CN110562269A - Method for processing fault of intelligent driving vehicle, vehicle-mounted equipment and storage medium - Google Patents
Method for processing fault of intelligent driving vehicle, vehicle-mounted equipment and storage medium Download PDFInfo
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- CN110562269A CN110562269A CN201910730806.9A CN201910730806A CN110562269A CN 110562269 A CN110562269 A CN 110562269A CN 201910730806 A CN201910730806 A CN 201910730806A CN 110562269 A CN110562269 A CN 110562269A
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
- B60W50/02—Ensuring safety in case of control system failures, e.g. by diagnosing, circumventing or fixing failures
- B60W50/0205—Diagnosing or detecting failures; Failure detection models
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Abstract
The embodiment of the disclosure relates to a method for processing faults of an intelligent driving vehicle, vehicle-mounted equipment and a storage medium, wherein the method comprises the following steps: respectively carrying out safety personnel detection and fault detection in the running process of the intelligent driving vehicle; after detecting the fault, determining a system fault level; and performing fault grading treatment based on the detection result of the security personnel and the system fault grade. In the embodiment of the disclosure, safety personnel detection and fault detection are simultaneously performed in the vehicle running process, the fault level of the system can be determined after the fault is detected, and the fault is processed in a grading manner based on the detection result of the safety personnel, so that the safety of the vehicle is ensured, the fault processing mode is enriched, and the vehicle is more flexible.
Description
Technical Field
The embodiment of the disclosure relates to the technical field of intelligent driving, in particular to a method for processing faults of an intelligent driving vehicle, vehicle-mounted equipment and a storage medium.
background
At present, if a fault occurs in the driving process of an intelligent driving vehicle, in order to ensure driving safety, an intelligent driving system can control the vehicle to brake and stop. Therefore, the current fault processing mode for the intelligent driving vehicle is single and not flexible enough.
The above description of the discovery process of the problems is only for the purpose of aiding understanding of the technical solutions of the present disclosure, and does not represent an admission that the above is prior art.
disclosure of Invention
To solve at least one problem of the prior art, at least one embodiment of the present disclosure provides a method of fault handling for an intelligent driving vehicle, an in-vehicle device, and a storage medium.
in a first aspect, an embodiment of the present disclosure provides a method for fault handling of a smart driving vehicle, where the method includes:
Respectively carrying out safety personnel detection and fault detection in the running process of the intelligent driving vehicle;
After detecting the fault, determining a system fault level;
And carrying out fault grading treatment based on the result of the detection of the security personnel and the fault grade of the system.
In a second aspect, an embodiment of the present disclosure further provides an on-board device, including: a processor and a memory; the processor is adapted to perform the steps of the method according to the first aspect by calling a program or instructions stored by the memory.
In a third aspect, the disclosed embodiments also propose a non-transitory computer-readable storage medium for storing a program or instructions for causing a computer to perform the steps of the method according to the first aspect.
Therefore, in at least one embodiment of the present disclosure, safety personnel detection and fault detection are performed simultaneously during the vehicle driving process, the system fault level can be determined after the fault is detected, and the fault is processed in a grading manner based on the safety personnel detection result, so that the vehicle safety is ensured, the fault processing manner is enriched, and the vehicle driving method is more flexible.
Drawings
To more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present disclosure, and other drawings can be obtained by those skilled in the art according to the drawings.
Fig. 1 is an overall architecture diagram of an intelligent driving vehicle provided by an embodiment of the present disclosure;
FIG. 2 is a block diagram of an intelligent driving system provided by an embodiment of the present disclosure;
FIG. 3 is a block diagram of a fault handling module provided by an embodiment of the present disclosure;
FIG. 4 is a block diagram of an in-vehicle device provided by an embodiment of the present disclosure;
Fig. 5 is a flowchart of a method for processing a fault of an intelligent driving vehicle according to an embodiment of the present disclosure.
Detailed Description
In order that the above objects, features and advantages of the present disclosure can be more clearly understood, the present disclosure will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the embodiments described are only a few embodiments of the present disclosure, and not all embodiments. The specific embodiments described herein are merely illustrative of the disclosure and are not intended to be limiting. All other embodiments derived by one of ordinary skill in the art from the described embodiments of the disclosure are intended to be within the scope of the disclosure.
It is noted that, in this document, relational terms such as "first" and "second," and the like, may be 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.
Aiming at the problems that the fault processing mode of an intelligent driving vehicle is single and not flexible enough in the prior art, the embodiment of the disclosure provides a fault processing scheme of the intelligent driving vehicle.
The scheme for processing the fault of the intelligent driving vehicle provided by the embodiment of the disclosure can be applied to the intelligent driving vehicle.
