Vehicle acceleration determination methods, devices and electronic equipment, storage media

Abstract

This application discloses a vehicle acceleration determination method, apparatus, electronic device, and storage medium. The method includes: acquiring an initial acceleration from a vehicle accelerometer; determining the zero-bias error parameter of the vehicle accelerometer; and updating the initial acceleration using the zero-bias error parameter of the vehicle accelerometer when it is determined that the vehicle is currently in a non-moving state. This application achieves adaptive adjustment of the zero-bias error parameter of the vehicle accelerometer, while eliminating high-frequency noise errors and improving data accuracy. This application can be used in autonomous vehicles.

Description

Technical Field

[0001] This application relates to the field of autonomous driving technology, and in particular to a method, apparatus, electronic device, and storage medium for determining vehicle acceleration. Background Technology

[0002] Due to safety requirements, autonomous vehicles need to obtain the vehicle's lateral or longitudinal acceleration information at the current moment as an input to the subsequent control module, so as to perform operations such as acceleration, deceleration or braking on the vehicle in real time.

[0003] In related technologies, autonomous vehicles obtain vehicle acceleration mainly through three methods: 1) directly through the Yaw-G sensor on the chassis; 2) through the accelerometer used in the positioning module; and 3) through differential calculation of vehicle speed. However, the information obtained through the Yaw-G sensor or accelerometer still contains noise, and the acceleration calculated by differential calculation of vehicle speed will have high-frequency noise and abrupt changes, and neither method is compatible with multiple vehicle models. Summary of the Invention

[0004] This application provides a method, apparatus, electronic device, and storage medium for determining vehicle acceleration, in order to dynamically calculate the error of the accelerometer and thus provide accurate positioning information.

[0005] The embodiments of this application adopt the following technical solutions:

[0006] In a first aspect, embodiments of this application provide a method for determining vehicle acceleration, which is used for autonomous vehicles. The method includes: acquiring an initial acceleration of a vehicle accelerometer; determining a zero-bias error parameter of the vehicle accelerometer; and updating the initial acceleration using the zero-bias error parameter of the vehicle accelerometer when it is determined that the vehicle is in a non-moving state at the current moment.

[0007] Secondly, embodiments of this application also provide a vehicle acceleration determination device, which is used for an autonomous vehicle. The device includes: an acquisition module for acquiring the initial acceleration of a vehicle accelerometer; a determination module for determining the zero-bias error parameter of the vehicle accelerometer; and an update module for updating the initial acceleration using the zero-bias error parameter of the vehicle accelerometer when it is determined that the vehicle is in a non-moving state at the current moment.

[0008] Thirdly, embodiments of this application also provide an electronic device, including: a processor; and a memory arranged to store computer-executable instructions, which, when executed, cause the processor to perform the above-described method.

[0009] Fourthly, embodiments of this application also provide a computer-readable storage medium that stores one or more programs, which, when executed by an electronic device including multiple applications, cause the electronic device to perform the above-described method.

[0010] The above-described technical solutions adopted in the embodiments of this application can achieve the following beneficial effects:

[0011] By adaptively learning the zero-bias error parameter of the accelerometer, when it is determined that the vehicle is in a non-moving state at the current moment, the initial acceleration can be updated by updating the zero-bias error parameter of the vehicle accelerometer, thereby eliminating the influence of high-frequency noise. Attached Figure Description

[0012] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0013] Figure 1 This is a flowchart illustrating the vehicle acceleration determination method in the embodiments of this application;

[0014] Figure 2 This is a schematic diagram of the vehicle acceleration determination device in the embodiments of this application;

[0015] Figure 3 This is a schematic diagram of the simulation results of the acceleration processing comparison in the embodiments of this application;

[0016] Figure 4 This is a schematic diagram of the simulation results of acceleration processing in the embodiments of this application;

[0017] Figure 5 This is a schematic diagram of the structure of an electronic device according to an embodiment of this application. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0019] To enable those skilled in the art to better understand this application, some technical terms appearing in the embodiments of this application are explained below:

[0020] IMU: Inertial Measurement Unit, is a device that measures the three-axis attitude angles (or angular rates) and acceleration of an object.

[0021] The raw data from an IMU typically consists of acceleration and angular velocity data that include bias (zero bias error parameter) and noise.

