CN109649395B

CN109649395B - Automatic driving adjustment method based on computing resources and vehicle-mounted equipment

Info

Publication number: CN109649395B
Application number: CN201811636306.0A
Authority: CN
Inventors: 马万里; 周小成
Original assignee: Uisee Technologies Beijing Co Ltd
Current assignee: Uisee Technologies Beijing Co Ltd
Priority date: 2018-12-29
Filing date: 2018-12-29
Publication date: 2023-12-01
Anticipated expiration: 2038-12-29
Also published as: CN109649395A

Abstract

The embodiment of the invention relates to an automatic driving adjustment method and vehicle-mounted equipment based on computing resources, wherein the method comprises the following steps: acquiring information of computing resources and information of vehicles; determining a computing resource abnormality level based on the information of the computing resource and the information of the vehicle; and determining automatic driving adjustment information based on the computing resource abnormality level. According to the embodiment of the invention, the abnormal level of the computing resource can be determined based on the information of the computing resource and the information of the vehicle, and then the automatic driving adjustment information can be determined based on the abnormal level of the computing resource, so that the processing capacity of the automatic driving system is reduced adaptively when the computing resource is abnormal, and the automatic driving system meets the real-time requirement and cannot generate unpredictable results.

Description

Automatic driving adjustment method based on computing resources and vehicle-mounted equipment

Technical Field

The embodiment of the invention relates to the technical field of automatic driving of vehicles, in particular to an automatic driving adjustment method based on computing resources and vehicle-mounted equipment.

Background

Currently, autonomous vehicles implement decision-making planning for autonomous driving through an autonomous driving system. The autopilot system is a software system running on an operating system, and the implementation of the autopilot decision-making requires the consumption of computing resources of the vehicle-mounted hardware system. The vehicle-mounted hardware system is a hardware system supporting the operation of an operating system, and the calculation capability of the vehicle-mounted hardware system can directly influence the reliability of the decision planning of the automatic driving system.

However, during the operation of the autopilot system, the computing power of the on-board hardware system may not meet the real-time requirement of the autopilot system, for example, the temperature of the on-board hardware system is too high, and the CPU (Central Processing Unit ) or the GPU (Graphics Processing Unit, graphics processor) in the on-board hardware system needs to be run down to reduce the temperature, so that the computing power of the on-board hardware system may not meet the real-time requirement of the autopilot system, making the decision planning of the autopilot system unreliable, resulting in unpredictable consequences of the autopilot system.

Disclosure of Invention

In order to solve the problems in the prior art, at least one embodiment of the invention provides an automatic driving adjustment method based on computing resources and vehicle-mounted equipment.

In a first aspect, an embodiment of the present invention provides a method for adjusting automatic driving based on computing resources, where the method includes:

acquiring information of computing resources and information of vehicles;

determining a computing resource abnormality level based on the information of the computing resource and the information of the vehicle;

and determining automatic driving adjustment information based on the computing resource abnormality level.

Based on the first aspect, in a first embodiment of the first aspect, the information of the vehicle includes: vehicle state information and/or in-vehicle environmental information.

Based on the first embodiment of the first aspect, in a second embodiment of the first aspect, the information of the computing resource includes: CPU temperature, chassis temperature, CPU main frequency or number of equipment fault codes; the vehicle state information includes a vehicle speed; the in-vehicle environmental information includes humidity.

Based on the first embodiment of the first aspect, in a third embodiment of the first aspect, the information of the computing resource includes: values of a plurality of parameters, the plurality of parameters comprising: at least two of CPU temperature, chassis temperature, CPU main frequency and number of equipment fault codes; the vehicle state information includes a vehicle speed; the in-vehicle environmental information includes humidity.

Based on the second embodiment of the first aspect, in a fourth embodiment of the first aspect, determining a computing resource abnormality level based on the information of the computing resource and the information of the vehicle includes:

if the information of the computing resource is abnormal and the vehicle speed is greater than a vehicle speed threshold, determining that the abnormal grade of the computing resource is a first grade;

if the information of the computing resource is abnormal and the vehicle speed is smaller than or equal to a vehicle speed threshold value, determining that the abnormal level of the computing resource is a second level;

wherein the first level is higher than the second level.

Based on the fourth embodiment of the first aspect, in a fifth embodiment of the first aspect, determining the automatic driving adjustment information based on the computing resource abnormality level includes:

if the abnormal level of the computing resource is the first level, the automatic driving adjustment information includes: the vehicle speed is the first speed limit, and the sensing range of the surrounding environment of the vehicle is the first range;

if the abnormal level of the computing resource is the second level, the automatic driving adjustment information includes: the vehicle speed is the second speed limit, and the sensing range of the surrounding environment of the vehicle is the second range;

wherein the first speed limit is less than the second speed limit; the first range is greater than the second range.

Based on the third embodiment of the first aspect, in a sixth embodiment of the first aspect, determining a computing resource abnormality level based on the information of the computing resource and the information of the vehicle includes:

if the values of the parameters are abnormal and the vehicle speed is greater than a vehicle speed threshold, determining that the abnormal grade of the computing resource is a first sub-grade in a third grade;

if the value of at least one parameter is abnormal and the vehicle speed is greater than a vehicle speed threshold, determining that the abnormal grade of the computing resource is a second sub-grade in the third grade;

If the values of the parameters are abnormal and the vehicle speed is smaller than or equal to a vehicle speed threshold value, determining that the abnormal grade of the computing resource is a first sub-grade in a fourth grade;

if the value of at least one parameter is abnormal and the vehicle speed is smaller than or equal to a vehicle speed threshold value, determining that the abnormal level of the computing resource is a second sub-level in the fourth level;

wherein the third level is higher than the fourth level; the first sub-level is higher in level than the second sub-level.

