WO2022056899A1 - 车辆测速装置的故障诊断方法和故障诊断装置 - Google Patents

车辆测速装置的故障诊断方法和故障诊断装置 Download PDF

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Publication number
WO2022056899A1
WO2022056899A1 PCT/CN2020/116411 CN2020116411W WO2022056899A1 WO 2022056899 A1 WO2022056899 A1 WO 2022056899A1 CN 2020116411 W CN2020116411 W CN 2020116411W WO 2022056899 A1 WO2022056899 A1 WO 2022056899A1
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WO
WIPO (PCT)
Prior art keywords
vehicle
speed
vehicle speed
reference object
fault diagnosis
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PCT/CN2020/116411
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English (en)
French (fr)
Inventor
吴自贤
李帅飞
周勇有
Original Assignee
华为技术有限公司
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 华为技术有限公司 filed Critical 华为技术有限公司
Priority to CN202080004603.2A priority Critical patent/CN112638738B/zh
Priority to PCT/CN2020/116411 priority patent/WO2022056899A1/zh
Priority to EP20953757.0A priority patent/EP4201777A4/en
Publication of WO2022056899A1 publication Critical patent/WO2022056899A1/zh
Priority to US18/186,390 priority patent/US20230227052A1/en

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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B60W50/02—Ensuring safety in case of control system failures, e.g. by diagnosing, circumventing or fixing failures
    • B60W50/0205—Diagnosing or detecting failures; Failure detection models
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/10—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to vehicle motion
    • B60W40/105—Speed
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01P—MEASURING LINEAR OR ANGULAR SPEED, ACCELERATION, DECELERATION, OR SHOCK; INDICATING PRESENCE, ABSENCE, OR DIRECTION, OF MOVEMENT
    • G01P21/00—Testing or calibrating of apparatus or devices covered by the preceding groups
    • G01P21/02—Testing or calibrating of apparatus or devices covered by the preceding groups of speedometers
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B60W50/02—Ensuring safety in case of control system failures, e.g. by diagnosing, circumventing or fixing failures
    • B60W50/0205—Diagnosing or detecting failures; Failure detection models
    • B60W2050/021—Means for detecting failure or malfunction
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B60W50/02—Ensuring safety in case of control system failures, e.g. by diagnosing, circumventing or fixing failures
    • B60W50/0205—Diagnosing or detecting failures; Failure detection models
    • B60W2050/0215—Sensor drifts or sensor failures
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2520/00—Input parameters relating to overall vehicle dynamics
    • B60W2520/10—Longitudinal speed
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2554/00—Input parameters relating to objects
    • B60W2554/20—Static objects
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2554/00—Input parameters relating to objects
    • B60W2554/80—Spatial relation or speed relative to objects

Definitions

  • the present application relates to the technical field of vehicles, and in particular, to a fault diagnosis method and device for a vehicle speed measuring device.
  • Vehicle speed information is an important information for vehicle driving control.
  • Various electronic control devices of automobiles such as electronic stability program (ESP), anti-lock braking system (ABS), traction control system (traction control system, TCS), vehicle navigation, etc., all need to use the vehicle speed information to generate control signals, and if the speed measurement device (such as a vehicle speed sensor) causes the vehicle speed measurement to be inaccurate, it will lead to errors in the control signal and also bring about Serious safety issues, so it is important to determine if the tachometer is faulty.
  • ESP electronic stability program
  • ABS anti-lock braking system
  • TCS traction control system
  • vehicle navigation etc.
  • the common diagnostic methods of speed measuring devices are to use the vehicle power information (which can be understood as information related to vehicle motion, or can be understood as information that affects vehicle speed) other than vehicle speed information as the basis for diagnosis, so as to determine whether the vehicle speed is accurate or not. , and then determine whether the speed measuring device is working properly.
  • vehicle power information which can be understood as information related to vehicle motion, or can be understood as information that affects vehicle speed
  • vehicle speed information other than vehicle speed information
  • the common diagnostic methods of speed measuring devices are to use the vehicle power information (which can be understood as information related to vehicle motion, or can be understood as information that affects vehicle speed) other than vehicle speed information as the basis for diagnosis, so as to determine whether the vehicle speed is accurate or not. , and then determine whether the speed measuring device is working properly.
  • the electrical fault diagnosis method when an electrical fault is detected, it can be determined that the initial speed sensor is not working properly, but this method can only detect connection faults such as open circuit or short circuit.
  • the speedometer was faulty, but this way it was impossible to diagnose when the vehicle was climbing a hill.
  • the method of diagnosing based on the wheel speed is equivalent to inferring a vehicle speed based on the wheel speed and comparing it with the speed measured by the speed measuring device. If it is inconsistent, it is considered that the speed measuring device is faulty. It is impossible to diagnose at the time of death.
  • the present application provides a fault diagnosis method and device for a vehicle speed measuring device, which has a wide range of diagnosis and can more accurately diagnose the fault of the speed measuring device.
  • a first aspect provides a fault diagnosis method for a vehicle speed measuring device, the method comprising: obtaining a first vehicle speed measured by the speed measuring device; obtaining reference information of N times of a static reference object, where N is an integer greater than 1, and the reference information Including the information of the positional relationship of the static reference object relative to the vehicle where the speed measuring device is located at each of the N moments; calculating the second vehicle speed according to the reference information; judging whether the speed measuring device is faulty according to the first vehicle speed and the second vehicle speed.
  • the vehicle speed is estimated by using a reference object other than the vehicle, and then compared with the vehicle speed measured by the speed measuring device, so as to determine whether the speed measuring device is faulty.
  • This solution does not need to rely on the vehicle's own vehicle power information. It will not be limited by the coverage of each kind of self-vehicle power information, so it can have a wider diagnostic range, so that the fault of the speed measuring device can be diagnosed more accurately.
  • the second vehicle speed is used to represent the estimated vehicle speed, or it can be understood as the calculated vehicle speed, and can also be understood as the vehicle speed measured by a non-speed measuring device.
  • the N times may be continuous times or discontinuous times, as long as they are close to the times corresponding to the first vehicle speed.
  • the time corresponding to the first vehicle speed may be selected as N time times with a time length within a certain threshold range. That is to say, assuming that the first vehicle speed is measured at time T1 and the time length threshold range is ⁇ t, N times within the range of [T1- ⁇ t, T1+ ⁇ t] can be selected. It should be understood that the N moments do not necessarily include T1. For example, N moments from T1+1 to T1+N may be selected. It should also be understood that the N moments may be continuous or discontinuous, and there is no limitation.
  • the static reference object may be directly set as a road sign or a traffic facility, so that once the road sign or traffic facility is identified, each step of fault diagnosis is started.
  • a static object can be selected from the recognized objects, and a static reference object can be selected from the static objects.
  • the fault diagnosis method before acquiring the reference information of the static reference object, the fault diagnosis method further includes: using a sensing device of the vehicle to identify objects around the vehicle; Select the static reference object in the .
  • the sensing device may include a camera or radar.
  • the following method when calculating the second vehicle speed, the following method may also be used: using the reference information at N times, that is, the static reference objects at N times relative to the vehicle At least one estimated vehicle speed is obtained by calculation, and the estimated vehicle speed is the average vehicle speed between any two of the N moments; the at least one estimated vehicle speed is processed to obtain the second vehicle speed.
  • the average velocity between the two moments is calculated.
  • the average speed between the two moments is calculated.
  • the following operations may be performed:
  • the displacement of the vehicle relative to the static reference object between the two moments is calculated
  • the average velocity between the two moments is calculated.
  • the average speed between the two moments is calculated.
  • the second vehicle speed may be obtained by means of averaging, taking the maximum value, taking the minimum value, or the like. Examples are given below. It should be understood that when there is only one estimated vehicle speed, the calculation result is equal to the estimated vehicle speed itself, regardless of the above-mentioned method of averaging, taking the maximum value, or taking the minimum value. In this case, the estimated vehicle speed is second speed. The following mainly introduces the processing method when there are multiple estimated vehicle speeds, but it should be understood that only one estimated vehicle speed can be regarded as a special case of multiple estimated vehicle speeds.
  • a plurality of estimated vehicle speeds may be averaged to obtain an average estimated vehicle speed, and the average estimated vehicle speed may be used as the second vehicle speed.
  • a maximum value may be selected as the second vehicle speed from among a plurality of estimated vehicle speeds.
  • a minimum value may be selected as the second vehicle speed from among a plurality of estimated vehicle speeds.
  • a maximum value and/or a minimum value may be removed, and the average value of the remaining estimated vehicle speeds may be used as the second vehicle speed.
  • mean or median filtering may be performed on the at least one estimated vehicle speed, and the vehicle speed obtained after filtering is as the second speed.
  • the difference is within a certain threshold range, it is considered that the first vehicle speed is correct and the speed measuring device is faultless.
  • the difference is not within the threshold range, it is considered that the first vehicle speed is wrong and the speed measuring device is faulty.
  • the following method when judging whether the speed measuring device is faulty according to the first vehicle speed and the second vehicle speed, the following method may be used:
  • the speed measuring device When the difference between the first vehicle speed and the second vehicle speed is less than or equal to the first preset threshold, it is considered that the speed measuring device is not faulty.
  • multiple second vehicle speeds may be obtained by calculation, and then when the errors between the multiple second vehicle speeds are large, do not Troubleshooting, that is, inaccurate estimates are not used as a basis for diagnosis.
  • the same static reference object may be used to obtain multiple second vehicle speeds according to the above method, or multiple static reference objects may be set, and the second vehicle speeds of the multiple reference objects may be obtained according to the above method.
  • the difference between the two second vehicle speeds and the first vehicle speed can be compared respectively. Only when the two difference values are less than or equal to the set threshold value, the vehicle speed sensor is considered to be faulty, otherwise, restart the vehicle. Get the second speed.
  • acquiring the reference information of the multiple static reference objects can be understood as acquiring the reference information of multiple times of each static reference object in the multiple static reference objects.
  • the second vehicle speed when judging whether the speed measuring device is faulty according to the first vehicle speed and the plurality of second vehicle speeds, when the difference between the plurality of second vehicle speeds is greater than the second preset When the threshold value is reached, the second vehicle speed can be obtained again; when the difference between the multiple second vehicle speeds is less than or equal to the second preset threshold value, it can be determined whether the speed measuring device is faulty.
  • a second aspect provides a fault diagnosis device for a vehicle speed measuring device, the device comprising a unit for executing the method of any one of the implementation manners of the first aspect above.
  • a third aspect provides a chip, the chip includes a processor and a data interface, the processor reads instructions stored in a memory through the data interface, and executes the method in any one of the implementations of the first aspect above .
  • the chip may further include a memory, in which instructions are stored, the processor is configured to execute the instructions stored in the memory, and when the instructions are executed, the The processor is configured to execute the method in any one of the implementation manners of the first aspect.
  • a vehicle which includes any one of the fault diagnosis devices of the second aspect, and a speed measuring device, where the fault diagnosis device is used to perform fault diagnosis on the speed measuring device.
  • a computer-readable medium stores program code for execution by a device, the program code including a method for performing any one of the implementations of the first aspect.
  • a computer program product comprising instructions, when the computer program product is run on a computer, the computer program product causes the computer to execute the method in any one of the implementation manners of the first aspect above.
  • FIG. 1 is a functional block diagram of a vehicle to which the embodiments of the present application are applied.
  • FIG. 2 is a schematic diagram of an automatic driving system to which an embodiment of the present application is applied.
  • FIG. 3 is a schematic diagram of an application of a cloud-side command to an autonomous driving vehicle according to an embodiment of the present application.
  • FIG. 4 is a schematic diagram of a fault diagnosis apparatus and its application according to an embodiment of the present application.
  • FIG. 5 is a schematic flowchart of a fault diagnosis method for a speed measuring device according to an embodiment of the present application.
  • FIG. 6 is a schematic flowchart of selecting a static reference object according to an embodiment of the present application.
  • FIG. 7 is a schematic flowchart of a method for estimating vehicle speed according to an embodiment of the present application.
  • FIG. 8 is a schematic diagram of straight-through vehicle speed estimation in a flat road scene according to an embodiment of the present application.
  • FIG. 9 is a schematic flowchart of another method for estimating vehicle speed according to an embodiment of the present application.
  • FIG. 10 is a schematic diagram of a cornering vehicle speed estimation in a flat road scene according to an embodiment of the present application.
  • FIG. 11 is a schematic diagram of vehicle speed estimation in a ramp scene according to an embodiment of the present application.
  • FIG. 12 is a schematic diagram of vehicle speed estimation in another ramp scene according to an embodiment of the present application.
  • FIG. 13 is a schematic diagram of a fault diagnosis device of a vehicle speed measuring device according to an embodiment of the present application.
  • FIG. 14 is a schematic diagram of a fault diagnosis device of a vehicle speed measuring device according to an embodiment of the present application.
  • the fault diagnosis method and/or device of the vehicle speed measuring device provided in the embodiments of the present application can be applied to various types of vehicles. These methods and/or devices can be applied to both manual driving, assisted driving, and automatic driving.
  • the technical solutions of the embodiments of the present application will be introduced below with reference to the accompanying drawings.
  • FIG. 1 is a functional block diagram of a vehicle to which the embodiments of the present application are applied.
  • the vehicle 100 may be a human-driven vehicle, or the vehicle 100 may be configured in a fully or partially autonomous driving mode.
  • the vehicle 100 may control the ego vehicle while in an autonomous driving mode, and may determine the current state of the vehicle and its surrounding environment through human manipulation, determine the possible behavior of at least one other vehicle in the surrounding environment, and A confidence level corresponding to the likelihood that other vehicles will perform the possible behavior is determined, and the vehicle 100 is controlled based on the determined information.
  • the vehicle 100 may be placed to operate without human interaction.
  • vehicle 100 Various subsystems may be included in vehicle 100 , such as travel system 110 , sensing system 120 , control system 130 , one or more peripherals 140 and power supply 160 , computer system 150 , and user interface 170 .
  • the vehicle 100 may include more or fewer subsystems, and each subsystem may include multiple elements. Additionally, each of the subsystems and elements of the vehicle 100 may be interconnected by wire or wirelessly.
  • the travel system 110 may include components for providing powered motion to the vehicle 100 .
  • travel system 110 may include engine 111, transmission 112, energy source 113, and wheels 114/tires.
  • the engine 111 may be an internal combustion engine, an electric motor, an air compression engine or other types of engine combinations; for example, a hybrid engine composed of a gasoline engine and an electric motor, or a hybrid engine composed of an internal combustion engine and an air compression engine.
  • Engine 111 may convert energy source 113 into mechanical energy.
  • the energy source 113 may include gasoline, diesel, other petroleum-based fuels, propane, other compressed gas-based fuels, ethanol, solar panels, batteries, and other sources of electricity.
  • the energy source 113 may also provide energy to other systems of the vehicle 100 .
  • transmission 112 may include a gearbox, a differential, and a driveshaft; wherein transmission 112 may transmit mechanical power from engine 111 to wheels 114 .
  • the transmission 112 may also include other devices, such as clutches.
  • the drive shafts may include one or more axles that may be coupled to one or more of the wheels 114 .
  • the sensing system 120 may include several sensors that sense information about the environment surrounding the vehicle 100 .
  • the sensing system 120 may include a positioning system 121 (eg, a global positioning system (GPS), BeiDou system, or other positioning system), an inertial measurement unit (IMU) 122, a radar 123, a laser Distance meter 124 , camera 125 and vehicle speed sensor 126 .
  • the sensing system 120 may also include sensors that monitor the internal systems of the vehicle 100 (eg, an in-vehicle air quality monitor, a fuel gauge, an oil temperature gauge, etc.). Sensor data from one or more of these sensors can be used to detect objects and their corresponding characteristics (position, shape, orientation, velocity, etc.). This detection and identification is a critical function for the safe operation of the autonomous vehicle 100 .
