CN109782744A - A kind of autonomous driving vehicle failure analysis methods, device and medium - Google Patents
A kind of autonomous driving vehicle failure analysis methods, device and medium Download PDFInfo
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- CN109782744A CN109782744A CN201910100197.9A CN201910100197A CN109782744A CN 109782744 A CN109782744 A CN 109782744A CN 201910100197 A CN201910100197 A CN 201910100197A CN 109782744 A CN109782744 A CN 109782744A
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Abstract
The application provides a kind of autonomous driving vehicle failure analysis methods, device and medium, is related to intelligent transportation testing field, to position to abnormal function module in autonomous driving vehicle.In this method, the output data of each each functional module divided in advance in autonomous driving vehicle is obtained；For each functional module, if the output data of the functional module is not in preset range of normal value, it is determined that the functional module is abnormal；Display is determined as abnormal functional module.In this way, it is detected by the output data to each functional module, can determine abnormal functional module occur, and abnormal functional module is shown, tester can be made to be directly acquainted with abnormal functional module, and the functional module of the exception is targetedly optimized.
Description
Technical field
This application involves intelligent transportation testing field more particularly to a kind of autonomous driving vehicle failure analysis methods, device
And medium.
Background technique
With the development of automobile industry, autonomous driving vehicle becomes focus concerned by people.Autonomous driving vehicle, also known as
Pilotless automobile, computer driving or wheeled mobile robot are that one kind realizes unmanned by autonomous driving vehicle
Intelligent automobile.
The complication system that autonomous driving vehicle is made of multiple modules.These modules have serial connection and parallel in system
Connection.Therefore, autonomous driving vehicle test in, when system when something goes wrong, the module to go wrong can not be accurately positioned, lead
Cause can not targetedly optimize system.
Summary of the invention
In order to during the test, be positioned to functional module abnormal in autonomous driving vehicle, the embodiment of the present application
A kind of autonomous driving vehicle failure analysis methods, device and medium are provided.
In a first aspect, the embodiment of the present application provides a kind of autonomous driving vehicle failure analysis methods, in this method, it is obtained from
The output data of each each functional module divided in advance in dynamic driving；For each functional module, if the functional module
Output data is not in preset range of normal value, it is determined that the functional module is abnormal；Display is determined as abnormal functional module.
Further, after determining the functional module exception, further includes:
Determine the output data of the functional module and the deviation of normal value；
Display is determined as abnormal functional module and specifically includes:
Display is determined as abnormal functional module and described and normal value deviation.
Further, if the output data of all functional modules is less than its corresponding preset range of normal value, institute
State method further include:
Anticipated output data when the output data and actual measurement of sensing module are compared；Wherein, the sensing module is pre-
For detecting the object and its attribute of surrounding enviroment in the functional module first divided；
If the output data of the sensing module is different from anticipated output data when actual measurement, it is determined that sensing module is abnormal.
Further, the method also includes:
If the output data of the sensing module is identical as anticipated output data when actual measurement, by the defeated of decision rule module
Data are made comparisons with anticipated output data when actual measurement out；Wherein, the decision rule module is used to be detected according to sensing module
Object carries out reasonable decision and planning to vehicle, and generates decision rule order and be sent to control module；
If the output data of the decision rule module is different from anticipated output data when the actual measurement, it is determined that decision rule
It is abnormal to draw module.
Further, the method also includes:
If the output data of the decision rule is identical as anticipated output data when the actual measurement, by the defeated of control module
Data are made comparisons with anticipated output data when actual measurement out；Wherein, the control module is used to pass through the decision rule order
Entire car controller is forwarded to line control system module；
If the output data of the control module is different from anticipated output data when the actual measurement, it is determined that control module is different
Often.
Further, the method also includes:
If the output data of the control module is identical as anticipated output data when the actual measurement, by line control system module
Output data make comparisons with anticipated output data when actual measurement；Wherein, the line control system module is described for receiving and executing
The decision rule order of entire car controller forwarding；
If the output data of the line control system module is different from anticipated output data when actual measurement, it is determined that the line traffic control system
Module of uniting is abnormal.
Further, before anticipated output data when the output data of sensing module and actual measurement being compared, the method
Further include:
Determine that the operation of vehicle is different from operation expected when actual measurement.
Second aspect, the embodiment of the present application also provide a kind of autonomous driving vehicle fail analysis device, which includes:
Module is obtained, for obtaining the output data of each each functional module divided in advance in autonomous driving vehicle；
Processing module, for being directed to each functional module, if the output data of the functional module is not in preset normal value
In range, it is determined that the functional module is abnormal；
Display module is determined as abnormal functional module for showing.
