WO2024087447A1 - 无人驾驶矿用车辆制动性能检测方法、装置、电子设备、存储介质及计算机程序产品 - Google Patents

无人驾驶矿用车辆制动性能检测方法、装置、电子设备、存储介质及计算机程序产品 Download PDF

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Publication number
WO2024087447A1
WO2024087447A1 PCT/CN2023/079494 CN2023079494W WO2024087447A1 WO 2024087447 A1 WO2024087447 A1 WO 2024087447A1 CN 2023079494 W CN2023079494 W CN 2023079494W WO 2024087447 A1 WO2024087447 A1 WO 2024087447A1
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WIPO (PCT)
Prior art keywords
target
deceleration
speed
braking
unmanned mining
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PCT/CN2023/079494
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English (en)
French (fr)
Inventor
周立岩
黄加勇
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北京易控智驾科技有限公司
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Publication of WO2024087447A1 publication Critical patent/WO2024087447A1/zh

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Classifications

    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60T—VEHICLE BRAKE CONTROL SYSTEMS OR PARTS THEREOF; BRAKE CONTROL SYSTEMS OR PARTS THEREOF, IN GENERAL; ARRANGEMENT OF BRAKING ELEMENTS ON VEHICLES IN GENERAL; PORTABLE DEVICES FOR PREVENTING UNWANTED MOVEMENT OF VEHICLES; VEHICLE MODIFICATIONS TO FACILITATE COOLING OF BRAKES
    • B60T17/00—Component parts, details, or accessories of power brake systems not covered by groups B60T8/00, B60T13/00 or B60T15/00, or presenting other characteristic features
    • B60T17/18—Safety devices; Monitoring
    • B60T17/22—Devices for monitoring or checking brake systems; Signal devices
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60R—VEHICLES, VEHICLE FITTINGS, OR VEHICLE PARTS, NOT OTHERWISE PROVIDED FOR
    • B60R16/00—Electric or fluid circuits specially adapted for vehicles and not otherwise provided for; Arrangement of elements of electric or fluid circuits specially adapted for vehicles and not otherwise provided for
    • B60R16/02—Electric or fluid circuits specially adapted for vehicles and not otherwise provided for; Arrangement of elements of electric or fluid circuits specially adapted for vehicles and not otherwise provided for electric constitutive elements
    • B60R16/023—Electric or fluid circuits specially adapted for vehicles and not otherwise provided for; Arrangement of elements of electric or fluid circuits specially adapted for vehicles and not otherwise provided for electric constitutive elements for transmission of signals between vehicle parts or subsystems
    • B60R16/0231—Circuits relating to the driving or the functioning of the vehicle
    • B60R16/0232—Circuits relating to the driving or the functioning of the vehicle for measuring vehicle parameters and indicating critical, abnormal or dangerous conditions

Definitions

  • the embodiments of the present disclosure are based on a Chinese patent application with application number 202211338783.5, application date October 28, 2022, and application name “Unmanned mining vehicle braking performance detection method, device, electronic device and storage medium”, and claim the priority of the Chinese patent application.
  • the entire content of the Chinese patent application is hereby introduced into the present disclosure as a reference.
  • the present disclosure relates to, but is not limited to, the field of unmanned driving technology, and in particular to a method, device, electronic equipment, storage medium and computer program product for detecting braking performance of an unmanned mining vehicle.
  • the electric control braking system is one of the most important wire-controlled components, playing an indispensable role in deceleration, obstacle avoidance, terminal parking, etc. If the braking system of an unmanned mining vehicle fails, it may not be able to decelerate and stop normally, which may lead to serious safety accidents.
  • the embodiments of the present disclosure provide a method and device for detecting the braking performance of an unmanned mining vehicle, an electronic device storage medium, and a computer program product.
  • the embodiment of the present disclosure provides a method for detecting the braking performance of an unmanned mining vehicle, comprising:
  • Acquire driving data of a target unmanned mining vehicle after receiving a braking instruction comprising a first moving distance of the target unmanned mining vehicle from a braking initial speed to a first speed, and a second moving distance of the target unmanned mining vehicle from the braking initial speed to a second speed, the first moving distance being less than the second moving distance;
  • the embodiment of the present disclosure also provides a braking performance detection device for an unmanned mining vehicle, comprising:
  • a driving data acquisition module used to acquire driving data of a target unmanned mining vehicle after receiving a braking instruction, wherein the driving data includes a first moving distance of the target unmanned mining vehicle from a braking initial speed to a first speed, and a second moving distance of the target unmanned mining vehicle from the braking initial speed to a second speed, wherein the first moving distance is less than the second moving distance;
  • a target deceleration acquisition module configured to obtain a target deceleration of the target unmanned mining vehicle on the braking route based on the first speed, the second speed, the first moving distance, and the second moving distance;
  • the performance index determination module is used to determine whether the target unmanned mining vehicle meets the target braking performance index based on the target deceleration.
  • An embodiment of the present disclosure also provides an electronic device, including: a processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the electronic device is running, the processor and the memory communicate via the bus, and when the machine-readable instructions are executed by the processor, the steps in the above-mentioned unmanned mining vehicle braking performance detection method are executed.
  • the embodiment of the present disclosure further provides a computer-readable storage medium having a computer program stored thereon.
  • the computer program is executed by a processor, the steps in the above-mentioned unmanned mining vehicle braking performance detection method are executed.
  • An embodiment of the present disclosure provides a computer program product, which includes a computer program or instructions.
  • the computer program or instructions When the computer program or instructions are run on an electronic device, the electronic device executes the steps in the above-mentioned unmanned mining vehicle braking performance detection method.
  • FIG1 is a flow chart of braking performance detection of an unmanned mining vehicle provided by an exemplary embodiment of the present disclosure
  • FIG2 is a flow chart of step S120 in FIG1 ;
  • FIG3 is a schematic block diagram of a functional module for detecting the braking performance of an unmanned mining vehicle provided by an exemplary embodiment of the present disclosure
  • FIG4 is a structural block diagram of an electronic device provided by an exemplary embodiment of the present disclosure.
  • FIG. 5 is a structural block diagram of a computer system provided by an exemplary embodiment of the present disclosure.
  • the embodiment of the present disclosure first provides a method for detecting the braking performance of an unmanned mining vehicle. As shown in FIG1 , the method may include the following steps:
  • step S110 the driving data of the target unmanned mining vehicle after receiving the braking instruction is obtained.
  • the driving data includes a first moving distance of the target unmanned mining vehicle from an initial braking speed to a first speed, and a second moving distance of the target unmanned mining vehicle from an initial braking speed to a second speed, and the first moving distance is smaller than the second moving distance.
  • a braking command is sent to the target unmanned mining vehicle through the console, and the initial braking speed of the target unmanned mining vehicle when receiving the braking command can be obtained.
  • the target unmanned mining vehicle starts braking until it stops, and the speed of the target unmanned mining vehicle will be reduced from the initial braking speed to the first speed, the second speed, etc., until the speed becomes zero.
  • the first speed and the second speed are two speeds between the initial braking speed and zero, and the two are not equal, and can be selected according to needs.
  • target unmanned mining vehicle in the embodiment may be a certain type of unmanned mining wide-body vehicle.
  • step S120 a target deceleration of the target unmanned mining vehicle is obtained based on the first speed, the second speed, the first moving distance, and the second moving distance.
  • the target unmanned mining vehicle is an unmanned mining wide-body vehicle as an example for explanation.
  • the performance of the braking system is specified based on the braking distance and braking deceleration.
  • the braking distance refers to the distance traveled from the start of the unmanned braking mining vehicle to the stop of the vehicle, but the braking distance is related to the initial braking speed, which refers to the speed when the unmanned driving wire control device begins to be braked.
  • the braking deceleration is used to characterize it.
  • the braking deceleration calculation uses the value of the braking intermediate speed segment to calculate, which largely avoids the error caused by the different initial braking conditions, and the braking deceleration is defined as the steady-state average deceleration.
  • the target deceleration of the target unmanned mining vehicle can be obtained by the following formula (1), where the target deceleration can be called the steady-state average deceleration.
  • a is the target deceleration
  • v b is the first speed
  • ve is the second speed
  • S b is the first moving distance
  • Se is the second moving distance
  • m is a constant, which can be taken as 25.92.
