Interactive autonomous driving control method and device, electronic equipment and storage medium

Abstract

The application provides an interactive automatic driving control method and device, electronic equipment and a storage medium. The interactive automatic driving control method comprises the following steps: acquiring a vehicle state of a target heavy-load train at a current time, wherein the vehicle state at least comprises a signal acquisition range and a driving position; acquiring offline induction data; and driving the target heavy-load train according to an automatic driving curve based on the vehicle state and the offline induction data. When the signal acquisition range of the target heavy-load train has a joint control point, a decision planning list is generated when the target heavy-load train reaches the joint control point. The automatic driving process of the target heavy-load train is performed according to a decision planning selected from the decision planning list by a user interaction interface. The application has the beneficial effects of improving train operation efficiency and reducing train energy consumption.

Description

Technical Field

[0001] This invention relates to the field of train automatic driving technology, and in particular to an interactive automatic driving control method, device, electronic device and storage medium. Background Technology

[0002] For heavy-haul trains, stopping should be avoided as much as possible except at the terminal station, because heavy-haul trains accelerate slowly, and stopping and restarting severely affects the operating efficiency of subsequent trains on the line. Before the train enters the station or in difficult sections, the train-machine communication is carried out in advance. The station dispatcher informs the driver in advance via telephone or onboard communication equipment whether the next station is a stop or a through stop, as well as the stopping location. Therefore, when the driver obtains the stopping information of the next station through the train-machine communication, the driver usually reduces the train speed in advance, extends the running time between sections, and reduces the number of stops. However, many locomotive automatic driving systems cannot obtain the train-machine communication information and can only default to the existing mode for speed planning, and cannot reduce the speed in advance. In order to avoid stopping, the driver often chooses to manually take over the train, resulting in too many automatic control interventions.

[0003] Furthermore, heavy-haul trains operate without fixed schedules, and there are no fixed stopping plans at stations along the line. Locomotive depots also adjust train operations due to scheduling and traffic volume factors. The addition of new stations along the line can also affect the overall operational plan. Therefore, even under the same signal conditions within the same section, driver operations can vary. The actual line operation curve is adjusted according to the needs of railway operations. However, intelligently adapting the planning of automatic driving to these demands is a significant challenge, as these demands are widespread, and automatic driving systems cannot be maintained or updated with substantial manpower and resources. Moreover, due to the large computational demands, it is currently difficult for automatic driving systems to achieve global optimization in their planning curves. Summary of the Invention

[0004] The main objective of this invention is to provide an interactive automatic driving control method, device, electronic device, and storage medium, which reduces the number of times manual intervention is required during automatic driving, improves train operation efficiency, and reduces train energy consumption.

[0005] One aspect of the present invention provides an interactive automatic driving control method, comprising:

[0006] Obtain the current vehicle status of the target heavy-haul train, where the vehicle status includes at least the signal acquisition range and the train's position.

[0007] Obtain offline guidance data, and based on the vehicle status and the offline guidance data, guide the target heavy-load train to travel according to the automatic driving curve;

[0008] When there is a control point within the signal acquisition range of the target heavy-load train, a decision planning list is generated when the target heavy-load train arrives at the control point.

[0009] Based on the decision plan selected from the decision plan list through the user interface, the automatic driving process of the target heavy-load train is executed.

[0010] According to the interactive automatic driving control method, acquiring offline guidance data and, based on the vehicle status and the offline guidance data, driving the target heavy-haul train according to the automatic driving curve includes:

[0011] Based on the location of the target heavy-haul train, the travel segment is determined. Based on the travel segment, the forward track data of the target heavy-haul train is determined, including slopes, curves, grid voltage, phase separation, track safety protection information, signal information, and the station to be reached. Based on the offline guidance data, a driving curve list is determined using simulation. An automatic driving curve for the travel segment is determined from the driving curve list using a genetic algorithm or particle swarm optimization based on the signal acquisition range, travel position, and forward track data.

[0012] According to the aforementioned interactive autonomous driving control method, the method further includes:

[0013] Obtain historical data for the driving segment, wherein the historical data is a manual driving curve;

[0014] By comparing the autonomous driving curve and the manual driving curve, driving differences are generated;

[0015] The driving differences are converted into induced speed, induced level, and induced condition, and the autonomous driving curve is updated based on the induced speed, induced level, and induced condition.