Based on the sixth embodiment of the first aspect, in a seventh embodiment of the first aspect, determining the automatic driving adjustment information based on the computing resource abnormality level includes:

if the abnormal level of the computing resource is the first sub-level in the third level, the automatic driving adjustment information includes: the vehicle speed is a first sub-speed limit in the third speed limit, and the sensing range of the surrounding environment of the vehicle is a first sub-range in the third range;

if the abnormal level of the computing resource is the second sub-level in the third level, the automatic driving adjustment information includes: the vehicle speed is a second sub-speed limit in the third speed limit, and the perceived range of the surrounding environment of the vehicle is a second sub-range in the third range;

If the abnormal level of the computing resource is the first sub-level in the fourth level, the automatic driving adjustment information includes: the vehicle speed is a first sub-speed limit in the fourth speed limit, and the perceived range of the surrounding environment of the vehicle is a first sub-range in the fourth range;

if the abnormal level of the computing resource is the second sub-level in the fourth level, the automatic driving adjustment information includes: the vehicle speed is a second sub-speed limit in the fourth speed limit and the perceived range of the vehicle surroundings is a second sub-range in the fourth range;

wherein the third speed limit is less than the fourth speed limit; the third range is greater than the fourth range; the first sub-speed limit is smaller than the second sub-speed limit; the first sub-range is greater than the second sub-range.

Based on the second embodiment of the first aspect, in an eighth embodiment of the first aspect, determining a computing resource abnormality level based on the information of the computing resource and the information of the vehicle includes:

if the information of the computing resource is abnormal and the humidity is greater than a humidity threshold, determining that the abnormal level of the computing resource is a fifth level;

if the information of the computing resource is abnormal and the humidity is smaller than or equal to a humidity threshold value, determining that the abnormal level of the computing resource is a sixth level;

Wherein the fifth level is higher than the sixth level.

Based on the eighth embodiment of the first aspect, in a ninth embodiment of the first aspect, determining the automatic driving adjustment information based on the computing resource abnormality level includes:

if the abnormal level of the computing resource is a fifth level, the automatic driving adjustment information includes: the vehicle speed is the fifth speed limit, and the sensing range of the surrounding environment of the vehicle is the fifth range;

if the abnormal level of the computing resource is a sixth level, the automatic driving adjustment information includes: the vehicle speed is the sixth speed limit, and the sensing range of the surrounding environment of the vehicle is the sixth range;

wherein the fifth speed limit is less than the sixth speed limit; the fifth range is greater than the sixth range.

Based on the third embodiment of the first aspect, in a tenth embodiment of the first aspect, determining a computing resource abnormality level based on the information of the computing resource and the information of the vehicle includes:

if the values of the parameters are abnormal and the humidity is greater than a humidity threshold, determining that the abnormal level of the computing resource is a first sub-level in a fifth level;

if the value of at least one parameter is abnormal and the humidity is greater than the humidity threshold, determining that the abnormal level of the computing resource is a second sub-level in the fifth level;

If the values of the parameters are abnormal and the humidity is smaller than or equal to a humidity threshold, determining that the abnormal level of the computing resource is a first sub-level in a sixth level;

if the value of at least one parameter is abnormal and the humidity is smaller than or equal to a humidity threshold value, determining that the abnormal level of the computing resource is a second sub-level in a sixth level;

wherein the fifth level is higher than the sixth level; the first sub-level is higher in level than the second sub-level.

Based on the tenth embodiment of the first aspect, in an eleventh embodiment of the first aspect, determining the automatic driving adjustment information based on the computing resource abnormality level includes:

if the abnormal level of the computing resource is the first sub-level in the fifth level, the automatic driving adjustment information includes: the vehicle speed is a first sub-speed limit in the fifth speed limit and the perceived range of the surrounding environment of the vehicle is a first sub-range in the fifth range;

if the abnormal level of the computing resource is the second sub-level in the fifth level, the automatic driving adjustment information includes: the vehicle speed is a second sub-speed limit in the fifth speed limit and the perceived range of the surrounding environment of the vehicle is a second sub-range in the fifth range;

If the abnormal level of the computing resource is the first sub-level in the sixth level, the automatic driving adjustment information includes: the vehicle speed is a first sub-speed limit in the sixth speed limit and the perceived range of the surrounding environment of the vehicle is a first sub-range in the sixth range;

if the abnormal level of the computing resource is the second sub-level in the sixth level, the automatic driving adjustment information includes: the vehicle speed is a second sub-speed limit in the sixth speed limit and the perceived range of the vehicle surroundings is a second sub-range in the sixth range;

wherein the fifth speed limit is less than the sixth speed limit; the fifth range is greater than the sixth range; the first sub-speed limit is smaller than the second sub-speed limit; the first sub-range is greater than the second sub-range.

Based on the second embodiment of the first aspect, in a twelfth embodiment of the first aspect, determining a computing resource abnormality level based on the information of the computing resource and the information of the vehicle includes:

if the information of the computing resource is abnormal, the humidity is greater than a humidity threshold value and the vehicle speed is greater than a vehicle speed threshold value, determining that the abnormal level of the computing resource is a seventh level;

if the information of the computing resource is abnormal, the humidity is smaller than or equal to a humidity threshold value and the vehicle speed is larger than a vehicle speed threshold value, determining that the abnormal level of the computing resource is an eighth level;

If the information of the computing resource is abnormal, the humidity is greater than a humidity threshold value and the vehicle speed is less than or equal to a vehicle speed threshold value, determining that the abnormal level of the computing resource is a ninth level;

if the information of the computing resource is abnormal, the humidity is smaller than or equal to a humidity threshold value and the vehicle speed is smaller than or equal to a vehicle speed threshold value, determining that the abnormal level of the computing resource is a tenth level;

wherein the seventh level, the eighth level, the ninth level, and the tenth level are sequentially decreased in level.