Another embodiment of the application additionally provides a kind of computing device, including at least one processor；And with it is described extremely
The memory of few processor communication connection；Wherein, the memory, which is stored with, to be executed by least one described processor
Instruction, described instruction is executed by least one described processor, so that at least one described processor is able to carry out the application
Any autonomous driving vehicle failure analysis methods that embodiment provides.
Another embodiment of the application additionally provides a kind of computer storage medium, wherein the computer storage medium is deposited
Computer executable instructions are contained, the computer executable instructions are for making computer execute any in the embodiment of the present application
Autonomous driving vehicle failure analysis methods.
Autonomous driving vehicle failure analysis methods, device and medium provided by the embodiments of the present application, by each function
The output data of module is detected, and can determine abnormal functional module occur, and abnormal functional module is shown,
Tester can be made to be directly acquainted with abnormal functional module, and the functional module of the exception is targetedly optimized.
Other features and advantage will illustrate in the following description, also, partly become from specification
It obtains it is clear that being understood and implementing the application.The purpose of the application and other advantages can be by written explanations
Specifically noted structure is achieved and obtained in book, claims and attached drawing.
Detailed description of the invention
In order to illustrate the technical solution of the embodiments of the present invention more clearly, will make below to required in the embodiment of the present invention
Attached drawing is briefly described, it should be apparent that, attached drawing described below is only some embodiments of the present invention, for
For those of ordinary skill in the art, without creative efforts, it can also be obtained according to these attached drawings other
Attached drawing.
Fig. 1 is a kind of autonomous driving vehicle failure analysis methods application scenarios schematic diagram in the embodiment of the present application；
Fig. 2 is a kind of autonomous driving vehicle failure analysis methods flow chart in the embodiment of the present application；
Fig. 3 is a kind of another flow chart of autonomous driving vehicle failure analysis methods in the embodiment of the present application；
Fig. 4 is a kind of autonomous driving vehicle failure analysis methods another application schematic diagram of a scenario in the embodiment of the present application；
Fig. 5 is a kind of autonomous driving vehicle failure analysis methods another application schematic diagram of a scenario in the embodiment of the present application；
Fig. 6 is a kind of autonomous driving vehicle fail analysis device schematic diagram in the embodiment of the present application；
Fig. 7 is the computing device schematic diagram according to the application embodiment.
Specific embodiment
To make the objectives, technical solutions, and advantages of the present invention clearer, below in conjunction with attached drawing to the present invention make into
It is described in detail to one step, it is clear that the described embodiments are only some of the embodiments of the present invention, rather than whole implementation
Example.Based on the embodiments of the present invention, obtained by those of ordinary skill in the art without making creative efforts
All other embodiment, shall fall within the protection scope of the present invention.
Autonomous driving vehicle is made of multiple modules, according to the function of modules, will be driven automatically in the embodiment of the present application
It sails automobile and is divided into following functional module:
Autonomous driving vehicle is made of multiple modules, generally comprises sensing module, decision rule module, control module and line
Control system module.Wherein, sensing module is used to detect the object and its attribute of surrounding enviroment；Decision rule module is used for according to sense
The object for knowing module detection carries out reasonable decision and planning to vehicle, and generates decision rule order and be sent to control module；
Control module is used to the decision rule order being forwarded to line control system module by entire car controller；Line control system module
For receiving and executing the decision rule order of the entire car controller forwarding, gear, braking, steering of vehicle etc. are controlled.
In autonomous driving vehicle, these functional modules have serial connection and parallel connection.Therefore, during the test, vapour
Vehicle when something goes wrong, can not accurately orient problem module.In view of this, the embodiment of the present application provides a kind of automatic Pilot vapour
Vehicle failure analysis methods, device and medium.In order to be clearly understood from technical solution provided by the embodiments of the present application, below to the party
The basic principle of case carries out simple illustration:
In autonomous driving vehicle test process, in order to which the functional module to go wrong, the embodiment of the present application is accurately positioned
A kind of autonomous driving vehicle failure analysis methods are provided.In this method, each each function divided in advance in autonomous driving vehicle is obtained
The output data of energy module；For each functional module, if the output data of the functional module is not in preset range of normal value
It is interior, it is determined that the functional module is abnormal；Display is determined as abnormal functional module.