[0016] According to the aforementioned interactive autonomous driving control method, the method further includes:

[0017] The updated autonomous driving curves are used to guide the updating of the offline guidance data.

[0018] According to the aforementioned interactive autonomous driving control method, the method further includes:

[0019] The induced update includes local induced updates and global induced updates, wherein the global induced update is obtained by combining multiple local induced updates of the target heavy-haul train in the train network or during its running time.

[0020] According to the interactive autonomous driving control method described above, the linkage points include:

[0021] The vehicle status is obtained, which also includes current speed, traction force, braking force, decompression, vehicle position, and driving conditions.

[0022] The control point and braking point are determined based on the data of the preceding track and the vehicle status. The control point is set between the braking point and the target heavy-load train, and the distance between the control point and the braking point is a safe distance.

[0023] According to the interactive automatic driving control method, when a control point exists within the signal acquisition range of the target heavy-haul train, a decision planning list is generated when the target heavy-haul train arrives at the control point, including:

[0024] When the control point is reached, the decision planning list is generated on the interactive interface. The decision planning list includes speed operation, deceleration, stopping outside the vehicle, stopping on the main line, passing on the main line, stopping on the siding, and passing on the siding.

[0025] Another aspect of the present invention provides an interactive automatic driving control device, comprising:

[0026] The first module is used to obtain the vehicle status of the target heavy-haul train at the current moment, wherein the vehicle status includes at least the signal acquisition range and the driving position.

[0027] The second module is used to acquire offline guidance data and, based on the vehicle status and the offline guidance data, to drive the target heavy-load train according to the automatic driving curve.

[0028] The third module is used to generate a decision planning list when the target heavy-load train arrives at the control point if there is a control point within the signal acquisition range of the target heavy-load train.

[0029] The fourth module is used to execute automatic driving processing of the target heavy-load train based on the decision plan selected from the decision plan list by the user interface.

[0030] Another aspect of the present invention provides an electronic device, including a processor and a memory;

[0031] The memory is used to store programs;

[0032] The processor executes the program to implement the method as described above.

[0033] This invention also discloses a computer program product or computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device can read the computer instructions from the computer-readable storage medium and execute the computer instructions, causing the computer device to perform the methods described above.

[0034] The beneficial effects of this invention are as follows: Offline guidance data guides the target heavy-load train to travel according to the automatic driving curve. Based on intelligent learning feedback from historical driving data, information not connected to the automatic driving system, such as vehicle-to-machine (V2M) control, is used to achieve reasonable automatic driving and speed control through the selection of planning and control scenarios in V2M control. This reduces the problems of frequent acceleration / deceleration and brake-induced stops that may occur when automatic driving is controlled according to interval speed control. Furthermore, information not connected to the automatic driving system, such as external vehicle control and special driving situations, is converted into automatic driving planning and control scenarios. By selecting unified planning and control scenarios, external information input interfaces are reduced, improving train operating efficiency and reducing train energy consumption. Attached Figure Description

[0035] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the description of the embodiments taken in conjunction with the following drawings, in which:

[0036] Figure 1 This is a block diagram of an interactive autonomous driving system according to an embodiment of the present invention.

[0037] Figure 2 This is a schematic diagram of an interactive autonomous driving process according to an embodiment of the present invention.

[0038] Figure 3 This is a schematic diagram of the autonomous driving curve generation process according to an embodiment of the present invention.

[0039] Figure 4 This is a schematic diagram of the autonomous driving curve update process according to an embodiment of the present invention.

[0040] Figure 5 This is a schematic diagram of the offline induced update process according to an embodiment of the present invention.

[0041] Figure 6 This is a schematic diagram illustrating the function selection of the driver human-machine interaction display screen according to an embodiment of the present invention.

[0042] Figure 7 This is a schematic diagram of an interactive autonomous driving device according to an embodiment of the present invention. Detailed Implementation

[0043] The embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings. Throughout the description, the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions. In the following description, suffixes such as "module," "part," or "unit" used to denote elements are used only for the purpose of illustrative purposes and have no specific meaning in themselves. Therefore, "module," "part," or "unit" can be used interchangeably. Terms such as "first," "second," etc., are used only to distinguish technical features and should not be construed as indicating or implying relative importance, or implicitly indicating the number of indicated technical features, or implicitly indicating the sequential relationship of the indicated technical features. In the following description, the consecutive reference numerals for method steps are for ease of review and understanding. Adjusting the implementation order of steps, in conjunction with the overall technical solution of the present invention and the logical relationship between the various steps, will not affect the technical effect achieved by the technical solution of the present invention. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0044] refer to Figure 1 , Figure 1 This is a block diagram of an interactive autonomous driving system according to an embodiment of the present invention. An example of this interactive autonomous driving system is as follows:

[0045] The safety protection module, a device for generating vehicle operation records and track protection information, sends track data, vehicle formation load, position, speed, and track protection information to the automatic driving control module. In urban rail and locomotives, it is usually an LKJ or ATP device.