Fig. 1 is an overall architecture diagram of an intelligent driving vehicle according to an embodiment of the present disclosure. As shown in fig. 1, the smart driving vehicle includes: sensor groups, smart driving system 100, vehicle floor management systems, and other components that may be used to propel a vehicle and control the operation of the vehicle.
And the sensor group is used for acquiring data of the external environment of the vehicle and detecting position data of the vehicle. The sensor group includes, for example, but not limited to, at least one of a camera, a laser radar, a millimeter wave radar, a GPS (Global Positioning System), and an IMU (Inertial Measurement Unit).
in some embodiments, the sensor group is further used for collecting dynamic data of the vehicle, and the sensor group further includes, for example and without limitation, at least one of a wheel speed sensor, a speed sensor, an acceleration sensor, a steering wheel angle sensor, and a front wheel angle sensor.
The intelligent driving system 100 is used for acquiring data of a sensor group, and all sensors in the sensor group transmit data at a high frequency in the driving process of the intelligent driving vehicle.
the intelligent driving system 100 is further configured to perform environment sensing and vehicle positioning based on the data of the sensor group, perform path planning and decision making based on the environment sensing information and the vehicle positioning information, and generate a vehicle control instruction based on the planned path, so as to control the vehicle to travel according to the planned path.
In some embodiments, the intelligent driving system 100 is further configured to perform a security guard detection and a fault detection during driving of the intelligent driving vehicle, respectively. The detection of the security personnel is continuously carried out during the running process of the vehicle, and is not carried out after a fault is detected. Further, the intelligent driving system 100 may perform a hierarchical process on the fault based on the result of the detection by the security officer after the fault is detected. In some embodiments, the smart driving system 100, upon detecting a fault, may determine a system fault level based on the detected fault; and then based on the result of the detection of the security personnel and the system fault level, carrying out hierarchical processing on the fault.
In some embodiments, the smart driving system 100 may be a software system, a hardware system, or a combination of software and hardware. For example, the smart driving system 100 is a software system running on an operating system, and the in-vehicle hardware system is a hardware system supporting the operating system.
In some embodiments, the smart driving system 100 is further configured to wirelessly communicate with a cloud server to interact with various information. In some embodiments, the smart driving system 100 and the cloud server communicate wirelessly via a wireless communication network (e.g., a wireless communication network including, but not limited to, a GPRS network, a Zigbee network, a Wifi network, a 3G network, a 4G network, a 5G network, etc.).
In some embodiments, the cloud server is used for overall coordination and management of the intelligent driving vehicle. In some embodiments, the cloud server may be configured to interact with one or more intelligent driving vehicles, orchestrate and coordinate the scheduling of multiple intelligent driving vehicles, and the like.
In some embodiments, the cloud server is a cloud server established by a vehicle service provider, and provides cloud storage and cloud computing functions. In some embodiments, the cloud server builds the vehicle-side profile. In some embodiments, the vehicle-side profile stores various information uploaded by the intelligent driving system 100. In some embodiments, the cloud server may synchronize the driving data generated by the vehicle side in real time.
in some embodiments, the cloud server may include a data warehouse and a data processing platform, wherein the data warehouse stores a vehicle-side file created by the cloud server. In some embodiments, the data warehouse can collect data from various source business systems into the data warehouse and process the data in the data processing platform for use by the vehicle end.
In some embodiments, the cloud server may be a server or a server group. The server group may be centralized or distributed. The distributed servers are beneficial to the distribution and optimization of tasks in a plurality of distributed servers, and the defects of resource shortage and response bottleneck of the traditional centralized server are overcome. In some embodiments, the cloud server may be local or remote.
In some embodiments, the cloud server may be configured to obtain information about Road monitoring units (RSUs) and smart driving vehicles, and may send the information to the smart driving vehicles. In some embodiments, the cloud server may send detection information corresponding to the smart driving vehicle in the road monitoring unit to the smart driving vehicle according to information of the smart driving vehicle.
in some embodiments, a road monitoring unit may be used to collect road monitoring information. In some embodiments, the road monitoring unit may be an environmental perception sensor, such as a camera, a lidar, etc., and may also be a road device, such as a V2X device, a roadside traffic light device, etc. In some embodiments, the road monitoring units may monitor road conditions pertaining to the respective road monitoring units, e.g., by type of vehicle, speed, priority level, etc. The road monitoring unit can send the road monitoring information to the cloud server after collecting the road monitoring information, and can also send the intelligent driving vehicle through the road.
and the vehicle bottom layer execution system is used for receiving the vehicle control instruction and realizing the control of vehicle running. In some embodiments, vehicle under-floor execution systems include, but are not limited to: a steering system, a braking system and a drive system. The steering system, the braking system and the driving system belong to mature systems in the field of vehicles, and are not described in detail herein.
