CN117369533A - Vehicle control method, device and equipment for serious weak network - Google Patents
Vehicle control method, device and equipment for serious weak network Download PDFInfo
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Abstract
The invention discloses a vehicle control method, device and equipment aiming at a serious weak network. Comprising the following steps: acquiring a network diagnosis condition through a network diagnosis module; when the network diagnosis condition is serious weak network, determining a target serious weak network end according to the network diagnosis module identification; generating a braking instruction according to the target serious weak network end, and controlling the vehicle to brake according to the braking instruction; and when the network diagnosis condition is network restoration, vehicle driving control is carried out according to the driving instruction input by the user. The vehicle end and the remote cockpit end are used for detecting network diagnosis conditions in parallel, when a serious weak network is detected, a corresponding control strategy is determined according to the target serious weak network end, and a braking instruction is generated to control the vehicle to brake, so that the vehicle driving control can be timely recovered after the network is recovered, good man-machine interaction experience is provided, the remote driving continuity is ensured, the front-end vehicle is not connected or the front-end vehicle can be recovered only by manual field intervention due to sudden serious weak network, and the operation cost is reduced.
Description
Technical Field
The invention relates to the technical field of remote driving, in particular to a vehicle control method, device and equipment aiming at serious weak networks.
Background
According to policy and safety requirements, in order to ensure the safety of front-end automatic driving Robotaxi vehicles in operation, real-time monitoring of the running Robotaxi vehicles at a far end is required, and when abnormal conditions exist in automatic driving, a remote driver takes over the problem vehicles in time through a remote cockpit to perform remote driving.
In the prior art, after a weak network is detected, the problem of network delay is reduced by a method for strategically reducing the number of backhaul cameras. However, in the method, delay is caused by the occurrence of own weak network, then the strategy is carried out and the strategy is to be validated, new delay is added, the danger of a remote driving scene is further increased, the continuity of remote driving cannot be ensured, and the driving safety in the case of sudden serious weak network cannot be ensured.
Disclosure of Invention
The invention provides a vehicle control method, device and equipment aiming at a serious weak network, which ensure the continuity of remote driving and can not lead to the disconnection of a front-end vehicle or the recovery of the front-end vehicle by manual field intervention due to sudden serious weak network.
According to an aspect of the present invention, there is provided a vehicle control method for a severe weak network, applied to a remote driving system, the method comprising:
acquiring a network diagnosis condition through a network diagnosis module;
when the network diagnosis condition is a serious weak network, determining a target serious weak network end according to the network diagnosis module identification, wherein the target serious weak network end is a remote cockpit end or a vehicle end;
generating a braking instruction according to the target serious weak network end, and controlling the vehicle to brake according to the braking instruction;
and when the network diagnosis condition is network restoration, vehicle driving control is carried out according to the driving instruction input by the user.
Optionally, obtaining, by the network diagnostic module, a network diagnostic condition includes: acquiring network detection information according to a specified time interval through a network diagnosis module, wherein the network detection information comprises network delay, instruction delay, video delay and video packet loss rate; judging whether the network detection information meets the diagnosis conditions, if so, determining that the network diagnosis condition is a serious weak network; otherwise, determining that the network diagnosis condition is normal.
Optionally, the diagnostic conditions include: the network delay is greater than the first preset time, the instruction delay is greater than the second preset time, the video delay is greater than the third preset time and/or the video packet loss rate is greater than the specified proportion.
Optionally, after determining that the network diagnosis condition is a severely weak network, the method further includes: acquiring network detection information according to a specified time interval through a network diagnosis module; and when the network detection information does not meet the diagnosis condition, determining that the network diagnosis condition is network recovery.
Optionally, generating a braking instruction according to the serious weak network end of the target, and controlling braking of the vehicle according to the braking instruction, including: when the target serious weak network end is a vehicle end, switching the vehicle state from a remote driving state to a temporary state through automatic driving application, generating a first braking instruction, and controlling the vehicle to brake based on the first braking instruction through automatic driving application; and when the target serious weak network end is a remote cockpit end, acquiring a second braking instruction input by a user based on the remote cockpit end, and sending the second braking instruction to an automatic driving application to control the braking of the vehicle.