  • v b ⁇ v 0
  • ve ⁇ v 0
  • v 0 is the initial braking speed of the target unmanned mining vehicle
  • the value range of ⁇ is (n ⁇ 1)
  • the value range of ⁇ is (0 ⁇ n)
  • n is a positive number less than 1.
  • v 0 is in km/h
  • S b is the distance traveled during the process of the vehicle speed decreasing from v 0 to v b
  • Se is the distance traveled during the process of the vehicle speed decreasing from v 0 to v e
  • Se -S b is the moving distance of the target unmanned mining vehicle from v b to v e .
  • ⁇ generally takes a value range of (0.5 ⁇ 1)
  • ⁇ generally takes a value range of (0 ⁇ 0.5).
  • the process of obtaining the target deceleration of the target unmanned mining vehicle on the braking route based on the corresponding moving distances of the target unmanned mining vehicle in different driving speed sections on the braking route it can be implemented in one of the following ways: 1
  • the moving distance corresponding to any speed section can be obtained, and the deceleration corresponding to the speed section can be calculated by the above formula (1), and the deceleration can be used as the target deceleration of the target unmanned mining vehicle.
  • the deceleration corresponding to the speed section that is usually calculated more accurately can be selected as the target deceleration based on multiple tests.
  • the moving distance corresponding to each speed section can be obtained by the above formula (1), and multiple decelerations can be obtained, and the target deceleration of the target unmanned mining vehicle on the braking route can be determined based on the multiple decelerations.
  • step S130 it is determined whether the target unmanned mining vehicle meets the target braking performance index based on the target deceleration.
  • the deceleration of the target unmanned mining vehicle after the target deceleration of the target unmanned mining vehicle is obtained in the above manner, it is possible to judge whether the braking performance of the target unmanned mining vehicle meets the target braking performance index according to the target deceleration.
  • the deceleration of the target unmanned mining vehicle only needs to meet a preset range. If the deceleration of the target unmanned mining vehicle is too large, it will cause wear and tear on the target unmanned mining vehicle. If the deceleration of the target unmanned mining vehicle is too small, it will cause the target unmanned mining vehicle to be unable to brake in time, which may cause an accident.
  • the preset range can be set according to actual tests or empirical values.
  • the unmanned mining vehicle braking performance detection method obtains the driving data of the target unmanned mining vehicle after receiving the braking command, and the driving data includes the target unmanned mining vehicle from the initial braking speed to the first speed and the second speed and the corresponding first moving distance and the second moving distance respectively.
  • the target deceleration of the target unmanned mining vehicle and based on the target deceleration, determine whether the braking performance of the target unmanned mining vehicle meets the target braking performance index. In this way, the braking performance of the unmanned mining vehicle is evaluated by the target deceleration, so that maintenance measures can be taken in time when there is a problem with the braking performance of the target unmanned mining vehicle, thereby improving the driving safety of the unmanned mining vehicle.
  • step S120 may further include the following steps:
  • step S121 a plurality of different travel speeds of the target unmanned mining vehicle on the braking route are obtained.
  • step S122 multiple sets of braking data are obtained.
  • Each set of braking data includes moving distances corresponding to two different speeds among a plurality of different driving speeds.
  • step S123 based on the multiple sets of braking data, multiple sets of decelerations of the target unmanned mining vehicle on the braking route are obtained, and the target deceleration is determined based on the multiple sets of decelerations.
  • the corresponding moving distances of the target unmanned mining vehicle in different driving speed sections on the braking route are obtained, so that the corresponding moving distances of the target unmanned mining vehicle in multiple speed sections can be obtained.
  • Table 1 shows the corresponding moving distances of the target unmanned mining vehicle in different driving speed sections on the braking route.
  • V1 ⁇ V2 represents the corresponding moving distance S1 of the target unmanned mining vehicle when the speed drops from V1 to V2
  • Vb ⁇ Ve represents the corresponding moving distance Sx of the target unmanned mining vehicle when the speed drops from Vb to Ve .
  • Sx can also be represented by Se - Sb in combination with the description of the following formula (1), where Se represents the distance traveled during the process of the vehicle speed dropping from v0 to ve , sb is the distance traveled during the process of the vehicle speed dropping from v0 to vb , and v0 is the initial braking speed of the target unmanned mining vehicle.
  • multiple groups of decelerations of the target unmanned mining vehicle on the braking route can be obtained by the above formula (1), so that the target deceleration of the target unmanned mining vehicle on the braking route can be determined based on the multiple groups of decelerations.
  • the target deceleration can be obtained by averaging the multiple groups of decelerations, which can avoid the error that may exist in the deceleration obtained by only using the corresponding moving distance within a certain group of speed segments, and then the target deceleration obtained by obtaining the multiple groups of decelerations can improve the accuracy of the calculation, and then the target unmanned mining vehicle can be determined. Better evaluation of the vehicle's braking performance.
  • the method may further include the following steps:
  • test samples include driving data of a plurality of unmanned mining vehicles of the same model, and each unmanned mining vehicle corresponds to a plurality of sets of driving data.
  • the driving data of multiple unmanned mining vehicles of the same model are obtained by the above method, and each unmanned mining vehicle corresponds to multiple groups of driving data to obtain multiple groups of decelerations, and the first average deceleration is obtained by averaging the multiple groups of decelerations.
  • the multiple groups of decelerations are compared with the first average deceleration, for example, by calculating the absolute value of the difference between the two, the deviation values of the multiple groups of decelerations from the first average deceleration are obtained, and the abnormal decelerations with deviation values greater than the threshold are selected from the multiple groups of decelerations.
  • the number of the abnormal decelerations is less than the preset number, it means that the multiple groups of decelerations meet the requirements, and the first average deceleration obtained by averaging them can be used as the target deceleration of the target unmanned mining vehicle.
  • the target deceleration can be obtained by obtaining multiple groups of driving data of multiple vehicles. Therefore, the method can also include the following steps:
  • test samples include driving data of a plurality of unmanned mining vehicles of the same model, and each unmanned mining vehicle corresponds to a plurality of sets of driving data.
  • a plurality of test samples are obtained by the above method, including driving data of a plurality of unmanned mining vehicles of the same model, and each unmanned mining vehicle corresponds to a plurality of groups of driving data and a plurality of groups of deceleration corresponding to the plurality of groups of driving data.
  • the second average deceleration is obtained by averaging the multiple groups of decelerations.
  • the multiple groups of decelerations are compared with the second average deceleration, for example, by calculating the absolute value of the difference between the two, the deviation values of the multiple groups of decelerations from the second average deceleration are obtained, and the abnormal decelerations with deviation values greater than the threshold value are selected from the multiple groups of decelerations, and the abnormal decelerations are removed from the multiple groups of decelerations to obtain a deceleration set including multiple decelerations.
  • the decelerations with values that deviate too much from the second average deceleration are removed from the deceleration set, and the obtained target deceleration can be more consistent with the actual value, and then the accuracy of the detection can be improved when the braking performance of the target unmanned mining vehicle is detected by the obtained target deceleration to see whether it meets the target braking performance index.
  • the driving speed of the target unmanned mining vehicle can be monitored, and a braking command can be sent to the target unmanned mining vehicle when the driving speed of the target unmanned mining vehicle reaches a target speed.
  • the target speed is the maximum driving speed of the target unmanned mining vehicle.
  • the above-mentioned average deceleration in the steady state can be obtained by performing a real vehicle test on a sample vehicle, and the target deceleration of the target unmanned mining vehicle can be calculated by obtaining test data at the highest vehicle speed using an automatic control program.
  • the number of samples should be greater than 20; 2) The road slope, road flatness, and road softness of each group of tests should be kept consistent as much as possible; 3) The vehicle load of each group of tests should be kept consistent; 4) The braking system of each vehicle should function normally under manual driving; 5) The brake air pressure of the test vehicle should be sufficient; 6) Each vehicle should be tested in no less than 3 groups.
  • the vehicle speed value and vehicle positioning information of the entire process need to be recorded, where the vehicle positioning information is used to obtain the vehicle's driving distance in different speed ranges. According to the above formula (1), the steady-state average deceleration value of each vehicle is calculated as the target deceleration.
  • the statistical principle is adopted to calculate and process the sample data using the three indicators of mean value, median and standard deviation, that is, to remove the abnormal deceleration.