  • the positioning system 121 may be used to estimate the geographic location of the vehicle 100 .
  • the IMU 122 may be used to sense position and orientation changes of the vehicle 100 based on inertial acceleration.
  • IMU 122 may be a combination of an accelerometer and a gyroscope.
  • the radar 123 may utilize radio signals to sense objects within the surrounding environment of the vehicle 100 .
  • radar 123 may be used to sense the speed and/or heading of objects.
  • the laser rangefinder 124 may utilize laser light to sense objects in the environment in which the vehicle 100 is located.
  • the laser rangefinder 124 may include one or more laser sources, laser scanners, and one or more detectors, among other system components.
  • camera 125 may be used to capture multiple images of the surrounding environment of vehicle 100 .
  • camera 125 may be a still camera or a video camera.
  • the vehicle speed sensor 126 may be used to measure the speed of the vehicle 100 .
  • real-time speed measurement of the vehicle can be performed.
  • the measured vehicle speed may be communicated to the control system 130 to effect control of the vehicle.
  • control system 130 controls the operation of the vehicle 100 and its components.
  • Control system 130 may include various elements, such as may include steering system 131 , throttle 132 , braking unit 133 , computer vision system 134 , route control system 135 , and obstacle avoidance system 136 .
  • the steering system 131 may operate to adjust the heading of the vehicle 100 .
  • it may be a steering wheel system.
  • the throttle 132 may be used to control the operating speed of the engine 111 and thus the speed of the vehicle 100 .
  • the braking unit 133 may be used to control the deceleration of the vehicle 100 ; the braking unit 133 may use friction to slow the wheels 114 . In other embodiments, the braking unit 133 may convert the kinetic energy of the wheels 114 into electrical current. The braking unit 133 may also take other forms to slow the wheels 114 to control the speed of the vehicle 100 .
  • computer vision system 134 is operable to process and analyze images captured by camera 125 in order to identify objects and/or features in the environment surrounding vehicle 100 .
  • Such objects and/or features may include traffic signals, road boundaries and obstacles.
  • Computer vision system 134 may use object recognition algorithms, structure from motion (SFM) algorithms, video tracking, and other computer vision techniques.
  • the computer vision system 134 may be used to map the environment, track objects, estimate the speed of objects, and the like.
  • the route control system 135 may be used to determine the route of travel of the vehicle 100 .
  • the route control system 135 may combine data from sensors, GPS, and one or more predetermined maps to determine a driving route for the vehicle 100 .
  • the obstacle avoidance system 136 may be used to identify, evaluate and avoid or otherwise traverse potential obstacles in the environment of the vehicle 100 .
  • control system 130 may additionally or alternatively include components in addition to those shown and described. Alternatively, some of the components shown above may be reduced.
  • the vehicle 100 may interact with external sensors, other vehicles, other computer systems or users through peripheral devices 140; wherein the peripheral devices 140 may include a wireless communication system 141, an on-board computer 142, a microphone 143 and/or a or speaker 144.
  • peripheral devices 140 may include a wireless communication system 141, an on-board computer 142, a microphone 143 and/or a or speaker 144.
  • peripherals 140 may provide a means for vehicle 100 to interact with user interface 170 .
  • the onboard computer 142 may provide information to the user of the vehicle 100 .
  • the user interface 116 can also operate the onboard computer 142 to receive user input; the onboard computer 142 can be operated through a touch screen.
  • peripheral device 140 may provide a means for vehicle 100 to communicate with other devices located within the vehicle.
  • microphone 143 may receive audio (eg, voice commands or other audio input) from a user of vehicle 100 .
  • speakers 144 may output audio to a user of vehicle 100 .
  • wireless communication system 141 may wirelessly communicate with one or more devices, either directly or via a communication network.
  • wireless communication system 141 may use 3G cellular communications; eg, code division multiple access (CDMA)), EVDO, global system for mobile communications (GSM)/general packet radio service (general packet radio service, GPRS), or 4G cellular communications, such as long term evolution (LTE); or, 5G cellular communications.
  • the wireless communication system 141 may communicate with a wireless local area network (WLAN) using wireless Internet access (WiFi).
  • WLAN wireless local area network
  • WiFi wireless Internet access
  • the wireless communication system 141 may communicate directly with the device using an infrared link, Bluetooth, or ZigBee; other wireless protocols, such as various vehicle communication systems, for example, the wireless communication system 141 may include an or A number of dedicated short range communications (DSRC) devices, which may include public and/or private data communications between vehicles and/or roadside stations.
  • DSRC dedicated short range communications
  • power supply 160 may provide power to various components of vehicle 100 .
  • the power source 160 may be a rechargeable lithium-ion battery or a lead-acid battery.
  • One or more battery packs of such a battery may be configured as a power source to provide power to various components of the vehicle 100 .
  • power source 160 and energy source 113 may be implemented together, such as in some all-electric vehicles.
  • a computer system 150 may include at least one processor 151 that executes execution in a non-transitory computer-readable medium stored in, for example, memory 152 .
  • Computer system 150 may also be multiple computing devices that control individual components or subsystems of vehicle 100 in a distributed fashion.
  • processor 151 may be any conventional processor, such as a commercially available central processing unit (CPU).
  • CPU central processing unit
  • the processor may be a dedicated device such as an application specific integrated circuit (ASIC) or other hardware-based processor.
  • FIG. 1 functionally illustrates a processor, memory, and other elements of the computer in the same block, one of ordinary skill in the art will understand that the processor, computer, or memory may actually include storage that may or may not be Multiple processors, computers or memories within the same physical enclosure.
  • the memory may be a hard drive or other storage medium located within an enclosure other than a computer.
  • reference to a processor or computer will be understood to include reference to a collection of processors or computers or memories that may or may not operate in parallel.
  • some components such as the steering and deceleration components, may each have its own processor that only performs computations related to component-specific functions .
  • a processor may be located remotely from the vehicle and in wireless communication with the vehicle. In other aspects, some of the processes described herein are performed on a processor disposed within the vehicle while others are performed by a remote processor, including taking steps necessary to perform a single maneuver.
  • memory 152 may contain instructions 153 (eg, program logic) that may be used by processor 151 to perform various functions of vehicle 100 , including those described above.
  • Memory 152 may also include additional instructions, such as including sending data to, receiving data from, interacting with, and/or performing data processing on one or more of travel system 110 , sensing system 120 , control system 130 , and peripherals 140 control commands.
  • memory 152 may store data such as road maps, route information, vehicle location, direction, speed, and other such vehicle data, among other information. Such information may be used by the vehicle 100 and the computer system 150 during operation of the vehicle 100 in autonomous, semi-autonomous and/or manual modes.
  • user interface 170 may be used to provide information to or receive information from a user of vehicle 100 .
  • user interface 170 may include one or more input/output devices within the set of peripheral devices 140 , eg, wireless communication system 141 , onboard computer 142 , microphone 143 , and speaker 144 .
  • computer system 150 may control functions of vehicle 100 based on input received from various subsystems (eg, travel system 110 , sensing system 120 , and control system 130 ) and from user interface 170 .
  • computer system 150 may utilize input from control system 130 to control braking unit 133 to avoid obstacles detected by sensing system 120 and obstacle avoidance system 136 .
  • computer system 150 is operable to provide control of various aspects of vehicle 100 and its subsystems.
  • one or more of these components described above may be installed or associated with the vehicle 100 separately.
  • memory 152 may exist partially or completely separate from vehicle 100 .
  • the above-described components may be communicatively coupled together in a wired and/or wireless manner.
  • FIG. 1 should not be construed as a limitation on the embodiments of the present application.
  • the vehicle 100 may be a self-driving car traveling on a road and may recognize objects in its surroundings to determine an adjustment to the current speed.
  • the objects may be other vehicles, traffic control devices, or other types of objects.
  • each identified object may be considered independently, and based on the object's respective characteristics, such as its current speed, acceleration, distance from the vehicle, etc., may be used to determine the speed at which the autonomous vehicle is to adjust.
  • the vehicle 100 or a computing device associated with the vehicle 100 eg, computer system 150, computer vision system 134, memory 152 of FIG. rain, ice on the road, etc.
  • a computing device associated with the vehicle 100 eg, computer system 150, computer vision system 134, memory 152 of FIG. rain, ice on the road, etc.
  • each of the identified objects is dependent on the behavior of the other, so it is also possible to predict the behavior of a single identified object by considering all of the identified objects together.
  • the vehicle 100 can adjust its speed based on the predicted behavior of the identified object.
  • the self-driving car can determine that the vehicle will need to adjust (eg, accelerate, decelerate, or stop) to a steady state based on the predicted behavior of the object.
  • other factors may also be considered to determine the speed of the vehicle 100, such as the lateral position of the vehicle 100 in the road being traveled, the curvature of the road, the proximity of static and dynamic objects, and the like.
  • the computing device may also provide instructions to modify the steering angle of the vehicle 100 so that the self-driving car follows a given trajectory and/or maintains contact with objects in the vicinity of the self-driving car (eg, , cars in adjacent lanes on the road) safe lateral and longitudinal distances.
  • objects in the vicinity of the self-driving car eg, , cars in adjacent lanes on the road
  • the above-mentioned vehicle 100 can be a car, a truck, a motorcycle, a bus, a boat, an airplane, a helicopter, a lawn mower, a recreational vehicle, a playground vehicle, construction equipment, a tram, a golf cart, a train, a cart, etc.
  • the application examples are not particularly limited.
  • the vehicle 100 shown in FIG. 1 may be an automatic driving vehicle, and the automatic driving system will be described in detail below.
  • FIG. 2 is a schematic diagram of an automatic driving system to which an embodiment of the present application is applied.
  • the automatic driving system shown in FIG. 2 includes a computer system 201, wherein the computer system 201 includes a processor 203, and the processor 203 is coupled with a system bus 205.
  • the processor 203 may be one or more processors, wherein each processor may include one or more processor cores.
  • a display adapter 207 (video adapter), which can drive a display 209, is coupled to the system bus 205.
  • the system bus 205 may be coupled to an input output (I/O) bus 213 through a bus bridge 211, and an I/O interface 215 may be coupled to the I/O bus.
  • I/O input output
  • I/O interface 215 communicates with various I/O devices, such as input device 217 (eg, keyboard, mouse, touch screen, etc.), media tray 221 (media tray), (eg, CD-ROM, multimedia interface, etc.) .
  • the transceiver 223 can send and/or receive radio communication signals, and the camera 255 can capture landscape and dynamic digital video images.
  • the interface connected to the I/O interface 215 may be the USB port 225 .
  • the processor 203 may be any conventional processor, such as a reduced instruction set computing (reduced instruction set computer, RISC) processor, a complex instruction set computing (complex instruction set computer, CISC) processor, or a combination of the above.
  • RISC reduced instruction set computer
  • CISC complex instruction set computing
  • the processor 203 may be a dedicated device such as an application specific integrated circuit (ASIC); the processor 203 may be a neural network processor or a combination of a neural network processor and the above-mentioned conventional processors.
  • ASIC application specific integrated circuit
  • computer system 201 may be located remotely from the autonomous vehicle and may communicate wirelessly with the autonomous vehicle.
  • some of the processes described herein are performed on a processor disposed within the autonomous vehicle and others are performed by a remote processor, including taking actions required to perform a single maneuver.
  • Network interface 229 may be a hardware network interface, such as a network card.
  • the network 227 may be an external network, such as the Internet, or an internal network, such as an Ethernet network or a virtual private network (VPN).
  • the network 227 may also be a wireless network, such as a WiFi network, a cellular network, and the like.
  • the hard disk drive interface is coupled with the system bus 205
  • the hardware drive interface 231 can be connected with the hard disk drive 233
  • the system memory 235 is coupled with the system bus 205 .
  • Data running in system memory 235 may include operating system 237 and application programs 243 .
  • the operating system 237 may include a parser (shell) 239 and a kernel (kernel) 241 .
  • the shell 239 is an interface between the user and the kernel of the operating system.
  • the shell can be the outermost layer of the operating system; the shell can manage the interaction between the user and the operating system, for example, waiting for user input, interpreting user input to the operating system, and processing various operating systems output result.
  • Kernel 241 may consist of those parts of the operating system that manage memory, files, peripherals, and system resources. Interacting directly with hardware, the operating system kernel usually runs processes and provides inter-process communication, providing CPU time slice management, interrupts, memory management, IO management, and more.
  • Application 243 includes programs that control the autonomous driving of the car, for example, programs that manage the interaction of the autonomous car with obstacles on the road, programs that control the route or speed of the autonomous car, and programs that control the interaction of the autonomous car with other autonomous vehicles on the road. .
  • Application 243 also exists on the system of software deployment server 249 . In one embodiment, the computer system 201 may download the application program from the software deployment server 249 when the autonomous driving related program 247 needs to be executed.
  • the application program 243 may also be a program for the autonomous vehicle to interact with the road lane lines, that is, a program that can track the lane lines in real time.
  • the application program 243 may also be a program for controlling the self-driving vehicle to perform automatic parking.
  • sensors 253 may be associated with computer system 201 , and sensors 253 may be used to detect the environment surrounding computer 201 .
  • the senor 253 can detect the lane on the road, such as the lane line, and can track the change of the lane line within a certain range in front of the vehicle in real time while the vehicle is moving (such as driving).
  • the sensor 253 can detect animals, cars, obstacles and pedestrian crossings, etc., and further sensors can also detect the environment around the above-mentioned animals, cars, obstacles and pedestrian crossings, such as: the environment around animals, for example, the environment around animals Other animals, weather conditions, ambient light levels, etc.
  • the sensors may be cameras, infrared sensors, chemical detectors, microphones, and the like.
  • the senor 253 can be used to detect the lane line in front of the vehicle, so that the vehicle can perceive the change of the lane during traveling, so as to plan and adjust the driving of the vehicle in real time accordingly.
  • the sensor 253 can be used to detect the size or position of the storage space and surrounding obstacles around the vehicle, so that the vehicle can perceive the distance between the storage space and surrounding obstacles, and when parking Collision detection is performed to prevent vehicles from colliding with obstacles.
  • the computer system 150 shown in FIG. 1 may also receive information from or transfer information to other computer systems.
  • the sensor data collected from the sensor system 120 of the vehicle 100 may be transferred to another computer for processing the data, which will be described below by taking FIG. 3 as an example.
  • FIG. 3 is a schematic diagram of an application of a cloud-side command to an autonomous driving vehicle according to an embodiment of the present application.
  • data from computer system 312 may be transmitted via a network to server 320 on the cloud side for further processing.
  • Networks and intermediate nodes may include various configurations and protocols, including the Internet, the World Wide Web, Intranets, Virtual Private Networks, Wide Area Networks, Local Area Networks, private networks using one or more of the company's proprietary communication protocols, Ethernet, WiFi and HTTP, and various combinations of the foregoing; such communications may be by any device capable of transferring data to and from other computers, such as modems and wireless interfaces.
  • server 320 may include a server having multiple computers, such as a load balancing server farm, that exchange information with different nodes of the network for the purpose of receiving, processing, and transmitting data from computer system 312 .
  • the server may be configured similarly to computer system 312 , with processor 330 , memory 340 , instructions 350 , and data 360 .
  • the data 360 of the server 320 may include information about road conditions around the vehicle.
  • server 320 may receive, detect, store, update, and transmit information related to vehicle road conditions.
  • the relevant information of the road conditions around the vehicle includes other vehicle information and obstacle information around the vehicle.
  • the vehicle speed measuring device is mainly a vehicle speed sensor, but in the actual use process, the vehicle speed sensor may fail or short-circuit, resulting in the inability to measure the speed or the measured vehicle speed is inaccurate. In order to ensure the safety of the vehicle, it is necessary to find the vehicle speed sensor in time. failure.