In some embodiments, the smart-drive vehicle may also include a vehicle CAN bus, not shown in FIG. 1, that connects to the vehicle's underlying implement system. Information interaction between the intelligent driving system 100 and the vehicle bottom layer execution system is transmitted through a vehicle CAN bus.
In some embodiments, the intelligent driving vehicle may control the vehicle to travel by both the driver and the intelligent driving system 100. In the manual driving mode, the driver drives the vehicle by operating devices for controlling the vehicle to run, such as, but not limited to, a brake pedal, a steering wheel, an accelerator pedal, and the like. The device for controlling the vehicle to run can directly operate the vehicle bottom layer execution system to control the vehicle to run.
In some embodiments, the intelligent driving vehicle may also be an unmanned vehicle, and the driving control of the vehicle is performed by the intelligent driving system 100.
Fig. 2 is a block diagram of an intelligent driving system 200 according to an embodiment of the present disclosure. In some embodiments, the intelligent driving system 200 may be implemented as the intelligent driving system 100 of fig. 1 or a part of the intelligent driving system 100 for controlling the vehicle to run.
As shown in fig. 2, the smart driving system 200 may be divided into a plurality of modules, for example, may include: a perception module 201, a planning module 202, a control module 203, a fault handling module 204, and other modules that may be used for intelligent driving.
the sensing module 201 is used for sensing and positioning the environment. In some embodiments, the sensing module 201 is used for acquired sensor data, V2X (Vehicle to X) data, high precision maps, and the like. In some embodiments, the sensing module 201 is configured to sense and locate the environment based on at least one of acquired sensor data, V2X (Vehicle to X) data, high-precision maps, and the like.
In some embodiments, the sensing module 201 is configured to generate sensing and positioning information, so as to sense an obstacle, identify a travelable area of a camera image, position a vehicle, and the like.
Environmental awareness (Environmental awareness) may be understood as a semantic classification of data with respect to the context of the scene understanding capabilities of the environment, such as the location of obstacles, the detection of road signs/markers, the detection of pedestrians/vehicles, etc. In some embodiments, the environmental sensing may be performed by fusing data of various sensors such as a camera, a laser radar, and a millimeter wave radar.
Localization (Localization) is part of the perception, and is the ability to determine the position of an intelligent driving vehicle relative to the environment. The positioning can be as follows: GPS positioning, wherein the positioning accuracy of the GPS is in the order of tens of meters to centimeters, and the positioning accuracy is high; the positioning method combining the GPS and the Inertial Navigation System (Inertial Navigation System) can also be used for positioning. The positioning may also be performed by using a SLAM (Simultaneous Localization And Mapping), where the target of the SLAM is to construct a map And to perform positioning using the map, And the SLAM determines the position of the current vehicle And the position of the current observed feature by using the environmental features that have been observed.
The V2X is a key technology of the intelligent transportation system, so that the vehicles, the vehicles and the base stations can communicate with each other, a series of traffic information such as real-time road conditions, road information and pedestrian information can be obtained, the intelligent driving safety is improved, the congestion is reduced, the traffic efficiency is improved, and vehicle-mounted entertainment information is provided.
The high accuracy map is the geographical map that uses in the intelligent driving field, compares with traditional map, and the difference lies in: 1) high-precision maps comprise a large amount of driving assistance information, for example by means of an accurate three-dimensional representation of the road network: including intersection places, landmark positions, and the like; 2) high-precision maps also include a large amount of semantic information, such as reporting the meaning of different colors on traffic lights, in turn, for example, indicating the speed limit of roads, and the location where left-turn lanes begin; 3) the high-precision map can reach centimeter-level precision, and the safe driving of the intelligent driving vehicle is ensured.
the planning module 202 is configured to perform path planning and decision making based on the perceptual positioning information generated by the perceptual positioning module.
In some embodiments, the planning module 202 is configured to perform path planning and decision-making based on the perceptual-positioning information generated by the perceptual-positioning module in combination with at least one of V2X data, high-precision maps, and the like.
in some embodiments, the planning module 202 is used to plan a path, deciding: the planning decision information is generated based on the behavior (e.g., including but not limited to following, passing, parking, detouring, etc.), vehicle heading, vehicle speed, desired acceleration of the vehicle, desired steering wheel angle, etc.
The control module 203 is configured to perform path tracking and trajectory tracking based on the planning decision information generated by the planning module 202.