  • the data processing adopts the method of eliminating the special values in rounds under the constraint of the overall sample, and the specific method is as follows:
  • the method of eliminating outliers is to eliminate the value that deviates the farthest from the median in each round;
  • the steady-state average deceleration can be approximately regarded as a general normal distribution. If the number of samples is large enough, the steady-state average deceleration can be approximately regarded as a general normal distribution, that is, n ⁇ N( ⁇ , ⁇ ), where ⁇ is the standard deviation, and the confidence interval for the general normal distribution is [ ⁇ -z ⁇ , ⁇ +z ⁇ ], and z is a coefficient determined according to the confidence level, which is generally 1. Due to the insufficient number of samples, the Wilson confidence interval calculation method is used to correct the above confidence interval to obtain the final lower limit and interval value of the wire control braking consistency.
  • the embodiment of the present disclosure provides an unmanned mining vehicle braking performance detection device, which can be a server or a chip applied to a server.
  • FIG3 is a schematic block diagram of the functional modules of the unmanned mining vehicle braking performance detection device provided by an exemplary embodiment of the present disclosure. As shown in FIG3, the unmanned mining vehicle braking performance detection device includes:
  • the driving data acquisition module 10 is used to acquire driving data of the target unmanned mining vehicle after receiving the braking instruction, wherein the driving data includes a first moving distance of the target unmanned mining vehicle from the braking initial speed to the first speed, and a second moving distance of the target unmanned mining vehicle from the braking initial speed to the second speed, wherein the first moving distance is less than the second moving distance;
  • a target deceleration acquisition module 20 configured to obtain a target deceleration of the target unmanned mining vehicle on the braking route based on the first speed, the second speed, the first moving distance, and the second moving distance;
  • the performance index determination module 30 is used to determine whether the target unmanned mining vehicle meets the target braking performance index based on the target deceleration.
  • the target deceleration acquisition module includes:
  • a driving speed acquisition submodule used to acquire a plurality of different driving speeds of the target unmanned mining vehicle on a braking route
  • a data acquisition submodule used to obtain multiple sets of braking data, wherein each set of braking data includes movement distances corresponding to two different speeds among the multiple different driving speeds;
  • the deceleration acquisition module is used to obtain multiple groups of decelerations of the target unmanned mining vehicle on the braking route based on the multiple groups of braking data, and determine the target deceleration based on the multiple groups of decelerations.
  • the device further includes:
  • a first sample acquisition module is used to acquire a plurality of test samples, wherein the plurality of test samples include driving data of a plurality of unmanned mining vehicles of the same model, and each of the unmanned mining vehicles corresponds to a plurality of sets of driving data;
  • a first calculation module configured to obtain a plurality of groups of decelerations based on the plurality of test samples, and average the plurality of groups of decelerations to obtain a first average deceleration
  • a first quantity acquisition module configured to acquire the quantity of abnormal decelerations in the plurality of groups of decelerations, wherein a deviation value between the abnormal deceleration and the first average deceleration is greater than a threshold
  • the first target deceleration determination module is configured to use the first average deceleration as the target deceleration when the number of the abnormal decelerations is less than a preset number.
  • the deceleration acquisition module includes:
  • a second sample acquisition module is used to acquire multiple test samples, wherein the multiple test samples include driving data of multiple unmanned mining vehicles of the same model, and each of the unmanned mining vehicles corresponds to multiple sets of driving data;
  • a second calculation module which obtains a plurality of groups of decelerations based on the plurality of test samples, and averages the plurality of groups of decelerations to obtain a second average deceleration;
  • an abnormal deceleration acquisition module for acquiring an abnormal deceleration among the plurality of groups of decelerations, wherein a deviation value between the abnormal deceleration and the second average deceleration is greater than a threshold;
  • a deceleration set acquisition module used for removing the abnormal deceleration from the multiple groups of decelerations to obtain a deceleration set including multiple decelerations
  • the second target deceleration determination module averages the values in the deceleration set to obtain a third average deceleration, and uses the third average deceleration as the target deceleration.
  • the device further includes:
  • a monitoring module used to monitor the travel speed of the target unmanned mining vehicle
  • the instruction sending module is used to send a braking instruction to the target unmanned mining vehicle when the driving speed of the target unmanned mining vehicle reaches a target speed, and execute the step of obtaining the driving data of the target unmanned mining vehicle after receiving the braking instruction; the target speed is the maximum driving speed of the target unmanned mining vehicle.
  • the unmanned mining vehicle braking performance detection device obtains the driving data of the target unmanned mining vehicle after receiving the braking command, and the driving data includes the target unmanned mining vehicle from the initial braking speed to the first speed and the second speed and the corresponding first moving distance and second moving distance, to obtain the target deceleration of the target unmanned mining vehicle, and determines whether the braking performance of the target unmanned mining vehicle meets the target braking performance index based on the target deceleration. In this way, the braking performance of the unmanned mining vehicle is evaluated by the target deceleration, so that maintenance measures can be taken in time when there is a problem with the braking performance of the target unmanned mining vehicle, thereby improving the driving safety of the unmanned mining vehicle.
  • An embodiment of the present disclosure also provides an electronic device, comprising: at least one processor; a memory for storing instructions executable by the at least one processor; wherein the at least one processor is configured to execute the instructions to implement the above method disclosed in the embodiment of the present disclosure.
  • Fig. 4 is a schematic diagram of the structure of an electronic device provided by an exemplary embodiment of the present disclosure.
  • the electronic device 1800 includes at least one processor 1801 and a memory 1802 coupled to the processor 1801, and the processor 1801 can execute the corresponding steps in the above method disclosed in the embodiment of the present disclosure.
  • the processor 1801 may also be referred to as a central processing unit (CPU), which may be an integrated circuit chip having signal processing capabilities. Each step in the method disclosed in the embodiment of the present disclosure may be completed by an integrated logic circuit of hardware in the processor 1801 or by instructions in the form of software.
  • the processor 1801 may be a general-purpose processor, a digital signal processor (DSP), an ASIC, a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components.
  • DSP digital signal processor
  • FPGA field-programmable gate array
  • a general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.
  • the steps of the method disclosed in the embodiment of the present disclosure may be directly embodied as being executed by a hardware decoding processor, or may be executed by a combination of hardware and software modules in a decoding processor.
  • the software module may be located in a memory 1802, such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, or other mature storage media in the art.
  • the processor 1801 reads the information in the memory 1802 and completes the steps of the method in combination with its hardware.
  • FIG. 5 is a block diagram of a computer system provided by an exemplary embodiment of the present disclosure.
  • Computer system 1900 is intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers.
  • Electronic devices can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices.
  • the components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and/or claimed herein.
  • the computer system 1900 includes a computing unit 1901, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 1902 or a computer program loaded from a storage unit 1908 into a random access memory (RAM) 1903.
  • ROM read-only memory
  • RAM random access memory
  • various programs and data required for the operation of the computer system 1900 can also be stored.
  • the computing unit 1901, ROM 1902, and RAM 1903 are connected to each other via a bus 1904.
  • An input/output (I/O) interface 1905 is also connected to the bus 1904.
  • a plurality of components in the computer system 1900 are connected to the I/O interface 1905, including: an input unit 1906, an output unit 1907, a storage unit 1908, and a communication unit 1909.
  • the input unit 1906 may be any type of device capable of inputting information to the computer system 1900, and the input unit 1906 may receive input digital or character information, and generate key signal input related to user settings and/or function control of the electronic device.
  • the output unit 1907 may be Any type of device capable of presenting information, and may include, but is not limited to, a display, a speaker, a video/audio output terminal, a vibrator, and/or a printer.
  • Storage unit 1908 may include, but is not limited to, a magnetic disk, an optical disk.
  • Communication unit 1909 allows computer system 1900 to exchange information/data with other devices over a network such as the Internet, and may include, but is not limited to, a modem, a network card, an infrared communication device, a wireless communication transceiver and/or a chipset, such as a BluetoothTM device, a WiFi device, a WiMax device, a cellular communication device, and/or the like.
  • the computing unit 1901 may be a variety of general and/or special processing components with processing and computing capabilities. Some examples of the computing unit 1901 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc.
  • the computing unit 1901 performs the various methods and processes described above. For example, in some embodiments, the above methods disclosed in the embodiments of the present disclosure may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 1908.