  • a vehicle speed is often calculated by means of other vehicle power information, and it can be judged whether the measured vehicle speed is normal by comparing with the measured vehicle speed, thereby inferring whether the speed measuring device is working normally.
  • each of the existing schemes cannot cover all working conditions. As mentioned above, it can only be effective in some specific cases, resulting in a very limited range of diagnosis.
  • the embodiments of the present application provide a new fault diagnosis method and device for a speed measuring device, which has a wide diagnostic range and can more accurately diagnose the fault of the speed measuring device. It mainly estimates the speed of the vehicle by using the static reference outside the vehicle, and then judges whether the speed measured by the speed measuring device is normal based on the estimated speed, so as to diagnose whether the speed measuring device is faulty.
  • This solution does not need to rely on the self-vehicle power information of the vehicle, so it will not be limited by the coverage of each type of self-vehicle power information, so it can have a wider diagnostic range, so that the speed measurement device can be diagnosed more accurately. Fault.
  • self-vehicle power information can be understood as information related to vehicle motion, or can be understood as information that affects vehicle speed, because self-vehicle power information (such as torque, wheel speed, etc.) is directly related to vehicle speed Yes, that is to say, the power information of the self-vehicle will directly affect the speed of the vehicle.
  • self-vehicle power information such as torque, wheel speed, etc.
  • the power information of the self-vehicle will directly affect the speed of the vehicle.
  • it does not conform to the known correlation relationship, which will lead to the inability to correctly infer the vehicle speed. For example, when climbing a hill, the relationship between torque and vehicle speed is broken, so that the torque can no longer be used to calculate the vehicle speed.
  • the vehicle speed is not zero
  • the vehicle speed inferred from the wheel speed is zero.
  • the vehicle speed can be inferred by using the power information of the self-vehicle, so as to judge whether the speed measuring device is working normally, but there are great limitations.
  • the vehicle speed is estimated by the movement of the vehicle relative to the external reference object, so it can be measured as long as the vehicle is moving, which can cover a wider range of diagnosis.
  • FIG. 4 is a schematic diagram of a fault diagnosis apparatus and its application according to an embodiment of the present application.
  • the fault diagnosis device 420 obtains some information from other devices, and can obtain the fault diagnosis result after certain processing.
  • the fault diagnosis apparatus 420 may include an acquisition unit 421 and a diagnosis unit 422 .
  • the obtaining unit 421 can be used to obtain some information (in this embodiment of the present application, the information can be understood as data), for example, the vehicle speed information from the speed measuring device 411 can be obtained, where the vehicle speed information includes the vehicle speed measured by the speed measuring device.
  • the vehicle speed information includes the vehicle speed measured by the speed measuring device.
  • reference object information from the environment detection device 413 may also be acquired, and the reference object includes information of at least one static reference object.
  • the environment detection device 413 may be a radar detection device, a camera, etc., for example, the radar 123 shown in FIG. 1 or FIG. 2 , the laser rangefinder 124 , the camera 125 or the camera 255 and the like.
  • the diagnosis unit 422 may be configured to process the information obtained by the obtaining unit 421 to obtain a fault diagnosis result.
  • the diagnosis unit 422 may calculate the estimated vehicle speed according to the reference object information, and then judge whether the vehicle speed measured by the speed measuring device is correct according to the estimated vehicle speed.
  • the fault diagnosis result is "no fault” or "normal operation”.
  • the result of the fault diagnosis is "failure” or "not working properly”.
  • a reference object other than the vehicle is used to estimate the vehicle speed, and then it is compared with the vehicle speed measured by the speed measuring device, so that it can be determined whether the speed measuring device is faulty without relying on the vehicle's own vehicle power device.
  • the embodiment of the present application can judge whether the speed measuring device is normal without relying on the power device of the vehicle, the power information of the vehicle can still be added for diagnosis, that is, on the basis of the above scheme , adding the self-vehicle power information as the expansion of the diagnosis basis will not affect the realization effect of the above scheme.
  • the fault detection device 420 may further include a sending unit for sending the fault diagnosis result to other modules or devices, for example, to a control system, for generating a control signal to control the vehicle.
  • a sending unit for sending the fault diagnosis result to other modules or devices, for example, to a control system, for generating a control signal to control the vehicle.
  • FIG. 5 is a schematic flowchart of a fault diagnosis method for a speed measuring device according to an embodiment of the present application. Each step in FIG. 5 is described below.
  • reference information of N times may be acquired, where N is an integer greater than 1.
  • the N moments may be continuous or non-continuous, and there is no limitation.
  • the reference information may include information about the positional relationship of the static reference object relative to the vehicle, and the vehicle may be understood as the vehicle where the speed measuring device to be diagnosed is located. That is to say, the positional relationship of the static reference object relative to the vehicle at each of the above N times can be obtained.
  • the positional relationship may include the distance and direction of the static reference object relative to the vehicle.
  • the direction of the static reference object relative to the vehicle may be represented by the included angle between the line connecting the static reference object and the vehicle and the traveling direction of the vehicle.
  • step 501 and step 502 may be executed simultaneously, or step 501 may be executed first, and step 502 may be executed first.
  • the static reference object may be directly set as a road sign or a traffic facility, so that once the road sign or traffic facility is identified, each step of fault diagnosis is started.
  • a static object can be selected from the recognized objects, and a static reference object can be selected from the static objects.
  • FIG. 6 is a schematic flowchart of selecting a static reference object according to an embodiment of the present application, and each step of FIG. 6 is described below.
  • the sensing device may include a camera, a radar.
  • a camera may be used to acquire images or videos, and then the images may be processed to identify objects in the images.
  • radar can also be used to sense objects and whether they are moving.
  • At least one static object can be selected from the recognized objects, and the static object can be used as a static reference.
  • step 603 can be executed.
  • FIG. 6 mainly provides a method for determining a static reference object, and step 603 may or may not be performed.
  • the second vehicle speed is used to represent the estimated vehicle speed, or it can be understood as the calculated vehicle speed, or it can be understood as the vehicle speed measured by the non-speed measuring device.
  • the second vehicle speed may be calculated by using the reference information at the above N moments, that is, the change in the displacement and/or angle of the vehicle relative to the static reference object within a period of time may be calculated and obtained within this period of time.
  • the average vehicle speed is taken as the second vehicle speed, or a plurality of average vehicle speeds can be obtained, and then a second vehicle speed can be obtained based on the multiple average vehicle speeds. Since there are many contents involved, the following will be introduced in conjunction with FIG. 7 to FIG. 12 , and will not be expanded here.
  • step 503 needs to be executed after step 502, but does not necessarily need to be executed after step 501, that is, step 502 and step 503 may be executed first, and then step 501 is executed. Step 501 may also be performed during any of steps 502 and 503, etc., without limitation.
  • the difference is within a certain threshold range, it is considered that the first vehicle speed is correct and the speed measuring device is faultless.
  • the difference is not within the threshold range, it is considered that the first vehicle speed is wrong and the speed measuring device is faulty.
  • multiple second vehicle speeds may be obtained by calculation, and then when the errors between the multiple second vehicle speeds are large, do not Troubleshooting, that is, inaccurate estimates are not used as a basis for diagnosis.
  • the same static reference object may be used to obtain multiple second vehicle speeds according to the above method, or multiple static reference objects may be set, and the second vehicle speeds of the multiple reference objects may be obtained according to the above method.
  • the difference between the two second vehicle speeds and the first vehicle speed can be compared respectively. Only when the two difference values are less than or equal to the set threshold value, the vehicle speed sensor is considered to be faulty, otherwise, restart the vehicle. Get the second speed.
  • FIG. 7 is a schematic flowchart of a method for estimating vehicle speed according to an embodiment of the present application.
  • the method shown in FIG. 7 can be applied to the estimation of a vehicle traveling in a straight line on a flat road.
  • the flat road scene shown in FIG. 8 drives in a straight line. 's estimate.
  • Each step in FIG. 7 will be introduced below.
  • the reference information may include information about the positional relationship of the static reference object relative to the vehicle, and the vehicle may be understood as the vehicle where the speed measuring device to be diagnosed is located. That is to say, the positional relationship of the static reference object relative to the vehicle at each of the above N times is acquired.
  • the N times may be continuous times or discontinuous times, as long as they are close to the times corresponding to the first vehicle speed.
  • the time corresponding to the first vehicle speed may be selected as N time times with a time length within a certain threshold range. That is to say, assuming that the first vehicle speed is measured at time T1 and the time length threshold range is ⁇ t, N times within the range of [T1- ⁇ t, T1+ ⁇ t] can be selected. It should be understood that the N moments do not necessarily include T1. For example, N moments from T1+1 to T1+N may be selected. It should also be understood that the N moments may be continuous or discontinuous, and there is no limitation.
  • the at least two moments refer to at least two moments in the above-mentioned N moments, and the two moments are not required to be consecutive.
  • the displacement ⁇ S between more than two time instants can be calculated, the displacement ⁇ S can be one or more.
  • the displacement can be calculated according to the distance between the static reference object and the vehicle at two moments, the angle between the vehicle's forward direction and the line connecting the static reference object and the vehicle.
  • the second vehicle speed may be obtained by means of averaging, taking the maximum value, taking the minimum value, or the like. Examples are given below. It should be understood that when there is only one estimated vehicle speed, the calculation result is equal to the estimated vehicle speed itself, regardless of the above-mentioned method of averaging, taking the maximum value, or taking the minimum value. In this case, the estimated vehicle speed is second speed. The following mainly introduces the processing method when there are multiple estimated vehicle speeds, but it should be understood that only one estimated vehicle speed can be regarded as a special case of multiple estimated vehicle speeds.
  • a plurality of estimated vehicle speeds may be averaged to obtain an average estimated vehicle speed, and the average estimated vehicle speed may be used as the second vehicle speed.
  • a maximum value may be selected as the second vehicle speed from among a plurality of estimated vehicle speeds.
  • a minimum value may be selected as the second vehicle speed from among a plurality of estimated vehicle speeds.
  • a maximum value and/or a minimum value may be removed, and the average value of the remaining estimated vehicle speeds may be used as the second vehicle speed.
  • FIG. 8 is a schematic diagram of straight-through vehicle speed estimation in a flat road scene according to an embodiment of the present application.
  • the vehicle In the scene shown in Figure 8, the vehicle is driving straight on the flat ground. Through the relative relationship between the vehicle and the static reference object at two different times shown in Figure 8, the vehicle displacement between two adjacent times can be obtained, thereby obtaining the current.
  • the speed of the vehicle is given below, and the specific calculation process is given as an example.
  • the displacement ⁇ S of the vehicle at the two times can be calculated according to the positional relationship between the vehicle at the i-th time and the i+1-th time adjacent to the static reference object, and it can be calculated from the geometric relationship in Figure 8:
  • Vi can be regarded as the estimated vehicle speed V' described in FIG. 7 .
  • N average velocities of N time intervals between time 1 and time N+1 may be calculated and obtained according to the same method as above.
  • the second vehicle speed may also be determined by means of filtering.
  • the filtering method may be one of filtering methods such as mean filtering and median filtering. For details, please refer to the relevant introduction in FIG. 7 and will not be repeated.
  • FIG. 9 is a schematic flowchart of another method for estimating vehicle speed according to an embodiment of the present application.
  • the method shown in FIG. 9 can be applied to vehicle speed estimation when a vehicle is turning on a flat road, such as the flat road turning scene shown in FIG. 10 . speed estimate.
  • a flat road such as the flat road turning scene shown in FIG. 10 .
  • speed estimate Each step in FIG. 9 will be introduced below.
  • the reference information may include information about the positional relationship of the static reference object relative to the vehicle, and the vehicle may be understood as the vehicle where the speed measuring device to be diagnosed is located. That is to say, the positional relationship of the static reference object relative to the vehicle at each of the above N times is acquired.
  • the N times may be continuous times or discontinuous times, as long as they are close to the times corresponding to the first vehicle speed.
  • the time corresponding to the first vehicle speed may be selected as N time times with a time length within a certain threshold range. That is to say, assuming that the first vehicle speed is measured at time T1 and the time length threshold range is ⁇ t, N times within the range of [T1- ⁇ t, T1+ ⁇ t] can be selected. It should be understood that the N moments do not necessarily include T1. For example, N moments from T1+1 to T1+N can be selected. It should also be understood that the N moments may be continuous or discontinuous, and there is no limitation.
  • the steering radius may be calculated according to the length of the vehicle body and the steering angle of the wheels, and then the steering angle may be calculated according to the geometric relationship between the steering radius and the steering angle.
  • the at least two moments refer to at least two moments in the above-mentioned N moments, and the two moments are not required to be consecutive.
  • the steering angle x may be one or multiple.
  • step 704 may be used to obtain the second vehicle speed, and the description will not be repeated.
  • FIG. 10 is a schematic diagram of a cornering vehicle speed estimation in a flat road scene according to an embodiment of the present application.
  • the vehicle is turning and driving on the flat ground.
  • the vehicle steering angle x between two adjacent times can be obtained, so that The current vehicle speed is obtained, and the specific calculation process is described below with an example.
  • the steering radius R i at the i-th moment can be calculated according to the body length L and the wheel steering angle ⁇ i at the i-th moment, and R i is taken as the steering radius from the i -th moment to the i+1-th moment, as marked in Figure 10 .
  • the steering angle x can be solved by the above two equations.
  • Vi can be considered as the estimated vehicle speed V' described in FIG. 9 .
  • N average velocities of N time intervals between time 1 and time N+1 may be calculated and obtained according to the same method as above.
  • the second vehicle speed may also be determined by filtering, and the filtering method may be one of filtering methods such as mean filtering and median filtering.
  • filtering methods such as mean filtering and median filtering.
  • FIG. 11 is a schematic diagram of vehicle speed estimation in a ramp scene according to an embodiment of the present application.
  • the static reference object and the vehicle are on the same slope.
  • the The ramp is located on a flat road, so the exact same calculation method as the flat road can be used.
  • Figure 7 or Figure 8 when driving in a straight line, the method shown in Figure 7 or Figure 8 can be used, and when driving in a turn, Figure 9 or Figure 9 can be used.
  • the method shown in 10 for the sake of brevity, will not be repeated here.
  • FIG. 12 is a schematic diagram of vehicle speed estimation in another ramp scene according to an embodiment of the present application.
  • the vehicle is driving on a ramp, but the static reference is on a flat road ahead of the ramp.
  • Fig. 12 shows a situation where the static reference object is not coplanar with the vehicle, but it should be understood that the scene shown in Fig. 12 can be regarded as the vehicle on the ramp and the static reference object is on the flat road, but it can also be seen
  • the action is that the vehicle is on a flat road, and the static reference object is on the ramp.
  • the slope of the slope is estimated by the vehicle acceleration sensor to be ⁇
  • the camera obtains the two adjacent moments of the i -th time and the i +1-th time
  • the distances Li and Li +1 of the static reference objects and the distance from the driving direction are obtained. Included angles ⁇ i , ⁇ i+1 .
  • the coordinate system is established by taking the plane where the vehicle's driving direction and the horizon (the dotted line shown in Figure 12) are located as the XZ plane. Assuming that the distance between point B and the origin is P, and the distance between point C and the origin is Q, the coordinates of points B and C are respectively are B(-M cos ⁇ , 0, M sin ⁇ ), C(-N cos ⁇ , 0, N sin ⁇ ). Let the coordinates of the static reference object be: A(X, Y, 0).
  • Li 2 (X+P cos ⁇ ) 2 +Y 2 +(P sin ⁇ ) 2 ;
  • Li +1 2 (X+Q cos ⁇ ) 2 +Y 2 +(Q sin ⁇ ) 2 .