In this way, detecting by the output data to each functional module, it can determine abnormal functional module occur,
And show abnormal functional module, tester can be made to be directly acquainted with abnormal functional module, and to the exception
Functional module targetedly optimized.
As shown in Figure 1, it is autonomous driving vehicle failure analysis methods application scenarios signal a kind of in the embodiment of the present application
Figure.Include in the scene, autonomous driving vehicle A, the autonomous driving vehicle A include sensing module A1, decision rule modules A 2,
Control module A3, line control system modules A 4；Detection module B.
Detection module B obtains the output data of each each functional module A1-A4 divided in advance in autonomous driving vehicle A.Needle
To control module A3, the output data of A3 determines that control module A3 is abnormal not in preset range of normal value.And display control mould
The information of block A3 exception.
Technical solution provided in an embodiment of the present invention is introduced with reference to the accompanying drawing.As shown in Fig. 2, it is the embodiment of the present application
A kind of middle autonomous driving vehicle failure analysis methods flow chart.The process the following steps are included:
Step 201: obtaining the output data of each each functional module divided in advance in autonomous driving vehicle.
Step 202: each functional module is directed to, if the output data of the functional module is not in preset range of normal value
It is interior, it is determined that the functional module is abnormal.
Step 203: display is determined as abnormal functional module.
When it is implemented, preset range of normal value is by traffic law specification and the self performance of vehicle, according to actual measurement
As a result it is arranged.
In this way, tester can be made according to the abnormal functional module of display, it is thus understood that abnormal function mould occur
Block, and the functional module is targetedly optimized.
Preferably, being directed to each functional module, after determining the functional module exception, the output number of the functional module is determined
According to and normal value deviation.In display, display is determined as abnormal functional module and described and normal value deviation.
In this way, not only tester can be made to recognize abnormal functional module, it can also be according to described and normal value
Deviation, binding test personnel experience targetedly optimizes functional module.
In a kind of possible embodiment, if the output data of all functional modules be less than its it is corresponding it is preset just
Constant value range also executes step 301-302 as shown in Figure 3:
Step 301: anticipated output data when the output data and actual measurement of sensing module are compared；Wherein, the perception
Module is in the functional module divided in advance for detecting the object and its attribute of surrounding enviroment.
Step 302: if the output data of the sensing module is different from anticipated output data when actual measurement, it is determined that perception mould
Block is abnormal.
Specifically, placing the objects such as barrier and traffic lights on the road of vehicle driving in actual measurement.Sensing module exists
After perceiving barrier, output data should include the instruction information of barrier and its attribute.If the output data of sensing module
Indicate clear, i.e., without aforementioned instruction information in output data, it is determined that sensing module is abnormal.
As shown in figure 4, it is autonomous driving vehicle failure analysis methods application scenarios signal a kind of in the embodiment of the present application
Figure.The scene includes: traffic lights 01, sensing module 02, detection module 03.
When actual measurement, traffic lights 01 show red light.Object and its attribute around the detection of sensing module 02, in output data simultaneously
It does not include the instruction information that traffic lights 01 show red light.It is pre- when detection module 03 is by the output data and actual measurement of sensing module 02
Phase output data is made comparisons, and determines that sensing module 02 is abnormal.
In this way, anticipated output data when passing through the output data and actual measurement of comparison sensing module, can determine sensing module
It is whether abnormal.In sensing module exception, tester can targetedly optimize sensing module.
If the output data of the sensing module is identical as anticipated output data when actual measurement, by the defeated of decision rule module
Data are made comparisons with anticipated output data when actual measurement out；Wherein, the decision rule module is used to be detected according to sensing module
Object carries out reasonable decision and planning to vehicle, and generates decision rule order and be sent to control module；
If the output data of the decision rule module is different from anticipated output data when the actual measurement, it is determined that decision rule
It is abnormal to draw module.
Specifically, sensing module detects the object of surrounding, and thingness is sent to decision rule module.At this point,
The object that decision rule module should be detected according to sensing module carries out reasonable decision and planning to vehicle.For example, slowing down, turning
To or braking etc..If the output data of the decision rule module indicates straight trip or accelerates, it is determined that decision rule module
Output data is different from anticipated output data when actual measurement, so that it is determined that decision rule module is abnormal.
In this way, sensing module under normal circumstances, determine decision that decision rule module is made and planning whether with actual measurement
When expected decision it is identical with planning, may thereby determine that whether decision rule module abnormal.In the output of decision rule module
When data (decision and planning) are different from output data (expected decision and the planning when actual measurement) when surveying, decision rule is determined
Module is abnormal.Tester can based on the analysis results optimize decision planning module.