[0046] Human-computer interaction module, which displays autonomous driving planning and real-time operating status information and facilitates human-computer interaction.

[0047] The braking system includes a braking control module and a basic braking unit, which receives braking commands, executes braking actions, and provides feedback on the braking status.

[0048] The TCU (Traction Control Unit) is the control core of the vehicle's traction drive system. It receives traction and regenerative braking commands and provides feedback on the traction status.

[0049] Historical data storage module, based on data storage for both autonomous and manual driving.

[0050] The vehicle automatic driving control module acquires vehicle status data, track data, protection information and other data from various related components, plans the target running curve for automatic train operation, outputs vehicle control commands, controls vehicle traction and braking, and completes the vehicle's following control of the automatic driving operation curve.

[0051] refer to Figure 2 ,in Figure 2This is a schematic diagram of an interactive autonomous driving process according to an embodiment of the present invention, which includes, but is not limited to, steps S100 to S400:

[0052] S100: Obtain the vehicle status of the target heavy-haul train at the current moment, where the vehicle status includes at least the signal acquisition range and the driving position.

[0053] S200 acquires offline guidance data and, based on the vehicle status and the offline guidance data, directs the target heavy-load train to travel according to the automatic driving curve.

[0054] In some embodiments, reference Figure 3 The diagram shows the process for generating autonomous driving curves. It includes, but is not limited to, steps S210 to S220:

[0055] S210 determines the travel section based on the location of the target heavy-haul train, and determines the forward track data of the target heavy-haul train based on the travel section. The forward track data includes gradients, curves, grid voltage, phase separation, track safety protection information, signal information, and the station ahead.

[0056] S220: Based on offline guidance data, a list of driving curves is determined by simulation. Then, an autonomous driving curve for the driving segment is determined from the list of driving curves using a combination of genetic algorithm and particle swarm optimization, based on signal acquisition range, driving position, and forward route data.

[0057] It is understandable that heavy-haul trains do not have fixed operating times, and there are no fixed stopping plans at stations along the line. At the same time, locomotive depots may adjust train operations due to scheduling and traffic volume, and the addition of stations in the middle of the line may also affect the overall operation plan. Therefore, the driver's operation may differ under the same signal conditions in the same section. The actual line operation curve is adjusted according to the needs of railway operation. However, automatic driving cannot invest a lot of manpower and resources for maintenance and updates, and due to the large amount of computation required, it is difficult for automatic driving to achieve global optimization in the planning curve.

[0058] In some embodiments, autonomous driving generates offline guidance by learning from the human driving data of the vehicle itself, and continuously iterates and updates the offline guidance method, which can iterate the planning curve to the local planning optimum or the global optimum, solve the problem of planning the optimal curve for autonomous driving, and can adaptively adjust to changes in railway demand.

[0059] In some embodiments, reference Figure 4 The diagram showing the autonomous driving curve update process includes, but is not limited to, steps S230 to S250:

[0060] S230, acquire historical data of the driving segment, including the manual driving curve;

[0061] S240 compares the autonomous driving curve and the manual driving curve to generate driving differences;

[0062] S250 converts driving differences into guidance speed, guidance level, and guidance conditions, and updates the autonomous driving curve based on these factors.

[0063] In some embodiments, the updated autonomous driving curve is used to induce updates to the offline guidance data.

[0064] In some embodiments, the induced update includes local induced updates and global induced updates. The global induced update is obtained by combining multiple local induced updates of the target heavy-haul train in the train network or during its running time. It can be understood that the local induced update refers to the update of a segment of the automatic driving curve (i.e., the distance between two control points), while the global induced update is a combination of all local induced updates of the heavy-haul train in the entire journey or train network. This combination can be adjusted according to the overall operational efficiency and coordination requirements of the train network.