In some embodiments, the control module 203 is configured to generate control commands for the vehicle floor-based execution system and issue the control commands, so that the vehicle floor-based execution system controls the vehicle to travel according to a desired path, for example, controls the steering wheel, the brake, and the throttle to control the vehicle laterally and longitudinally.
in some embodiments, the control module 203 is further configured to calculate a front wheel steering angle based on a path tracking algorithm.
In some embodiments, the expected path curve in the path tracking process is independent of time parameters, and during tracking control, the intelligent driving vehicle can be assumed to advance at a constant speed at the current speed, so that the driving path approaches to the expected path according to a certain cost rule; during track tracking, the expected path curve is related to both time and space, and the intelligent driving vehicle is required to reach a certain preset reference path point within a specified time.
Path tracking differs from trajectory tracking in that it is not subject to time constraints and only requires the desired path to be tracked within a certain error range.
The fault processing module 204 is used for respectively performing security personnel detection and fault detection in the running process of the intelligent driving vehicle. The detection of the security personnel is continuously carried out during the running process of the vehicle, and is not carried out after a fault is detected. Further, the fault handling module 204 may perform a hierarchical handling of the fault based on the results of the security personnel detection after the fault is detected. In some embodiments, the fault handling module 204, upon detecting a fault, may determine a system fault level based on the detected fault; and then based on the result of the detection of the security personnel and the system fault level, carrying out hierarchical processing on the fault.
In some embodiments, the function of the fault handling module 204 may be integrated into the sensing module 201, the planning module 202, or the control module 203, or may be configured as a module separate from the intelligent driving system 200, and the fault handling module 204 may be a software module, a hardware module, or a module combining software and hardware. For example, the fault handling module 204 is a software module running on an operating system, and the in-vehicle hardware system is a hardware system supporting the operating system.
Fig. 3 is a block diagram of a fault handling module 300 according to an embodiment of the disclosure. In some embodiments, the fault handling module 300 may be implemented as the fault handling module 204 or as part of the fault handling module 204 in fig. 2.
as shown in fig. 3, the fault handling module 300 may include, but is not limited to, the following elements: a security guard detection unit 301, a fault detection unit 302, a fault classification unit 303, and a classification processing unit 304.
the security officer detection unit 301 is used for detecting security officers in the driving process of the intelligent driving vehicle. In some embodiments, the security officer detection performed by the security officer detection unit 301 may be to detect whether a security officer is present. In some embodiments, the security officer detection unit 301 detects the presence of a security officer by one or more means, which may include, but are not limited to, the following, for example: 1) detecting whether a registration report exists in the intelligent driving system, if so, indicating that a security officer actively registers the report to the intelligent driving system and is responsible for monitoring the running condition of the intelligent driving system; 2) detecting whether a safety person exists or not through pressure sensor data of a seat at the position of the driver, and if the pressure sensor data is larger than a preset threshold value, indicating that the safety person exists and sitting on the seat at the position of the driver; 3) whether the security officer is on the seat at the driver position is judged through a video image, wherein the video image can be shot by a camera installed in the vehicle.
in some embodiments, the security personnel detection unit 301 is further configured to perform security personnel action detection after the failure detection unit 302 detects a failure, and further determine whether a security personnel actively intervenes in the failure processing. In some embodiments, the safer detection unit 301 continuously detects the safer's actions during the driving of the smart driving vehicle. In some embodiments, the security officer detection unit 301 performs security officer action detection by one or more means, which may include, but are not limited to, the following means, for example: 1) detecting whether a safety officer actively rotates a steering wheel; 2) detecting whether a safety personnel actively steps on a brake pedal; 3) whether the emergency stop button is pressed by the security officer.
The fault detection unit 302 is used for detecting faults of each component in the intelligent driving vehicle and each module in the intelligent driving system. For example, detecting a failure of a sensor group installed in a smart driving vehicle; and for example, detecting faults of functional modules such as a perception module, a planning module and a control module of the intelligent driving system. In some embodiments, the failure detection unit 302 may detect a failure by detecting information such as heartbeats of each module or component, and an operating state and data reported by each module or component, and determining whether each module or component has a failure, and if a failure is detected, reporting the failure to the failure classification unit 303 immediately for classification processing. In some embodiments, the manner in which the fault detection unit 302 detects the fault is an existing manner, and is not described again.
The fault classification unit 303 is configured to perform classification processing on the fault reported by the fault detection unit 302. In some embodiments, the fault classification unit 303 classifies the fault, and may determine a system fault level, which may be understood as a fault level of the smart driving system. In some embodiments, different system fault levels may be predefined, for example, different system fault levels may be defined based on the implementation degree of the intelligent driving system function, where the table one is defined for different system fault levels, and different implementation degrees of the intelligent driving system function are embodied as different system behaviors in the table one.