  • part or all of the computer program may be loaded and/or installed on the electronic device 1900 via the ROM 1902 and/or the communication unit 1909.
  • the computing unit 1901 may be configured to perform the above methods disclosed in the embodiments of the present disclosure in any other appropriate manner (e.g., by means of firmware).
  • the embodiment of the present disclosure further provides a computer-readable storage medium, wherein when instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device is enabled to execute the above method disclosed in the embodiment of the present disclosure.
  • the computer-readable storage medium in the disclosed embodiments may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment.
  • the computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or equipment, or any suitable combination of the foregoing.
  • the computer-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
  • RAM random access memory
  • ROM read-only memory
  • EPROM or flash memory erasable programmable read-only memory
  • CD-ROM portable compact disk read-only memory
  • CD-ROM compact disk read-only memory
  • magnetic storage device or any suitable combination of the foregoing.
  • the computer-readable medium may be included in the electronic device, or may exist independently without being incorporated into the electronic device.
  • the embodiments of the present disclosure further provide a computer program product, including a computer program, wherein when the computer program is executed by a processor, the above method disclosed in the embodiments of the present disclosure is implemented.
  • the program for executing the program may be written in one or more programming languages or a combination thereof.
  • the computer program code for the operation of the present disclosure includes but is not limited to object-oriented programming languages, such as Java, Smalltalk, C++, and also includes conventional procedural programming languages, such as "C" language or similar programming languages.
  • the program code can be executed entirely on the user's computer, partially on the user's computer, as an independent software package, partially on the user's computer and partially on a remote computer, or completely on a remote computer or server.
  • the remote computer can be connected to the user's computer through any type of network (including a local area network (LAN) or a wide area network (WAN)), or it can be connected to an external computer.
  • LAN local area network
  • WAN wide area network
  • each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function.
  • the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved.
  • each square box in the block diagram and/or flow chart, and the combination of the square boxes in the block diagram and/or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.
  • modules, components or units involved in the embodiments described in the present disclosure may be implemented by software or hardware, wherein the names of the modules, components or units do not, in some cases, limit the modules, components or units themselves.
  • exemplary hardware logic components include, without limitation, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chip (SOCs), complex programmable logic devices (CPLDs), and the like.
  • FPGAs field programmable gate arrays
  • ASICs application specific integrated circuits
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Abstract

一种无人驾驶矿用车辆制动性能检测方法、装置、电子设备(1800)、存储介质及计算机程序产品，方法包括：S110、获取目标无人驾驶矿用车辆在接收到制动指令后的行驶数据，行驶数据包括目标无人驾驶矿用车辆从制动初始速度到第一速度之间行驶的第一移动距离，及目标无人驾驶矿用车辆从制动初始速度到第二速度之间行驶的第二移动距离；S120、基于行驶数据获得目标无人驾驶矿用车辆的目标减速度；S130、基于目标减速度确定目标无人驾驶矿用车辆是否满足目标制动性能指标。通过该目标减速度来对于无人驾驶矿用车辆的制动性能进行评估，以便在目标无人驾驶矿用车辆的制动性能出现问题时可以及时采取维修等措施，进而可以提高无人驾驶矿用车辆的行驶安全。