Based on the twelfth embodiment of the first aspect, in a thirteenth embodiment of the first aspect, determining the automatic driving adjustment information based on the computing resource abnormality level includes:

if the abnormal level of the computing resource is a seventh level, the automatic driving adjustment information includes: the vehicle speed is the seventh speed limit, and the sensing range of the surrounding environment of the vehicle is the seventh range;

if the abnormal level of the computing resource is an eighth level, the automatic driving adjustment information includes: the vehicle speed is the eighth speed limit, and the sensing range of the surrounding environment of the vehicle is the eighth range;

if the abnormal level of the computing resource is a ninth level, the automatic driving adjustment information includes: the vehicle speed is the ninth speed limit, and the sensing range of the surrounding environment of the vehicle is the ninth range;

If the abnormal level of the computing resource is a tenth level, the automatic driving adjustment information includes: the vehicle speed is the tenth speed limit, and the sensing range of the surrounding environment of the vehicle is the tenth range;

wherein the seventh speed limit, the eighth speed limit, the ninth speed limit, and the tenth speed limit are sequentially increased;

the seventh range, the eighth range, the ninth range, and the tenth range decrease in order.

In a fourteenth embodiment of the first aspect, based on the information of the computing resource and the information of the vehicle, determining a computing resource abnormality level includes:

if the values of the parameters are abnormal, the humidity is greater than a humidity threshold value and the vehicle speed is greater than a vehicle speed threshold value, determining that the abnormal level of the computing resource is a first sub-level in a seventh level;

if the value of at least one parameter is abnormal, the humidity is greater than a humidity threshold value and the vehicle speed is greater than a vehicle speed threshold value, determining that the abnormal level of the computing resource is a second sub-level in a seventh level;

if the values of the parameters are abnormal, the humidity is smaller than or equal to a humidity threshold value and the vehicle speed is larger than a vehicle speed threshold value, determining that the abnormal grade of the computing resource is a first sub-grade in an eighth grade;

If the value of at least one parameter is abnormal, the humidity is smaller than or equal to a humidity threshold value and the vehicle speed is larger than a vehicle speed threshold value, determining that the abnormal level of the computing resource is a second sub-level in the eighth level;

if the values of the parameters are abnormal, the humidity is greater than a humidity threshold value and the vehicle speed is less than or equal to a vehicle speed threshold value, determining that the abnormal grade of the computing resource is a first sub-grade in a ninth grade;

if the value of at least one parameter is abnormal, the humidity is greater than a humidity threshold value and the vehicle speed is less than or equal to a vehicle speed threshold value, determining that the abnormal level of the computing resource of the vehicle-mounted hardware system is a second sub-level in a ninth level;

if the values of the parameters are abnormal, the humidity is smaller than or equal to a humidity threshold value and the vehicle speed is smaller than or equal to a vehicle speed threshold value, determining that the abnormal grade of the computing resource is a first sub-grade in a tenth grade;

if the value of at least one parameter is abnormal, the humidity is smaller than or equal to a humidity threshold value and the vehicle speed is smaller than or equal to a vehicle speed threshold value, determining that the abnormal level of the computing resource is a second sub-level in a tenth level;

wherein the seventh level, the eighth level, the ninth level, and the tenth level are sequentially lower in level; the first sub-level is higher in level than the second sub-level.

Based on the fourteenth embodiment of the first aspect, in the fifteenth embodiment of the first aspect, determining the automatic driving adjustment information based on the computing resource abnormality level includes:

if the abnormal level of the computing resource is the first sub-level in the seventh level, the automatic driving adjustment information includes: the vehicle speed is a first sub-speed limit in the seventh speed limit and the perceived range of the vehicle surroundings is a first sub-range in the seventh range;

if the abnormal level of the computing resource is the second sub-level in the seventh level, the automatic driving adjustment information includes: the vehicle speed is a second sub-speed limit in the seventh speed limit and the perceived range of the vehicle surroundings is a second sub-range in the seventh range;

if the abnormal level of the computing resource is the first sub-level in the eighth level, the automatic driving adjustment information includes: the vehicle speed is a first sub-speed limit in the eighth speed limit and the perceived range of the surrounding environment of the vehicle is a first sub-range in the eighth range;

if the abnormal level of the computing resource is the second sub-level in the eighth level, the automatic driving adjustment information includes: the vehicle speed is a second sub-speed limit in the eighth speed limit and the perceived range of the vehicle surroundings is a second sub-range in the eighth range;

If the abnormal level of the computing resource is the first sub-level in the ninth level, the automatic driving adjustment information includes: the vehicle speed is a first sub-speed limit in the ninth speed limit and the perceived range of the surrounding environment of the vehicle is a first sub-range in the ninth range;

if the abnormal level of the computing resource is the second sub-level in the ninth level, the automatic driving adjustment information includes: the vehicle speed is a second sub-speed limit in the ninth speed limit and the perceived range of the vehicle surroundings is a second sub-range in the ninth range;

if the abnormal level of the computing resource is the first sub-level in the tenth level, the automatic driving adjustment information includes: the vehicle speed is a first sub-speed limit in a tenth speed limit and the perceived range of the vehicle surroundings is a first sub-range in the tenth range;

if the abnormal level of the computing resource is the second sub-level in the tenth level, the automatic driving adjustment information includes: the vehicle speed is a second sub-speed limit in the tenth speed limit and the perceived range of the vehicle surroundings is a second sub-range in the tenth range;