TABLE-different System Fault level definitions
as can be seen, the system failure level is defined as five levels: E1-E5 respectively correspond to different realization degrees of the functions of the intelligent driving system. Those skilled in the art will appreciate that different levels of system faults may be predefined based on different factors, such as the degree of impact on the functionality of the intelligent driving system; also, for example, different system failure levels are defined based on different countermeasures; and for example define different levels of system failure based on the importance of different functional modules.
In some embodiments, after different system fault levels are predefined, for different objects, the system fault level corresponding to the fault of the different object may be predefined, so as to obtain the system fault level preset by the different object. In some embodiments, the system fault level corresponding to the fault of the different object may be determined based on the degree of influence on the intelligent driving system when the different object has the fault. In some embodiments, the system failure level corresponding to the failure of different objects may be determined based on the importance of the different objects. In some embodiments, when an object fails, one system failure level may be corresponded, and a plurality of system failure levels may be corresponded, depending on the failure degree of the object. In some embodiments, when determining the system fault level after detecting the fault, the fault classification unit 303 may determine the system fault level by determining an object in which the fault occurs, and further, based on the system fault level preset by the object.
In some embodiments, if there is one failed object, the fault classifying unit 303 determines a system fault level preset based on the object when determining the system fault level, for example, if there is one system fault level when the object fails, the fault classifying unit 303 determines the system fault level as the system fault level preset by the object.
In some embodiments, if there are multiple failed objects, the fault classification unit 303 determines the system fault level as: the highest level among the preset system failure levels of the plurality of objects.
The classification processing unit 304 is configured to perform fault classification processing based on the security personnel detection result of the security personnel detection unit 301 and the system fault classification determined by the fault classification unit 303. In some embodiments, in order to prevent the security officer from competing for the vehicle control right and ensure that the security officer has the control right with the highest priority, the security officer takes processing measures to fully exert the role of the security officer, so if the security officer detection unit 301 detects the security officer, the classification processing unit 304 notifies the security officer of the information of the fault, the system fault level and the processing measures corresponding to the system fault level, thereby prompting the security officer of the processing measures that can be taken.
in some embodiments, different system failure levels correspond to different processing measures, for example, for the level of the unused system failure defined in table one, the following processing measures are taken as examples:
the corresponding processing measures of the system fault level L1 are as follows: warning and displaying fault information;
The corresponding processing measures of the system fault level L2 are as follows: speed limit driving and displaying fault information;
The corresponding processing measures of the system fault level L3 are as follows: limiting the speed, braking and stopping at a nearby safe place, and displaying fault information;
the corresponding processing measures of the system fault level L4 are as follows: weak braking and displaying fault information;
The corresponding processing measures of the system fault level L5 are as follows: brake strongly and display failure information.
In some embodiments, if a safety officer is detected, but the safety officer does not actively intervene in fault processing within a preset time, in order to ensure that the safety and the controllability of the vehicle are ensured, the intelligent driving system takes corresponding processing measures. In some embodiments, the preset time is a reaction time given to a security officer on the basis of ensuring the safety of the vehicle, and may be set according to actual needs, and the specific value of the preset time is not limited herein. In some embodiments, after the security personnel detection unit 301 detects a security personnel and the classification processing unit 304 notifies the security personnel of the information of the fault, the system fault level and the processing measure corresponding to the system fault level, if the security personnel detection unit 301 does not detect the action of the security personnel within a preset time, the classification processing unit 304 performs fault processing based on the processing measure corresponding to the system fault level, so as to ensure the safety of the vehicle.
in some embodiments, if no security personnel is detected, in order to ensure that the vehicle is safe and controllable, the intelligent driving system takes corresponding processing measures. In some embodiments, when the classification processing unit 304 performs fault classification processing, if the security personnel detection unit 301 does not detect a security personnel, the classification processing unit 304 performs fault processing based on a processing measure corresponding to a system fault level, so as to ensure vehicle safety.
In some embodiments, after the hierarchical processing unit 304 performs the fault hierarchical processing, a recovery processing may be further performed based on a recovery measure corresponding to the system fault level. In some embodiments, a plurality of recovery measures may be preset, and one recovery measure may correspond to one system failure level or a plurality of system failure levels. In some embodiments, the four preset recovery measures are as follows:
And (4) recovery measure A: automatic control is immediately recovered without manual interference;
And (4) recovery measure B: the control can be recovered after manual confirmation at any time;
and C, recovery measure C: and the vehicle can be automatically controlled after reaching a target state (such as parking beside) without manual interference.
and (4) recovery measure D: after the vehicle reaches the target state, the vehicle is manually confirmed and then is controlled again.