If the output data of the decision rule is identical as anticipated output data when the actual measurement, by the defeated of control module
Data are made comparisons with anticipated output data when actual measurement out；Wherein, the control module is used to pass through the decision rule order
Entire car controller is forwarded to line control system module；If anticipated output data when the output data of the control module and the actual measurement
It is different, it is determined that control module is abnormal.
Specifically, decision rule module be directed to sensing module detection object, be made that braking and acceleration be-
6.5m/s2.Control module receives the decision rule order of decision rule module, it should be transferred to the decision rule order
Entire car controller, but there is no the data for indicating decision rule order in the output data of control module.So determining control
Molding block is abnormal.
In this way, mould can be controlled determining by anticipated output data when the output data and actual measurement of comparison control module
After block exception, tester can be optimized control module according to the information.
If the output data of the control module is identical as anticipated output data when the actual measurement, by line control system module
Output data make comparisons with anticipated output data when actual measurement；Wherein, the line control system module is described for receiving and executing
The decision rule order of entire car controller forwarding；
If the output data of the line control system module is different from anticipated output data when actual measurement, it is determined that the line traffic control system
Module of uniting is abnormal.
Specifically, after the decision rule order that line traffic control system module receives entire car controller forwarding, it should according to
The decision rule order controls gear and the braking of vehicle, so that vehicle is according to decision and plans with the acceleration of -6.5m/s gradually
Stopping traveling gradually.Do not include the control by gear and braking to vehicle in the output data of line traffic control system module, so,
Determine that line control system module is abnormal.
In this way, can output data and actual measurement by comparing line traffic control system module when anticipated output data compared with, determine
Whether line control system module is abnormal.In line traffic control system module exception, tester can be optimized line control system module.
Preferably, if the output data of the line control system module is identical as anticipated output data when actual measurement, it is determined that institute
It is normal to state autonomous driving vehicle.In this way, can be according to sensing module, decision rule module, control module, line control system module
Process determines whether each functional module is abnormal, and tester then can targetedly optimize abnormal functional module.
When it is implemented, determining that the operation of vehicle is different from operation expected when actual measurement before executing above-mentioned steps 301.
Specifically, placing barrier on the road of vehicle driving when actual measurement, it is contemplated that operation should be brake or steering.And
In actual measurement, vehicle is there is no turning to also not brake, so determining that the operation of vehicle is different from operation expected when actual measurement.
If the output data of each functional module, in the preset range of normal value of the functional module, and the operation of vehicle
It is identical as expected operation, then it can determine that autonomous driving vehicle does not break down.By the above method, each function is specifically being detected
Can module whether before exception, determine that the operation of vehicle is different with expected operation, the time can be saved, raising autonomous driving vehicle
Accident analysis efficiency.
In order to more clearly understand a kind of autonomous driving vehicle failure analysis methods provided by the embodiments of the present application, in conjunction with figure
5 pairs of programs are described further.Fig. 5 is a kind of applied field of autonomous driving vehicle failure analysis methods in the embodiment of the present application
Scape schematic diagram.The scene include: traffic lights 01, vehicle A0 autonomous driving vehicle A include sensing module A1, decision rule module
A2, control module A3 and line control system modules A 4；Detection module 02.
When actual measurement, traffic lights 01 show red light.Detection module 02 obtains each each function divided in advance in autonomous driving vehicle A
The output data of energy modules A 1-A4, and it is corresponding to determine that the output data of all functional modules is less than each functional module
Preset range of normal value.But detection module 02 determines that vehicle A0 makes a dash across the red light, it is different from expected operation.Detection module 02 will
The output data of sensing module A1 is made comparisons with anticipated output data when actual measurement.Before including in the output data of sensing module A1
Square 10m has the instruction information of red light, and detection module 02 determines that sensing module A1 is normal.Detection module 02 is by decision rule modules A 2
Output data make comparisons with anticipated output data when actual measurement.It include with -6.5m/ in the output data of decision rule modules A 2
s2Acceleration brake decision.Detection module 02 determines that decision rule modules A 2 is normal.So by the output of control module A3
Data are made comparisons with anticipated output data when actual measurement.Do not include in the output data of control module A3 with -6.5m/s2Acceleration
The decision of brake determines that control module A3 is abnormal.The information of 02 display control module A3 exception of detection module.