With reference to the twelfth or thirteenth embodiment of the first aspect, in a sixteenth embodiment of the first aspect, if the information of the computing resource is a CPU temperature, the information of the computing resource is abnormal in that the CPU temperature is greater than a temperature threshold;

if the information of the computing resource is the case temperature, the information of the computing resource is abnormal, namely the case temperature is larger than a case temperature threshold value;

if the information of the computing resource is the CPU main frequency, the information of the computing resource is abnormal, namely the CPU main frequency is larger than a CPU main frequency threshold value;

if the information of the computing resource is the number of the equipment fault codes, the information of the computing resource is abnormal, and the number of the equipment fault codes is larger than a number threshold.

In a second aspect, an embodiment of the present invention further proposes a vehicle-mounted device, including:

a processor, a memory, and a user interface;

the processor, memory, and user interface are coupled together by a bus system;

the processor is configured to perform the steps of the method according to the first aspect by calling a program or instructions stored in the memory.

It can be seen that in at least one embodiment of the present invention, by acquiring the information of the computing resource and the information of the vehicle, the abnormal level of the computing resource may be determined based on the two information, and then the automatic driving adjustment information may be determined based on the abnormal level of the computing resource, and when the computing resource is abnormal, the processing capability of the automatic driving system is adaptively reduced, so that the automatic driving system meets the real-time requirement, and the automatic driving system will not generate unpredictable results.

Drawings

Fig. 1 is a schematic structural diagram of a vehicle-mounted device according to an embodiment of the present invention;

fig. 2 is a schematic diagram of an architecture of an autopilot system, an operating system, and a vehicle-mounted hardware system according to an embodiment of the present invention;

fig. 3 is a flowchart of an automatic driving adjustment method based on computing resources according to an embodiment of the present invention.

Detailed Description

In order that the above-recited objects, features and advantages of the present invention can be more clearly understood, a more particular description of the invention will be rendered by reference to specific embodiments thereof which are illustrated in the appended drawings. It is to be understood that the depicted embodiments are some, but not all, embodiments of the present invention. The specific embodiments described herein are to be considered in an illustrative rather than a restrictive sense. All other embodiments, which are obtained by a person skilled in the art based on the described embodiments of the invention, fall within the scope of protection of the invention.

It should be noted that in this document, relational terms such as "first" and "second" and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions.

Fig. 1 is a schematic structural diagram of a vehicle-mounted device according to an embodiment of the present invention.

The in-vehicle apparatus shown in fig. 1 includes: at least one processor 101, at least one memory 102, and other user interfaces 103. The various components in the in-vehicle device are coupled together by a bus system 104. It is understood that the bus system 104 is used to enable connected communications between these components. The bus system 104 includes a power bus, a control bus, and a status signal bus in addition to the data bus. But for clarity of illustration the various buses are labeled as bus system 104 in fig. 1.

The user interface 103 may include, among other things, a display, a keyboard, or a pointing device (e.g., a mouse, a trackball (trackball), or a touch pad, etc.).

It will be appreciated that the memory 102 in this embodiment may be either volatile memory or nonvolatile memory, or may include both volatile and nonvolatile memory. The non-volatile memory may be a Read-only memory (ROM), a programmable Read-only memory (ProgrammableROM, PROM), an erasable programmable Read-only memory (ErasablePROM, EPROM), an electrically erasable programmable Read-only memory (ElectricallyEPROM, EEPROM), or a flash memory, among others. The volatile memory may be a random access memory (RandomAccessMemory, RAM) that acts as an external cache. By way of example, and not limitation, many forms of RAM are available, such as Static RAM (SRAM), dynamic random access memory (DynamicRAM, DRAM), synchronous dynamic random access memory (SynchronousDRAM, SDRAM), double data rate synchronous dynamic random access memory (ddr SDRAM), enhanced Synchronous Dynamic Random Access Memory (ESDRAM), synchronous link dynamic random access memory (SynchlinkDRAM, SLDRAM), and direct memory bus random access memory (DirectRambusRAM, DRRAM). The memory 102 described herein is intended to comprise, without being limited to, these and any other suitable types of memory.

In some implementations, the memory 102 stores the following elements, executable units or data structures, or a subset thereof, or an extended set thereof: an operating system 1021, and application programs 1022.

The operating system 1021 includes various system programs, such as a framework layer, a core library layer, a driver layer, and the like, for implementing various basic services and processing hardware-based tasks. The application programs 1022 include various application programs such as a media player (MediaPlayer), a Browser (Browser), and the like for implementing various application services. A program for implementing the method of the embodiment of the present invention may be included in the application program 1022.

In the embodiment of the present invention, the processor 101 is configured to execute the method steps provided in the embodiments of the automatic driving adjustment method based on computing resources by calling the program or the instruction stored in the memory 102, specifically, the program or the instruction stored in the application 1022, for example, including the following steps one to four:

step one, acquiring information of computing resources and information of vehicles;

step two, determining an abnormal level of the computing resource based on the information of the computing resource and the information of the vehicle;

And step three, determining automatic driving adjustment information based on the computing resource abnormal grade.