[0022] The inventors discovered that when autonomous vehicles need to obtain information about their lateral / longitudinal acceleration at the current moment, the commonly used methods have the following drawbacks:

[0023] 1. When using the Yaw-G sensor, data from this sensor cannot be obtained because not all vehicle models are equipped with this type of sensor, or many vehicle models do not have an open protocol for this type of data.

[0024] 2. When the information obtained using the Yaw-G sensor and accelerometer contains noise, such as installation errors of the sensor itself, device errors of the sensor, and random errors caused by external factors such as temperature, it will be impossible to guarantee the accuracy of the data when using fixed error parameters.

[0025] 3. Acceleration calculated using the derivative of vehicle speed will contain high-frequency noise and abrupt changes.

[0026] Based on the above, the embodiments of this application use an adaptive error calculation model and a low-pass filter, combined with the speed information provided by the positioning module, to process the raw accelerometer data. This eliminates the influence of high-frequency noise while dynamically calculating the accelerometer error. Furthermore, it ensures that the same system can be applied to different vehicle models, saving time on parameter adjustment and configuration.

[0027] The technical solutions provided by the various embodiments of this application are described in detail below with reference to the accompanying drawings.

[0028] This application provides a method for determining vehicle acceleration, such as... Figure 1 The diagram shows a flowchart of a vehicle acceleration determination method according to an embodiment of this application. The method includes at least the following steps S110 to S130:

[0029] Step S110: Obtain the initial acceleration of the vehicle accelerometer.

[0030] For autonomous vehicles, there are body sensors or a positioning module. The vehicle's velocity data at a standstill, i.e., the initial acceleration from the accelerometer and the vehicle's velocity, can be obtained from the data from the body sensors or the output of the positioning module. Here, "standstill" refers to the moment when the vehicle is not running or the vehicle's velocity is close to zero. The vehicle's velocity may fluctuate slightly due to vibrations, but this does not affect the calculation results.

[0031] Before the vehicle is powered on and before the autonomous driving mode is activated, speed and acceleration information (close to 0) are acquired during a set time period when the vehicle is stationary. The vehicle's IMU includes a gyroscope and an accelerometer. Here, we primarily refer to the accelerometer.

[0032] It's understandable that the time period is set based on the actual situation.

[0033] It should be noted that when the autonomous vehicle is an autonomous taxi or autonomous minibus, a safety operator needs to activate or deactivate the autonomous driving mode.

[0034] Step S120: Determine the zero bias error parameter of the vehicle accelerometer.

[0035] When the vehicle is stationary, the accelerometer output during this time period can be set to the initial zero-bias error parameter bias of the accelerometer, denoted as bias. acc Furthermore, if the time period exceeds the aforementioned set time frame, further judgment will be made as follows.

[0036] Step S130: If it is determined that the vehicle is in a non-moving state at the current moment, the initial acceleration is updated by the zero bias error parameter of the vehicle accelerometer.

[0037] First, determine whether the vehicle is in a stationary or moving state at the current moment. For example, if the current speed is higher than a preset threshold, the vehicle is considered to be in motion; conversely, if the current speed is lower than the preset threshold, the vehicle is considered to be stationary. For example, the preset threshold could be 0.01 m / s.

[0038] Then, if it is determined that the vehicle is in a non-moving state at the current moment, the initial acceleration needs to be updated using the zero bias error parameter of the vehicle accelerometer.

[0039] Furthermore, the zero-bias error parameter of the vehicle accelerometer is adaptively adjusted. Simultaneously, when the vehicle is not in motion, the zero-bias error parameter of the vehicle accelerometer is updated at each corresponding moment in the non-motion state. It is important to note that the update process follows a chronological order, updating the initial acceleration first, and then updating the corresponding acceleration (the acceleration at the next moment after the initial acceleration) as time progresses.

[0040] In some embodiments, if the vehicle stops N times, then bias is applied. acc The bias was obtained after N updates. acc The different values ​​ensured the adaptive effect. Afterwards, the bias was updated N times. acc As the acceleration at the current moment (stopped moment / non-stopped moment).

[0041] like Figure 3 As shown, the vehicle stopped three times, affecting bias. acc Three updates were performed, and the bias obtained in each update was... acc The values ​​are different.