Based on identical inventive concept, the embodiment of the present application also provides a kind of autonomous driving vehicle fail analysis device.Such as
It is a kind of autonomous driving vehicle fail analysis device schematic diagram in the embodiment of the present application shown in Fig. 6, which includes:
Module 601 is obtained, for obtaining the output data of each each functional module divided in advance in autonomous driving vehicle；
Processing module 602, for being directed to each functional module, if the output data of the functional module is not preset normal
It is worth in range, it is determined that the functional module is abnormal；
Display module 603 is determined as abnormal functional module for showing.
Further, processing module 602 is also used to determine the output data of the functional module and the deviation of normal value；
Display module 603 is specifically used for display and is determined as abnormal functional module and described and normal value deviation.
Optionally, if the output data of all functional modules is less than its corresponding preset range of normal value, the dress
It sets further include:
Comparison module, for comparing anticipated output data when the output data and actual measurement of sensing module；Wherein, described
Sensing module is in the functional module divided in advance for detecting the object and its attribute of surrounding enviroment；
Determining module, if the output data for the sensing module is different from anticipated output data when actual measurement, it is determined that
Sensing module is abnormal.
Further, the comparison module is also used to, if anticipated output when the output data and actual measurement of the sensing module
Data are identical, then the output data of decision rule module are made comparisons with anticipated output data when actual measurement；Wherein, the decision rule
The object that module is used to detect according to sensing module is drawn, reasonable decision and planning are carried out to vehicle, and generates decision rule life
Order is sent to control module；
The determining module, if being also used to anticipated output number when the output data and the actual measurement of the decision rule module
According to difference, it is determined that decision rule module is abnormal.
Optionally, the comparison module is also used to: if the output data of the decision rule and expection when the actual measurement are defeated
Data are identical out, then the output data of control module are made comparisons with anticipated output data when actual measurement；Wherein, the control module
For being forwarded to line control system module by entire car controller for the decision rule order；
The determining module, if being also used to the output data of the control module and anticipated output data are not when the actual measurement
Together, it is determined that control module is abnormal.
Optionally, the comparison module is also used to, if the output data of the control module and expection when the actual measurement are defeated
Data are identical out, then the output data of line control system module are made comparisons with anticipated output data when actual measurement；Wherein, the line traffic control
System module is used to receive and execute the decision rule order of the entire car controller forwarding；
The determining module, if being also used to when the output data and actual measurement of the line control system module anticipated output data not
Together, it is determined that the line control system module is abnormal.
Further, anticipated output data are done when the comparison module is also used to output data and actual measurement by sensing module
Before comparing, determine that the operation of vehicle is different from operation expected when actual measurement.
After the autonomous driving vehicle failure analysis methods and device for describing the application illustrative embodiments, connect down
Come, introduces the computing device of the another exemplary embodiment according to the application.
Person of ordinary skill in the field it is understood that the various aspects of the application can be implemented as system, method or
Program product.Therefore, the various aspects of the application can be with specific implementation is as follows, it may be assumed that complete hardware embodiment, complete
The embodiment combined in terms of full Software Implementation (including firmware, microcode etc.) or hardware and software, can unite here
Referred to as circuit, " module " or " system ".
In some possible embodiments, at least one processing can be included at least according to the computing device of the application
Device and at least one processor.Wherein, memory is stored with program code, when program code is executed by processor, so that
Processor executes the autonomous driving vehicle failure according to the various illustrative embodiments of the application point of this specification foregoing description
Step in analysis method.For example, processor can execute step 201-203 as shown in Figure 2 or step as shown in Figure 3
301-302。
The computing device 130 of this embodiment according to the application is described referring to Fig. 7.The calculating that Fig. 7 is shown
Device 130 is only an example, should not function to the embodiment of the present application and use scope bring any restrictions.
As shown in fig. 7, computing device 130 is showed in the form of general-purpose calculating appts.The component of computing device 130 can wrap
Include but be not limited to: at least one above-mentioned processor 131, above-mentioned at least one processor 132, the different system components of connection (including
Memory 132 and processor 131) bus 133.
Bus 133 indicates one of a few class bus structures or a variety of, including memory bus or Memory Controller,
Peripheral bus, processor or the local bus using any bus structures in a variety of bus structures.
Memory 132 may include the readable medium of form of volatile memory, such as random access memory (RAM)
1321 and/or cache memory 1322, it can further include read-only memory (ROM) 1323.
Memory 132 can also include program/utility 1325 with one group of (at least one) program module 1324,
Such program module 1324 includes but is not limited to: operating system, one or more application program, other program modules and
It may include the realization of network environment in program data, each of these examples or certain combination.