  • a flat road can be regarded as a special case of the scene shown in Fig. 12. That is to say, the scenarios shown in Fig. 7 and Fig. 8 can be regarded as a special case in which the gradient ⁇ in Fig. 11 and Fig. 12 is 0. In this case, the above formula is also applicable.
  • FIGS. 7 to 11 are all cases where the vehicle and the static reference object are on the same road plane, wherein, FIGS. 7 to 10 are on a level ground, and FIG. 11 is on a sloped ground.
  • the vehicle and the static reference are no longer on the same road plane. It can be seen that no matter whether the vehicle and the static reference 5 are on the same road plane, whether the vehicle is driving in a straight line or turning, the positional relationship between the vehicle and the static reference object can be used to calculate the average vehicle speed at one end of the time interval, Thereby, the second vehicle speed is obtained, and whether the first vehicle speed is accurate is determined by using the second vehicle speed.
  • the speed of the vehicle is estimated by using a static reference outside the vehicle, and then based on the estimated speed, it is judged whether the speed measured by the speed measuring device is normal, so that whether the speed measuring device is faulty can be diagnosed.
  • This solution does not need to rely on the self-vehicle power information of the vehicle, so it will not be limited by the coverage of each type of self-vehicle power information, so it can have a wider diagnostic range, so that the speed measurement device can be diagnosed more accurately. Fault.
  • the fault diagnosis method of the vehicle speed measuring device according to the embodiment of the present application is described above, and the following describes the fault diagnosis device of the vehicle speed measuring device according to the embodiment of the present application. It should be understood that the fault diagnosing apparatus introduced hereinafter can perform each process of the fault diagnosing method of the embodiments of the present application, and the repeated description will be appropriately omitted when the embodiments of the apparatus are introduced below.
  • FIG. 13 is a schematic diagram of a fault diagnosis device of a vehicle speed measuring device according to an embodiment of the present application.
  • the device 2000 includes an acquisition unit 2001 and a processing unit 2002 .
  • the device 2000 can be used to execute each step of the fault diagnosis method of the vehicle speed measuring device according to the embodiment of the present application.
  • the acquiring unit 2001 can be used to perform steps 501 and 502 in the method shown in FIG. 5
  • the processing unit 2002 can be used to perform steps 503 and 504 in the method shown in FIG. 5
  • the obtaining unit 2001 may be configured to execute step 601 in the method shown in FIG. 6
  • the processing unit 2002 may be configured to execute step 602 of the method shown in FIG. 6 .
  • obtaining Unit 2001 may also be used to perform step 603 .
  • the acquiring unit 2001 may be configured to perform step 701 in the method shown in FIG. 7
  • the processing unit 2002 may be configured to perform steps 702 to 704 in the method shown in FIG. 7 .
  • the acquiring unit 2001 may be configured to execute step 901 in the method shown in FIG. 9
  • the processing unit 2002 may be configured to execute steps 902 to 904 in the method shown in FIG. 9 .
  • the above-mentioned apparatus 2000 may also be used to execute each step in the method shown in FIG. 8 , FIG. 10 , FIG. 11 , and FIG. 12 .
  • the above-mentioned apparatus 2000 may be the fault diagnosis apparatus 420 shown in FIG. 4 , wherein the acquisition unit 2001 may be equivalent to the acquisition unit 421 , and the processing unit 2002 may be equivalent to the diagnosis unit 422 .
  • FIG. 14 is a schematic diagram of a fault diagnosis device of a vehicle speed measuring device according to an embodiment of the present application.
  • the apparatus 3000 includes a memory 3001 , a processor 3002 , a communication interface 3003 and a bus 3004 .
  • the memory 3001 , the processor 3002 , and the communication interface 3003 are connected to each other through the bus 3004 for communication.
  • the memory 3001 may be a read only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM).
  • the memory 3001 can store a program.
  • the processor 3002 and the communication interface 3003 are used to execute each step of the fault diagnosis method of the vehicle speed measuring device of the embodiment of the present application.
  • the memory 3001 may have the function of the memory 152 shown in FIG. 1 , the function of the system memory 235 shown in FIG. 2 , or the function of the memory 340 shown in FIG. 4 to realize the above function of storing programs.
  • the processor 3002 may adopt a general-purpose CPU, a microprocessor, an ASIC, a graphics processing unit (graphic processing unit, GPU), or one or more integrated circuits, for executing related programs, so as to implement the functions of the embodiments of the present application. Functions required to be performed by the units in the fault diagnosis apparatus, or perform various steps of the fault diagnosis method of the embodiments of the present application.
  • the processor 3002 may have the function of the processor 151 shown in FIG. 1 , or the function of the processor 203 shown in FIG. 2 , or the function of the processor 330 shown in FIG. 3 , so as to realize the above-mentioned function of executing related programs .
  • the processor 3002 may also be an integrated circuit chip with signal processing capability.
  • each step of the fault diagnosis method of the embodiment of the present application may be completed by an integrated logic circuit of hardware in a processor or an instruction in the form of software.
  • the above-mentioned processor 3002 may also be a general-purpose processor, a digital signal processor (digital signal processing, DSP), an application-specific integrated circuit (ASIC), an off-the-shelf programmable gate array (field programmable gate array, FPGA) or other Programming logic devices, discrete gate or transistor logic devices, discrete hardware components.
  • DSP digital signal processing
  • ASIC application-specific integrated circuit
  • FPGA field programmable gate array
  • Programming logic devices discrete gate or transistor logic devices, discrete hardware components.
  • a general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
  • the steps of the method disclosed in conjunction with the embodiments of the present application can be directly embodied as being executed by a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor.
  • the software modules may be located in random access memory, flash memory, read-only memory, programmable read-only memory or electrically erasable programmable memory, registers and other storage media mature in the art.
  • the storage medium is located in the memory, and the processor reads the information in the memory and, in combination with its hardware, completes the functions required to be performed by the units included in the fault diagnosis device of the vehicle speed measurement device of the embodiment of the present application, or executes the vehicle speed measurement device of the embodiment of the present application.
  • the various steps of the fault diagnosis method of the device may be located in random access memory, flash memory, read-only memory, programmable read-only memory or electrically erasable programmable memory, registers and other storage media mature in the art.
  • the storage medium is located in the memory, and the processor reads the information in the memory and, in combination with its hardware, completes the functions required to be performed by the units included in the fault diagnosis device of the vehicle speed measurement device of the embodiment of the present application, or executes the vehicle speed measurement device of the
  • the communication interface 3003 may use a transceiver device such as, but not limited to, a transceiver to implement communication between the device and other devices or a communication network.
  • a transceiver device such as, but not limited to, a transceiver to implement communication between the device and other devices or a communication network.