[0042] Figure 3 This is an example of acceleration processing results, removing high-frequency noise and accelerometer bias. acc The revised data (black) compared to the original data (gray) better meets the requirements of the subsequent autonomous driving control module.

[0043] Figure 4 An example of acceleration processing results is the bias parameter of the adaptive accelerometer zero bias of an autonomous vehicle.

[0044] In practical implementation, if the current time (let's say time k) is used, the following formula can be used for bias. acc Update:

[0045] bias acc =(bias) acc *ω+bias k ) / (ω+1)

[0046] In some embodiments, bias needs to be adjusted. k Amplitude limiting filtering is performed to prevent sudden changes caused by abnormal data.

[0047] In some embodiments, ω is an integer multiple of 1 and can be set according to the actual test results.

[0048] In one embodiment of this application, the method further includes: when it is determined that the vehicle is in motion at the current moment, filtering the initial acceleration of the vehicle accelerometer using the zero bias error parameter.

[0049] In practice, if it is determined that the vehicle is in motion at the current moment, the initial acceleration of the vehicle accelerometer is filtered using the zero-bias error parameter. That is, if the current speed is higher than a preset threshold, the vehicle is considered to be in motion and has not stopped; the zero-bias error parameter bias is then directly applied. acc The accelerometer data (initial acceleration) is low-pass filtered.

[0050] It should be noted that when an autonomous vehicle is in motion, the lateral or longitudinal acceleration information of the vehicle body at the current moment is relatively stable. After passing through a low-pass filter, it can meet the requirements for smoothness and latency.

[0051] In one embodiment of this application, when it is determined that the vehicle is in a non-moving state at the current moment, updating the initial acceleration using the zero-bias error parameter of the vehicle accelerometer includes: when it is determined that the vehicle is in a non-moving state at the current moment at time K, updating the initial acceleration at time K using the Kth zero-bias error parameter of the vehicle accelerometer; when it is determined that the vehicle is in a non-moving state at the current moment at time K+N, adaptively updating the acceleration at time K+N using the K+Nth zero-bias error parameter of the vehicle accelerometer, where K and N are integers.

[0052] In specific implementation, if it is determined that the vehicle is in a non-moving state at the current time (K-th time), the initial acceleration at the K-th time is updated using the K-th zero-bias error parameter of the vehicle accelerometer. When a non-moving state occurs at the K-th time, the zero-bias error parameter is updated once as the K-th zero-bias error parameter, and the initial acceleration (taking the initial state as an example) is updated based on the K-th zero-bias error parameter.

[0053] Furthermore, if it is determined that the vehicle is in a non-moving state at the current time (K+N), the acceleration at time (K+N) is adaptively updated using the zero-bias error parameter of the vehicle accelerometer. That is, if the vehicle is in a non-moving state at the next (K+N) time, the zero-bias error parameter is updated again as the zero-bias error parameter for the (K+N)th time, and the acceleration (not the initial acceleration) is updated based on the zero-bias error parameter for the (K+N)th time.

[0054] In some embodiments, when the autonomous vehicle is in autonomous driving mode, the vehicle's positioning module acquires the vehicle's acceleration information and inputs it into the vehicle's control module as one of the input parameters for positioning perception. Considering that during driving, the acceleration calculated from the differential of the vehicle's speed may contain high-frequency noise and abrupt changes, often leading to high-frequency noise interference, and that using a fixed zero-bias error parameter cannot guarantee the accuracy of the vehicle's acceleration at the current moment, further investigation is needed.

[0055] By adaptively learning the bias parameter of the accelerometer from the vehicle, the effects of changes in vehicle equipment conditions (such as changes in tire pressure) caused by excessive driving time and the influence of external factors (such as rainy weather) are eliminated.

[0056] In one embodiment of this application, determining the zero-bias error parameter of the vehicle accelerometer includes: calculating the zero-bias error parameter of the accelerometer using the vehicle's velocity and acceleration at a stationary moment; and updating the zero-bias error parameter in real time according to a preset formula when it is determined that the vehicle is in a non-moving state at the current moment.

[0057] In practice, when determining the zero-bias error parameter of the vehicle accelerometer, the vehicle speed and acceleration at a stationary moment (before starting or before the vehicle is powered on and the autonomous driving mode is activated) can be used to calculate the zero-bias error parameter of the accelerometer.