Optionally, after controlling the vehicle brake based on the first brake command by the autopilot application, further comprising: the vehicle state is switched from the temporary state to the autonomous state by the autonomous application.
Optionally, the method further comprises: when the network diagnosis condition is serious weak network or network recovery, generating prompt information according to the network diagnosis condition; and sending the prompt information to a remote cockpit end display device for display.
According to another aspect of the present invention, there is provided a vehicle control apparatus for a severe weak network, the apparatus comprising:
the network diagnosis condition acquisition module is used for acquiring the network diagnosis condition through the network diagnosis module;
the system comprises a target serious weak network end determining module, a remote cockpit end determining module and a remote control module, wherein the target serious weak network end determining module is used for determining a target serious weak network end according to the identification of the network diagnosis module when the network diagnosis condition is serious weak network;
the braking instruction generation and vehicle braking module is used for generating a braking instruction according to the target serious weak network end and controlling the vehicle to brake according to the braking instruction;
and the network recovery and vehicle control module is used for carrying out vehicle driving control according to the driving instruction input by the user when the network diagnosis condition is network recovery.
According to another aspect of the present invention, there is provided an electronic apparatus including:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform a vehicle control method for a severe weak network according to any one of the embodiments of the present invention.
According to another aspect of the present invention, there is provided a computer readable storage medium storing computer instructions for causing a processor to implement a vehicle control method for a severe weak network according to any of the embodiments of the present invention when executed.
According to the technical scheme, the network diagnosis condition is detected in parallel through the vehicle end and the remote cockpit end, when the serious weak network is detected, the corresponding control strategy is determined according to the target serious weak network end, and the braking instruction is generated to control the vehicle to brake, so that the vehicle driving control can be timely restored after the network is restored, good man-machine interaction experience is provided, the remote driving continuity is ensured, the front-end vehicle is not connected due to sudden serious weak network or can be restored only by manual field intervention, and the operation cost is reduced.
It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the invention or to delineate the scope of the invention. Other features of the present invention will become apparent from the description that follows.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a vehicle control method for a severe weak network according to a first embodiment of the present invention;
fig. 2 is a schematic process diagram of a control strategy of a vehicle-end serious weak network according to a first embodiment of the present invention;
fig. 3 is a schematic process diagram of a control strategy of a severe weak network at a remote cockpit according to a first embodiment of the present invention;
fig. 4 is a flowchart of another vehicle control method for a severe weak network according to the second embodiment of the present invention;
fig. 5 is a schematic structural diagram of a vehicle control device for a severe weak net according to a third embodiment of the present invention;
fig. 6 is a schematic structural diagram of an electronic device implementing a vehicle control method for a severe weak network according to an embodiment of the present invention.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Example 1
Fig. 1 is a flowchart of a vehicle control method for a severe weak network, which is applicable to a scenario in which a severe weak network occurs when a remote Robotaxi vehicle is provided in an embodiment of the present invention, the method may be performed by a vehicle control device for a severe weak network, the vehicle control device for a severe weak network may be implemented in a form of hardware and/or software, and the vehicle control device for a severe weak network may be configured in a remote driving system. As shown in fig. 1, the method includes:
s110, acquiring a network diagnosis condition through a network diagnosis module.
The remote driving system comprises a vehicle end and a remote cockpit end, a driver does not need to enter the vehicle end, and the vehicle end can be operated by remote control at the remote cockpit end, so that unmanned driving is realized. The remote cockpit end is divided into an industrial personal computer running a software module and a large screen displaying video and alarm information; the vehicle end is mainly an industrial personal computer running a software module and an automobile chassis executing instructions. The vehicle end and the remote cockpit end are both provided with network diagnosis modules, and can autonomously diagnose the current network delay, instruction delay, video delay and video packet loss rate. The network diagnosis condition comprises the conditions of normal network, serious weak network, network recovery and the like.
And S120, when the network diagnosis condition is a serious weak network, determining a target serious weak network end according to the network diagnosis module identification, wherein the target serious weak network end is a remote cockpit end or a vehicle end.