In some embodiments, the correspondence between the system failure level and the recovery measure is as follows:
the system fault level L1 corresponds to recovery measure a;
the system failure level L2 corresponds to recovery measure B;
The system failure level L3 corresponds to recovery measure C;
The system failure level L4 and the system failure level L5 correspond to the recovery measure D.
in some embodiments, the division of each unit in the fault processing module 300 is only one logical function division, and there may be another division manner in actual implementation, for example, the security guard detecting unit 301, the fault detecting unit 302, the fault classifying unit 303, and the classifying processing unit 304 may be implemented as one unit; the security guard detection unit 301, the fault detection unit 302, the fault classification unit 303, or the classification processing unit 304 may also be divided into a plurality of sub-units. It will be understood that the various units or sub-units may be implemented in electronic hardware, or a combination 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 implementation. Skilled artisans may implement the described functionality in varying ways for each particular application.
Fig. 4 is a schematic structural diagram of an in-vehicle device provided in an embodiment of the present disclosure. The vehicle-mounted equipment can support the operation of the intelligent driving system.
As shown in fig. 4, the vehicle-mounted apparatus includes: at least one processor 401, at least one memory 402, and at least one communication interface 403. The various components in the in-vehicle device are coupled together by a bus system 404. A communication interface 403 for information transmission with an external device. It is understood that the bus system 404 is used to enable communications among the components. The bus system 404 includes a power bus, a control bus, and a status signal bus in addition to a data bus. For clarity of illustration, the various buses are labeled as bus system 404 in fig. 4.
It will be appreciated that the memory 402 in this embodiment can be either volatile memory or nonvolatile memory, or can include both volatile and nonvolatile memory.
In some embodiments, memory 402 stores the following elements, executable units or data structures, or a subset thereof, or an expanded set thereof: an operating system and an application program.
The operating system includes various system programs, such as a framework layer, a core library layer, a driver layer, and the like, and is used for implementing various basic services and processing hardware-based tasks. The application programs, including various application programs such as a Media Player (Media Player), a Browser (Browser), etc., are used to implement various application services. The program for implementing the method for processing the fault of the intelligent driving vehicle provided by the embodiment of the disclosure can be contained in an application program.
in the embodiment of the present disclosure, the processor 401 is configured to execute the steps of the method for processing the fault of the intelligent driving vehicle provided by the embodiment of the present disclosure by calling a program or an instruction stored in the memory 402, specifically, a program or an instruction stored in an application program.
The method for processing the fault of the intelligent driving vehicle provided by the embodiment of the disclosure can be applied to the processor 401, or implemented by the processor 401. The processor 401 may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware or instructions in the form of software in the processor 401. The processor 401 may be a general-purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, or discrete hardware components. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The steps of the method for intelligently handling the vehicle fault provided by the embodiment of the disclosure can be directly embodied as the execution of a hardware decoding processor, or the execution of the hardware decoding processor and a software unit in the decoding processor is combined. The software elements may be located in ram, flash, rom, prom, or eprom, registers, among other storage media that are well known in the art. The storage medium is located in a memory 402, and the processor 401 reads information in the memory 402 and performs the steps of the method in combination with its hardware.
Fig. 5 is a flowchart of a method for processing a fault of an intelligent driving vehicle according to an embodiment of the present disclosure. The execution subject of the method is the vehicle-mounted equipment, and in some embodiments, the execution subject of the method is an intelligent driving system supported by the vehicle-mounted equipment.
As shown in fig. 5, the method of smart driving vehicle fault handling may include the following steps 501 to 503:
501. And respectively carrying out safety personnel detection and fault detection in the running process of the intelligent driving vehicle. In some embodiments, performing a security officer detection may be detecting whether a security officer is present. In some embodiments, the presence of a security officer is detected by one or more means, which may include, for example and without limitation, the following: 1) detecting whether a registration report exists in the intelligent driving system, if so, indicating that a security officer actively registers the report to the intelligent driving system and is responsible for monitoring the running condition of the intelligent driving system; 2) detecting whether a safety person exists or not through pressure sensor data of a seat at the position of the driver, and if the pressure sensor data is larger than a preset threshold value, indicating that the safety person exists and sitting on the seat at the position of the driver; 3) whether the security officer is on the seat at the driver position is judged through a video image, wherein the video image can be shot by a camera installed in the vehicle.
in some embodiments, after a fault is detected, a safety officer action detection is performed to determine whether a safety officer actively intervenes in the fault handling. In some embodiments, the actions of the security officer may be continuously detected during the driving of the smart driving vehicle. In some embodiments, the detection of the action of the security officer is performed by one or more means, which may include, but are not limited to, the following, for example: 1) detecting whether a safety officer actively rotates a steering wheel; 2) detecting whether a safety personnel actively steps on a brake pedal; 3) whether the emergency stop button is pressed by the security officer.