[0058] Furthermore, if it is determined that the vehicle is in a non-moving state at the current moment, the zero bias error parameter is calculated according to the preset formula bias. acc =(bias) acc *ω+bias k ) / (ω+1) is updated in real time.

[0059] In one embodiment of this application, determining that the vehicle is in a non-moving state at the current moment includes: determining that the vehicle is in a non-moving state at the current moment by checking whether the vehicle speed is lower than a preset threshold.

[0060] In practice, since only the vehicle speed information is used to determine the vehicle's motion state, and the acceleration is not directly obtained by using the differential of the vehicle speed, the effects of time difference caused by data transmission and the effects of the vehicle speed's own error during vehicle operation can be eliminated.

[0061] In one embodiment of this application, the updated initial acceleration is further subjected to low-pass filtering using a low-pass filter.

[0062] In practice, low-pass filters of different orders can be used to analyze and process the raw data, depending on the actual requirements for smoothness and delay.

[0063] acc k =α*acc k +(1-α)*acc k-1 ,

[0064] Where α is a parameter related to the filter cutoff frequency, and acc k This indicates the raw acceleration measured by the accelerometer.

[0065] Preferably, the analysis and processing of the raw data can eliminate the influence of noise accordingly.

[0066] In one embodiment of this application, the method further includes: smoothing the acceleration in the accelerometer in the vehicle positioning and sensing module and inputting it to the vehicle control module.

[0067] In practical implementation, considering that the frequency of the control module in an autonomous vehicle is much lower than the frequency of the accelerometer output, the positioning module performs low-pass filtering, followed by median filtering, mean filtering, or cascaded median or mean filtering to further smooth the data, thereby meeting the requirements of the autonomous vehicle control module.

[0068] Preferably, median filtering, mean filtering, or cascaded median or mean filtering are used to further smooth the data, which can eliminate the influence of noise accordingly.

[0069] The method in this application embodiment uses the vehicle's speed data and acceleration data at a stationary moment to calculate the zero bias error parameter of the accelerometer and determines the vehicle's driving state based on the vehicle's speed during autonomous driving. If the speed requirement is met, the acceleration data is dynamically updated, and a low-pass filter is used to process the acceleration data.

[0070] This application embodiment also provides a device 200, such as Figure 2 As shown, a structural schematic diagram of an embodiment of this application is provided. The device 200 includes at least: an acquisition module 210, a determination module 220, and an update module 230, wherein:

[0071] The acquisition module 210 is used to acquire the initial acceleration of the vehicle accelerometer;

[0072] Module 220 is used to determine the zero-bias error parameters of the vehicle accelerometer; and

[0073] The update module 230 is used to update the initial acceleration by means of the zero bias error parameter of the vehicle accelerometer when it is determined that the vehicle is in a non-moving state at the current moment.

[0074] In one embodiment of this application, the acquisition module 210 is specifically used for: for an autonomous vehicle, which has body sensors or a positioning module, the vehicle's speed data at a stationary moment, i.e., the initial acceleration of the accelerometer and the vehicle speed, can be obtained based on the body sensor data or the output of the positioning module. It is assumed that the vehicle is not started, i.e., the vehicle speed is close to 0. And due to vehicle vibration and other factors, there may be some small fluctuations, but these do not affect the calculation results.

[0075] Before the vehicle is powered on and before the autonomous driving mode is activated, speed and acceleration information (close to 0) are acquired during a set time period when the vehicle is stationary. The vehicle's IMU includes a gyroscope and an accelerometer. Here, we primarily refer to the accelerometer.

[0076] It's understandable that the time period is set based on the actual situation.

[0077] It should be noted that when the autonomous vehicle is an autonomous taxi or autonomous minibus, a safety operator needs to activate or deactivate the autonomous driving mode.

[0078] In one embodiment of this application, the determining module 220 is specifically used to: based on the vehicle's stationary state, the output of the accelerometer during this time period can be set to the initial zero-bias error parameter bias of the accelerometer, denoted as bias. acc Furthermore, if the time period exceeds the aforementioned set time frame, further judgment will be made as follows.

