing the above methods. Technical details not described in detail in the embodiments of the present invention can be found in the methods provided in all the foregoing embodiments of the present invention.

[0130] Figure 5A schematic diagram of an electronic device 10 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0131] like Figure 5 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 may also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0132] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0133] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as dead reckoning methods.

[0134] In some embodiments, the dead reckoning method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the dead reckoning method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the dead reckoning method by any other suitable means (e.g., by means of firmware).

[0135] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0136] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0137] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0138] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0139] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0140] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0141] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0142] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A dead reckoning method, characterized by, The method comprises the following steps: According to the forward speed information of the target vehicle at the current time, the current state quantity corresponding to the filter is constructed, and the state quantity prediction result of the filter is obtained by predicting the current state quantity; Obtain the residual between the current observed vehicle speed value corresponding to the target vehicle and the state quantity prediction result, determine the residual replacement quantity matched with the residual, and update the state quantity prediction result of the filter according to the residual replacement quantity; According to the updated state quantity prediction result and the steering information of the target vehicle, the dead reckoning result corresponding to the target vehicle is determined; Before constructing the current state quantity corresponding to the filter according to the forward speed information of the target vehicle at the current time, the method further comprises the following steps: Obtain the forward acceleration measurement value and the forward speed measurement value of the target vehicle in the body coordinate system at the current time through the sensor on the target vehicle; obtain the preset forward acceleration bias corresponding to the target vehicle at the current time; generate the forward speed information of the target vehicle at the current time according to the forward speed measurement value, the forward acceleration measurement value and the preset forward acceleration bias; Wherein, the forward acceleration bias is determined in real time according to the forward acceleration measurement value obtained by the inertial measurement unit and the forward speed measurement value obtained by the odometer; the method further comprises:

2. The method of claim 1, wherein, Obtain the forward speed measurement values corresponding to the target vehicle at the current time and the next time through the odometer, calculate the forward acceleration according to the two forward speed measurement values, compare the forward acceleration with the forward acceleration measurement value obtained by the inertial measurement unit, and determine the forward acceleration bias according to the comparison result. The method further comprises the following steps: According to the preset time interval, construct the state transition matrix corresponding to the target vehicle; 3. The method of claim 1, wherein, According to the state transition matrix and the current state quantity, the state quantity prediction result of the filter is calculated. Before obtaining the residual between the current observed vehicle speed value corresponding to the target vehicle and the state quantity prediction result, the method further comprises the following steps: Obtain the current speed value collected by the odometer on the target vehicle at the current time; 4. The method of claim 2, wherein, Calibrate the current speed value according to the speed coefficient calibrated in advance by the target vehicle to obtain the current observed vehicle speed value. The method further comprises the following steps: Input the residual into the pre-constructed neural dynamics model, calculate at least one feedback quantity corresponding to the residual through the neural dynamics model; According to each feedback quantity, determine the residual replacement quantity matched with the residual; 5. The method of claim 4, wherein, According to the residual replacement quantity and the preset Kalman gain, update the state quantity prediction result of the filter. According to each feedback quantity, determine the residual replacement quantity matched with the residual, which comprises the following steps: Integrate each feedback quantity with respect to time to obtain the integral result corresponding to each feedback quantity; Add up the integral results corresponding to each feedback quantity to obtain the residual replacement quantity matched with the residual.

6. The method of claim 4, wherein, After the current state quantity is predicted to obtain a state quantity prediction result of the filter, the method further comprises: obtaining a corresponding original covariance matrix of the filter at the current time; predicting the original covariance matrix according to the state transition matrix and a preset noise matrix corresponding to the filter to obtain a covariance matrix prediction result; before updating the state quantity prediction result of the filter according to the residual replacement quantity and a preset Kalman gain, the method further comprises: obtaining the Kalman gain according to the covariance matrix prediction result, a preset observation matrix corresponding to the filter and a preset observation noise matrix.

7. The method of claim 6, wherein, after updating the state quantity prediction result of the filter, the method further comprises: updating the covariance matrix prediction result according to the Kalman gain, a preset unit matrix and the preset observation matrix corresponding to the filter, and taking the updated covariance matrix prediction result as the original covariance matrix corresponding to the filter at the next time.

8. A dead reckoning device characterized by comprising: comprise: a state prediction module configured to construct a current state quantity corresponding to the filter according to forward speed information of the target vehicle at the current time, and predict the current state quantity to obtain a state quantity prediction result of the filter; a prediction result updating module configured to obtain a residual between a current observed vehicle speed value of the target vehicle and the state quantity prediction result, determine a residual replacement quantity matched with the residual, and update the state quantity prediction result of the filter according to the residual replacement quantity; a calculation result determination module configured to determine a dead reckoning result corresponding to the target vehicle according to the updated state quantity prediction result and steering information of the target vehicle. The state prediction module comprises: an acceleration acquisition unit configured to acquire a forward acceleration measurement value and a forward speed measurement value of the target vehicle in a body coordinate system at the current time through a sensor on the target vehicle; an offset acquisition unit configured to acquire a preset forward acceleration offset corresponding to the target vehicle at the current time; a speed information determination unit configured to generate forward speed information of the target vehicle at the current time according to the forward speed measurement value, the forward acceleration measurement value and the forward acceleration offset; wherein the forward acceleration offset is determined in real time according to the forward acceleration measurement value acquired by an inertial measurement unit and the forward speed measurement value acquired by an odometer; and the device is further configured to: acquire forward speed measurement values of the target vehicle at the current time and the next time through the odometer, calculate a forward acceleration according to the two forward speed measurement values, compare the forward acceleration with a forward acceleration measurement value acquired by the inertial measurement unit, and determine the forward acceleration offset according to a comparison result.

9. An electronic device, comprising: The electronic device comprises: at least one processor; and a memory connected with the at least one processor in communication; wherein the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to execute the dead reckoning method in any one of claims 1-7.

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