The method disclosed in the above embodiment of the present invention may be applied to the processor 101 or implemented by the processor 101. The processor 101 may be an integrated circuit chip with signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in the processor 101 or instructions in the form of software. The processor 101 may be a general purpose processor, a digital signal processor (DigitalSignalProcessor, DSP), an application specific integrated circuit (application specific IntegratedCircuit, ASIC), an off-the-shelf programmable gate array (FieldProgrammableGateArray, FPGA) or other programmable logic device, a discrete gate or transistor logic device, a discrete hardware component. The disclosed methods, steps, and logic blocks in the embodiments of the present invention may be implemented or performed. 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 connection with the embodiments of the present invention may be embodied directly in the execution of a hardware decoding processor, or in the execution of a combination of hardware and software elements in a decoding processor. The software elements may be located in a random access memory, flash memory, read-only memory, programmable read-only memory or electrically erasable programmable memory, registers, etc. as well known in the art. The storage medium is located in the memory 102, and the processor 101 reads information in the memory 102, and in combination with its hardware, performs the steps of the method described above.

It is to be understood that the embodiments described herein may be implemented in hardware, software, firmware, middleware, microcode, or a combination thereof. For a hardware implementation, the processing units may be implemented within one or more Application Specific Integrated Circuits (ASICs), digital Signal Processors (DSPs), digital signal processing devices (dsppdevices), programmable Logic Devices (PLDs), field Programmable Gate Arrays (FPGAs), general purpose processors, controllers, micro-controllers, microprocessors, other electronic units adapted to perform the functions described herein, or a combination thereof.

For a software implementation, the techniques described herein may be implemented by means of units that perform the functions described herein. The software codes may be stored in a memory and executed by a processor. The memory may be implemented within the processor or external to the processor.

Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.

It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, and are not repeated herein.

In the embodiments provided in the present application, it should be understood that the order of execution may be arbitrarily adjusted, unless there is an explicit order of precedence between the steps of the method embodiments. The disclosed apparatus and method may be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.

The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.

The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on such understanding, the technical solution of the embodiments of the present invention may be embodied in essence or a part contributing to the prior art or a part of the technical solution, in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in the embodiments of the present invention. And the aforementioned storage medium includes: a usb disk, a removable hard disk, a ROM, a RAM, a magnetic disk, or an optical disk, etc.

Fig. 2 is a schematic diagram of an architecture of an autopilot system, an operating system, and an on-board hardware system, and in fig. 2, the autopilot system is a software system running on the operating system, which consumes computing resources of the on-board hardware system for implementing autopilot decision planning. The vehicle-mounted hardware system is a hardware system supporting the operation of an operating system, and the calculation capability of the vehicle-mounted hardware system can directly influence the reliability of the decision planning of the automatic driving system. The automatic driving adjustment device based on the computing resource is a software architecture for realizing the automatic driving adjustment method based on the computing resource, and is also a software system running on an operating system.

Fig. 3 is a schematic diagram of an automatic driving adjustment method based on computing resources according to an embodiment of the present invention, which may include the following steps 301 to 303:

301. acquiring information of computing resources and information of vehicles;

302. determining a computing resource abnormality level based on the information of the computing resource and the information of the vehicle;

303. and determining automatic driving adjustment information based on the computing resource abnormality level.

In this embodiment, by acquiring the information of the computing resource and the information of the vehicle, the abnormal level of the computing resource can be determined based on the two information, and then the automatic driving adjustment information can be determined based on the abnormal level of the computing resource, and when the computing resource is abnormal, the processing capability of the automatic driving system is adaptively reduced, so that the automatic driving system meets the real-time requirement, and the automatic driving system cannot generate unpredictable results.

In some embodiments, the information of the vehicle includes, but is not limited to, vehicle status information. The vehicle state information includes, but is not limited to, vehicle speed. The information of the computing resource is any one of the CPU temperature, the chassis temperature, the CPU main frequency and the number of the equipment fault codes.

Accordingly, step 302 is to determine an abnormal level of the computing resource based on the information of the computing resource and the vehicle state information, and may specifically include the following contents (1) and (2):

(1) If the information of the computing resource is abnormal and the vehicle speed is greater than the vehicle speed threshold, determining the abnormal grade of the computing resource as a first grade;

(2) If the information of the computing resource is abnormal and the vehicle speed is smaller than or equal to the vehicle speed threshold value, determining the abnormal level of the computing resource as a second level;

wherein the first hierarchical level is higher than the second hierarchical level.

Accordingly, determining autopilot adjustment information based on the computing resource anomaly level, as described in step 303, may include the following (1) and (2):

(1) If the abnormal grade of the computing resource is the first grade, the automatic driving adjustment information comprises: the vehicle speed is the first speed limit, and the sensing range of the surrounding environment of the vehicle is the first range;

(2) If the abnormal grade of the computing resource is the second grade, the automatic driving adjustment information comprises: the vehicle speed is the second speed limit, and the sensing range of the surrounding environment of the vehicle is the second range;

In some embodiments, the information of the vehicle includes, but is not limited to, vehicle status information. The vehicle state information includes, but is not limited to, vehicle speed. The information of the computing resource includes values of a plurality of parameters including, but not limited to, at least two of a CPU temperature, a chassis temperature, a CPU primary frequency, and a number of device fault codes.