In some embodiments, fault detection is specific to detecting faults of components in a smart driving vehicle and modules in a smart driving system. For example, detecting a failure of a sensor group installed in a smart driving vehicle; and for example, detecting faults of functional modules such as a perception module, a planning module and a control module of the intelligent driving system. In some embodiments, the failure detection mode may be to determine whether each module or component fails by detecting information such as heartbeats of each module or component, and an operating state and data reported by each module or component. In some embodiments, the failure detection mode is the existing mode and is not described in detail.
502. Upon detecting a fault, a system fault level is determined. The system fault class is understood to be the fault class of the intelligent driving system. In some embodiments, different system fault levels may be predefined, for example, different system fault levels may be defined based on the implementation degree of the intelligent driving system function, where the table one is defined for different system fault levels, and different implementation degrees of the intelligent driving system function are embodied as different system behaviors in the table one.
TABLE-different System Fault level definitions
As can be seen, the system failure level is defined as five levels: E1-E5 respectively correspond to different realization degrees of the functions of the intelligent driving system. Those skilled in the art will appreciate that different levels of system faults may be predefined based on different factors, such as the degree of impact on the functionality of the intelligent driving system; also, for example, different system failure levels are defined based on different countermeasures; and for example define different levels of system failure based on the importance of different functional modules.
in some embodiments, after different system fault levels are predefined, for different objects, the system fault level corresponding to the fault of the different object may be predefined, so as to obtain the system fault level preset by the different object. In some embodiments, the system fault level corresponding to the fault of the different object may be determined based on the degree of influence on the intelligent driving system when the different object has the fault. In some embodiments, the system failure level corresponding to the failure of different objects may be determined based on the importance of the different objects. In some embodiments, when an object fails, one system failure level may be corresponded, and a plurality of system failure levels may be corresponded, depending on the failure degree of the object.
In some embodiments, when determining the system fault level after detecting the fault, the system fault level may be determined by determining the object in which the fault occurred, and further based on the system fault level preset by the object.
in some embodiments, if there is one failed object, the system fault level is determined based on a system fault level preset by the object, for example, if there is one system fault level corresponding to the object, the system fault level is determined to be the system fault level preset by the object.
in some embodiments, if there are multiple failed objects, the system failure level is determined as: the highest level among the preset system failure levels of the plurality of objects.
503. And carrying out fault grading treatment based on the result of the detection of the security personnel and the fault grade of the system. In some embodiments, in order to not compete with the security officer for the vehicle control right and ensure that the security officer has the control right with the highest priority, the security officer takes the processing measures to fully play the role of the security officer, so that if the security officer is detected, the information of the fault, the system fault level and the processing measures corresponding to the system fault level are notified to the security officer, thereby prompting the security officer of the processing measures that can be taken.
In some embodiments, different system failure levels correspond to different processing measures, for example, for the level of the unused system failure defined in table one, the following processing measures are taken as examples:
The corresponding processing measures of the system fault level L1 are as follows: warning and displaying fault information;
the corresponding processing measures of the system fault level L2 are as follows: speed limit driving and displaying fault information;
the corresponding processing measures of the system fault level L3 are as follows: limiting the speed, braking and stopping at a nearby safe place, and displaying fault information;
The corresponding processing measures of the system fault level L4 are as follows: weak braking and displaying fault information;
The corresponding processing measures of the system fault level L5 are as follows: brake strongly and display failure information.
in some embodiments, if a safety officer is detected, but the safety officer does not actively intervene in fault processing within a preset time, in order to ensure that the safety and the controllability of the vehicle are ensured, the intelligent driving system takes corresponding processing measures. In some embodiments, the preset time is a reaction time given to a security officer on the basis of ensuring the safety of the vehicle, and may be set according to actual needs, and the specific value of the preset time is not limited herein. In some embodiments, after a security officer is detected and information of a fault, a system fault level and a processing measure corresponding to the system fault level are notified to the security officer, if the action of the security officer is not detected within a preset time, fault processing is performed based on the processing measure corresponding to the system fault level, and vehicle safety is ensured.