Computing device 130 can also be communicated with one or more external equipments 134 (such as keyboard, sensing equipment etc.), also
Can be enabled a user to one or more equipment interacted with computing device 130 communication, and/or with make the computing device
The 130 any equipment (such as router, modem etc.) that can be communicated with one or more of the other computing device are led to
Letter.This communication can be carried out by input/output (I/O) interface 135.Also, computing device 130 can also be suitable by network
Orchestration 136 and one or more network (such as local area network (LAN), wide area network (WAN) and/or public network, such as because of spy
Net) communication.As shown, network adapter 136 is communicated by bus 133 with other modules for computing device 130.It should
Understand, although not shown in the drawings, other hardware and/or software module can be used in conjunction with computing device 130, including but unlimited
In: microcode, device driver, redundant processor, external disk drive array, RAID system, tape drive and data
Backup storage system etc..
In some possible embodiments, the various aspects of autonomous driving vehicle failure analysis methods provided by the present application
It is also implemented as a kind of form of program product comprising program code, when program product is run on a computing device,
Program code be used to making computer equipment execute this specification foregoing description according to the various illustrative embodiments of the application
Step in autonomous driving vehicle failure analysis methods, for example, computer equipment can execute step 201- as shown in Figure 2
203 or step 301-302 as shown in Figure 3.
Program product can be using any combination of one or more readable mediums.Readable medium can be readable signal Jie
Matter or readable storage medium storing program for executing.Readable storage medium storing program for executing for example may be-but not limited to-electricity, magnetic, optical, electromagnetic, infrared
The system of line or semiconductor, device or device, or any above combination.The more specific example of readable storage medium storing program for executing is (non-
The list of exhaustion) include: electrical connection with one or more conducting wires, portable disc, hard disk, random access memory (RAM),
Read-only memory (ROM), erasable programmable read only memory (EPROM or flash memory), optical fiber, the read-only storage of portable compact disc
Device (CD-ROM), light storage device, magnetic memory device or above-mentioned any appropriate combination.
The program product for autonomous driving vehicle accident analysis of presently filed embodiment can be using portable tight
It gathers disk read-only memory (CD-ROM) and including program code, and can run on the computing device.However, the program of the application
Product is without being limited thereto, and in this document, readable storage medium storing program for executing can be any tangible medium for including or store program, the program
Execution system, device or device use or in connection can be commanded.
Readable signal medium may include in a base band or as the data-signal that carrier wave a part is propagated, wherein carrying
Readable program code.The data-signal of this propagation can take various forms, including --- but being not limited to --- electromagnetism letter
Number, optical signal or above-mentioned any appropriate combination.Readable signal medium can also be other than readable storage medium storing program for executing it is any can
Read medium, the readable medium can send, propagate or transmit for by instruction execution system, device or device use or
Program in connection.
The program code for including on readable medium can transmit with any suitable medium, including --- but being not limited to ---
Wirelessly, wired, optical cable, RF etc. or above-mentioned any appropriate combination.
Can with any combination of one or more programming languages come write for execute the application operation program
Code, programming language include object oriented program language-Java, C++ etc., further include conventional process
Formula programming language-such as " C " language or similar programming language.Program code can be calculated fully in user
It executes on device, partly execute on a user device, executing, as an independent software package partially in user's computing device
Upper part executes on remote computing device or executes on remote computing device or server completely.It is being related to remotely counting
In the situation for calculating device, remote computing device can pass through the network of any kind --- including local area network (LAN) or wide area network
(WAN)-it is connected to user's computing device, or, it may be connected to external computing device (such as provided using Internet service
Quotient is connected by internet).
It should be noted that although being referred to several unit or sub-units of device in the above detailed description, this stroke
It point is only exemplary not enforceable.In fact, according to presently filed embodiment, it is above-described two or more
The feature and function of unit can embody in a unit.Conversely, the feature and function of an above-described unit can
It is to be embodied by multiple units with further division.
In addition, although describing the operation of the application method in the accompanying drawings with particular order, this do not require that or
Hint must execute these operations in this particular order, or have to carry out shown in whole operation be just able to achieve it is desired
As a result.Additionally or alternatively, it is convenient to omit multiple steps are merged into a step and executed by certain steps, and/or by one
Step is decomposed into execution of multiple steps.