Accordingly, step 302 is to determine a computing resource abnormality level based on the information of the computing resource and the vehicle state information, and may specifically include the following (1) to (4):

(1) If the values of the parameters are abnormal and the vehicle speed is greater than the vehicle speed threshold, determining that the abnormal grade of the computing resource is a first sub-grade in the third grade;

(2) If the value of at least one parameter is abnormal and the vehicle speed is greater than the vehicle speed threshold, determining that the abnormal grade of the computing resource is a second sub-grade in the third grade;

(3) If the values of the parameters are abnormal and the vehicle speed is smaller than or equal to the vehicle speed threshold value, determining that the abnormal grade of the computing resource is a first sub-grade in the fourth grade;

(4) If the value of at least one parameter is abnormal and the vehicle speed is less than or equal to the vehicle speed threshold value, determining that the abnormal grade of the computing resource is a second sub-grade in the fourth grade;

Accordingly, determining the autopilot adjustment information based on the computing resource anomaly level, as described in step 303, may include the following (1) through (4):

(1) If the abnormal level of the computing resource is the first sub-level in the third level, the automatic driving adjustment information includes: the vehicle speed is a first sub-speed limit in the third speed limit, and the sensing range of the surrounding environment of the vehicle is a first sub-range in the third range;

(2) If the abnormal level of the computing resource is the second sub-level in the third level, the automatic driving adjustment information includes: the vehicle speed is a second sub-speed limit in the third speed limit, and the perceived range of the surrounding environment of the vehicle is a second sub-range in the third range;

(3) If the abnormal level of the computing resource is the first sub-level in the fourth level, the automatic driving adjustment information includes: the vehicle speed is a first sub-speed limit in the fourth speed limit, and the perceived range of the surrounding environment of the vehicle is a first sub-range in the fourth range;

(4) If the abnormal level of the computing resource is the second sub-level in the fourth level, the automatic driving adjustment information includes: the vehicle speed is a second sub-speed limit in the fourth speed limit and the perceived range of the vehicle surroundings is a second sub-range in the fourth range;

In some embodiments, the information of the vehicle includes, but is not limited to, in-vehicle environmental information. In-vehicle environmental information includes, but is not limited to, humidity. The information of the computing resource is any one of the CPU temperature, the chassis temperature, the CPU main frequency and the number of the equipment fault codes.

Accordingly, step 302 is to determine an anomaly level of the computing resource based on the information of the computing resource and the in-vehicle environment information, and may specifically include the following contents (1) and (2):

(1) If the information of the computing resource is abnormal and the humidity is greater than the humidity threshold, determining that the abnormal level of the computing resource is a fifth level;

(2) If the information of the computing resource is abnormal and the humidity is smaller than or equal to the humidity threshold value, determining that the abnormal level of the computing resource is a sixth level;

wherein the fifth level is higher than the sixth level.

(1) If the abnormal level of the computing resource is the fifth level, the automatic driving adjustment information includes: the vehicle speed is the fifth speed limit, and the sensing range of the surrounding environment of the vehicle is the fifth range;

(2) If the abnormal level of the computing resource is the sixth level, the automatic driving adjustment information includes: the vehicle speed is the sixth speed limit, and the sensing range of the surrounding environment of the vehicle is the sixth range;

In some embodiments, the information of the vehicle includes, but is not limited to, in-vehicle environmental information. In-vehicle environmental information includes, but is not limited to, humidity. The information of the computing resource includes values of a plurality of parameters including, but not limited to, at least two of a CPU temperature, a chassis temperature, a CPU primary frequency, and a number of device fault codes.

Accordingly, step 302 is to determine an anomaly level of the computing resource based on the information of the computing resource and the in-vehicle environment information, and may specifically include the following contents (1) to (4):

(1) If the values of the parameters are abnormal and the humidity is greater than the humidity threshold, determining that the abnormal level of the computing resource is a first sub-level in the fifth level;

(2) If the value of at least one parameter is abnormal and the humidity is greater than the humidity threshold, determining that the abnormal level of the computing resource is a second sub-level in the fifth level;

(3) If the values of the parameters are abnormal and the humidity is less than or equal to the humidity threshold, determining that the abnormal level of the computing resource is a first sub-level in the sixth level;

(4) If the value of at least one parameter is abnormal and the humidity is less than or equal to the humidity threshold, determining that the abnormal level of the computing resource is a second sub-level in the sixth level;

(1) If the abnormal level of the computing resource is the first sub-level in the fifth level, the automatic driving adjustment information includes: the vehicle speed is a first sub-speed limit in the fifth speed limit and the perceived range of the surrounding environment of the vehicle is a first sub-range in the fifth range;

(2) If the abnormal level of the computing resource is the second sub-level in the fifth level, the automatic driving adjustment information includes: the vehicle speed is a second sub-speed limit in the fifth speed limit and the perceived range of the surrounding environment of the vehicle is a second sub-range in the fifth range;

(3) If the abnormal level of the computing resource is the first sub-level in the sixth level, the automatic driving adjustment information includes: the vehicle speed is a first sub-speed limit in the sixth speed limit and the perceived range of the surrounding environment of the vehicle is a first sub-range in the sixth range;

(4) If the abnormal level of the computing resource is the second sub-level in the sixth level, the automatic driving adjustment information includes: the vehicle speed is a second sub-speed limit in the sixth speed limit and the perceived range of the vehicle surroundings is a second sub-range in the sixth range;

In some embodiments, the information of the vehicle includes, but is not limited to, vehicle state information and in-vehicle environment information. The vehicle state information includes, but is not limited to, vehicle speed. In-vehicle environmental information includes, but is not limited to, humidity. The information of the computing resource is any one of the CPU temperature, the chassis temperature, the CPU main frequency and the number of the equipment fault codes.