  • the bus 3004 may include pathways for transferring information between various components of the device (eg, memory, processor, communication interface).
  • the embodiments of the present application further provide a computer program product including instructions, and when the instructions are executed by a computer, the instructions cause the computer to implement the methods in the foregoing method embodiments.
  • the network device involved in the embodiment of the present application includes a hardware layer, an operating system layer running on the hardware layer, and an application layer running on the operating system layer.
  • the hardware layer may include hardware such as CPU, memory management unit (MMU), and memory (also called main memory).
  • the operating system of the operating system layer may be any one or more computer operating systems that implement business processing through processes, such as a Linux operating system, a Unix operating system, an Android operating system, an iOS operating system, or a Windows operating system.
  • the application layer may include applications such as browsers, address books, word processing software, and instant messaging software.
  • the embodiments of the present application do not specifically limit the specific structure of the execution body of the methods provided by the embodiments of the present application, as long as the program in which the codes of the methods provided by the embodiments of the present application are recorded can be executed to execute the methods according to the embodiments of the present application. Just communicate.
  • Computer readable media may include, but are not limited to, magnetic storage devices (eg, hard disks, floppy disks, or magnetic tapes, etc.), optical disks (eg, compact discs (CDs), digital versatile discs (DVDs), etc. ), smart cards and flash memory devices (eg, erasable programmable read-only memory (EPROM), cards, stick or key drives, etc.).
  • magnetic storage devices eg, hard disks, floppy disks, or magnetic tapes, etc.
  • optical disks eg, compact discs (CDs), digital versatile discs (DVDs), etc.
  • smart cards and flash memory devices eg, erasable programmable read-only memory (EPROM), cards, stick or key drives, etc.
  • the various storage media described herein may represent one or more devices and/or other machine-readable media for storing information.
  • the term "machine-readable medium” may include, but is not limited to, wireless channels and various other media capable of storing, containing, and/or carrying instructions and/or data.
  • the processor is a general-purpose processor, DSP, ASIC, FPGA or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components
  • the memory storage module
  • memory described herein is intended to include, but not be limited to, these and any other suitable types of memory.
  • the disclosed apparatus and method may be implemented in other manners.
  • the apparatus embodiments described above are only illustrative.
  • the division of the units is only a logical function division. In actual implementation, there may be other division methods.
  • multiple units or components may be combined or Can be integrated into another system, or some features can be ignored, or not implemented.
  • the shown or discussed mutual coupling or direct coupling or communication connection may be through some interfaces, indirect coupling or communication connection of devices or units, which may be in electrical, mechanical or other forms.
  • the units described as separate components may or may not be physically separated, and components displayed as units may or may not be physical units, that is, may be located in one place, or may be distributed to multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution in this embodiment.
  • each functional unit in each embodiment of the present application may be integrated into one unit, or each unit may exist physically alone, or two or more units may be integrated into one unit.
  • the functions, if implemented in the form of software functional units and sold or used as independent products, may be stored in a computer-readable storage medium.
  • the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution can be embodied in the form of a computer software product, and the computer software product is stored in a storage
  • the computer software product includes several instructions, the instructions are used to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application.
  • the aforementioned storage medium may include, but is not limited to, various media that can store program codes, such as a U disk, a removable hard disk, a ROM, a RAM, a magnetic disk, or an optical disk.

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Abstract

一种车辆测速装置的故障诊断方法和故障诊断装置，该方法包括：获取测速装置测得的第一车速；获取静态参照物的N个时刻的参照信息，N为大于1的整数，该参照信息包括静态参照物在该N个时刻中的每个时刻相对于测速装置所在的车辆的位置关系的信息；根据参照信息计算第二车速；根据第一车速和第二车速，判断测速装置是否故障。该方法不需要依赖于车辆的自车动力信息，因此不会受每种自车动力信息所覆盖范围的局限，应用在智能汽车、网联汽车、新能源汽车、自动驾驶/智能驾驶汽车上，具有更广的诊断范围，从而可以更为准确地诊断出测速装置的故障。

Description

车辆测速装置的故障诊断方法和故障诊断装置 技术领域
本申请涉及车辆技术领域，尤其涉及一种车辆测速装置的故障诊断方法和装置。
背景技术
车速信息是车辆行驶控制的重要信息，汽车的各类电子控制装置，例如车身电子稳定系统(electronic stability program，ESP)、防抱死制动系统(anti-lock braking system，ABS)、牵引力控制系统(traction control system，TCS)、车辆导航等等，都需要利用车速信息来产生控制信号，而如果测速装置(例如车速传感器)导致车速测量不准，就会导致控制信号的错误，还会带来严重的安全问题，因此确定测速装置是否存在故障是十分重要的。
常见的测速装置的诊断方法均是将除了车速信息以外的自车动力信息(可以理解为与车辆运动相关的信息，或者可以理解为会影响车速的信息)作为诊断的依据，从而判断车速是否准确，进而确定测速装置是否工作正常。例如，采用电气性故障诊断方法，当检测到电气故障的时候就可以判定初速传感器无法正常工作，但这种方法只能检测开路或短路等连接性故障。又例如，依据发动机转速转矩及换挡制动信号来诊断的方式，相当于根据转速转矩、制动信号这些信息来推算出一个车速跟测速装置测到的车速来比较，如果不一致就认为测速装置存在故障，但这种方式在车辆爬坡的时候是无法诊断的。再例如，依据车轮的轮速来诊断的方式，相当于根据轮速推断出一个车速跟测速装置测到的车速来比较，如果不一致就认为测速装置存在故障，但这种方式在车轮打滑或卡死的时候是无法诊断的。
简而言之，上述利用其他自车动力信息作为诊断依据的诊断方法，但只能在其各自特定的情况下才能有效，诊断范围非常局限，一旦超出其诊断范围就会失效。
因此，如何更准确地诊断出测速装置的故障，是亟待解决的问题。
发明内容
本申请提供了一种车辆测速装置的故障诊断方法和装置，诊断范围广，能够更准确地诊断出测速装置的故障。
第一方面，提供一种车辆测速装置的故障诊断方法，该方法包括：获取测速装置测得的第一车速；获取静态参照物的N个时刻的参照信息，N为大于1的整数，参照信息包括静态参照物在N个时刻中的每个时刻相对于测速装置所在的车辆的位置关系的信息；根据参照信息计算第二车速；根据第一车速和第二车速，判断测速装置是否故障。
在本申请技术方案中，利用车辆以外的参照物估算车速，进而跟测速装置测得的车速进行比较，从而可以判定测速装置是否故障，这种方案不需要依赖于车辆的自车动力信息，因此不会受每种自车动力信息所覆盖范围的局限，所以可以具有更广的诊断范围，从而可以更为准确地诊断出测速装置的故障。
需要说明的是，在本申请实施例中，第二车速用于表示估计得到的车速，或者可以理 解为推算出的车速，还可以理解为非测速装置测得的车速。
可选地，该N个时刻可以是连续的时刻，也可以是不连续的时刻，只要与第一车速所对应的时刻接近即可。
可选地，可以选取与第一车速所对应的时刻的是时间长度在一定阈值范围内的N个时刻。也就是说，假设第一车速是在T1时刻测得的，时间长度阈值范围为Δt，就可以选取[T1-Δt，T1+Δt]范围内的N个时刻。应理解，该N个时刻不一定必须包括T1，例如可以选取T1+1到T1+N这N个时刻，还应理解N个时刻可以是连续的，也可是不连续的，不存在限定。
可选地，可以直接设定静态参照物为某种路标或交通设施等，使得一旦识别到该路标或交通设施就开始执行故障诊断的各个步骤。
可选地，还可以通过获取车辆周围物体，从识别出的物体中选择出静态的物体，以及从静态的物体中选取静态参照物。
结合第一方面，在第一方面的某些实现方式中，在获取静态参照物的参照信息之前，故障诊断方法还包括：利用车辆的感知设备，识别车辆周围的物体；以及从识别出的物体中选取静态参照物。
可选地，该感知设备可以包括摄像头或雷达。
需要说明的是，在本申请实施例中，无论是平面道路还是有坡度的道路、无论是车辆直线行驶还是转弯行驶，都可以根据静态参照物与车辆的相对关系以及获取静态参照物的时间间隔来估算出第二车速。下面分别针对不同的道路情况和不同的车辆行驶情况进行介绍。
结合第一方面，在第一方面的某些实现方式中，在计算第二车速时，还可以采用下面的方法：利用N个时刻的参照信息，也就是N个时刻的静态参照物相对于车辆的位置关系，计算得到至少一个估计车速，估计车速为N个时刻中的任意两个时刻之间的平均车速；对至少一个估计车速进行处理，得到第二车速。
结合第一方面，在第一方面的某些实现方式中，当车辆与静态参照物在同一道路平面且车辆为直线行驶时，可以执行下面的操作：
利用静态参照物相对于车辆的距离、静态参照物与车辆行驶方向之间夹角，计算得到两个时刻之间车辆相对于静态参照物的位移；
利用位移和两个时刻之间的时间长度，计算得到两个时刻之间的平均速度。
结合第一方面，在第一方面的某些实现方式中，当车辆与静态参照物在同一道路平面且车辆为转弯行驶时，可以执行下面的操作：
利用静态参照物与车辆之间的距离、车辆的车轮转向角、车辆的长度，计算得到两个时刻之间车辆的转向角度、车辆的转向半径；
利用转向角度、转向半径和两个时刻之间的时间长度，计算得到两个时刻之间的平均速度。
结合第一方面，在第一方面的某些实现方式中，当车辆与静态参照物不在同一道路平面且车辆为直线行驶时，可以执行下面的操作：
建立任意三维坐标系，并表示出静态参照物的坐标、车辆在两个时刻的位置坐标；
根据静态参照物的坐标、车辆在两个时刻的位置坐标，计算得到两个时刻之间车辆相 对于静态参照物的位移；
利用位移和两个时刻之间的时间长度，计算得到两个时刻之间的平均速度。
结合第一方面，在第一方面的某些实现方式中，当车辆与静态参照物不在同一道路平面且车辆为转弯行驶时，可以执行下面的操作：
建立任意三维坐标系，并表示出静态参照物的坐标、车辆在两个时刻的位置的坐标；
利用静态参照物与车辆之间的距离、车辆的车轮转向角、车辆的长度，计算得到两个时刻之间车辆的转向角度、车辆的转向半径；
利用转向角度、转向半径和两个时刻之间的时间长度，计算得到两个时刻之间的平均速度。
可选地，可以通过求均值、取最大值、取最小值等方式获得第二车速。下面分别举例说明。应理解，只有一个估计车速的时候，无论是上述求平均值、取最大值、取最小值中的任意一种方式，计算结果都等于估计车速本身，在这种情况下，该估计车速即为第二车速。下面主要介绍当为多个估计车速的时候的处理方式，但应理解，只有一个估计车速可以看作是多个估计车速的特例。
例如，可以将多个估计车速求平均值，得到平均估计车速，并将该平均估计车速作为第二车速。
又例如，可以从多个估计车速中，选择最大值作为第二车速。
又例如，可以从多个估计车速中，选择最小值作为第二车速。
又例如，可以去掉一个最大值和/或去掉一个最小值，再将剩余的估计车速的平均值作为第二车速。
结合第一方面，在第一方面的某些实现方式中，在对至少一个估计车速进行处理得到第二车速时，可以对至少一个估计车速进行均值滤波或中值滤波，将滤波之后得到的车速作为第二车速。
可选地，可以根据第一车速和第二车速的差值，判断测速装置是否故障。当该差值在一定阈值范围内的时候，认为第一车速正确，测速装置无故障。当该差值不在该阈值范围内的时候，认为第一车速错误，测速装置故障。
结合第一方面，在第一方面的某些实现方式中，在根据第一车速和第二车速判断测速装置是否故障时，可以采用下面的方法：
当第一车速与第二车速之间的差距大于第一预设阈值时，认为测速装置故障；
当第一车速与第二车速之间的差距小于或等于第一预设阈值时，认为测速装置没有故障。
可选地，为了防止第二车速估算错误或者误差较大，导致对于故障的诊断出现错误，可以计算得到多个第二车速，之后当多个第二车速之间的误差较大的时候，不进行故障诊断，也就是说，估算不准确的时候不作为诊断依据。