It should be understood by those skilled in the art that, embodiments herein can provide as method, system or computer program
Product.Therefore, complete hardware embodiment, complete software embodiment or reality combining software and hardware aspects can be used in the application
Apply the form of example.Moreover, it wherein includes the computer of computer usable program code that the application, which can be used in one or more,
The computer program implemented in usable storage medium (including but not limited to magnetic disk storage, CD-ROM, optical memory etc.) produces
The form of product.
The application is referring to method, the process of equipment (system) and computer program product according to the embodiment of the present application
Figure and/or block diagram describe.It should be understood that every one stream in flowchart and/or the block diagram can be realized by computer program instructions
The combination of process and/or box in journey and/or box and flowchart and/or the block diagram.It can provide these computer programs
Instruct the processor of general purpose computer, special purpose computer, Embedded Processor or other programmable data processing devices to produce
A raw machine, so that being generated by the instruction that computer or the processor of other programmable data processing devices execute for real
The device for the function of being specified in present one or more flows of the flowchart and/or one or more blocks of the block diagram.
These computer program instructions, which may also be stored in, is able to guide computer or other programmable data processing devices with spy
Determine in the computer-readable memory that mode works, so that it includes referring to that instruction stored in the computer readable memory, which generates,
Enable the manufacture of device, the command device realize in one box of one or more flows of the flowchart and/or block diagram or
The function of being specified in multiple boxes.
These computer program instructions also can be loaded onto a computer or other programmable data processing device, so that counting
Series of operation steps are executed on calculation machine or other programmable devices to generate computer implemented processing, thus in computer or
The instruction executed on other programmable devices is provided for realizing in one or more flows of the flowchart and/or block diagram one
The step of function of being specified in a box or multiple boxes.
Although the preferred embodiment of the application has been described, it is created once a person skilled in the art knows basic
Property concept, then additional changes and modifications can be made to these embodiments.So it includes excellent that the following claims are intended to be interpreted as
It selects embodiment and falls into all change and modification of the application range.
Obviously, those skilled in the art can carry out various modification and variations without departing from the essence of the application to the application
Mind and range.In this way, if these modifications and variations of the application belong to the range of the claim of this application and its equivalent technologies
Within, then the application is also intended to include these modifications and variations.
Claims (10)
1. a kind of autonomous driving vehicle failure analysis methods, which is characterized in that the described method includes:
Obtain the output data of each each functional module divided in advance in autonomous driving vehicle；
For each functional module, if the output data of the functional module is not in preset range of normal value, it is determined that the function
It can module exception；
Display is determined as abnormal functional module.
2. the method according to claim 1, wherein after determining the functional module exception, further includes:
Determine the output data of the functional module and the deviation of normal value；
Display is determined as abnormal functional module and specifically includes:
Display is determined as abnormal functional module and described and normal value deviation.
3. the method according to claim 1, wherein if the output data of all functional modules to be less than its right
The preset range of normal value answered, the method also includes:
Anticipated output data when the output data and actual measurement of sensing module are compared；Wherein, the sensing module is to draw in advance
For detecting the object and its attribute of surrounding enviroment in the functional module divided；
If the output data of the sensing module is different from anticipated output data when actual measurement, it is determined that sensing module is abnormal.
4. according to the method described in claim 3, it is characterized in that, the method also includes:
If the output data of the sensing module is identical as anticipated output data when actual measurement, by the output number of decision rule module
It makes comparisons according to anticipated output data when actual measurement；Wherein, the decision rule module is used for the object detected according to sensing module,
Reasonable decision and planning are carried out to vehicle, and generates decision rule order and is sent to control module；
If the output data of the decision rule module is different from anticipated output data when the actual measurement, it is determined that decision rule mould
Block is abnormal.
5. according to the method described in claim 4, it is characterized in that, the method also includes:
If the output data of the decision rule is identical as anticipated output data when the actual measurement, by the output number of control module
It makes comparisons according to anticipated output data when actual measurement；Wherein, the control module is used to the decision rule order passing through vehicle
Controller is forwarded to line control system module；
If the output data of the control module is different from anticipated output data when the actual measurement, it is determined that control module is abnormal.
6. according to the method described in claim 5, it is characterized in that, the method also includes:
If the output data of the control module is identical as anticipated output data when the actual measurement, by the defeated of line control system module
Data are made comparisons with anticipated output data when actual measurement out；Wherein, the line control system module is for receiving and executing the vehicle
The decision rule order of controller forwarding；
If the output data of the line control system module is different from anticipated output data when actual measurement, it is determined that the line control system mould
Block is abnormal.