[0079] In one embodiment of this application, the update module 230 is specifically used to: first, determine whether the vehicle is in a non-moving state or a moving state at the current moment during its driving process. For example, if the current speed is higher than a preset threshold, the vehicle can be considered to be in a moving state at this moment; conversely, if the current speed is lower than the preset threshold, the vehicle can be considered to be stationary at this moment. For example, the preset threshold here can be 0.01 m / s.

[0080] Then, if it is determined that the vehicle is in a non-moving state at the current moment, the initial acceleration needs to be updated using the zero bias error parameter of the vehicle accelerometer.

[0081] Furthermore, the zero-bias error parameter of the vehicle accelerometer is adaptively adjusted. Simultaneously, when the vehicle is not in motion, the zero-bias error parameter of the vehicle accelerometer is updated at each corresponding moment in the non-motion state. It is important to note that the update process follows a chronological order, updating the initial acceleration first, and then updating the corresponding acceleration (the acceleration at the next moment after the initial acceleration) as time progresses.

[0082] In some embodiments, if the vehicle stops N times, then bias is applied. acc The bias was obtained after N updates. acc The different values ​​ensured the adaptive effect. Afterwards, the bias was updated N times. acc As the acceleration at the current moment (stopped moment / non-stopped moment).

[0083] In practice, the following formula is used for bias. acc Update:

[0084] bias acc =(bias) acc *ω+bias k ) / (ω+1)

[0085] In some embodiments, bias needs to be adjusted. k Amplitude limiting filtering is performed to prevent sudden changes caused by abnormal data.

[0086] In some embodiments, ω is an integer multiple of 1 and can be set according to the actual test results.

[0087] It is understood that the above-mentioned vehicle acceleration determination device can implement each step of the vehicle acceleration determination method provided in the foregoing embodiments. The relevant explanations of the vehicle acceleration determination method are applicable to the vehicle acceleration determination device, and will not be repeated here.

[0088] Figure 5 This is a schematic diagram of the structure of an electronic device according to an embodiment of this application. Please refer to it. Figure 5 At the hardware level, the electronic device includes a processor, and optionally also includes an internal bus, a network interface, and memory. The memory may include main memory, such as high-speed random-access memory (RAM), or non-volatile memory, such as at least one disk drive. Of course, the electronic device may also include other hardware required for other business operations.

[0089] The processor, network interface, and memory can be interconnected via an internal bus, which can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus, etc. This bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 5 The symbol is represented by a single double-headed arrow, but this does not mean that there is only one bus or one type of bus.

[0090] Memory is used to store programs. Specifically, programs may include program code, which includes computer operation instructions. Memory may include main memory and non-volatile memory, and provides instructions and data to the processor.

[0091] The processor reads the corresponding computer program from non-volatile memory into main memory and then executes it, forming the vehicle acceleration determination device at the logical level. The processor executes the program stored in memory and specifically performs the following operations:

[0092] Obtain the initial acceleration from the vehicle's accelerometer;

[0093] Determine the zero-bias error parameters of the vehicle accelerometer; and

[0094] If it is determined that the vehicle is in a non-moving state at the current moment, the initial acceleration is updated by the zero bias error parameter of the vehicle accelerometer.

[0095] The above is as stated in this application. Figure 1The method for determining vehicle acceleration disclosed in the illustrated embodiment can be applied to a processor or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method can be completed by integrated logic circuits in the processor's hardware or by instructions in software form. The processor can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in the embodiments of this application can be directly embodied in the execution of a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software module can reside in a mature storage medium in the field, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or registers. This storage medium is located in memory, and the processor reads information from the memory and, in conjunction with its hardware, completes the steps of the above method.

[0096] The electronic device can also perform Figure 1 The method for executing the vehicle acceleration determination device, and the realization of the vehicle acceleration determination device in Figure 1 The functions of the embodiments shown are not described in detail here.

[0097] This application also proposes a computer-readable storage medium that stores one or more programs, the programs including instructions that, when executed by an electronic device including multiple applications, enable the electronic device to perform... Figure 1 The method executed by the vehicle acceleration determination device in the illustrated embodiment is specifically used to perform:

[0098] Obtain the initial acceleration from the vehicle's accelerometer;

[0099] Determine the zero-bias error parameters of the vehicle accelerometer; and

[0100] If it is determined that the vehicle is in a non-moving state at the current moment, the initial acceleration is updated by the zero bias error parameter of the vehicle accelerometer.