Accordingly, step 302 is to determine an abnormality level of the computing resource based on the information of the computing resource, the vehicle state information and the in-vehicle environment information, and may specifically include the following contents (1) to (4):

(1) If the information of the computing resource is abnormal, the humidity is greater than the humidity threshold value and the vehicle speed is greater than the vehicle speed threshold value, determining that the abnormal level of the computing resource is a seventh level;

(2) If the information of the computing resource is abnormal, the humidity is smaller than or equal to the humidity threshold value and the vehicle speed is larger than the vehicle speed threshold value, determining that the abnormal level of the computing resource is an eighth level;

(3) If the information of the computing resource is abnormal, the humidity is greater than the humidity threshold value, and the vehicle speed is less than or equal to the vehicle speed threshold value, determining the abnormal level of the computing resource as a ninth level;

(4) If the information of the computing resource is abnormal, the humidity is smaller than or equal to a humidity threshold value and the vehicle speed is smaller than or equal to a vehicle speed threshold value, determining the abnormal grade of the computing resource as a tenth grade;

wherein the seventh level, eighth level, ninth level, and tenth level are sequentially decreased in level.

(1) If the abnormal level of the computing resource is a seventh level, the automatic driving adjustment information includes: the vehicle speed is the seventh speed limit, and the sensing range of the surrounding environment of the vehicle is the seventh range;

(2) If the abnormal level of the computing resource is the eighth level, the automatic driving adjustment information includes: the vehicle speed is the eighth speed limit, and the sensing range of the surrounding environment of the vehicle is the eighth range;

(3) If the abnormal level of the computing resource is the ninth level, the automatic driving adjustment information includes: the vehicle speed is the ninth speed limit, and the sensing range of the surrounding environment of the vehicle is the ninth range;

(4) If the abnormal level of the computing resource is a tenth level, the automatic driving adjustment information includes: the vehicle speed is the tenth speed limit, and the sensing range of the surrounding environment of the vehicle is the tenth range;

wherein the seventh speed limit, the eighth speed limit, the ninth speed limit and the tenth speed limit are sequentially increased; the seventh range, eighth range, ninth range, and tenth range decrease in order.

In some embodiments, the information of the vehicle includes, but is not limited to, vehicle state information and in-vehicle environment information. The vehicle state information includes, but is not limited to, vehicle speed. In-vehicle environmental information includes, but is not limited to, humidity. The information of the computing resource includes values of a plurality of parameters including, but not limited to, at least two of a CPU temperature, a chassis temperature, a CPU primary frequency, and a number of device fault codes.

Accordingly, step 302 is to determine an abnormality level of the computing resource based on the information of the computing resource, the vehicle state information and the in-vehicle environment information, and may specifically include the following contents (1) to (8):

(1) If the values of the parameters are abnormal, the humidity is greater than the humidity threshold value and the vehicle speed is greater than the vehicle speed threshold value, determining that the abnormal grade of the computing resource is a first sub-grade in the seventh grade;

(2) If the value of at least one parameter is abnormal, the humidity is greater than the humidity threshold value and the vehicle speed is greater than the vehicle speed threshold value, determining that the abnormal grade of the computing resource is a second sub-grade in the seventh grade;

(3) If the values of the parameters are abnormal, the humidity is smaller than or equal to the humidity threshold value and the vehicle speed is larger than the vehicle speed threshold value, determining that the abnormal grade of the computing resource is a first sub-grade in the eighth grade;

(4) If the value of at least one parameter is abnormal, the humidity is smaller than or equal to the humidity threshold value and the vehicle speed is larger than the vehicle speed threshold value, determining that the abnormal grade of the computing resource is a second sub-grade in the eighth grade;

(5) If the values of the parameters are abnormal, the humidity is greater than the humidity threshold value and the vehicle speed is less than or equal to the vehicle speed threshold value, determining that the abnormal grade of the computing resource is a first sub-grade in the ninth grade;

(6) If the value of at least one parameter is abnormal, the humidity is greater than the humidity threshold value and the vehicle speed is less than or equal to the vehicle speed threshold value, determining that the abnormal level of the computing resource of the vehicle-mounted hardware system is a second sub-level in the ninth level;

(7) If the values of the parameters are abnormal, the humidity is smaller than or equal to the humidity threshold value and the vehicle speed is smaller than or equal to the vehicle speed threshold value, determining the abnormal grade of the computing resource as a first sub-grade in a tenth grade;

(8) If the value of at least one parameter is abnormal, the humidity is smaller than or equal to the humidity threshold value and the vehicle speed is smaller than or equal to the vehicle speed threshold value, determining the abnormal grade of the computing resource as a second sub-grade in the tenth grade;

wherein the seventh level, eighth level, ninth level, and tenth level are sequentially decreased in level; the first sub-level is higher in level than the second sub-level.

Accordingly, determining the autopilot adjustment information based on the computing resource anomaly level, as described in step 303, may include the following (1) through (8):

(1) If the abnormal level of the computing resource is the first sub-level in the seventh level, the automatic driving adjustment information includes: the vehicle speed is a first sub-speed limit in the seventh speed limit and the perceived range of the vehicle surroundings is a first sub-range in the seventh range;

(2) If the computing resource abnormality level is the second sub-level of the seventh level, the automatic driving adjustment information includes: the vehicle speed is a second sub-speed limit in the seventh speed limit and the perceived range of the vehicle surroundings is a second sub-range in the seventh range;

(3) If the computing resource abnormality level is the first sub-level of the eighth level, the automatic driving adjustment information includes: the vehicle speed is a first sub-speed limit in the eighth speed limit and the perceived range of the surrounding environment of the vehicle is a first sub-range in the eighth range;

(4) If the computing resource abnormality level is the second sub-level of the eighth level, the automatic driving adjustment information includes: the vehicle speed is a second sub-speed limit in the eighth speed limit and the perceived range of the vehicle surroundings is a second sub-range in the eighth range;