7. according to the method described in claim 6, it is characterized in that, by anticipated output when the output data and actual measurement of sensing module
Before data compare, the method also includes:
Determine that the operation of vehicle is different from operation expected when actual measurement.
8. a kind of autonomous driving vehicle fail analysis device, which is characterized in that described device includes:
Module is obtained, for obtaining the output data of each each functional module divided in advance in autonomous driving vehicle；
Processing module, for being directed to each functional module, if the output data of the functional module is not in preset range of normal value
It is interior, it is determined that the functional module is abnormal；
Display module is determined as abnormal functional module for showing.
9. a kind of computer-readable medium, is stored with computer executable instructions, which is characterized in that the computer is executable to be referred to
It enables for executing the method as described in any claim in claim 1-7.
10. a kind of computing device characterized by comprising at least one processor；And it is logical at least one described processor
Believe the memory of connection；Wherein, the memory is stored with the instruction that can be executed by least one described processor, described instruction
It is executed by least one described processor, so that at least one described processor is able to carry out such as power any in claim 1-7
Benefit requires the method.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110361201A (en) * | 2019-07-29 | 2019-10-22 | 深兰科技（上海）有限公司 | A kind of vehicle control parameters scaling method and equipment |
| CN113641170A (en) * | 2021-08-31 | 2021-11-12 | 北京三快在线科技有限公司 | Method and device for intelligently detecting unmanned takeover problem |
| CN114205223A (en) * | 2021-11-29 | 2022-03-18 | 中汽研（天津）汽车工程研究院有限公司 | Tracing and positioning method and device for vehicle intelligent driving function abnormal event |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH09146630A (en) * | 1995-11-24 | 1997-06-06 | Jatco Corp | Fault diagnostic device |
| CN106094830A (en) * | 2016-07-11 | 2016-11-09 | 百度在线网络技术（北京）有限公司 | For the method and apparatus controlling automatic driving vehicle |
| CN107063713A (en) * | 2017-04-27 | 2017-08-18 | 百度在线网络技术（北京）有限公司 | Method of testing and device applied to pilotless automobile |
| CN108762226A (en) * | 2018-05-14 | 2018-11-06 | 济南浪潮高新科技投资发展有限公司 | A kind of automatic driving vehicle test method, apparatus and system |
| CN108845556A (en) * | 2018-04-28 | 2018-11-20 | 济南浪潮高新科技投资发展有限公司 | A kind of automatic driving vehicle test method and test device |
| CN109213126A (en) * | 2018-09-17 | 2019-01-15 | 安徽江淮汽车集团股份有限公司 | Autonomous driving vehicle test macro and method |
-
2019
- 2019-01-31 CN CN201910100197.9A patent/CN109782744A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH09146630A (en) * | 1995-11-24 | 1997-06-06 | Jatco Corp | Fault diagnostic device |
| CN106094830A (en) * | 2016-07-11 | 2016-11-09 | 百度在线网络技术（北京）有限公司 | For the method and apparatus controlling automatic driving vehicle |
| CN107063713A (en) * | 2017-04-27 | 2017-08-18 | 百度在线网络技术（北京）有限公司 | Method of testing and device applied to pilotless automobile |
| CN108845556A (en) * | 2018-04-28 | 2018-11-20 | 济南浪潮高新科技投资发展有限公司 | A kind of automatic driving vehicle test method and test device |
| CN108762226A (en) * | 2018-05-14 | 2018-11-06 | 济南浪潮高新科技投资发展有限公司 | A kind of automatic driving vehicle test method, apparatus and system |
| CN109213126A (en) * | 2018-09-17 | 2019-01-15 | 安徽江淮汽车集团股份有限公司 | Autonomous driving vehicle test macro and method |
Non-Patent Citations (1)
| Title |
|---|
| 车云等: "《智能汽车：决战2020》", 30 April 2018, 北京理工大学出版社 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110361201A (en) * | 2019-07-29 | 2019-10-22 | 深兰科技（上海）有限公司 | A kind of vehicle control parameters scaling method and equipment |
| CN113641170A (en) * | 2021-08-31 | 2021-11-12 | 北京三快在线科技有限公司 | Method and device for intelligently detecting unmanned takeover problem |
| CN113641170B (en) * | 2021-08-31 | 2023-02-03 | 北京三快在线科技有限公司 | Method and device for intelligently detecting unmanned driving takeover problem |
| CN114205223A (en) * | 2021-11-29 | 2022-03-18 | 中汽研（天津）汽车工程研究院有限公司 | Tracing and positioning method and device for vehicle intelligent driving function abnormal event |
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