[0065] In some embodiments, reference Figure 5 The diagram shown illustrates the offline induced update process. Based on historical manual driving data and historical human-computer interaction results, it intelligently iterates the planning curve to match the driving habits and operating characteristics of most drivers, and adjusts it for different railway operating times.

[0066] In some implementations, the control point is determined in the following ways: obtaining the vehicle status, which includes current speed, traction force, braking force, decompression, vehicle position, and driving conditions; determining the control point and braking point based on the data of the track ahead and the vehicle status, wherein the control point is set between the braking point and the target heavy-haul train, and the distance between the control point and the braking point is a safe distance.

[0067] S300: When there is a control point within the signal acquisition range of the target heavy-load train, a decision planning list is generated when the target heavy-load train arrives at the control point.

[0068] In some embodiments, reference Figure 6 The diagram shown illustrates the function selection of the driver's human-machine interface display screen, which includes automatic braking modes such as speed operation, deceleration, external parking, mainline parking, mainline passage, lateral parking, and lateral passage.

[0069] In some embodiments, the braking point may be a station, a difficult driving point, etc.

[0070] S400, based on the decision plan selected from the decision plan list through the user interface, execute the automatic driving process for the target heavy-load train.

[0071] Figure 7 This is a schematic diagram of an interactive autonomous driving device according to an embodiment of the present invention. The device includes a first module 710, a second module 720, a third module 730, and a fourth module 740.

[0072] The system comprises four modules: a first module for acquiring the current vehicle status of the target heavy-load train, including at least the signal acquisition range and the train's position; a second module for acquiring offline guidance data and guiding the target heavy-load train according to the automatic driving curve based on the vehicle status and the offline guidance data; a third module for generating a decision planning list when the target heavy-load train reaches a control point if a control point exists within its signal acquisition range; and a fourth module for executing the automatic driving process of the target heavy-load train based on the decision planning selected from the decision planning list via the user interface.

[0073] For example, with the cooperation of the first, second, third, and fourth modules in the device, the embodiment device can implement any of the aforementioned interactive automatic driving control methods, namely, obtaining the vehicle status of the target heavy-load train at the current moment, wherein the vehicle status includes at least the signal acquisition range and the driving position; obtaining offline guidance data, and driving the target heavy-load train according to the automatic driving curve based on the vehicle status and the offline guidance data; when there is a control point in the signal acquisition range of the target heavy-load train, generating a decision planning list when the target heavy-load train reaches the control point; and executing the automatic driving processing of the target heavy-load train according to the decision planning selected from the decision planning list by the user interface. The beneficial effects of this invention are as follows: Offline guidance data guides the target heavy-load train to travel according to the automatic driving curve. Based on intelligent learning feedback from historical driving data, information not connected to the automatic driving system, such as vehicle-to-machine (V2M) control, is used to achieve reasonable automatic driving and speed control through the selection of planning and control scenarios in V2M control. This reduces the problems of frequent acceleration / deceleration and brake-induced stops that may occur when automatic driving is controlled according to interval speed control. Furthermore, information not connected to the automatic driving system, such as external vehicle control and special driving situations, is converted into automatic driving planning and control scenarios. By selecting unified planning and control scenarios, external information input interfaces are reduced, improving train operating efficiency and reducing train energy consumption.

[0074] This invention also provides an electronic device, which includes a processor and a memory;

[0075] The memory stores the program;

[0076] The processor executes a program to perform the aforementioned interactive autonomous driving control method; the electronic device has the function of carrying and running the interactive autonomous driving control software system provided in the embodiments of the present invention, such as a personal computer, minicomputer, mainframe, workstation, network or distributed computing environment, standalone or integrated computer platform, or communicating with charged particle tools or other imaging devices, etc.

[0077] This invention also provides a computer-readable storage medium storing a program that is executed by a processor to implement the interactive autonomous driving control method described above.

[0078] In some alternative embodiments, the functions / operations mentioned in the block diagrams may not occur in the order shown in the operation diagrams. For example, depending on the functions / operations involved, two consecutively shown blocks may actually be executed substantially simultaneously, or the blocks may sometimes be executed in reverse order. Furthermore, the embodiments presented and described in the flowcharts of this invention are provided by way of example to provide a more comprehensive understanding of the technology. The disclosed methods are not limited to the operations and logic flows presented herein. Alternative embodiments are contemplated in which the order of various operations is altered and sub-operations described as part of a larger operation are executed independently.