It should be noted that, the remote driving end and the vehicle end execute network diagnosis in parallel, so when the network diagnosis condition is serious and weak network, the serious and weak network end of the target needs to be determined, and the network diagnosis module identifiers of the vehicle end and the remote driving cabin end are different, so the serious and weak network end of the target can be determined by determining the network diagnosis module identifier for sending the network diagnosis condition through the controller. The remote driving end corresponding identifier 01 and the vehicle end corresponding identifier 02 are exemplary, and when the controller receives the network diagnosis condition sent by the network diagnosis module 02, the corresponding serious weak network end of the target can be determined to be the vehicle end.
S130, generating a braking instruction according to the target serious weak network end, and controlling the vehicle to brake according to the braking instruction.
Optionally, generating a braking instruction according to the serious weak network end of the target, and controlling braking of the vehicle according to the braking instruction, including: when the target serious weak network end is a vehicle end, switching the vehicle state from a remote driving state to a temporary state through automatic driving application, generating a first braking instruction, and controlling the vehicle to brake based on the first braking instruction through automatic driving application; and when the target serious weak network end is a remote cockpit end, acquiring a second braking instruction input by a user based on the remote cockpit end, and sending the second braking instruction to an automatic driving application to control the braking of the vehicle.
Optionally, after controlling the vehicle brake based on the first brake command by the autopilot application, further comprising: the vehicle state is switched from the temporary state to the autonomous state by the autonomous application.
Specifically, when a serious weak network is detected at a vehicle end, an automatic driving application Canbus can be triggered through a communication gateway, the Canbus can be switched from a remote driving state to a temporary state standby at the first time, and the Canbus in the standby state can directly send a first brake instruction to an actuator to enable the brake actuator to execute brake operation, so that a safety event can be avoided in the fastest time, after the Canbus executes the brake instruction, the vehicle state can be automatically changed from the standby state to a normal state automatic driving state (auto), and the automatic driving state is changed to be not a remote taking over state, so that the remote driver does not take over the vehicle in time when the network is restored, and the driving safety is better ensured.
Further, when the target serious weak network end is a remote cockpit end, a second braking instruction input by a user is acquired based on the remote cockpit end, the user refers to a driver of the remote cockpit end, and the second braking instruction is sent to an automatic driving application to control vehicle braking.
And S140, when the network diagnosis condition is network restoration, vehicle driving control is carried out according to the driving instruction input by the user.
Optionally, the method further comprises: when the network diagnosis condition is serious weak network or network recovery, generating prompt information according to the network diagnosis condition; and sending the prompt information to a remote cockpit end display device for display.
Specifically, the remote cockpit end display device refers to a display large screen of the remote cockpit end, and when the serious weak network or the network recovery is diagnosed, the network diagnosis module can generate prompt information to be displayed on the display large screen, so that a user can know the conditions of the serious weak network and the network recovery in time, and the user can take over and control the vehicle in time.
In a specific implementation manner, fig. 2 is a schematic diagram of a process of providing a control strategy for a serious weak network at a vehicle end in the first embodiment of the present invention, as shown in fig. 2, when the vehicle end detects the serious weak network, an autopilot application Canbus may be triggered by a communication gateway, the Canbus may be switched from a remote driving state to a temporary state standby at the first time, the Canbus in the standby state may directly send a first brake command to an actuator, the brake actuator performs a brake operation, so that a safety event can be avoided in the fastest time, after the Canbus performs the brake command, the vehicle state may be automatically changed from the standby state to a normal state autopilot state (auto), the communication gateway receives a vehicle state message (including a vehicle driving state field) of the Canbus, the communication gateway notifies a cabin end of a message that the vehicle state has been switched to auto, the cabin end large screen changes the vehicle state from remote driving to the autopilot state, and the cabin end ends the remote takeover state to the vehicle, and the vehicle returns to the remote monitoring state. When the network diagnosis condition is network recovery, the network diagnosis module can timely display the recovery normal network delay information on the display device, so that a driver can reinitiate a driving instruction to a vehicle end, the driving instruction refers to a taking over and controlling instruction of the vehicle end, and after the vehicle receives a new driving instruction of a cabin end, the Canbus can be switched from an automatic driving state to a remote driving state and execute the received first instruction at the same time, and thus, the front-end Robotaxi vehicle reenters the remote driving state.