可选地，可以利用同一个静态参照物，根据上述方法得到多个第二车速，也可以设置多个静态参照物，根据上述方法得到该多个参照物的第二车速。
以两个第二车速为例，可以分别比较两个第二车速与第一车速之间的差值，只有当两个差值均小于或等于设定阈值的时候才认为车速传感器故障，否则重新获取第二车速。
又例如还可以设定为，当两个第二车速中任意一个第二车速与第一车速的差值大于某 一阈值的时候，就重新获取第二车速。
三个以上第二车速的情况与之相似，不再重复介绍。
结合第一方面，在第一方面的某些实现方式中，静态参照物可以为多个，在这种情况下可以获取该多个静态参照物的参照信息；根据参照信息，计算得到多个第二车速；根据第一车速和多个第二车速，判断测速装置是否故障。
需要说明的是，获取该多个静态参照物的参照信息，可以理解为获取该多个静态参照物中的每个静态参照物的多个时刻的参照信息。
结合第一方面，在第一方面的某些实现方式中，在根据第一车速和多个第二车速，判断测速装置是否故障时，当多个第二车速之间的差距大于第二预设阈值时，可以重新获取第二车速；当多个第二车速之间的差距小于或等于第二预设阈值时，可以判断测速装置是否故障。
第二方面，提供一种车辆测速装置的故障诊断装置，该装置包括用于执行上述第一方面的任意一种实现方式的方法的单元。
第三方面，提供一种芯片，所述芯片包括处理器与数据接口，所述处理器通过所述数据接口读取存储器上存储的指令，执行上述第一方面的任意一种实现方式中的方法。
可选地，作为一种实现方式，所述芯片还可以包括存储器，所述存储器中存储有指令，所述处理器用于执行所述存储器上存储的指令，当所述指令被执行时，所述处理器用于执行第一方面的任意一种实现方式中的方法。
第四方面，提供一种车辆，该车辆包括第二方面任意一种故障诊断装置，以及测速装置，该故障诊断装置用于对该测速装置进行故障诊断。
第五方面，提供一种计算机可读介质，该计算机可读介质存储用于设备执行的程序代码，该程序代码包括用于执行第一方面的任意一种实现方式中的方法。
第六方面，提供一种包含指令的计算机程序产品，当该计算机程序产品在计算机上运行时，使得计算机执行上述第一方面的任意一种实现方式中的方法。
附图说明
图1是本申请实施例适用的一种车辆的功能框图。
图2是本申请实施例适用的一种自动驾驶系统的示意图。
图3是本申请实施例的一种云侧指令自动驾驶车辆的应用示意图。
图4是本申请实施例的故障诊断装置及其应用的示意图。
图5是本申请实施例的测速装置的故障诊断方法的示意性流程图。
图6是本申请实施例的选择静态参照物的示意性流程图。
图7是本申请实施例的一种估算车速的方法的示意性流程图。
图8是本申请实施例的平面道路场景的直行车速估算示意图。
图9是本申请实施例的另一种估算车速的方法的示意性流程图。
图10是本申请实施例的平面道路场景的转弯车速估算示意图。
图11是本申请实施例的一种坡道场景的车速估算示意图。
图12是本申请实施例的另一种坡道场景的车速估算示意图。
图13是本申请实施例的一种车辆测速装置的故障诊断装置的示意图。
图14是本申请实施例的一种车辆测速装置的故障诊断装置的示意图。
具体实施方式
本申请实施例所提供的车辆测速装置的故障诊断方法和/或装置可以应用于各类车辆。这些方法和/或装置既可以应用于人工驾驶，又可以应用于辅助驾驶，还可以应用于自动驾驶。下面结合附图，对本申请实施例的技术方案进行介绍。
图1是本申请实施例适用的一种车辆的功能框图。其中，车辆100可以是人工驾驶车辆，或者可以将车辆100配置可以为完全或部分地自动驾驶模式。
在一个示例中，车辆100可以在处于自动驾驶模式中的同时控制自车，并且可通过人为操作来确定车辆及其周边环境的当前状态，确定周边环境中的至少一个其他车辆的可能行为，并确定其他车辆执行可能行为的可能性相对应的置信水平，基于所确定的信息来控制车辆100。在车辆100处于自动驾驶模式中时，可以将车辆100置为在没有和人交互的情况下操作。
车辆100中可以包括各种子系统，例如，行进系统110、传感系统120、控制系统130、一个或多个外围设备140以及电源160、计算机系统150和用户接口170。
可选地，车辆100可以包括更多或更少的子系统，并且每个子系统可包括多个元件。另外，车辆100的每个子系统和元件可以通过有线或者无线互连。
示例性地，行进系统110可以包括用于向车辆100提供动力运动的组件。在一个实施例中，行进系统110可以包括引擎111、传动装置112、能量源113和车轮114/轮胎。其中，引擎111可以是内燃引擎、电动机、空气压缩引擎或其他类型的引擎组合；例如，汽油发动机和电动机组成的混动引擎，内燃引擎和空气压缩引擎组成的混动引擎。引擎111可以将能量源113转换成机械能量。
示例性地，能量源113可以包括汽油、柴油、其他基于石油的燃料、丙烷、其他基于压缩气体的燃料、乙醇、太阳能电池板、电池和其他电力来源。能量源113也可以为车辆100的其他系统提供能量。
示例性地，传动装置112可以包括变速箱、差速器和驱动轴；其中，传动装置112可以将来自引擎111的机械动力传送到车轮114。
在一个实施例中，传动装置112还可以包括其他器件，比如离合器。其中，驱动轴可以包括可耦合到一个或多个车轮114的一个或多个轴。
示例性地，传感系统120可以包括感测关于车辆100周边的环境的信息的若干个传感器。
例如，传感系统120可以包括定位系统121(例如，全球定位系统(global positioning system,GPS)、北斗系统或者其他定位系统)、惯性测量单元(inertial measurement unit，IMU)122、雷达123、激光测距仪124、相机125以及车速传感器126。传感系统120还可以包括被监视车辆100的内部系统的传感器(例如，车内空气质量监测器、燃油量表、机油温度表等)。来自这些传感器中的一个或多个的传感器数据可用于检测对象及其相应特性(位置、形状、方向、速度等)。这种检测和识别是自主车辆100的安全操作的关键功能。
其中，定位系统121可以用于估计车辆100的地理位置。IMU 122可以用于基于惯性 加速度来感测车辆100的位置和朝向变化。在一个实施例中，IMU 122可以是加速度计和陀螺仪的组合。
示例性地，雷达123可以利用无线电信号来感测车辆100的周边环境内的物体。在一些实施例中，除了感测物体以外，雷达123还可用于感测物体的速度和/或前进方向。
示例性地，激光测距仪124可以利用激光来感测车辆100所位于的环境中的物体。在一些实施例中，激光测距仪124可以包括一个或多个激光源、激光扫描器以及一个或多个检测器，以及其他系统组件。
示例性地，相机125可以用于捕捉车辆100的周边环境的多个图像。例如，相机125可以是静态相机或视频相机。
示例性地，车速传感器126可以用于测量车辆100的速度。例如，可以对车辆进行实时测速。测得的车速可以传送给控制系统130以实现对车辆的控制。
如图1所示，控制系统130为控制车辆100及其组件的操作。控制系统130可以包括各种元件，比如可以包括转向系统131、油门132、制动单元133、计算机视觉系统134、路线控制系统135以及障碍规避系统136。
示例性地，转向系统131可以操作来调整车辆100的前进方向。例如，在一个实施例中可以为方向盘系统。油门132可以用于控制引擎111的操作速度并进而控制车辆100的速度。
示例性地，制动单元133可以用于控制车辆100减速；制动单元133可以使用摩擦力来减慢车轮114。在其他实施例中，制动单元133可以将车轮114的动能转换为电流。制动单元133也可以采取其他形式来减慢车轮114转速从而控制车辆100的速度。
如图1所示，计算机视觉系统134可以操作来处理和分析由相机125捕捉的图像以便识别车辆100周边环境中的物体和/或特征。上述物体和/或特征可以包括交通信号、道路边界和障碍物。计算机视觉系统134可以使用物体识别算法、运动中恢复结构(structure from motion，SFM)算法、视频跟踪和其他计算机视觉技术。在一些实施例中，计算机视觉系统134可以用于为环境绘制地图、跟踪物体、估计物体的速度等等。
示例性地，路线控制系统135可以用于确定车辆100的行驶路线。在一些实施例中，路线控制系统135可结合来自传感器、GPS和一个或多个预定地图的数据以为车辆100确定行驶路线。
如图1所示，障碍规避系统136可以用于识别、评估和避免或者以其他方式越过车辆100的环境中的潜在障碍物。
在一个实例中，控制系统130可以增加或替换地包括除了所示出和描述的那些以外的组件。或者也可以减少一部分上述示出的组件。
如图1所示，车辆100可以通过外围设备140与外部传感器、其他车辆、其他计算机系统或用户之间进行交互；其中，外围设备140可包括无线通信系统141、车载电脑142、麦克风143和/或扬声器144。
在一些实施例中，外围设备140可以提供车辆100与用户接口170交互的手段。例如，车载电脑142可以向车辆100的用户提供信息。用户接口116还可操作车载电脑142来接收用户的输入；车载电脑142可以通过触摸屏进行操作。在其他情况中，外围设备140可以提供用于车辆100与位于车内的其它设备通信的手段。例如，麦克风143可以从车辆 100的用户接收音频(例如，语音命令或其他音频输入)。类似地，扬声器144可以向车辆100的用户输出音频。
如图1所述，无线通信系统141可以直接地或者经由通信网络来与一个或多个设备无线通信。例如，无线通信系统141可以使用3G蜂窝通信；例如，码分多址(code division multiple access，CDMA))、EVD0、全球移动通信系统(global system for mobile communications，GSM)/通用分组无线服务(general packet radio service，GPRS)，或者4G蜂窝通信，例如长期演进(long term evolution，LTE)；或者，5G蜂窝通信。无线通信系统141可以利用无线上网(WiFi)与无线局域网(wireless local area network，WLAN)通信。
在一些实施例中，无线通信系统141可以利用红外链路、蓝牙或者紫蜂协议(ZigBee)与设备直接通信；其他无线协议，例如各种车辆通信系统，例如，无线通信系统141可以包括一个或多个专用短程通信(dedicated short range communications，DSRC)设备，这些设备可包括车辆和/或路边台站之间的公共和/或私有数据通信。
如图1所示，电源160可以向车辆100的各种组件提供电力。在一个实施例中，电源160可以为可再充电锂离子电池或铅酸电池。这种电池的一个或多个电池组可被配置为电源为车辆100的各种组件提供电力。在一些实施例中，电源160和能量源113可一起实现，例如一些全电动车中那样。
示例性地，车辆100的部分或所有功能可以受计算机系统150控制，其中，计算机系统150可以包括至少一个处理器151，处理器151执行存储在例如存储器152中的非暂态计算机可读介质中的指令153。计算机系统150还可以是采用分布式方式控制车辆100的个体组件或子系统的多个计算设备。
例如，处理器151可以是任何常规的处理器，诸如商业可获得的中央处理器(central processing unit，CPU)。
可选地，该处理器可以是诸如专用集成电路(application specific integrated circuit，ASIC)或其它基于硬件的处理器的专用设备。尽管图1功能性地图示了处理器、存储器、和在相同块中的计算机的其它元件，但是本领域的普通技术人员应该理解该处理器、计算机、或存储器实际上可以包括可以或者可以不存储在相同的物理外壳内的多个处理器、计算机或存储器。例如，存储器可以是硬盘驱动器或位于不同于计算机的外壳内的其它存储介质。因此，对处理器或计算机的引用将被理解为包括对可以或者可以不并行操作的处理器或计算机或存储器的集合的引用。不同于使用单一的处理器来执行此处所描述的步骤，诸如转向组件和减速组件的一些组件每个都可以具有其自己的处理器，所述处理器只执行与特定于组件的功能相关的计算。
在此处所描述的各个方面中，处理器可以位于远离该车辆并且与该车辆进行无线通信。在其它方面中，此处所描述的过程中的一些在布置于车辆内的处理器上执行而其它则由远程处理器执行，包括采取执行单一操纵的必要步骤。
在一些实施例中，存储器152可包含指令153(例如，程序逻辑)，指令153可以被处理器151来执行车辆100的各种功能，包括以上描述的那些功能。存储器152也可包括额外的指令，比如包括向行进系统110、传感系统120、控制系统130和外围设备140中的一个或多个发送数据、从其接收数据、与其交互和/或对其进行控制的指令。
示例性地，除了指令153以外，存储器152还可存储数据，例如，道路地图、路线信息，车辆的位置、方向、速度以及其它这样的车辆数据，以及其他信息。这种信息可在车辆100在自主、半自主和/或手动模式中操作期间被车辆100和计算机系统150使用。
如图1所示，用户接口170可以用于向车辆100的用户提供信息或从其接收信息。可选地，用户接口170可以包括在外围设备140的集合内的一个或多个输入/输出设备，例如，无线通信系统141、车载电脑142、麦克风143和扬声器144。
在本申请的实施例中，计算机系统150可以基于从各种子系统(例如，行进系统110、传感系统120和控制系统130)以及从用户接口170接收的输入来控制车辆100的功能。例如，计算机系统150可以利用来自控制系统130的输入以便控制制动单元133来避免由传感系统120和障碍规避系统136检测到的障碍物。在一些实施例中，计算机系统150可操作来对车辆100及其子系统的许多方面提供控制。
可选地，上述这些组件中的一个或多个可与车辆100分开安装或关联。例如，存储器152可以部分或完全地与车辆100分开存在。上述组件可以按有线和/或无线方式来通信地耦合在一起。
可选地，上述组件只是一个示例，实际应用中，上述各个模块中的组件有可能根据实际需要增添或者删除，图1不应理解为对本申请实施例的限制。
可选地，车辆100可以是在道路行进的自动驾驶汽车，可以识别其周围环境内的物体以确定对当前速度的调整。物体可以是其它车辆、交通控制设备、或者其它类型的物体。在一些示例中，可以独立地考虑每个识别的物体，并且基于物体的各自的特性，诸如它的当前速度、加速度、与车辆的间距等，可以用来确定自动驾驶汽车所要调整的速度。
可选地，车辆100或者与车辆100相关联的计算设备(如图1的计算机系统150、计算机视觉系统134、存储器152)可以基于所识别的物体的特性和周围环境的状态(例如，交通、雨、道路上的冰等等)来预测所述识别的物体的行为。
可选地，每一个所识别的物体都依赖于彼此的行为，因此，还可以将所识别的所有物体全部一起考虑来预测单个识别的物体的行为。车辆100能够基于预测的所述识别的物体的行为来调整它的速度。换句话说，自动驾驶汽车能够基于所预测的物体的行为来确定车辆将需要调整到(例如，加速、减速、或者停止)稳定状态。在这个过程中，也可以考虑其它因素来确定车辆100的速度，诸如，车辆100在行驶的道路中的横向位置、道路的曲率、静态和动态物体的接近度等等。
除了提供调整自动驾驶汽车的速度的指令之外，计算设备还可以提供修改车辆100的转向角的指令，以使得自动驾驶汽车遵循给定的轨迹和/或维持与自动驾驶汽车附近的物体(例如，道路上的相邻车道中的轿车)的安全横向和纵向距离。
上述车辆100可以为轿车、卡车、摩托车、公共汽车、船、飞机、直升飞机、割草机、娱乐车、游乐场车辆、施工设备、电车、高尔夫球车、火车、和手推车等，本申请实施例不做特别的限定。
在一种可能的实现方式中，上述图1所示的车辆100可以是自动驾驶车辆，下面对自动驾驶系统的进行详细描述。
图2是本申请实施例适用的一种自动驾驶系统的示意图。
如图2所示的自动驾驶系统包括计算机系统201，其中，计算机系统201包括处理器 203，处理器203和系统总线205耦合。处理器203可以是一个或者多个处理器，其中，每个处理器都可以包括一个或多个处理器核。显示适配器207(video adapter)，显示适配器可以驱动显示器209，显示器209和系统总线205耦合。系统总线205可以通过总线桥211和输入输出(I/O)总线213耦合，I/O接口215和I/O总线耦合。I/O接口215和多种I/O设备进行通信，比如，输入设备217(如：键盘，鼠标，触摸屏等)，媒体盘221(media tray),(例如，CD-ROM，多媒体接口等)。收发器223可以发送和/或接受无线电通信信号，摄像头255可以捕捉景田和动态数字视频图像。其中，和I/O接口215相连接的接口可以是USB端口225。
其中，处理器203可以是任何传统处理器，比如，精简指令集计算(reduced instruction set computer，RISC)处理器、复杂指令集计算(complex instruction set computer，CISC)处理器或上述的组合。
可选地，处理器203可以是诸如专用集成电路(ASIC)的专用装置；处理器203可以是神经网络处理器或者是神经网络处理器和上述传统处理器的组合。
可选地，在一些实施例中，计算机系统201可位于远离自动驾驶车辆的地方，并且可与自动驾驶车辆无线通信。在其它方面，本申请所述的一些过程在设置在自动驾驶车辆内的处理器上执行，其它由远程处理器执行，包括采取执行单个操纵所需的动作。
计算机系统201可以通过网络接口229和软件部署服务器249通信。网络接口229可以是硬件网络接口，比如，网卡。网络227可以是外部网络，比如，因特网，也可以是内部网络，比如以太网或者虚拟私人网络(virtual private network，VPN)。可选地，网络227还可以是无线网络，比如WiFi网络，蜂窝网络等。
如图2所示，硬盘驱动接口和系统总线205耦合，硬件驱动器接口231可以与硬盘驱动器233相连接，系统内存235和系统总线205耦合。运行在系统内存235的数据可以包括操作系统237和应用程序243。其中，操作系统237可以包括解析器(shell)239和内核(kernel)241。shell 239是介于使用者和操作系统之内核(kernel)间的一个接口。shell可以是操作系统最外面的一层；shell可以管理使用者与操作系统之间的交互，比如，等待使用者的输入，向操作系统解释使用者的输入，并且处理各种各样的操作系统的输出结果。内核241可以由操作系统中用于管理存储器、文件、外设和系统资源的那些部分组成。直接与硬件交互，操作系统内核通常运行进程，并提供进程间的通信，提供CPU时间片管理、中断、内存管理、IO管理等等。应用程序243包括控制汽车自动驾驶相关的程序，比如，管理自动驾驶的汽车和路上障碍物交互的程序，控制自动驾驶汽车路线或者速度的程序，控制自动驾驶汽车和路上其他自动驾驶汽车交互的程序。应用程序243也存在于软件部署服务器249的系统上。在一个实施例中，在需要执行自动驾驶相关程序247时，计算机系统201可以从软件部署服务器249下载应用程序。
例如，应用程序243还可以是自动驾驶汽车和路上车道线交互的程序，也就是说可以实时跟踪车道线的程序。
例如，应用程序243还可以是控制自动驾驶车辆进行自动泊车的程序。
示例性地，传感器253可以与计算机系统201关联，传感器253可以用于探测计算机201周围的环境。
举例来说，传感器253可以探测路上的车道，比如可以探测到车道线，并能够在车辆 移动(如正在行驶)过程中实时跟踪到车辆前方一定范围内的车道线变化。又例如，传感器253可以探测动物，汽车，障碍物和人行横道等，进一步传感器还可以探测上述动物，汽车，障碍物和人行横道等物体周围的环境，比如：动物周围的环境，例如，动物周围出现的其他动物，天气条件，周围环境的光亮度等。