[0101] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0102] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0103] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0104] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0105] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0106] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0107] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0108] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0109] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0110] The above description is merely an embodiment of this application and is not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for determining vehicle acceleration, wherein, For use in autonomous vehicles, the method includes: Obtain the initial acceleration from the vehicle's accelerometer; Before the vehicle is powered on and started, and before the autonomous driving mode is activated, acquire speed and acceleration information for a set time period when the vehicle is stationary. Determine the zero-bias error parameters of the vehicle accelerometer; and When the vehicle is stationary, the accelerometer output during this time period can be set to the initial accelerometer zero-bias error parameter, denoted as bias. acc The zero bias error parameter of the vehicle accelerometer will be adaptively adjusted. If it is determined that the vehicle is in a non-moving state at the current moment, the initial acceleration is updated by the zero bias error parameter of the vehicle accelerometer; When the vehicle is not moving, the zero-bias error parameter of the vehicle accelerometer is updated at each moment corresponding to the non-moving state. If the current moment k is given, the bias can be calculated using the following formula. acc Update: bias acc =(bias) acc *ω+bias k ) / (ω+1), where ω is an integer multiple of 1; If the current speed is higher than the preset threshold, the vehicle can be considered to be in motion at this moment and has not stopped. In this case, the zero bias error parameter bias is used directly. acc The initial acceleration of the accelerometer is subjected to low-pass filtering.

2. The method as described in claim 1, wherein, If it is determined that the vehicle is in a non-moving state at the current moment, the initial acceleration is updated using the zero-bias error parameter of the vehicle accelerometer, including: If it is determined that the vehicle is in a non-moving state at time K, the initial acceleration at time K is updated by the Kth zero bias error parameter of the vehicle accelerometer. If it is determined that the vehicle is in a non-moving state at the current time, the acceleration at the K+N time is adaptively updated using the K+Nth zero bias error parameter of the vehicle accelerometer, where K and N are integers.

3. The method as described in claim 2, wherein: The determination of the zero-bias error parameter of the vehicle accelerometer includes: The zero-bias error parameter of the accelerometer is calculated using the vehicle's velocity and acceleration at a standstill. And when it is determined that the vehicle is in a non-moving state at the current moment, the zero bias error parameter is updated in real time according to a preset formula.

4. The method as described in claim 1, wherein, The determination that the vehicle is in a non-moving state at the current moment includes: The system determines whether the vehicle is in a non-moving state at any given moment by checking whether its speed is below a preset threshold.

5. The method as described in claim 1, wherein, It also includes: using a low-pass filter to perform low-pass filtering on the updated initial acceleration.

6. The method of claim 1, wherein, Also includes: The acceleration in the vehicle accelerometer is smoothed.

7. A vehicle acceleration determining device, wherein, For use in autonomous vehicles, the device includes: The acquisition module is used to acquire the initial acceleration of the vehicle's accelerometer. Before the vehicle is powered on and started, and before the autonomous driving mode is activated, acquire speed and acceleration information for a set time period when the vehicle is stationary. The determination module is used to determine the zero-bias error parameters of the vehicle accelerometer; and When the vehicle is stationary, the accelerometer output during this time period can be set to the initial accelerometer zero-bias error parameter, denoted as bias. acc The zero bias error parameter of the vehicle accelerometer will be adaptively adjusted. The update module is used to update the initial acceleration by using the zero bias error parameter of the vehicle accelerometer when it is determined that the vehicle is in a non-moving state at the current moment. When the vehicle is not moving, the zero-bias error parameter of the vehicle accelerometer is updated at each moment corresponding to the non-moving state. If the current moment k is given, the bias can be calculated using the following formula. acc Update: bias acc =(bias) acc *ω+bias k ) / (ω+1), where ω is an integer multiple of 1; If the current speed is higher than the preset threshold, the vehicle can be considered to be in motion at this moment and has not stopped. In this case, the zero bias error parameter bias is used directly. acc The initial acceleration of the accelerometer is subjected to low-pass filtering.

8. An electronic device, comprising: processor; as well as A memory configured to store computer-executable instructions, which, when executed, cause the processor to perform the method of any one of claims 1 to 6.

9. A computer-readable storage medium storing one or more programs, which, when executed by an electronic device including a plurality of applications, cause the electronic device to perform the method of any one of claims 1 to 6.

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