In a specific implementation manner, fig. 3 is a schematic diagram of a process of providing a control strategy of a serious weak network at a remote cockpit end according to the first embodiment of the present invention, where, as shown in fig. 3, when the remote cockpit end detects the serious weak network, a serious weak network alarm message is displayed on a large screen at the remote cockpit end; after the driver at the cabin end sees the alarm message, the driver can step on the brake for the first time; the remote brake command is sent to the Canbus through the communication gateway, and the Canbus executes the brake command, so that the driving state of the Canbus vehicle is kept unchanged. After the network is restored, the remote cockpit driver continues to remotely drive the vehicle.
In summary, the vehicle end and the remote cockpit end execute the vehicle serious weak network control strategy in parallel, and if the vehicle end strategy is executed first and then the cabin end strategy is executed, the cabin end strategy cannot continue to take effect, because the vehicle state is not the remote driving state; however, if the cabin-end strategy is executed first, the method of the vehicle-end strategy may still be effective in rare cases, such as that the cabin-end vehicle-end detects an abnormal situation at the same time. The remote cockpit end and the vehicle end are used for simultaneously carrying out network diagnosis, and control strategies are given under the serious weak network condition, and the two control strategies support parallel execution, so that the safety of the remote Robotaxi vehicle is further ensured.
According to the technical scheme, the network diagnosis condition is detected in parallel through the vehicle end and the remote cockpit end, when the serious weak network is detected, the corresponding control strategy is determined according to the target serious weak network end, and the braking instruction is generated to control the vehicle to brake, so that the vehicle driving control can be timely restored after the network is restored, good man-machine interaction experience is provided, the remote driving continuity is ensured, the front-end vehicle is not connected due to sudden serious weak network or can be restored only by manual field intervention, and the operation cost is reduced.
Example two
Fig. 4 is a flowchart of a vehicle control method for a serious weak network according to a second embodiment of the present invention, where a specific process of acquiring a network diagnosis situation through a network diagnosis module is added on the basis of the first embodiment. The specific contents of steps S250-S270 are substantially the same as steps S120-S140 in the first embodiment, so that a detailed description is omitted in this embodiment. As shown in fig. 4, the method includes:
s210, acquiring network detection information through a network diagnosis module according to a specified time interval, wherein the network detection information comprises network delay, instruction delay, video delay and video packet loss rate.
Specifically, the vehicle end and the remote cockpit end are connected through a 5G public network, and the vehicle end and the remote cockpit end are provided with own network diagnosis modules. Continuous heartbeat messages exist between the vehicle end and the remote cockpit end, the sending frequency is once every 20ms, and the delay of the unidirectional heartbeat messages is generally about 20ms through practical tests. For example, the specified time interval may be 300ms, and the network diagnostic module may detect the network delay, the command delay, the video delay, and the video packet loss rate at a frequency of 300ms, i.e., every 300 ms.
S220, judging whether the network detection information meets the diagnosis condition, if so, executing S230, otherwise, executing S240.
Optionally, the diagnostic conditions include: the network delay is greater than the first preset time, the instruction delay is greater than the second preset time, the video delay is greater than the third preset time and/or the video packet loss rate is greater than the specified proportion.
In particular, there are four conditions for diagnosing a severely weak network: 1. the network delay is greater than a first preset time, the first preset time can be 100ms, namely, one round trip time of the heartbeat messages of the vehicle end and the cabin end is longer than 100ms, and if the network delay exceeds 100ms, the network is considered to be in a serious weak network state. Because even if the 5G network has a certain oscillation, the network delay exceeds 100ms, but once the network delay occurs, the security of remote driving is threatened very much. 2. The instruction delay is greater than a second preset time, the second preset time can be 120ms (two rounds of normal instructions), the interval period of the instructions is 20ms, one instruction unidirectional transmission is about 20ms, the round-trip time is 40ms, and the round-trip time of two normal instructions plus 1 interval period is 100ms. The two back and forth setting is to avoid instruction delay caused by some common weak network conditions, and is considered as serious weak network, so that the front-end vehicle frequently stops suddenly. 3. The video delay is greater than the third preset time, the third preset time can be 250ms, the normal video delay is about 150ms, and the threshold value is set to be 250ms, so that on one hand, the fluctuation of the 5G network is considered, on the other hand, the acquisition imaging delay of the camera can be influenced by a scene, and the complex scene delay can be increased. 4. The video packet loss rate is greater than the specified proportion, the specified proportion can be 10%, the threshold value is set with two limiting conditions, on one hand, the video stream is optimized based on the network quality, on the other hand, the experience of a driver of the remote cockpit needs to be considered, if the video packet loss rate is greater than the threshold value, the experience of the driver can be reduced in geometric level, the fatigue degree of the driver is increased, and the safety of the remote driving is further affected.