Description

无人驾驶矿用车辆制动性能检测方法、装置、电子设备、存储介质及计算机程序产品
相关申请的交叉引用
本公开实施例基于申请号为202211338783.5、申请日为2022年10月28日、申请名称为“无人驾驶矿用车辆制动性能检测方法、装置、电子设备及存储介质”的中国专利申请提出，并要求该中国专利申请的优先权，该中国专利申请的全部内容在此引入本公开作为参考。
技术领域
本公开涉及但不限于无人驾驶技术领域，尤其涉及一种无人驾驶矿用车辆制动性能检测方法、装置、电子设备、存储介质及计算机程序产品。
背景技术
对于无人驾驶矿用车辆而言，电控制动系统是极其重要的线控部件之一，在减速避障、终点停车等环节起到不可或缺的作用。如果无人驾驶的矿用车辆的制动系统出现故障，则可能导致无法正常的减速停车，进而可能导致造成严重的安全事故。
目前的矿用宽体车通常采用气压制动的方式制动，从制动原理上来讲，不同车辆的制动性能很难保证一致性，并且随着车辆使用时间的增长，车辆的制动性能也会出现不同程度的衰减。对于有人驾驶的车辆来说，可以通过司机的主观感受确定车辆的制动能力是否正常，但是对于无人驾驶矿用车辆来说，目前还没有明确的评测方法来对某一型号车辆的制动性能进行评估，以保证无人驾驶矿用车辆的行驶安全。
发明内容
本公开实施例提供一种无人驾驶矿用车辆制动性能检测方法、装置、电子设备存储介质及计算机程序产品。
本公开实施例提供了一种无人驾驶矿用车辆制动性能检测方法，包括：
获取目标无人驾驶矿用车辆在接收到制动指令后的行驶数据，所述行驶数据包括所述目标无人驾驶矿用车辆从制动初始速度到第一速度之间行驶的第一移动距离，及所述目标无人驾驶矿用车辆从所述制动初始速度到第二速度之间行驶的第二移动距离，所述第一移动距离小于所述第二移动距离；
基于所述第一速度、所述第二速度、所述第一移动距离和所述第二移动距离，获得所述目标无人驾驶矿用车辆的目标减速度；
基于所述目标减速度确定所述目标无人驾驶矿用车辆的是否满足目标制动性能指 标。
本公开实施例还提供了一种无人驾驶矿用车辆制动性能检测装置，包括：
行驶数据获取模块，用于获取目标无人驾驶矿用车辆在接收到制动指令后的行驶数据，所述行驶数据包括所述目标无人驾驶矿用车辆从制动初始速度到第一速度之间行驶的第一移动距离，及所述目标无人驾驶矿用车辆从所述制动初始速度到第二速度之间行驶的第二移动距离，所述第一移动距离小于所述第二移动距离；
目标减速度获取模块，用于基于所述第一速度、所述第二速度、所述第一移动距离和所述第二移动距离，获得所述目标无人驾驶矿用车辆在所述制动路线上的目标减速度；
性能指标确定模块，用于基于所述目标减速度确定所述目标无人驾驶矿用车辆的是否满足目标制动性能指标。
本公开实施例还提供一种电子设备，包括：处理器、存储器和总线，所述存储器存储有所述处理器可执行的机器可读指令，当电子设备运行时，所述处理器与所述存储器之间通过总线通信，所述机器可读指令被所述处理器执行时执行上述无人驾驶矿用车辆制动性能检测方法中的步骤。
本公开实施例还提供一种计算机可读存储介质，该计算机可读存储介质上存储有计算机程序，该计算机程序被处理器运行时执行上述无人驾驶矿用车辆制动性能检测方法中的步骤。
本公开实施例提供一种计算机程序产品，所述计算机程序产品包括计算机程序或指令，在所述计算机程序或指令在电子设备上运行的情况下，使得所述电子设备执行上述无人驾驶矿用车辆制动性能检测方法中的步骤。
关于上述无人驾驶矿用车辆制动性能检测装置、电子设备、计算机可读存储介质及计算机程序产品的效果描述参见上述无人驾驶矿用车辆制动性能检测方法的说明。
为使本公开的上述目的、特征和优点能更明显易懂，下文特举实施例，并配合所附附图，作详细说明如下。应当理解的是，以上的一般描述和后文的细节描述仅是示例性和解释性的，而非限制本公开。
附图说明
为了更清楚地说明本公开实施例的技术方案，下面将对实施例中所需要使用的附图作简单地介绍，此处的附图被并入说明书中并构成本说明书中的一部分，这些附图示出了符合本公开的实施例，并与说明书一起用于说明本公开的技术方案。应当理解，以下附图仅示出了本公开的某些实施例，因此不应被看作是对范围的限定，对于本领域普通 技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他相关的附图。
图1为本公开一示例性实施例提供的无人驾驶矿用车辆制动性能检测的流程图；
图2为图1中步骤S120的流程图；
图3为本公开一示例性实施例提供的无人驾驶矿用车辆制动性能检测的功能模块示意性框图；
图4为本公开一示例性实施例提供的电子设备的结构框图；
图5为本公开一示例性实施例提供的计算机系统的结构框图。
具体实施方式
下面将参照附图更详细地描述本公开的实施例。虽然附图中显示了本公开的某些实施例，然而应当理解的是，本公开可以通过各种形式来实现，而且不应该被解释为限于这里阐述的实施例，相反提供这些实施例是为了更加透彻和完整地理解本公开。应当理解的是，本公开的附图及实施例仅用于示例性作用，并非用于限制本公开的保护范围。
应当理解，本公开的方法实施方式中记载的各个步骤可以按照不同的顺序执行，和/或并行执行。此外，方法实施方式可以包括附加的步骤和/或省略执行示出的步骤。本公开的范围在此方面不受限制。
本文使用的术语“包括”及其变形是开放性包括，即“包括但不限于”。术语“基于”是“至少部分地基于”。术语“一个实施例”表示“至少一个实施例”；术语“另一实施例”表示“至少一个另外的实施例”；术语“一些实施例”表示“至少一些实施例”。其他术语的相关定义将在下文描述中给出。需要注意，本公开中提及的“第一”、“第二”等概念仅用于对不同的装置、模块或单元进行区分，并非用于限定这些装置、模块或单元所执行的功能的顺序或者相互依存关系。
需要注意，本公开中提及的“一个”、“多个”的修饰是示意性而非限制性的，本领域技术人员应当理解，除非在上下文另有明确指出，否则应该理解为“一个或多个”。
本公开实施方式中的多个装置之间所交互的消息或者信息的名称仅用于说明性的目的，而并不是用于对这些消息或信息的范围进行限制。
由于目前的矿用宽体车通常采用气压制动的方式，从制动原理上来讲，不同车辆的制动性能很难保证一致性，并且随着车辆使用时间的增加，宽体车的制动性能也会出现不同程度的衰减。而对于有人驾驶的车辆来说，可以通过司机的主观感受确定车辆的制动能力是否正常，但是对于无人驾驶矿用宽体车来说，目前还没有明确的评测方法来 对某一型号车辆的制动一致性进行评估。对于同一型号的车辆来说，需要明确单车功能正常时的制动能力边界，当某一车辆的制动能力低于一致性的能力边界后，证明该车辆的制动系统需要检修，不再适合继续运营，否则可能产生安全风险。因此如何确定能够表征线控制动性能一致性的能力边界指标就具有重要的意义。
因此，为了对无人驾驶矿用车辆制动性能进行检测，以提高无人驾驶矿用车辆的安全性，本公开实施例首先提供了一种无人驾驶矿用车辆制动性能检测方法，如图1所示，该方法可以包括如下步骤：
在步骤S110中，获取目标无人驾驶矿用车辆在接收到制动指令后的行驶数据。
其中，该行驶数据包括目标无人驾驶矿用车辆从制动初始速度到第一速度之间行驶的第一移动距离，及目标无人驾驶矿用车辆从制动初始速度到第二速度之间行驶的第二移动距离，第一移动距离小于第二移动距离。
实施例中，为了对目标无人驾驶矿用车辆的制动性能进行检测，在目标无人驾驶矿用车辆行驶过程中，通过控制台向该目标无人驾驶矿用车辆发送制动指令，可以获得目标无人驾驶矿用车辆在接收到制动指令时的制动初始速度，目标无人驾驶矿用车辆在接收到制动指令后开始制动，直到停止，目标无人驾驶矿用车辆的速度会从制动初始速度降低至第一速度、第二速度等，直到速度变为零。其中，第一速度和第二速度是制动初始速度和零之间的两个速度，二者不相等，可根据需要进行选取。
需要说明的是，实施例中的目标无人驾驶矿用车辆可以是某一型号的无人驾驶矿用宽体车。
在步骤S120中，基于第一速度、第二速度、第一移动距离和第二移动距离，获得目标无人驾驶矿用车辆的目标减速度。
实施例中，以目标无人驾驶矿用车辆为无人驾驶矿用宽体车辆为例进行说明，对于无人驾驶矿用宽体车辆而言，制动系统的性能是基于制动距离和制动减速度来规定的。一般情况下，制动距离是指从无人驾驶制动矿用车辆开始被制动至车辆停车所行驶的距离，但是制动距离和制动初始速度有关，制动初始速度是指无人驾驶线控装置开始被制动时的速度，在进行试验时无法精准地保证每一组测试的制动初始速度是完全一致的。因此采用制动减速度来表征，同时为了排除制动初期制动系统起作用的时间以及制动末期车速变化缓慢等因素的影响，该制动减速度计算采用制动中间车速段的数值进行计算，在很大程度上避免了以为制动初始条件的不同导致的误差，将该制动减速度定义为稳态平均减速度。