In some embodiments, if no security personnel is detected, in order to ensure that the vehicle is safe and controllable, the intelligent driving system takes corresponding processing measures. In some embodiments, when fault classification processing is performed, if a safety personnel is not detected, fault processing is performed based on processing measures corresponding to the system fault level, and vehicle safety is guaranteed.
In some embodiments, after the fault classification processing, recovery processing may be further performed based on a recovery measure corresponding to the system fault classification. In some embodiments, a plurality of recovery measures may be preset, and one recovery measure may correspond to one system failure level or a plurality of system failure levels. In some embodiments, the four preset recovery measures are as follows:
And (4) recovery measure A: automatic control is immediately recovered without manual interference;
And (4) recovery measure B: the control can be recovered after manual confirmation at any time;
and C, recovery measure C: and the vehicle can be automatically controlled after reaching a target state (such as parking beside) without manual interference.
And (4) recovery measure D: after the vehicle reaches the target state, the vehicle is manually confirmed and then is controlled again.
In some embodiments, the correspondence between the system failure level and the recovery measure is as follows:
The system fault level L1 corresponds to recovery measure a;
The system failure level L2 corresponds to recovery measure B;
the system failure level L3 corresponds to recovery measure C;
The system failure level L4 and the system failure level L5 correspond to the recovery measure D.
It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of action combinations, but those skilled in the art will understand that the present disclosure embodiments are not limited by the described action sequences, because some steps may be performed in other sequences or simultaneously according to the present disclosure embodiments (for example, whether a security officer is present or not may be continuously detected during the driving process of the intelligent driving vehicle, or the actions of the security officer may be continuously detected during the driving process of the intelligent driving vehicle, and the two detections may be performed simultaneously; for example, the actions of the security officer may be detected after a fault is detected). In addition, those skilled in the art can appreciate that the embodiments described in the specification all belong to alternative embodiments.
Embodiments of the present disclosure also provide a non-transitory computer-readable storage medium storing a program or instructions, where the program or instructions cause a computer to perform steps of various embodiments of a method for processing a fault of an intelligent driving vehicle, and in order to avoid repeated descriptions, the steps are not described herein again.
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 an … …" 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 included in other embodiments, rather than others, combinations of features of different embodiments are meant to be within the scope of the disclosure and form different embodiments.
Those skilled in the art will appreciate that the description of each embodiment has a respective emphasis, and reference may be made to the related description of other embodiments for those parts of an embodiment that are not described in detail.
Although the embodiments of the present disclosure have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present disclosure, and such modifications and variations fall within the scope defined by the appended claims.
Claims (11)
1. A method of fault handling for a smart-driven vehicle, comprising:
respectively carrying out safety personnel detection and fault detection in the running process of the intelligent driving vehicle;
after detecting the fault, determining a system fault level;
And carrying out fault grading treatment based on the result of the detection of the security personnel and the fault grade of the system.
2. The method of claim 1, wherein said determining a system failure level comprises:
determining a failed object;
And determining the system fault level based on the system fault level preset by the object.
3. The method of claim 2, wherein said determining a system failure level comprises:
if a plurality of objects have faults, determining the system fault level as follows: the highest level among the preset system failure levels of the plurality of objects.
4. the method according to any one of claims 1 to 3, wherein the system fault levels are divided based on the degree of implementation of the smart driving system function.
5. the method according to claim 2 or 3, wherein the preset system fault level of the object is determined based on the degree of influence on the smart driving system function when the object is in fault, or the importance degree of the object.
6. The method of claim 1, wherein performing fault classification processing based on the results of the security officer detection and the system fault level comprises:
and if the safety personnel is detected, informing the safety personnel of the fault information, the system fault level and the processing measures corresponding to the system fault level.
7. The method of claim 6, wherein after notifying the security officer of the information of the fault, the system fault level, and the handling measures corresponding to the system fault level, the method further comprises:
Detecting an action of the security officer;
And if the action of the safety personnel is not detected within the preset time, carrying out fault treatment based on the treatment measures corresponding to the system fault level.
8. The method of claim 1, wherein performing fault classification processing based on the results of the security officer detection and the system fault level comprises:
and if the safety personnel are not detected, performing fault processing based on the processing measures corresponding to the system fault level.
9. The method of claim 1, wherein after performing fault classification processing, the method further comprises:
And carrying out recovery processing based on the recovery measures corresponding to the system fault level.
10. An in-vehicle apparatus, characterized by comprising: a processor and a memory;
The processor is adapted to perform the steps of the method of any one of claims 1 to 9 by calling a program or instructions stored in the memory.
11. A non-transitory computer-readable storage medium storing a program or instructions for causing a computer to perform the steps of the method according to any one of claims 1 to 9.
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