In summary, when any of the following occurs: the network delay is greater than 100ms, the instruction delay is greater than 120ms, the video delay is greater than 250ms, and the video packet loss rate is greater than 10%. I.e. considered as a serious weak network, the problem of network delay cannot be solved by means of algorithms, software or hardware, etc. Of course, as the 5G network of the operator evolves, the quality of the 5G network will continue to increase, and the threshold may be adjusted for iteration according to new situations.
S230, determining that the network diagnosis condition is a serious weak network.
Optionally, after determining that the network diagnosis condition is a severely weak network, the method further includes: acquiring network detection information according to a specified time interval through a network diagnosis module; and when the network detection information does not meet the diagnosis condition, determining that the network diagnosis condition is network recovery.
Specifically, after the network diagnosis condition is determined to be serious and weak, the network diagnosis module also continues to perform network diagnosis, acquires network detection information according to a specified time interval, and determines that the network is recovered when the network detection information is determined to be unsatisfied according to a preset condition.
S240, determining that the network diagnosis condition is that the network is normal.
Specifically, when the above situation does not occur, the network diagnosis situation is determined to be normal, and the remote driving system continuously performs the network diagnosis according to the specified time interval.
S250, when the network diagnosis condition is a serious weak network, determining a target serious weak network end according to the network diagnosis module identification, wherein the target serious weak network end is a remote cockpit end or a vehicle end.
And S260, generating a braking instruction according to the target serious weak network end, and controlling the vehicle to brake according to the braking instruction.
Optionally, generating a braking instruction according to the serious weak network end of the target, and controlling braking of the vehicle according to the braking instruction, including: when the target serious weak network end is a vehicle end, switching the vehicle state from a remote driving state to a temporary state through automatic driving application, generating a first braking instruction, and controlling the vehicle to brake based on the first braking instruction through automatic driving application; and when the target serious weak network end is a remote cockpit end, acquiring a second braking instruction input by a user based on the remote cockpit end, and sending the second braking instruction to an automatic driving application to control the braking of the vehicle.
Optionally, after controlling the vehicle brake based on the first brake command by the autopilot application, further comprising: the vehicle state is switched from the temporary state to the autonomous state by the autonomous application.
And S270, when the network diagnosis condition is network restoration, vehicle driving control is performed according to the driving instruction input by the user.
Optionally, the method further comprises: when the network diagnosis condition is serious weak network or network recovery, generating prompt information according to the network diagnosis condition; and sending the prompt information to a remote cockpit end display device for display.
According to the technical scheme, the network diagnosis condition is detected in parallel through the vehicle end and the remote cockpit end, when the serious weak network is detected, the corresponding control strategy is determined according to the target serious weak network end, and the braking instruction is generated to control the vehicle to brake, so that the vehicle driving control can be timely restored after the network is restored, good man-machine interaction experience is provided, the remote driving continuity is ensured, the front-end vehicle is not connected due to sudden serious weak network or can be restored only by manual field intervention, and the operation cost is reduced.