可以通过下述公式(1)来获取目标无人驾驶矿用车辆目标减速度，这里的目标减速度可以称为稳态平均减速度。
其中，a为目标减速度，vb为第一速度，ve为第二速度，Sb为第一移动距离，Se为第二移动距离，m为常数，可以取值为25.92。vb＝αv0，ve＝βv0；v0为目标无人驾驶矿用车辆的制动初始速度，α的取值范围为(n～1)，β的取值范围为(0～n)，n为小于1的正数。具体的，v0单位为km/h，Sb为车速从v0下降到vb过程中行驶的距离，Se为车速从v0下降到ve过程中行驶的距离，Se-Sb即为目标无人驾驶矿用车辆从vb下降到ve的移动距离。其中，α一般取值范围为(0.5～1)，β一般取值范围为(0～0.5)。
需要说明的是，在本公开提供的实施例中，在基于目标无人驾驶矿用车辆在制动路线上的不同行驶速度段内对应的移动距离，获得目标无人驾驶矿用车辆在制动路线上的目标减速度的过程中，可以包括以下方式之一实现，①可以获取任一速度段对应的移动距离，并通过上述公式(1)来计算出该速度段对应的减速度，并将该减速度作为目标无人驾驶矿用车辆的目标减速度。另外，实施例中还可以根据多次测试，选择通常计算比较准确的速度段对应的减速度作为目标减速度等等。②通过上述公式(1)方式获取各个速度段对应的移动距离，并得到多个减速度，并基于该多个减速度来确定目标无人驾驶矿用车辆在制动路线上的目标减速度。
在步骤S130中，基于目标减速度确定目标无人驾驶矿用车辆是否满足目标制动性能指标。
本公开实施例中，在通过上述方式获得目标无人驾驶矿用车辆的目标减速度后，就可以根据该目标减速度来判断目标无人驾驶矿用车辆的制动性能是否满足目标制动性能指标。一般而言，目标无人驾驶矿用车辆的减速度满足一个预设范围即可，目标无人驾驶矿用车辆的减速度太大会对目标无人驾驶矿用车辆造成磨损，如果目标无人驾驶矿用车辆的减速度太小又会导致目标无人驾驶矿用车辆无法及时制动，可能会导致事故的发生。其中，该预设范围可以根据实际的测试或者经验值等方式来设定。
本公开实施例提供的无人驾驶矿用车辆制动性能检测方法，通过获取目标无人驾驶矿用车辆在接收到制动指令后的行驶数据，该行驶数据包括目标无人驾驶矿用车辆从制动初始速度分别到第一速度和第二速度及分别对应的第一移动距离和第二移动距离， 来获得目标无人驾驶矿用车辆的目标减速度，并基于该目标减速度来确定目标无人驾驶矿用车辆的制动性能是否满足目标制动性能指标。这样通过该目标减速度来对于无人驾驶矿用车辆的制动性能进行评估，以便在目标无人驾驶矿用车辆的制动性能出现问题时可以及时采取维修等措施，进而可以提高无人驾驶矿用车辆的行驶安全。
基于上述实施例，在本公开提供的又一实施例中，如图2所示，上述步骤S120具体还可以包括如下步骤：
在步骤S121中，获取目标无人驾驶矿用车辆在制动路线上的多个不同的行驶速度。
在步骤S122中，获得多组制动数据。
其中，该每组制动数据包括多个不同的行驶速度中两个不同速度下对应的移动距离。
在步骤S123中，基于多组制动数据，获得目标无人驾驶矿用车辆在制动路线上的多组减速度，并基于多组减速度确定目标减速度。
在目标无人驾驶矿用车辆制动过程中，获取目标无人驾驶矿用车辆在制动路线上不同行驶速度段内对应的移动距离，这样可以获得多组速度段内目标无人驾驶矿用车辆对应的移动距离。
表1：
示例性的，如表1所示，表1为目标无人驾驶矿用车辆在制动路线上不同行驶速度段内对应的移动距离，例如，V1～V2表示目标无人驾驶矿用车辆在从速度V1下降到V2时对应的移动距离S1，Vb～Ve表示目标无人驾驶矿用车辆在从速度Vb下降到Ve时对应的移动距离Sx，这样可以得到目标无人驾驶矿用车辆在多组不同速度段下对应的移动距离。当然，还可以结合下述公式(1)的描述方式，通过Se-Sb来表示Sx，其中，Se表示车速从v0下降到ve过程中行驶的距离，sb为车速从v0下降到vb过程中行驶的距离，v0为目标无人驾驶矿用车辆的制动初始速度。
实施例中，可以通过上述公式(1)来获得目标无人驾驶矿用车辆在制动路线上的多组减速度，这样可以基于该多组减速度来确定目标无人驾驶矿用车辆在制动路线上的目标减速度。例如，可以对该多组减速度求平均等方式来获得目标减速度，可以避免因只通过某一组速度段内对应的移动距离来获得的减速度可能存在的误差，进而通过获得的多组减速度获得的目标减速度能够提高计算的准确性，进而可以对目标无人驾驶矿用 车辆的制动性能更好的评价。
基于上述实施例，在本公开的又一实施例中，可以通过获得多辆车辆的多个组行驶数据，以便可以更好的获得目标减速度，因此，该方法还可以包括以下步骤：
S11，获取多个测试样本，多个测试样本包括同一车型的多辆无人驾驶矿用车辆的行驶数据，且每辆无人驾驶矿用车辆对应有多组行驶数据。
S12，基于多个测试样本获得多组减速度，并对多组减速度求平均，得到第一平均减速度。
S13，获取多组减速度中异常减速度的数量。其中，该异常减速度与第一平均减速度之间的偏离值大于阈值。
S14，在异常减速度的数量小于预设数量时，并将第一平均减速度作为目标减速度。
本公开实施例中，在通过上述方式获得同一车型的多辆无人驾驶矿用车辆的行驶数据，且每辆无人驾驶矿用车辆对应有多组行驶数据得到的多组减速度，通过对该多组减速度求平均，得到第一平均减速度。实施例中将多组减速度分别与该第一平均减速度进行比较，例如通过计算二者差值的绝对值，得到多组减速度分别与第一平均减速度偏离值，从该多组减速度中选择出偏离值大于阈值的异常减速度，如果该异常减速度的数量小于预设数量，说明该多组减速度满足要求，可以将其求均值得到的第一平均减速度作为目标无人驾驶矿用车辆目标减速度。
基于上述实施例，在本公开的又一实施例中，可以通过获得多辆车辆的多个组行驶数据，进而来获得目标减速度，因此，该方法还可以包括以下步骤：
S21，获取多个测试样本，多个测试样本包括同一车型的多辆无人驾驶矿用车辆的行驶数据，且每辆无人驾驶矿用车辆对应有多组行驶数据。
S22，基于多个测试样本获得多组减速度，并对多组减速度求平均，得到第二平均减速度。
S23，获取多组减速度中的异常减速度。其中，该异常减速度与第二平均减速度之间的偏离值大于阈值。
S24，将异常减速度从多组减速度中去除，得到包括多个减速度的减速度集合。
S25，对减速度集合中的数值求平均，得到第三平均减速度，并将第三平均减速度作为目标减速度。
本公开实施例中通过上述方式获得多个测试样本包括同一车型的多辆无人驾驶矿用车辆的行驶数据，且每辆无人驾驶矿用车辆对应有多组行驶数据对应的多组减速 度，通过对该多组减速度求平均，得到第二平均减速度。实施例中将多组减速度分别与该第二平均减速度进行比较，例如通过计算二者差值的绝对值，得到多组减速度分别与第二平均减速度偏离值，从该多组减速度中选择出偏离值大于阈值的异常减速度，并将该异常减速度从该多组减速度中去除，来得到包括多个减速度的减速度集合。这样将数值偏离第二平均减速度太大的减速度从减速度集合中去除，得到的目标减速度可以更加符合实际数值，进而通过得到的目标减速度来检测目标无人驾驶矿用车辆的制动性能是否满足目标制动性能指标时，可以提高检测的准确性。
实施例中，可以通过监测目标无人驾驶矿用车辆的行驶速度，并在目标无人驾驶矿用车辆的行驶速度达到目标速度时，向目标无人驾驶矿用车辆发送制动指令。其中，目标速度为目标无人驾驶矿用车辆的最大行驶速度。例如，可以通过对样本车辆进行实车测试来获得上述稳态的平均减速度，确定采用自动控制程序实现最高车速下获得测试数据来计算目标无人驾驶矿用车辆的目标减速度。
在进行样本获取的数据采集过程中，可以遵循以下原则：1)样本数量应大于20；2)每组测试的路面坡度，路面平整度，路面松软程度尽量保持一致；3)每组测试的车辆载重保持一致；4)每个车辆在人工驾驶下制动系统功能正常；5)测试车辆制动气压保证充足；6)每个车辆的测试不少于3组。测试过程中需要记录整个过程中的车速值以及车辆定位信息，其中车辆定位信息是为了获得车辆在不同速度段内的行驶距离。根据上述公式(1)计算获得每个车辆的稳态平均减速度值作为目标减速度。
实施例中，在上述过程中，采用统计学原理，采用平均值，中位数及标准差三个指标对样本数据进行计算处理，即将异常减速度去除。数据处理采用满足整体样本约束下的逐轮淘汰特异值的方法，具体方式如下：
1)明确整体样本约束为平均值偏离中位数不超过10％，标准差不大于σm；
2)明确淘汰特异值的方法为每轮剔除偏离中位数最远的一个值；
3)按照2)的要求逐轮淘汰特异值样本，直到所有样本满足条件1)。
4)取满足条件的样本的平均值作为参考的平均发出的平均减速度μ。