可选地，如果计算机201位于自动驾驶的汽车上，传感器可以是摄像头，红外线感应器，化学检测器，麦克风等。
示例性地，在车道线跟踪的场景中，传感器253可以用于探测车辆前方的车道线，从而使得车辆能够感知在行进过程中车道的变化，以据此对车辆的行驶进行实时规划和调整。
示例性地，在自动泊车的场景中，传感器253可以用于探测车辆周围的库位和周边障碍物的尺寸或者位置，从而使得车辆能够感知库位和周边障碍物的距离，在泊车时进行碰撞检测，防止车辆与障碍物发生碰撞。
在一个示例中，图1所示的计算机系统150还可以从其它计算机系统接收信息或转移信息到其它计算机系统。或者，从车辆100的传感系统120收集的传感器数据可以被转移到另一个计算机对此数据进行处理，下面以图3为例进行介绍。
图3是本申请实施例的一种云侧指令自动驾驶车辆的应用示意图。如图3所示，来自计算机系统312的数据可以经由网络被传送到云侧的服务器320用于进一步的处理。网络以及中间节点可以包括各种配置和协议，包括因特网、万维网、内联网、虚拟专用网络、广域网、局域网、使用一个或多个公司的专有通信协议的专用网络、以太网、WiFi和HTTP、以及前述的各种组合；这种通信可以由能够传送数据到其它计算机和从其它计算机传送数据的任何设备，诸如调制解调器和无线接口。
在一个示例中，服务器320可以包括具有多个计算机的服务器，例如负载均衡服务器群，为了从计算机系统312接收、处理并传送数据的目的，其与网络的不同节点交换信息。该服务器可以被类似于计算机系统312配置，具有处理器330、存储器340、指令350、和数据360。
示例性地，服务器320的数据360可以包括车辆周围道路情况的相关信息。例如，服务器320可以接收、检测、存储、更新、以及传送与车辆道路情况的相关信息。
例如，车辆周围道路情况的相关信息包括与车辆周围的其它车辆信息以及障碍物信息。
目前车辆的测速装置主要为车速传感器，但在实际使用过程中，车速传感器会出现故障或短路断路等情况，导致不能测速或者测得的车速不准确，为了保证车辆的安全就需要及时发现车速传感器的故障。在现有技术的方案中，往往借助于其他自车动力信息来推算出一个车速，与测得的车速比较就可以判断出测得的车速是否正常，从而推断出测速装置是否正常工作。但是现有方案的每种方案都没有办法覆盖所有工况，正如上文提到的，只能在一些特定的情况下才能有效，导致诊断范围非常局限。
针对上述问题，本申请实施例提供一种新的测速装置的故障诊断方法和装置，诊断范围广，能够更准确地诊断出测速装置的故障。主要通过利用车辆外部的静态参照物来估算车辆的车速，然后以估算的车速为依据来判断测速装置测得的车速是否正常，从而可以诊断出测速装置是否存在故障。这种方案不需要依赖于车辆的自车动力信息，因此不会受每 种自车动力信息所覆盖范围的局限，所以可以具有更广的诊断范围，从而可以更为准确地诊断出测速装置的故障。
需要说明的是，自车动力信息可以理解为与车辆运动相关的信息，或者可以理解为会影响车速的信息，由于自车动力信息(例如转矩、轮速等)与车速是有直接关联性的，也就是说，自车动力信息会直接影响到车速。但是自车动力信息会存在变化，在一些场景下不符合已知的关联性关系，就会导致不能正确推断出车速。例如，在爬坡的时候，转矩与车速的关联性关系被打破，导致不能再利用转矩推算出车速。又例如，在车轮打滑的时候，车辆是继续移动的(车速不为零)，但根据轮速推断出来的车速却为零。还有众多其他例子，不再一一列举。简而言之，虽然可以利用自车动力信息推断出车速，从而判断测速装置是否正常工作，但是存在很大的局限性。而在本本申请实施例用来估算车速的是车辆相对于外部参照物的移动，所以只要车辆在动就可以测得，可以覆盖更广的诊断范围。
图4是本申请实施例的故障诊断装置及其应用的示意图。如图4所示，故障诊断装置420从其他装置中获取一些信息，经过一定处理后就可以得到故障诊断结果。
可选地，故障诊断装置420可以包括获取单元421和诊断单元422。
获取单元421可以用于获取一些信息(在本申请实施例中信息可以理解为数据)，例如可以获取来自于测速装置411的车速信息，该车速信息包括测速装置所测得的车速。又例如还可以获取来自于环境检测装置413的参照物信息，该参照物中包括至少一个静态参照物的信息。
可选地，环境检测装置413可以是雷达探测装置、摄像头等，例如可以是图1或图2所示的雷达123、激光测距仪124、相机125或摄像头255等。
诊断单元422可以用于对获取单元421获取的信息进行处理，得到故障诊断结果。
可选地，诊断单元422可以根据参照物信息推算出估计车速，再根据估计车速判断测速装置测得的车速是否正确。当判定车速正确时，故障诊断结果即为“无故障”或“正常工作”。当判定车速错误时(没有测得车速为车速错误的一个例子)，故障诊断结果即为“故障”或“未正常工作”。需要说明的是，诊断单元422的具体诊断过程会在下面结合各图进行介绍，在此不再展开介绍。
在该方案中，利用车辆以外的参照物估算车速，进而跟测速装置测得的车速进行比较，从而可以判定测速装置是否故障，不需要依赖于车辆的自车动力装置。
需要说明的是，虽然本申请实施例不需要依赖于车辆的自车动力装置就可以判断测速装置是否正常，但是依然可以加入自车动力信息进行诊断，也就是说，可以在上述方案的基础上，增加自车动力信息作为诊断依据的扩充，并不会影响上述方案的实现效果。
可选地，故障检测装置420还可以包括发送单元，用于将故障诊断结果发送给其他模块或装置，例如发送给控制系统，用于生成控制信号，对车辆进行控制。
图5是本申请实施例的测速装置的故障诊断方法的示意性流程图。下面对图5各个步骤进行介绍。
501、获取测速装置测得的第一车速。
502、获取静态参照物的参照信息。
可选地，可以获取N个时刻的参照信息，N为大于1的整数。N个时刻可以为连续的，也可以为非连续的，不存在限定。
可选地，该参照信息可以包括该静态参照物相对于车辆的位置关系的信息，该车辆可以理解为待诊断的测速装置所在的车辆。也就是说，可以获取上述N个时刻中的每个时刻时该静态参照物相对于车辆的位置关系。
可选地，该位置关系可以包括静态参照物相对于车辆的距离和方向。
可选地，静态参照物相对于车辆的方向可以利用静态参照物与车辆的连线与车辆行进方向之间的夹角表示。
需要说明的是，步骤501和步骤502不存在执行顺序的限定，可以同时执行，也可以先执行步骤501，还可以先执行步骤502。
可选地，可以直接设定静态参照物为某种路标或交通设施等，使得一旦识别到该路标或交通设施就开始执行故障诊断的各个步骤。
可选地，还可以通过获取车辆周围物体，从识别出的物体中选择出静态的物体，以及从静态的物体中选取静态参照物。
图6是本申请实施例的选择静态参照物的示意性流程图，下面对图6各个步骤进行介绍。
601、利用感知设备识别车辆周围物体。
可选地，该感知设备可以包括摄像头、雷达。
可选地，可以利用摄像头获取图像或视频，然后对图像进行处理，识别出图像中的物体。
可选地，还可以利用雷达感测物体以及物体是否移动。
602、从识别出的物体中选取至少一个静态参照物。
也就是说，可以从识别出的物体中选取出至少一个静态的物体，以及将静态的物体作为静态参照物。
当选定静态参照物之后，就可以获取静态参照物信息，该静态参照物信息包括静态参照物相对于车辆的距离和静态参照物相对于车辆的方位/方向。即可以执行步骤603。
603、获取静态参照物相对于车辆的距离和方向信息。
需要说明的是，图6主要提供了一种确定静态参照物的方法，步骤603可以执行也可以不执行。
503、根据参照信息，计算第二车速。
需要说明的是，在本申请实施例中，第二车速用于表示估计得到的车速，或者可以理解为推算出的车速，还可以理解为非测速装置测得的车速。
可选地，可以利用上述N个时刻的参照信息计算得到第二车速，也就是说，可以根据一段时间内车辆相对于静态参照物的位移和/或角度的变化量来计算得到这段时间内的平均车速，并将该平均车速作为第二车速，或者还可以得到多个平均车速，然后基于该多个平均车速来得到一个第二车速。由于涉及内容较多，下面会结合图7至图12进行介绍，在此不再展开。
需要说明的是，步骤503需要在步骤502之后执行，但不一定需要在步骤501之后执行，也就是说，可以先执行步骤502和步骤503，再执行步骤501。还可以在执行步骤502和步骤503的任意期间执行步骤501，等等，不存在限定。
504、根据第一车速和第二车速，判断测速装置是否故障。
可选地，可以根据第一车速和第二车速的差值，判断测速装置是否故障。当该差值在一定阈值范围内的时候，认为第一车速正确，测速装置无故障。当该差值不在该阈值范围内的时候，认为第一车速错误，测速装置故障。
可选地，为了防止第二车速估算错误或者误差较大，导致对于故障的诊断出现错误，可以计算得到多个第二车速，之后当多个第二车速之间的误差较大的时候，不进行故障诊断，也就是说，估算不准确的时候不作为诊断依据。
可选地，可以利用同一个静态参照物，根据上述方法得到多个第二车速，也可以设置多个静态参照物，根据上述方法得到该多个参照物的第二车速。
以两个第二车速为例，可以分别比较两个第二车速与第一车速之间的差值，只有当两个差值均小于或等于设定阈值的时候才认为车速传感器故障，否则重新获取第二车速。
又例如还可以设定为，当两个第二车速中任意一个第二车速与第一车速的差值大于某一阈值的时候，就重新获取第二车速。
三个以上第二车速的情况与之相似，不再重复介绍。
图7是本申请实施例的一种估算车速的方法的示意性流程图，图7所示方法可以适用于车辆在平面道路上直线行驶的估算，例如图8所示的平面道路场景中直线行驶的估算。下面对图7各个步骤进行介绍。
701、获取静态参照物的N个时刻的参照信息，N为大于1的整数。
可选地，该参照信息可以包括该静态参照物相对于车辆的位置关系的信息，该车辆可以理解为待诊断的测速装置所在的车辆。也就是说，获取上述N个时刻中的每个时刻时该静态参照物相对于车辆的位置关系。
可选地，该N个时刻可以是连续的时刻，也可以是不连续的时刻，只要与第一车速所对应的时刻接近即可。
可选地，可以选取与第一车速所对应的时刻的是时间长度在一定阈值范围内的N个时刻。也就是说，假设第一车速是在T1时刻测得的，时间长度阈值范围为Δt，就可以选取[T1-Δt，T1+Δt]范围内的N个时刻。应理解，该N个时刻不一定必须包括T1，例如可以选取T1+1到T1+N这N个时刻，还应理解N个时刻可以是连续的，也可是不连续的，不存在限定。
702、计算至少两个时刻之间的至少一个位移ΔS。
需要说明的是，该至少两个时刻是指上述N个时刻中的至少两个时刻，该两个时刻不要求必须是连续的。
还应理解，由于可以计算多于两个时刻之间的位移ΔS，所以位移ΔS可以为一个，也可以为多个。
可选地，可以根据两个时刻静态参照物相对于车辆的距离、车辆前进方向与静态参照物和车辆的连线之间的夹角，计算得到位移。
703、根据上述至少一个位移ΔS和上述至少两个时刻之间的时间长度，计算得到至少一个估计车速V’。
704、根据上述至少一个估计车速V’，得到第二车速。
可选地，可以通过求均值、取最大值、取最小值等方式获得第二车速。下面分别举例说明。应理解，只有一个估计车速的时候，无论是上述求平均值、取最大值、取最小值中 的任意一种方式，计算结果都等于估计车速本身，在这种情况下，该估计车速即为第二车速。下面主要介绍当为多个估计车速的时候的处理方式，但应理解，只有一个估计车速可以看作是多个估计车速的特例。
例如，可以将多个估计车速求平均值，得到平均估计车速，并将该平均估计车速作为第二车速。
又例如，可以从多个估计车速中，选择最大值作为第二车速。
又例如，可以从多个估计车速中，选择最小值作为第二车速。
又例如，可以去掉一个最大值和/或去掉一个最小值，再将剩余的估计车速的平均值作为第二车速。
图8是本申请实施例的平面道路场景的直行车速估算示意图。在图8所示场景中，车辆在平地上直线行驶，通过图8所示车辆在两个不同时刻与静态参照物的相对关系，就可以获得两个相邻时刻间的车辆位移，从而获得当前的车辆速度，下面对具体计算过程进行举例介绍。
假设从摄像头获取了相邻N+1个采样时刻下静态参照物与车辆的位置关系。在第i时刻，所选静态参照物与车辆的距离L i，所选静态参照物与当前车辆连线和车辆行驶方向的夹角α i；在第i+1时刻，所选静态参照物与车辆的距离L i+1，所选静态参照物与当前车辆连线和车辆行驶方向的夹角α i+1。
可以根据车辆在第i时刻和第i+1时刻两个相邻时刻与静态参照物的位置关系，计算车辆在两个时刻的位移ΔS，由图8中几何关系可以推算出：
Figure PCTCN2020116411-appb-000001
第i时刻和第i+1时刻的时间间隔为Δt，因此，可以推算出第i时刻至第i+1时刻之间的平均速度v i为：v i＝ΔS/Δt。
可以将v i看作是图7中所述的估计车速V’。
可选地，可以根据上述同样的方法，计算得到1至N+1时刻之间的N个时间间隔的N个平均速度。
可选地，还可以通过滤波的方式确定第二车速，滤波方式可以是均值滤波、中值滤波等滤波方式的一种，具体可以参考图7相关介绍，不再重复。
图9是本申请实施例的另一种估算车速的方法的示意性流程图，图9所示方法可以适用于车辆在平面道路上转弯行驶的车速估算，例如图10所示的平面道路转弯场景的车速估算。下面对图9各个步骤进行介绍。
901、获取静态参照物的N个时刻的参照信息，N为大于1的整数。
可选地，该参照信息可以包括该静态参照物相对于车辆的位置关系的信息，该车辆可以理解为待诊断的测速装置所在的车辆。也就是说，获取上述N个时刻中的每个时刻时该静态参照物相对于车辆的位置关系。
可选地，该N个时刻可以是连续的时刻，也可以是不连续的时刻，只要与第一车速所对应的时刻接近即可。
可选地，可以选取与第一车速所对应的时刻的是时间长度在一定阈值范围内的N个时刻。也就是说，假设第一车速是在T1时刻测得的，时间长度阈值范围为Δt，就可以选取 [T1-Δt，T1+Δt]范围内的N个时刻。应理解，该N个时刻不一定必须包括T1，例如可以选取T1+1到T1+N这N个时刻，还应理解N个时刻可以实连续的，也可是不连续的，不存在限定。
902、计算至少两个时刻之间的至少一个转向角度x。
可选地，可以根据车身长度和车轮转向角计算得到转向半径，再根据转向半径、转向角度之间的几何关系来计算得到转向角度。
需要说明的是，该至少两个时刻是指上述N个时刻中的至少两个时刻，该两个时刻不要求必须是连续的。
还应理解，由于可以计算多于两个时刻之间的转向角度x，所以转向角度x可以为一个，也可以为多个。
903、根据上述至少一个转向角度x，计算得到至少一个估计车速V’。
904、根据上述至少一个估计车速V’，得到第二车速。
可选地，可选采用与步骤704相同的方法得到第二车速，不再重复介绍。
图10是本申请实施例的平面道路场景的转弯车速估算示意图。在图10所示场景中，车辆在平地上转弯行驶，通过图10所示车辆在两个不同时刻与静态参照物的相对关系，就可以获得两个相邻时刻间的车辆转向角度x，从而获得当前的车辆速度，下面对具体计算过程进行举例介绍。
假设从摄像头获取了相邻N个采样时刻下静态参照物与车辆的位置关系，在第i时刻，所选静态参照物与车辆的距离L i，所选静态参照物与当前车辆连线和车辆行驶方向的夹角α i，以及车辆的车轮转向角δ i；在第i+1时刻，所选静态参照物与车辆的距离L i+1，所选静态参照物与当前车辆连线和车辆行驶方向的夹角α i+1，以及车辆的车轮转向角δ i+1。
可以根据车身长度L和第i时刻的车轮转向角δ i，计算得到第i时刻的转向半径R i，并将R i作为第i时刻至第i+1时刻的转向半径，如图10中标注。
根据图10所示的几何关系，第i时刻至第i+1时刻车辆所行驶过的转向角度x与转向半径R i等几何量之间的关系为：
x+(90°-α i)＝y+(90°-α i+1)，
L i 2+L i+1 2-2L iL i+1cosy＝2R i 2-2R i 2cosx。
通过上面两个式子就可以求解出转向角x。
第i时刻和第i+1时刻的时间间隔为Δt，因此，可以推算出第i时刻至第i+1时刻之间的平均速度v i为：v i＝x R i/Δt。
可以将v i看作是图9中所述的估计车速V’。
可选地，可以根据上述同样的方法，计算得到1至N+1时刻之间的N个时间间隔的N个平均速度。
可选地，还可以通过滤波的方式确定第二车速，滤波方式可以是均值滤波、中值滤波等滤波方式的一种，具体可以参考上文相关介绍，不再重复。
图11是本申请实施例的一种坡道场景的车速估算示意图。在图11所示场景中，静态参照物与车辆同处于坡道，从图11中可以看出，在这种情况下，相当于静态参照物和车辆在同一个平面道路中，此处为在坡道所在平面道路中，因此可以采用与平面道路时完全相同的计算方法，例如，当为直线行驶时可以采用图7或图8所示方法，当为转弯行驶时， 可以采用图9或图10所示的方法，为了简洁，在此不再重复介绍。
图12是本申请实施例的另一种坡道场景的车速估算示意图。如图12所示，车辆在坡道上行驶，但静态参照物在坡道前方的平面道路上。可以理解为，图12所示是一种静态参照物与车辆不共面的情况，但应理解，图12所示场景可以看作是车辆在坡道静态参照物在平面道路，但也可以看作是车辆在平面道路，静态参照物在坡道。
对于图12所示场景，可以通过建立三维坐标系的方法，标注出静态参照物的坐标(图12中A点坐标)、车辆在第i时刻的坐标(图12中B点坐标)、车辆在第i+1时刻的坐标(图12中C点坐标)，从而可以根据三者的几何关系估算出车辆在第i时刻至第i+1时刻之间的平均车速v i，进而可以得到第二车速。下面进行举例说明。
假设通过车辆加速度传感器估算所处坡道的坡度为γ，通过摄像头获得第i时刻和第i+1时刻相邻两个时刻，静态参照物的距离L i、L i+1及与行驶方向的夹角α i、α i+1。
以车辆行驶方向与地平线(图12中所示虚线)所在的面为XZ面建立坐标系，假设B点与原点距离为P，C点与原点距离为Q，则B、C两点的坐标分别为B(-M cosγ，0，M sinγ)、C(-N cosγ，0，N sinγ)。设静态参照物的坐标为：A(X，Y，0)。
三者之间的几何关系满足下面的式子：
cos α i＝(X+P cosγ，Y，-P sinγ)(-cosγ，0，-sinγ)/(P-Q)/L i；
cos α i+1＝(X+Q cosγ，Y，-Q sinγ)(-cosγ，0，-sinγ)/(P-Q)/L i+1；
L i  2＝(X+P cosγ) 2+Y 2+(P sinγ) 2；
L i+1 2＝(X+Q cosγ) 2+Y 2+(Q sinγ) 2。
通过上面几个式子，可以求解出P和Q的值。
第i时刻和第i+1时刻的时间间隔为Δt，因此，可以推算出第i时刻至第i+1时刻之间的平均速度v i为：v i＝(P-Q)/Δt。
需要说明的是，图12所示方法可以理解为是将图7和图8所示方法从二维空间推广到了三维空间，因此，对于平面道路可以看作是图12所示场景的一个特例，也就是说，图7和图8所示场景可以看作是图11和图12中坡度γ为0的特例，此时，上面的公式同样适用。
还应理解，对于车辆和静态参照物不在同一道路平面且车辆为转弯行驶的情况，同样可以采用建立坐标系的方式进行计算，可以看作是将图9和图10所示方法从二维空间推广到三维空间，为了简洁，不再重复介绍。
还需理解，图7至图11均为车辆和静态参照物在同一道路平面的情况，其中，图7至图10是在水平地面上，图11是在有坡度的地面上。而图12是车辆和静态参照物不再同一道路平面上。可以看出，无论车辆和静态参照五是不是在同一道路平面，无论车辆是直线行驶还是转弯行驶，都可以利用车辆与静态参照物之间的位置关系来求算出一端时间间隔内的平均车速，从而得到第二车速，以及利用第二车速来判断第一车速是否准确。