Example III
Fig. 5 is a schematic structural diagram of a vehicle control device for a severe weak net according to a third embodiment of the present invention. As shown in fig. 5, the apparatus includes: a network diagnosis condition acquisition module 310, configured to acquire a network diagnosis condition through the network diagnosis module;
the target serious weak network end determining module 320 is configured to determine a target serious weak network end according to the identifier of the network diagnostic module when the network diagnostic condition is serious weak network, where the target serious weak network end is a remote cockpit end or a vehicle end;
the braking instruction generation and vehicle braking module 330 is configured to generate a braking instruction according to the target serious weak network end, and control vehicle braking according to the braking instruction;
the network recovery and vehicle control module 340 is configured to perform vehicle driving control according to a driving instruction input by a user when the network diagnosis condition is network recovery.
Optionally, the network diagnosis condition acquisition module 310 is specifically configured to: acquiring network detection information according to a specified time interval through a network diagnosis module, wherein the network detection information comprises network delay, instruction delay, video delay and video packet loss rate; judging whether the network detection information meets the diagnosis conditions, if so, determining that the network diagnosis condition is a serious weak network; otherwise, determining that the network diagnosis condition is normal.
Optionally, the apparatus further comprises: the network recovery detection module is used for: after the network diagnosis condition is determined to be a serious weak network, acquiring network detection information according to a specified time interval through a network diagnosis module; and when the network detection information does not meet the diagnosis condition, determining that the network diagnosis condition is network recovery.
Optionally, the brake command generating and vehicle braking module 330 specifically includes: a brake instruction generating unit for: when the target serious weak network end is a vehicle end, switching the vehicle state from a remote driving state to a temporary state through automatic driving application, generating a first braking instruction, and controlling the vehicle to brake based on the first braking instruction through automatic driving application; and when the target serious weak network end is a remote cockpit end, acquiring a second braking instruction input by a user based on the remote cockpit end, and sending the second braking instruction to an automatic driving application to control the braking of the vehicle.
Optionally, the apparatus further comprises: a state switching module, configured to: after controlling the vehicle brake by the autopilot application based on the first brake command, the vehicle state is switched from the temporary state to the autopilot state by the autopilot application.
Optionally, the apparatus further comprises: the prompt module is used for: when the network diagnosis condition is serious weak network or network recovery, generating prompt information according to the network diagnosis condition; and sending the prompt information to a remote cockpit end display device for display.
According to the technical scheme, the network diagnosis condition is detected in parallel through the vehicle end and the remote cockpit end, when the serious weak network is detected, the corresponding control strategy is determined according to the target serious weak network end, and the braking instruction is generated to control the vehicle to brake, so that the vehicle driving control can be timely restored after the network is restored, good man-machine interaction experience is provided, the remote driving continuity is ensured, the front-end vehicle is not connected due to sudden serious weak network or can be restored only by manual field intervention, and the operation cost is reduced.
The vehicle control device for the serious weak network provided by the embodiment of the invention can execute the vehicle control method for the serious weak network provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.
Example IV
Fig. 6 shows a schematic diagram of the structure of an electronic device 10 that may be used to implement an embodiment of the invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. Electronic equipment may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.
As shown in fig. 6, the electronic device 10 includes at least one processor 11, and a memory, such as a Read Only Memory (ROM) 12, a Random Access Memory (RAM) 13, etc., communicatively connected to the at least one processor 11, in which the memory stores a computer program executable by the at least one processor, and the processor 11 may perform various appropriate actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or the computer program loaded from the storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 may also be stored. The processor 11, the ROM 12 and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.
Various components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, etc.; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, an optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.
The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, digital Signal Processors (DSPs), and any suitable processor, controller, microcontroller, etc. The processor 11 performs the various methods and processes described above, such as a vehicle control method for a severely weak network. Namely: acquiring a network diagnosis condition through a network diagnosis module; when the network diagnosis condition is a serious weak network, determining a target serious weak network end according to the network diagnosis module identification, wherein the target serious weak network end is a remote cockpit end or a vehicle end; generating a braking instruction according to the target serious weak network end, and controlling the vehicle to brake according to the braking instruction; and when the network diagnosis condition is network restoration, vehicle driving control is carried out according to the driving instruction input by the user.
In some embodiments, a vehicle control method for a severe weak network may be implemented as a computer program tangibly embodied on a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM 12 and/or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of a vehicle control method for a severe weak network described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform a vehicle control method for a severely weak network in any other suitable way (e.g., by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
A computer program for carrying out methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be implemented. The computer program may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) through which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the internet.