如果样本数量足够多，稳态平均减速度近似堪称一般正态分布如果样本数量足够多，稳态平均减速度近似看成一般正态分布，即n～N(μ，σ)，其中σ为标准差，对于一般正态分布的置信区间为[μ-zσ，μ+zσ]，z为根据置信度确定的系数，一般取1。由于样本数量不够，因此采用威尔逊置信区间的计算方式对上述置信区间进行修正获得最终的线控制动一致性下限及区间值。
在采用对应各个功能划分各个功能模块的情况下，本公开实施例提供了一种无人驾驶矿用车辆制动性能检测装置，该无人驾驶矿用车辆制动性能检测装置可以为服务器或应用于服务器的芯片。图3为本公开一示例性实施例提供的无人驾驶矿用车辆制动性能检测装置的功能模块示意性框图。如图3所示，该无人驾驶矿用车辆制动性能检测装置包括：
行驶数据获取模块10，用于获取目标无人驾驶矿用车辆在接收到制动指令后的行驶数据，所述行驶数据包括所述目标无人驾驶矿用车辆从制动初始速度到第一速度之间行驶的第一移动距离，及所述目标无人驾驶矿用车辆从所述制动初始速度到第二速度之间行驶的第二移动距离，所述第一移动距离小于所述第二移动距离；
目标减速度获取模块20，用于基于所述第一速度、所述第二速度、所述第一移动距离和所述第二移动距离，获得所述目标无人驾驶矿用车辆在所述制动路线上的目标减速度；
性能指标确定模块30，用于基于所述目标减速度确定所述目标无人驾驶矿用车辆的是否满足目标制动性能指标。
在本公开提供的又一实施例中，所述目标减速度获取模块，包括：
行驶速度获取子模块，用于获取所述目标无人驾驶矿用车辆在制动路线上的多个不同的行驶速度；
数据获取子模块，用于获得多组制动数据，其中，每组制动数据包括所述多个不同的行驶速度中两个不同速度下对应的移动距离；
减速度获取模块，用于基于所述多组制动数据，获得所述目标无人驾驶矿用车辆在所述制动路线上的多组减速度，并基于所述多组减速度确定所述目标减速度。
在本公开提供的又一实施例中，所述装置还包括：
第一样本获取模块，用于获取多个测试样本，所述多个测试样本包括同一车型的多辆无人驾驶矿用车辆的行驶数据，且每辆所述无人驾驶矿用车辆对应有多组行驶数据；
第一计算模块，用于基于所述多个测试样本获得多组减速度，并对所述多组减速度求平均，得到第一平均减速度；
第一数量获取模块，用于获取所述多组减速度中异常减速度的数量，所述异常减速度与所述第一平均减速度之间的偏离值大于阈值；
第一目标减速度确定模块，用于在所述异常减速度的数量小于预设数量时，将所述第一平均减速度作为所述目标减速度。
在本公开提供的又一实施例中，所述减速度获取模块，包括：
第二样本获取模块，用于获取多个测试样本，所述多个测试样本包括同一车型的多辆无人驾驶矿用车辆的行驶数据，且每辆所述无人驾驶矿用车辆对应有多组行驶数据；
第二计算模块，基于所述多个测试样本获得多组减速度，并对所述多组减速度求平均，得到第二平均减速度；
异常减速度获取模块，获取所述多组减速度中的异常减速度，所述异常减速度与所述第二平均减速度之间的偏离值大于阈值；
减速度集合获取模块，用于将所述异常减速度从所述多组减速度中去除，得到包括多个减速度的减速度集合；
第二目标减速度确定模块，对所述减速度集合中的数值求平均，得到第三平均减速度，并将所述第三平均减速度作为目标减速度。
在本公开提供的又一实施例中，所述装置还包括：
监测模块，用于监测所述目标无人驾驶矿用车辆的行驶速度；
指令发送模块，用于在所述目标无人驾驶矿用车辆的行驶速度达到目标速度时，向所述目标无人驾驶矿用车辆发送制动指令，并执行所述获取目标无人驾驶矿用车辆在接收到制动指令后的行驶数据的步骤；所述目标速度为所述目标无人驾驶矿用车辆的最大行驶速度。
本公开实施例提供的无人驾驶矿用车辆制动性能检测装置，通过获取目标无人驾驶矿用车辆在接收到制动指令后的行驶数据，该行驶数据包括目标无人驾驶矿用车辆从制动初始速度分别到第一速度和第二速度及分别对应的第一移动距离和第二移动距离，来获得目标无人驾驶矿用车辆的目标减速度，并基于该目标减速度来确定目标无人驾驶矿用车辆的制动性能是否满足目标制动性能指标。这样通过该目标减速度来对于无人驾驶矿用车辆的制动性能进行评估，以便在目标无人驾驶矿用车辆的制动性能出现问题时可以及时采取维修等措施，进而可以提高无人驾驶矿用车辆的行驶安全。
本公开实施例还提供一种电子设备，包括：至少一个处理器；用于存储所述至少一个处理器可执行指令的存储器；其中，所述至少一个处理器被配置为执行所述指令，以实现本公开实施例公开的上述方法。
图4为本公开一示例性实施例提供的电子设备的结构示意图。如图4所示，该电子设备1800包括至少一个处理器1801以及耦接至处理器1801的存储器1802，该处理器1801可以执行本公开实施例公开的上述方法中的相应步骤。
上述处理器1801还可以称为中央处理单元(central processing unit，CPU)，其可以是一种集成电路芯片，具有信号的处理能力。本公开实施例公开的上述方法中的各步骤可以通过处理器1801中的硬件的集成逻辑电路或者软件形式的指令完成。上述的处理器1801可以是通用处理器、数字信号处理器(digital signal processing，DSP)、ASIC、现成可编程门阵列(field-programmable gate array，FPGA)或者其他可编程逻辑器件、分立门或者晶体管逻辑器件、分立硬件组件。通用处理器可以是微处理器或者该处理器也可以是任何常规的处理器等。结合本公开实施例所公开的方法的步骤可以直接体现为硬件译码处理器执行完成，或者用译码处理器中的硬件及软件模块组合执行完成。软件模块可以位于存储器1802中，例如随机存储器，闪存、只读存储器，可编程只读存储器或者电可擦写可编程存储器、寄存器等本领域成熟的存储介质。处理器1801读取存储器1802中的信息，结合其硬件完成上述方法的步骤。
另外，根据本公开的各种操作/处理在通过软件和/或固件实现的情况下，可从存储介质或网络向具有专用硬件结构的计算机系统，例如图5所示的计算机系统1900安装构成该软件的程序，该计算机系统在安装有各种程序时，能够执行各种功能，包括诸如前文所述的功能等等。图5为本公开一示例性实施例提供的计算机系统的结构框图。
计算机系统1900旨在表示各种形式的数字电子的计算机设备，诸如，膝上型计算机、台式计算机、工作台、个人数字助理、服务器、刀片式服务器、大型计算机、和其它适合的计算机。电子设备还可以表示各种形式的移动装置，诸如，个人数字处理、蜂窝电话、智能电话、可穿戴设备和其它类似的计算装置。本文所示的部件、它们的连接和关系、以及它们的功能仅仅作为示例，并且不意在限制本文中描述的和/或者要求的本公开的实现。
如图5所示，计算机系统1900包括计算单元1901，该计算单元1901可以根据存储在只读存储器(ROM)1902中的计算机程序或者从存储单元1908加载到随机存取存储器(RAM)1903中的计算机程序，来执行各种适当的动作和处理。在RAM 1903中，还可存储计算机系统1900操作所需的各种程序和数据。计算单元1901、ROM 1902以及RAM 1903通过总线1904彼此相连。输入/输出(I/O)接口1905也连接至总线1904。
计算机系统1900中的多个部件连接至I/O接口1905，包括：输入单元1906、输出单元1907、存储单元1908以及通信单元1909。输入单元1906可以是能向计算机系统1900输入信息的任何类型的设备，输入单元1906可以接收输入的数字或字符信息，以及产生与电子设备的用户设置和/或功能控制有关的键信号输入。输出单元1907可以是 能呈现信息的任何类型的设备，并且可以包括但不限于显示器、扬声器、视频/音频输出终端、振动器和/或打印机。存储单元1908可以包括但不限于磁盘、光盘。通信单元1909允许计算机系统1900通过网络诸如因特网的与其他设备交换信息/数据，并且可以包括但不限于调制解调器、网卡、红外通信设备、无线通信收发机和/或芯片组，例如蓝牙TM设备、WiFi设备、WiMax设备、蜂窝通信设备和/或类似物。
计算单元1901可以是各种具有处理和计算能力的通用和/或专用处理组件。计算单元1901的一些示例包括但不限于中央处理单元(CPU)、图形处理单元(GPU)、各种专用的人工智能(AI)计算芯片、各种运行机器学习模型算法的计算单元、数字信号处理器(DSP)、以及任何适当的处理器、控制器、微控制器等。计算单元1901执行上文所描述的各个方法和处理。例如，在一些实施例中，本公开实施例公开的上述方法可被实现为计算机软件程序，其被有形地包含于机器可读介质，例如存储单元1908。在一些实施例中，计算机程序的部分或者全部可以经由ROM 1902和/或通信单元1909而被载入和/或安装到电子设备1900上。在一些实施例中，计算单元1901可以通过其他任何适当的方式(例如，借助于固件)而被配置为执行本公开实施例公开的上述方法。