在本申请实施例中，通过利用车辆外部的静态参照物来估算车辆的车速，然后以估算的车速为依据来判断测速装置测得的车速是否正常，从而可以诊断出测速装置是否存在故障。这种方案不需要依赖于车辆的自车动力信息，因此不会受每种自车动力信息所覆盖范围的局限，所以可以具有更广的诊断范围，从而可以更为准确地诊断出测速装置的故障。
上文对本申请实施例的车辆测速装置的故障诊断方法进行了介绍，下面对本申请实施 例的车辆测速装置的故障诊断装置进行介绍。应理解，下文中介绍的故障诊断装置能够执行本申请实施例的故障诊断方法的各个过程，下面在介绍装置的实施例时，会适当省略重复的描述。
图13是本申请实施例的一种车辆测速装置的故障诊断装置的示意图，该装置2000包括获取单元2001和处理单元2002。该装置2000可以用于执行本申请实施例的车辆测速装置的故障诊断方法的各步骤。例如，获取单元2001可以用于执行图5所示方法中的步骤501和步骤502，处理单元2002可以用于执行图5所示方法中的步骤503和步骤504。又例如，获取单元2001可以用于执行图6所示方法中的步骤601，处理单元2002可以用于执行图6所示方法中的步骤602，当图6所示方法包括执行步骤603时，获取单元2001还可以用于执行步骤603。又例如，获取单元2001可以用于执行图7所示方法中的步骤701，处理单元2002可以用于执行图7所示方法中的步骤702至步骤704。又例如，获取单元2001可以用于执行图9所示方法中的步骤901，处理单元2002可以用于执行图9所示方法中的步骤902至步骤904。
又例如，上述装置2000还可以用于执行图8、图10、图11、图12所示方法中的各步骤。
上述装置2000可以为图4所示故障诊断装置420，其中获取单元2001可以相当于获取单元421，处理单元2002可以相当于诊断单元422。
图14是本申请实施例的一种车辆测速装置的故障诊断装置的示意图。该装置3000包括存储器3001、处理器3002、通信接口3003以及总线3004。其中，存储器3001、处理器3002、通信接口3003通过总线3004实现彼此之间的通信连接。
可选地，存储器3001可以是只读存储器(read only memory，ROM)，静态存储设备，动态存储设备或者随机存取存储器(random access memory，RAM)。存储器3001可以存储程序，当存储器3001中存储的程序被处理器3002执行时，处理器3002和通信接口3003用于执行本申请实施例的车辆测速装置的故障诊断方法的各个步骤。
可选地，存储器3001可以具有图1所示存储器152的功能或者具有图2所示系统内存235的功能，或者具有图4所示存储器340的功能，以实现上述存储程序的功能。可选地，处理器3002可以采用通用的CPU，微处理器，ASIC，图形处理器(graphic processing unit，GPU)或者一个或多个集成电路，用于执行相关程序，以实现本申请实施例的故障诊断装置中的单元所需执行的功能，或者执行本申请实施例的故障诊断方法的各个步骤。
可选地，处理器3002可以具有图1所示处理器151的功能或者具有图2所示处理器203的功能，或者具有图3所示处理器330的功能，以实现上述执行相关程序的功能。
可选地，处理器3002还可以是一种集成电路芯片，具有信号的处理能力。在实现过程中，本申请实施例的故障诊断方法的各个步骤可以通过处理器中的硬件的集成逻辑电路或者软件形式的指令完成。
可选地，上述处理器3002还可以是通用处理器、数字信号处理器(digital signal processing，DSP)、专用集成电路(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。可以实现或者执行本申请实施例中的公开的各方法、步骤及逻辑框图。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。结合本申请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中。该存储介质位于存储器，处理器读取存储器中的信息，结合其硬件完成本申请实施例的车辆测速装置的故障诊断装置中包括的单元所需执行的功能，或者执行本申请实施例的车辆测速装置的故障诊断方法的各个步骤。
可选地，通信接口3003可以使用例如但不限于收发器一类的收发装置，来实现装置与其他设备或通信网络之间的通信。
总线3004可包括在装置各个部件(例如，存储器、处理器、通信接口)之间传送信息的通路。
本申请实施例还提供一种包含指令的计算机程序产品，该指令被计算机执行时使得该计算机实现上述方法实施例中的方法。
上述提供的任一种故障诊断装置中相关内容的解释及有益效果均可参考上文提供的对应的方法实施例，此处不再赘述。
除非另有定义，本申请所使用的所有的技术和科学术语与属于本申请的技术领域的技术人员通常理解的含义相同。本申请中在本申请的说明书中所使用的术语只是为了描述具体的实施例的目的，不是旨在于限制本申请。
可选地，本申请实施例中涉及的网络设备包括硬件层、运行在硬件层之上的操作系统层，以及运行在操作系统层上的应用层。其中，硬件层可以包括CPU、内存管理单元(memory management unit，MMU)和内存(也称为主存)等硬件。操作系统层的操作系统可以是任意一种或多种通过进程(process)实现业务处理的计算机操作系统，例如，Linux操作系统、Unix操作系统、Android操作系统、iOS操作系统或windows操作系统等。应用层可以包含浏览器、通讯录、文字处理软件、即时通信软件等应用。
本申请实施例并未对本申请实施例提供的方法的执行主体的具体结构进行特别限定，只要能够通过运行记录有本申请实施例提供的方法的代码的程序，以根据本申请实施例提供的方法进行通信即可。
本申请的各个方面或特征可以实现成方法、装置或使用标准编程和/或工程技术的制品。本申请中使用的术语“制品”可以涵盖可从任何计算机可读器件、载体或介质访问的计算机程序。例如，计算机可读介质可以包括但不限于：磁存储器件(例如，硬盘、软盘或磁带等)，光盘(例如，压缩盘(compact disc，CD)、数字通用盘(digital versatile disc，DVD)等)，智能卡和闪存器件(例如，可擦写可编程只读存储器(erasable programmable read-only memory，EPROM)、卡、棒或钥匙驱动器等)。
本申请描述的各种存储介质可代表用于存储信息的一个或多个设备和/或其它机器可读介质。术语“机器可读介质”可以包括但不限于：无线信道和能够存储、包含和/或承载指令和/或数据的各种其它介质。
需要说明的是，当处理器为通用处理器、DSP、ASIC、FPGA或者其他可编程逻辑器件、分立门或者晶体管逻辑器件、分立硬件组件时，存储器(存储模块)可以集成在处理器中。
还需要说明的是，本申请描述的存储器旨在包括但不限于这些和任意其它适合类型的 存储器。
本领域普通技术人员可以意识到，结合本申请中所公开的实施例描述的各示例的单元及步骤，能够以电子硬件、或者计算机软件和电子硬件的结合来实现。这些功能究竟以硬件还是软件方式来执行，取决于技术方案的特定应用和设计约束条件。专业技术人员可以对每个特定的应用来使用不同方法来实现所描述的功能，但是这种实现不应认为超出本申请的保护范围。
所属领域的技术人员可以清楚地了解到，为描述的方便和简洁，上述描述的装置和单元的具体工作过程，可以参考前述方法实施例中的对应过程，在此不再赘述。
在本申请所提供的几个实施例中，应该理解到，所揭露的装置和方法，可以通过其它的方式实现。例如，以上所描述的装置实施例仅仅是示意性的，例如，所述单元的划分，仅仅为一种逻辑功能划分，实际实现时可以有另外的划分方式，例如多个单元或组件可以结合或者可以集成到另一个系统，或一些特征可以忽略，或不执行。此外，所显示或讨论的相互之间的耦合或直接耦合或通信连接可以是通过一些接口，装置或单元的间接耦合或通信连接，可以是电性，机械或其它的形式。
所述作为分离部件说明的单元可以是或者也可以不是物理上分开的，作为单元显示的部件可以是或者也可以不是物理单元，即可以位于一个地方，或者也可以分布到多个网络单元上。可以根据实际的需要选择其中的部分或者全部单元来实现本实施例方案的目的。
另外，在本申请各个实施例中的各功能单元可以集成在一个单元中，也可以是各个单元单独物理存在，也可以两个或两个以上单元集成在一个单元中。
所述功能如果以软件功能单元的形式实现并作为独立的产品销售或使用时，可以存储在一个计算机可读取存储介质中。基于这样的理解，本申请的技术方案本质上，或者说对现有技术做出贡献的部分，或者该技术方案的部分，可以以计算机软件产品的形式体现出来，该计算机软件产品存储在一个存储介质中，该计算机软件产品包括若干指令，该指令用以使得一台计算机设备(可以是个人计算机，服务器，或者网络设备等)执行本申请各个实施例所述方法的全部或部分步骤。前述的存储介质可以包括但不限于：U盘、移动硬盘、ROM、RAM、磁碟或者光盘等各种可以存储程序代码的介质。
以上所述，仅为本申请的具体实施方式，但本申请的保护范围并不局限于此，任何熟悉本技术领域的技术人员在本申请揭露的技术范围内，可轻易想到变化或替换，都应涵盖在本申请的保护范围之内。因此，本申请的保护范围应以所述权利要求的保护范围为准。

Claims (25)

  1. 一种车辆测速装置的故障诊断方法，其特征在于，包括：
    获取测速装置测得的第一车速；
    获取静态参照物的N个时刻的参照信息，N为大于1的整数，所述参照信息包括所述静态参照物在所述N个时刻中的每个时刻相对于所述测速装置所在的车辆的位置关系的信息；
    根据所述参照信息，计算第二车速；
    根据所述第一车速和所述第二车速，判断所述测速装置是否故障。
  2. 如权利要求1所述的故障诊断方法，其特征在于，在获取所述参照信息之前，所述故障诊断方法还包括：
    利用所述车辆的感知设备，识别所述车辆周围的物体；
    从识别出的所述物体中选取所述静态参照物。
  3. 如权利要求1或2所述的故障诊断方法，其特征在于，所述根据所述参照信息，计算第二车速，包括：
    利用所述N个时刻的参照信息，计算得到至少一个估计车速，所述估计车速为所述N个时刻中的任意两个时刻之间的平均车速；
    对至少一个所述估计车速进行处理，得到所述第二车速。
  4. 如权利要求3所述的故障诊断方法，其特征在于，所述利用所述N个时刻的参照信息，计算得到至少一个估计车速，所述估计车速为所述N个时刻中的任意两个时刻之间的平均车速，包括：
    当所述车辆与所述静态参照物在同一道路平面且所述车辆为直线行驶时，执行下面的操作：
    利用所述静态参照物相对于所述车辆的距离、所述静态参照物与所述车辆行驶方向之间夹角，计算得到所述两个时刻之间所述车辆相对于所述静态参照物的位移；
    利用所述位移和所述两个时刻之间的时间长度，计算得到所述两个时刻之间的所述平均速度。
  5. 如权利要求3所述的故障诊断方法，其特征在于，所述利用所述N个时刻的参照信息，计算得到至少一个估计车速，所述估计车速为所述N个时刻中的任意两个时刻之间的平均车速，包括：
    当所述车辆与所述静态参照物在同一道路平面且所述车辆为转弯行驶时，执行下面的操作：
    利用所述静态参照物与所述车辆之间的距离、所述车辆的车轮转向角、所述车辆的长度，计算得到所述两个时刻之间所述车辆的转向角度、所述车辆的转向半径；
    利用所述转向角度、所述转向半径和所述两个时刻之间的时间长度，计算得到所述两个时刻之间的所述平均速度。
  6. 如权利要求3所述的故障诊断方法，其特征在于，所述利用所述N个时刻的参照信息，计算得到至少一个估计车速，所述估计车速为所述N个时刻中的任意两个时刻之间 的平均车速，包括：
    当所述车辆与所述静态参照物不在同一道路平面且所述车辆为直线行驶时，执行下面的操作：
    建立任意三维坐标系，并表示出所述静态参照物的坐标、所述车辆在所述两个时刻的位置坐标；
    根据所述静态参照物的坐标、所述车辆在所述两个时刻的位置坐标，计算得到所述两个时刻之间所述车辆相对于所述静态参照物的位移；
    利用所述位移和所述两个时刻之间的时间长度，计算得到所述两个时刻之间的所述平均速度。
  7. 如权利要求3所述的故障诊断方法，其特征在于，所述利用所述N个时刻的参照信息，计算得到至少一个估计车速，所述估计车速为所述N个时刻中的任意两个时刻之间的平均车速，包括：
    当所述车辆与所述静态参照物不在同一道路平面且所述车辆为转弯行驶时，执行下面的操作：
    建立任意三维坐标系，并表示出所述静态参照物的坐标、所述车辆在所述两个时刻的位置的坐标；
    利用所述静态参照物与所述车辆之间的距离、所述车辆的车轮转向角、所述车辆的长度，计算得到所述两个时刻之间所述车辆的转向角度、所述车辆的转向半径；
    利用所述转向角度、所述转向半径和所述两个时刻之间的时间长度，计算得到所述两个时刻之间的所述平均速度。
  8. 如权利要求3至7中任一项所述的故障诊断方法，其特征在于，所述对至少一个所述估计车速进行处理，得到所述第二车速，包括：
    对至少一个所述估计车速进行均值滤波或中值滤波，将滤波之后得到的车速作为所述第二车速。
  9. 如权利要求1至8中任一项所述的故障诊断方法，其特征在于，所述根据所述第一车速和所述第二车速，判断所述测速装置是否故障，包括：
    当所述第一车速与所述第二车速之间的差距大于第一预设阈值时，认为所述测速装置故障；
    当所述第一车速与所述第二车速之间的差距小于或等于所述第一预设阈值时，认为所述测速装置没有故障。
  10. 如权利要求1至9中任一项所述的故障诊断方法，其特征在于，所述静态参照物为多个，所述故障诊断方法还包括：
    获取多个所述静态参照物的所述参照信息；
    根据所述参照信息，计算得到多个所述第二车速；
    根据所述第一车速和多个所述第二车速，判断所述测速装置是否故障。
  11. 如权利要求10所述的故障诊断方法，其特征在于，所述根据所述第一车速和多个所述第二车速，判断所述测速装置是否故障，包括：
    当多个所述第二车速之间的差距大于第二预设阈值时，重新获取所述第二车速；
    当多个所述第二车速之间的差距小于或等于所述第二预设阈值时，判断所述测速装置 是否故障。
  12. 一种车辆测速装置的故障诊断装置，其特征在于，包括：
    获取单元，用于获取测速装置测得的第一车速；
    所述获取单元还用于，获取静态参照物的N个时刻的参照信息，N为大于1的整数，所述参照信息包括所述静态参照物在所述N个时刻中的每个时刻相对于所述测速装置所在的车辆的位置关系的信息；
    处理单元，用于根据所述参照信息，计算第二车速；
    所述处理单元还用于，根据所述第一车速和所述第二车速，判断所述测速装置是否故障。
  13. 如权利要求12所述的故障诊断装置，其特征在于，在获取所述参照信息之前，所述获取单元还用于，利用所述车辆的感知设备，识别所述车辆周围的物体；
    所述处理单元还用于，从识别出的所述物体中选取所述静态参照物。
  14. 如权利要求12或13所述的故障诊断装置，其特征在于，所述获取单元具体用于，利用所述N个时刻的参照信息，计算得到至少一个估计车速，所述估计车速为所述N个时刻中的任意两个时刻之间的平均车速；
    所述处理单元还用于，对至少一个所述估计车速进行处理，得到所述第二车速。
  15. 如权利要求14所述的故障诊断装置，其特征在于，当所述车辆与所述静态参照物在同一道路平面且所述车辆为直线行驶时，所述获取单元具体用于执行下面的操作：
    利用所述静态参照物相对于所述车辆的距离、所述静态参照物与所述车辆行驶方向之间夹角，计算得到所述两个时刻之间所述车辆相对于所述静态参照物的位移；
    利用所述位移和所述两个时刻之间的时间长度，计算得到所述两个时刻之间的所述平均速度。
  16. 如权利要求14所述的故障诊断装置，其特征在于，当所述车辆与所述静态参照物在同一道路平面且所述车辆为转弯行驶时，所述获取单元具体用于执行下面的操作：
    利用所述静态参照物与所述车辆之间的距离、所述车辆的车轮转向角、所述车辆的长度，计算得到所述两个时刻之间所述车辆的转向角度、所述车辆的转向半径；
    利用所述转向角度、所述转向半径和所述两个时刻之间的时间长度，计算得到所述两个时刻之间的所述平均速度。
  17. 如权利要求14所述的故障诊断装置，其特征在于，当所述车辆与所述静态参照物不在同一道路平面且所述车辆为直线行驶时，所述获取单元具体用于执行下面的操作：
    建立任意三维坐标系，并表示出所述静态参照物的坐标、所述车辆在所述两个时刻的位置坐标；
    根据所述静态参照物的坐标、所述车辆在所述两个时刻的位置坐标，计算得到所述两个时刻之间所述车辆相对于所述静态参照物的位移；
    利用所述位移和所述两个时刻之间的时间长度，计算得到所述两个时刻之间的所述平均速度。
  18. 如权利要求14所述的故障诊断装置，其特征在于，当所述车辆与所述静态参照物不在同一道路平面且所述车辆为转弯行驶时，所述获取单元具体用于执行下面的操作：
    建立任意三维坐标系，并表示出所述静态参照物的坐标、所述车辆在所述两个时刻的 位置的坐标；
    利用所述静态参照物与所述车辆之间的距离、所述车辆的车轮转向角、所述车辆的长度，计算得到所述两个时刻之间所述车辆的转向角度、所述车辆的转向半径；
    利用所述转向角度、所述转向半径和所述两个时刻之间的时间长度，计算得到所述两个时刻之间的所述平均速度。
  19. 如权利要求14至18中任一项所述的故障诊断装置，其特征在于，所述获取单元具体用于，对至少一个所述估计车速进行均值滤波或中值滤波，将滤波之后得到的车速作为所述第二车速。
  20. 如权利要求12至19中任一项所述的故障诊断装置，其特征在于，所述获取单元具体用于执行以下操作：
    当所述第一车速与所述第二车速之间的差距大于第一预设阈值时，认为所述测速装置故障；
    当所述第一车速与所述第二车速之间的差距小于或等于所述第一预设阈值时，认为所述测速装置没有故障。
  21. 如权利要求12至20中任一项所述的故障诊断装置，其特征在于，所述静态参照物为多个，所述获取单元还用于，获取多个所述静态参照物的所述参照信息；
    所述处理单元还用于，根据所述参照信息，计算得到多个所述第二车速；以及根据所述第一车速和多个所述第二车速，判断所述测速装置是否故障。
  22. 如权利要求21所述的故障诊断装置，其特征在于，所述获取单元还用于，当多个所述第二车速之间的差距大于第二预设阈值时，重新获取所述第二车速；
    所述处理单元还用于，当多个所述第二车速之间的差距小于或等于所述第二预设阈值时，判断所述测速装置是否故障。
  23. 一种芯片，其特征在于，所述芯片包括处理器与数据接口，所述处理器通过所述数据接口读取存储器上存储的指令，以执行如权利要求1至11中任一项所述的故障诊断方法。
  24. 一种计算机可读存储介质，其特征在于，所述计算机可读介质存储用于设备执行的程序代码，该程序代码包括用于执行如权利要求1至11中任一项所述的故障诊断方法的指令。
  25. 一种车辆，其特征在于，所述车辆包括如权利要求12至22中任一项所述的故障诊断装置，以及测速装置，所述故障诊断装置用于对所述测速装置进行故障诊断。
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