The computing system may include clients and servers. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service are overcome.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present invention may be performed in parallel, sequentially, or in a different order, so long as the desired results of the technical solution of the present invention are achieved, and the present invention is not limited herein.
The above embodiments do not limit the scope of the present invention. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.
Claims (10)
1. A vehicle control method for a severe weak network, applied to a remote driving system, comprising:
acquiring a network diagnosis condition through a network diagnosis module;
when the network diagnosis condition is a serious weak network, determining a target serious weak network end according to a network diagnosis module identifier, wherein the target serious weak network end is a remote cockpit end or a vehicle end;
generating a braking instruction according to the target serious weak network end, and controlling the vehicle to brake according to the braking instruction;
and when the network diagnosis condition is network restoration, vehicle driving control is carried out according to the driving instruction input by the user.
2. The method of claim 1, wherein the obtaining, by the network diagnostic module, the network diagnostic condition comprises:
acquiring network detection information according to a specified time interval through a network diagnosis module, wherein the network detection information comprises network delay, instruction delay, video delay and video packet loss rate;
judging whether the network detection information meets diagnosis conditions or not, if so, determining that the network diagnosis condition is a serious weak network;
otherwise, determining that the network diagnosis condition is normal.
3. The method of claim 2, wherein the diagnostic conditions comprise:
the network delay is greater than a first preset time, the instruction delay is greater than a second preset time, the video delay is greater than a third preset time and/or the video packet loss rate is greater than a specified proportion.
4. A method according to claim 3, further comprising, after said determining that said network diagnostic condition is a severely weak network:
acquiring network detection information according to a specified time interval through a network diagnosis module;
and when the network detection information does not meet the diagnosis condition, determining that the network diagnosis condition is network recovery.
5. The method of claim 1, wherein the generating a braking command according to the target severe weak network end, and controlling braking of the vehicle according to the braking command, comprises:
when the target serious weak network terminal is a vehicle terminal, switching a vehicle state from a remote driving state to a temporary state through an automatic driving application, generating a first braking instruction, and controlling vehicle braking based on the first braking instruction through the automatic driving application;
and when the target serious weak network end is a remote cockpit end, acquiring a second braking instruction input by a user based on the remote cockpit end, and sending the second braking instruction to an automatic driving application to control vehicle braking.
6. The method of claim 5, further comprising, after said controlling vehicle braking by said autopilot application based on said first braking instruction:
the vehicle state is switched from the temporary state to the autonomous state by the autonomous application.
7. The method according to claim 4, wherein the method further comprises:
when the network diagnosis condition is serious weak network or network recovery, generating prompt information according to the network diagnosis condition;
and sending the prompt information to the remote cockpit end display device for display.
8. A vehicle control apparatus for a severe weak network, comprising:
the network diagnosis condition acquisition module is used for acquiring the network diagnosis condition through the network diagnosis module;
the target serious weak network end determining module is used for determining a target serious weak network end according to the identification of the network diagnosis module when the network diagnosis condition is serious weak network, wherein the target serious weak network end is a remote cockpit end or a vehicle end;
the brake instruction generation and vehicle brake module is used for generating a brake instruction according to the target serious weak network end and controlling the vehicle brake according to the brake instruction;
and the network recovery and vehicle control module is used for carrying out vehicle driving control according to the driving instruction input by the user when the network diagnosis condition is network recovery.
9. An electronic device, the electronic device comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-7.
10. A computer storage medium storing computer instructions for causing a processor to perform the method of any one of claims 1-7 when executed.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN118092406A (en) * | 2024-04-25 | 2024-05-28 | 安徽中科星驰自动驾驶技术有限公司 | Remote driving method and remote control device based on automatic driving system |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN118092406A (en) * | 2024-04-25 | 2024-05-28 | 安徽中科星驰自动驾驶技术有限公司 | Remote driving method and remote control device based on automatic driving system |
| CN118092406B (en) * | 2024-04-25 | 2024-07-12 | 安徽中科星驰自动驾驶技术有限公司 | Remote driving method and remote control device based on automatic driving system |
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