本公开实施例还提供一种计算机可读存储介质，其中，当所述计算机可读存储介质中的指令由电子设备的处理器执行时，使得所述电子设备能够执行本公开实施例公开的上述方法。
本公开实施例中的计算机可读存储介质可以是有形的介质，其可以包含或存储以供指令执行系统、装置或设备使用或与指令执行系统、装置或设备结合地使用的程序。上述计算机可读存储介质可以包括但不限于电子的、磁性的、光学的、电磁的、红外的、或半导体系统、装置或设备，或者上述内容的任何合适组合。更具体的，上述计算机可读存储介质可以包括基于一个或多个线的电气连接、便携式计算机盘、硬盘、随机存取存储器(RAM)、只读存储器(ROM)、可擦除可编程只读存储器(EPROM或快闪存储器)、光纤、便捷式紧凑盘只读存储器(CD-ROM)、光学储存设备、磁储存设备、或上述内容的任何合适组合。
上述计算机可读介质可以是上述电子设备中所包含的；也可以是单独存在，而未装配入该电子设备中。
本公开实施例还提供一种计算机程序产品，包括计算机程序，其中，所述计算机程序被处理器执行时实现本公开实施例公开的上述方法。
在本公开的实施例中，可以以一种或多种程序设计语言或其组合来编写用于执行 本公开的操作的计算机程序代码，上述程序设计语言包括但不限于面向对象的程序设计语言，诸如Java、Smalltalk、C++，还包括常规的过程式程序设计语言，诸如“C”语言或类似的程序设计语言。程序代码可以完全地在用户计算机上执行、部分地在用户计算机上执行、作为一个独立的软件包执行、部分在用户计算机上部分在远程计算机上执行、或者完全在远程计算机或服务器上执行。在涉及远程计算机的情形中，远程计算机可以通过任意种类的网络(包括局域网(LAN)或广域网(WAN))连接到用户计算机，或者，可以连接到外部计算机。
附图中的流程图和框图，图示了按照本公开各种实施例的系统、方法和计算机程序产品的可能实现的体系架构、功能和操作。在这点上，流程图或框图中的每个方框可以代表一个模块、程序段、或代码的一部分，该模块、程序段、或代码的一部分包含一个或多个用于实现规定的逻辑功能的可执行指令。也应当注意，在有些作为替换的实现中，方框中所标注的功能也可以以不同于附图中所标注的顺序发生。例如，两个接连地表示的方框实际上可以基本并行地执行，它们有时也可以按相反的顺序执行，这依所涉及的功能而定。也要注意的是，框图和/或流程图中的每个方框、以及框图和/或流程图中的方框的组合，可以用执行规定的功能或操作的专用的基于硬件的系统来实现，或者可以用专用硬件与计算机指令的组合来实现。
描述于本公开实施例中所涉及到的模块、部件或单元可以通过软件的方式实现，也可以通过硬件的方式来实现。其中，模块、部件或单元的名称在某种情况下并不构成对该模块、部件或单元本身的限定。
本文中以上描述的功能可以至少部分地由一个或多个硬件逻辑部件来执行。例如，非限制性地，可以使用的示例性的硬件逻辑部件包括：现场可编程门阵列(FPGA)、专用集成电路(ASIC)、专用标准产品(ASSP)、片上系统(SOC)、复杂可编程逻辑设备(CPLD)等等。
以上描述仅为本公开的一些实施例以及对所运用技术原理的说明。本领域技术人员应当理解，本公开中所涉及的公开范围，并不限于上述技术特征的特定组合而成的技术方案，同时也应涵盖在不脱离上述公开构思的情况下，由上述技术特征或其等同特征进行任意组合而形成的其它技术方案。例如上述特征与本公开中公开的(但不限于)具有类似功能的技术特征进行互相替换而形成的技术方案。
虽然已经通过示例对本公开的一些特定实施例进行了详细说明，但是本领域的技术人员应该理解，以上示例仅是为了进行说明，而不是为了限制本公开的范围。本领域的 技术人员应该理解，可在不脱离本公开的范围和精神的情况下，对以上实施例进行修改。本公开的范围由所附权利要求来限定。

Claims (11)

  1. 一种无人驾驶矿用车辆制动性能检测方法，包括：
    获取目标无人驾驶矿用车辆在接收到制动指令后的行驶数据，所述行驶数据包括所述目标无人驾驶矿用车辆从制动初始速度到第一速度之间行驶的第一移动距离，及所述目标无人驾驶矿用车辆从所述制动初始速度到第二速度之间行驶的第二移动距离，所述第一移动距离小于所述第二移动距离；
    基于所述第一速度、所述第二速度、所述第一移动距离和所述第二移动距离，获得所述目标无人驾驶矿用车辆的目标减速度；
    基于所述目标减速度确定所述目标无人驾驶矿用车辆是否满足目标制动性能指标。
  2. 根据权利要求1所述的方法，其中，所述获得所述目标无人驾驶矿用车辆的目标减速度，包括：
    通过下述方式获得所述目标减速度：
    其中，a为所述目标减速度，vb为所述第一速度，ve为所述第二速度，Sb为所述第一移动距离，Se为所述第二移动距离，m为常数。
  3. 根据权利要求2所述的方法，其中，所述第一速度和所述第二速度满足以下关系：
    vb＝αv0，ve＝βv0；
    其中，v0为所述制动初始速度，α的取值范围为(n～1)，β的取值范围为(0～n)，n为小于1的正数。
  4. 根据权利要求1所述的方法，其中，所述获得所述目标无人驾驶矿用车辆的目标减速度，包括：
    获取所述目标无人驾驶矿用车辆在制动路线上的多个不同的行驶速度；
    获得多组制动数据，其中，每组制动数据包括所述多个不同的行驶速度中两个不同速度下对应的移动距离；
    基于所述多组制动数据，获得所述目标无人驾驶矿用车辆在所述制动路线上的多组减速度，并基于所述多组减速度确定所述目标减速度。
  5. 根据权利要求1所述的方法，其中，所述方法还包括：
    获取多个测试样本，所述多个测试样本包括同一车型的多辆无人驾驶矿用车辆的行驶数据，且每辆所述无人驾驶矿用车辆对应有多组行驶数据；
    基于所述多个测试样本获得多组减速度，并对所述多组减速度求平均，得到第一平均减速度；
    获取所述多组减速度中异常减速度的数量，所述异常减速度与所述第一平均减速度之间的偏离值大于阈值；
    在所述异常减速度的数量小于预设数量时，将所述第一平均减速度作为所述目标减速度。
  6. 根据权利要求1所述的方法，其中，所述方法还包括：
    获取多个测试样本，所述多个测试样本包括同一车型的多辆无人驾驶矿用车辆的行驶数据，且每辆所述无人驾驶矿用车辆对应有多组行驶数据；
    基于所述多个测试样本获得多组减速度，并对所述多组减速度求平均，得到第二平均减速度；
    获取所述多组减速度中的异常减速度，所述异常减速度与所述第二平均减速度之间的偏离值大于阈值；
    将所述异常减速度从所述多组减速度中去除，得到包括多个减速度的减速度集合；
    对所述减速度集合中的数值求平均，得到第三平均减速度，并将所述第三平均减速度作为目标减速度。
  7. 根据权利要求1～6任一所述的方法，其中，所述方法还包括：
    监测所述目标无人驾驶矿用车辆的行驶速度；
    在所述目标无人驾驶矿用车辆的行驶速度达到目标速度时，向所述目标无人驾驶矿用车辆发送制动指令，并执行所述获取目标无人驾驶矿用车辆在接收到制动指令后的行驶数据的步骤；所述目标速度为所述目标无人驾驶矿用车辆的最大行驶速度。
  8. 一种无人驾驶矿用车辆制动性能检测装置，包括：
    行驶数据获取模块，用于获取目标无人驾驶矿用车辆在接收到制动指令后的行驶数据，所述行驶数据包括所述目标无人驾驶矿用车辆从制动初始速度到第一速度之间行驶的第一移动距离，及所述目标无人驾驶矿用车辆从所述制动初始速度到第二速度之间行驶的第二移动距离，所述第一移动距离小于所述第二移动距离；
    目标减速度获取模块，用于基于所述第一速度、所述第二速度、所述第一移动距离和所述第二移动距离，获得所述目标无人驾驶矿用车辆在所述制动路线上的目标减速度；
    性能指标确定模块，用于基于所述目标减速度确定所述目标无人驾驶矿用车辆的 是否满足目标制动性能指标。
  9. 一种电子设备，包括：
    至少一个处理器；
    用于存储所述至少一个处理器可执行指令的存储器；
    其中，所述至少一个处理器被配置为执行所述指令，以实现如权利要求1-7中任一项所述的方法。
  10. 一种计算机可读存储介质，当所述计算机可读存储介质中的指令由电子设备的处理器执行时，使得所述电子设备能够执行如权利要求1-7中任一项所述的方法。
  11. 一种计算机程序产品，所述计算机程序产品包括计算机程序或指令，在所述计算机程序或指令在电子设备上运行的情况下，使得所述电子设备执行权利要求1至7中任一项所述的方法。
PCT/CN2023/079494 2022-10-28 2023-03-03 无人驾驶矿用车辆制动性能检测方法、装置、电子设备、存储介质及计算机程序产品 WO2024087447A1 (zh)

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