(5) If the abnormal level of the computing resource is the first sub-level in the ninth level, the automatic driving adjustment information includes: the vehicle speed is a first sub-speed limit in the ninth speed limit and the perceived range of the surrounding environment of the vehicle is a first sub-range in the ninth range;

(6) If the computing resource abnormality level is the second sub-level of the ninth level, the automatic driving adjustment information includes: the vehicle speed is a second sub-speed limit in the ninth speed limit and the perceived range of the vehicle surroundings is a second sub-range in the ninth range;

(7) If the abnormal level of the computing resource is the first sub-level in the tenth level, the automatic driving adjustment information includes: the vehicle speed is a first sub-speed limit in a tenth speed limit and the perceived range of the vehicle surroundings is a first sub-range in the tenth range;

(8) If the computing resource abnormality level is the second sub-level of the tenth level, the automatic driving adjustment information includes: the vehicle speed is a second sub-speed limit in the tenth speed limit and the perceived range of the vehicle surroundings is a second sub-range in the tenth range;

In some embodiments, if the information of the computing resource is a CPU temperature, the information of the computing resource is abnormal in that the CPU temperature is greater than a temperature threshold;

if the information of the computing resource is the case temperature, the information of the computing resource is abnormal, namely the case temperature is larger than the case temperature threshold;

if the information of the computing resource is the number of the equipment fault codes, the information of the computing resource is abnormal, and the number of the equipment fault codes is larger than a number threshold value.

The various thresholds, the various speed limits, and the various ranges mentioned in the above embodiments may be set according to actual situations, and specific values are not limited herein.

It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.

Those skilled in the art will appreciate that while some embodiments described herein include some features but not others included in other embodiments, combinations of features of different embodiments are meant to be within the scope of the invention and form different embodiments.

Although embodiments of the present invention have been described in connection with the accompanying drawings, various modifications and variations may be made by those skilled in the art without departing from the spirit and scope of the invention, and such modifications and variations fall within the scope of the invention as defined by the appended claims.

Claims

1. An automatic driving adjustment method based on computing resources, comprising:

acquiring information of computing resources and information of vehicles;

determining a computing resource abnormality level based on the information of the computing resource and the information of the vehicle; the abnormal computing resource represents that the computing capability of the vehicle-mounted hardware system cannot meet the real-time requirement of the automatic driving system;

determining automatic driving adjustment information based on the abnormal grade of the computing resource, wherein the automatic driving adjustment information is used for reducing the processing capacity of an automatic driving system so that the automatic driving system meets the real-time requirement;

The determining a computing resource abnormality level based on the information of the computing resource and the information of the vehicle includes: if the information of the computing resource is abnormal and the vehicle speed is greater than the vehicle speed threshold, determining that the abnormal grade of the computing resource is a first grade; if the information of the computing resource is abnormal and the vehicle speed is smaller than or equal to a vehicle speed threshold value, determining that the abnormal level of the computing resource is a second level; wherein the first level is higher than the second level;

the determining automatic driving adjustment information based on the computing resource abnormality level includes: if the abnormal level of the computing resource is the first level, the automatic driving adjustment information includes: the vehicle speed is the first speed limit, and the sensing range of the surrounding environment of the vehicle is the first range; if the abnormal level of the computing resource is the second level, the automatic driving adjustment information includes: the vehicle speed is the second speed limit, and the sensing range of the surrounding environment of the vehicle is the second range; wherein the first speed limit is less than the second speed limit; the first range is greater than the second range, and both the first range and the second range are less than a perceived range of the computing resource when normal.

2. The method of claim 1, wherein the information of the vehicle comprises: vehicle state information and/or in-vehicle environmental information.

3. The method of claim 2, wherein the information of the computing resource comprises: CPU temperature, chassis temperature, CPU main frequency or number of equipment fault codes; the vehicle state information includes a vehicle speed; the in-vehicle environmental information includes humidity.

4. The method of claim 2, wherein the information of the computing resource comprises: values of a plurality of parameters, the plurality of parameters comprising: at least two of CPU temperature, chassis temperature, CPU main frequency and number of equipment fault codes; the vehicle state information includes a vehicle speed; the in-vehicle environmental information includes humidity.

5. The method of claim 4, wherein the step of determining the position of the first electrode is performed,

determining a computing resource anomaly level based on the information of the computing resource and the information of the vehicle, comprising:

6. The method of claim 5, wherein the step of determining the position of the probe is performed,

determining autopilot adjustment information based on the computing resource anomaly class, comprising:

7. The method of claim 3, wherein the step of,

Wherein the fifth level is higher than the sixth level.

8. The method of claim 7, wherein the step of determining the position of the probe is performed,

9. The method of claim 4, wherein the step of determining the position of the first electrode is performed,

10. The method of claim 9, wherein the step of determining the position of the substrate comprises,

11. The method of claim 3, wherein the step of,

12. The method of claim 11, wherein the step of determining the position of the probe is performed,

13. The method of claim 4, wherein the step of determining the position of the first electrode is performed,

14. The method of claim 13, wherein the step of determining the position of the probe is performed,

15. The method according to claim 11 or 12, wherein,

if the information of the computing resource is CPU temperature, the information of the computing resource is abnormal, namely the CPU temperature is larger than a temperature threshold;

16. An in-vehicle apparatus, characterized by comprising:

a processor, a memory, and a user interface;

the processor, memory, and user interface are coupled together by a bus system;

the processor is adapted to perform the steps of the method according to any one of claims 1 to 15 by invoking a program or instruction stored in the memory.