[0079] This invention also discloses a computer program product or computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device can read the computer instructions from the computer-readable storage medium and execute the computer instructions, causing the computer device to perform the aforementioned interactive automatic driving control method.

[0080] Furthermore, although the invention has been described in the context of functional modules, it should be understood that, unless otherwise stated, one or more of the described functions and / or features may be integrated into a single physical device and / or software module, or one or more functions and / or features may be implemented in a separate physical device or software module. It is also understood that a detailed discussion of the actual implementation of each module is unnecessary for understanding the invention. Rather, given the properties, functions, and internal relationships of the various functional modules in the apparatus disclosed herein, the actual implementation of the module will be understood within the scope of conventional skill of an engineer. Therefore, those skilled in the art can implement the invention as set forth in the claims using ordinary techniques without excessive experimentation. It is also understood that the specific concepts disclosed are merely illustrative and not intended to limit the scope of the invention, which is determined by the full scope of the appended claims and their equivalents.

[0081] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0082] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0083] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0084] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0085] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0086] Although embodiments of the invention have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

[0087] The above is a detailed description of the preferred embodiments of the present invention, but the present invention is not limited to the embodiments described. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present invention, and these equivalent modifications or substitutions are all included within the scope defined by the claims of this application.

Claims

1. An interactive automatic driving control method, characterized in that, include: Obtain the current vehicle status of the target heavy-haul train, where the vehicle status includes at least the signal acquisition range and the train's position. Obtain offline guidance data, and based on the vehicle status and the offline guidance data, guide the target heavy-load train to travel according to the automatic driving curve; When there is a control point within the signal acquisition range of the target heavy-load train, a decision planning list is generated when the target heavy-load train arrives at the control point. Based on the decision plan selected from the decision plan list through the user interface, the automatic driving process of the target heavy-load train is executed.

2. The interactive automatic driving control method according to claim 1, characterized in that, The step of acquiring offline guidance data and, based on the vehicle status and the offline guidance data, directing the target heavy-haul train according to the automatic driving curve includes: Based on the location of the target heavy-haul train, the travel segment is determined. Based on the travel segment, the forward track data of the target heavy-haul train is determined, including slopes, curves, grid voltage, phase separation, track safety protection information, signal information, and the station to be reached. Based on the offline guidance data, a driving curve list is determined using simulation. An automatic driving curve for the travel segment is determined from the driving curve list using a genetic algorithm or particle swarm optimization based on the signal acquisition range, travel position, and forward track data.

3. The interactive automatic driving control method according to claim 2, characterized in that, The method further includes: Obtain historical data for the driving segment, wherein the historical data is a manual driving curve; By comparing the autonomous driving curve and the manual driving curve, driving differences are generated; The driving differences are converted into induced speed, induced level, and induced condition, and the autonomous driving curve is updated based on the induced speed, induced level, and induced condition.

4. The interactive automatic driving control method according to claim 3, characterized in that, The method further includes: The updated autonomous driving curves are used to guide the updating of the offline guidance data.

5. The interactive automatic driving control method according to claim 4, characterized in that, The method further includes: The induced update includes local induced updates and global induced updates, wherein the global induced update is obtained by combining multiple local induced updates of the target heavy-haul train in the train network or during its running time.

6. The interactive automatic driving control method according to claim 2, characterized in that, The joint control points include: The vehicle status is obtained, which also includes current speed, traction force, braking force, decompression, vehicle position, and driving conditions. The control point and braking point are determined based on the data of the preceding track and the vehicle status. The control point is set between the braking point and the target heavy-load train, and the distance between the control point and the braking point is a safe distance.

7. The interactive automatic driving control method according to claim 1, characterized in that, When a control point exists within the signal acquisition range of the target heavy-haul train, a decision planning list is generated when the target heavy-haul train arrives at the control point, including: Upon reaching the control point, the decision planning list is generated on the interactive interface. The decision planning list includes speed operation, deceleration, stopping outside the vehicle, stopping on the main line, passing on the main line, stopping on the siding, and passing on the siding.

8. An interactive automatic driving control device, characterized in that, include: The first module is used to obtain the vehicle status of the target heavy-haul train at the current moment, wherein the vehicle status includes at least the signal acquisition range and the driving position. The second module is used to acquire offline guidance data and, based on the vehicle status and the offline guidance data, to drive the target heavy-load train according to the automatic driving curve. The third module is used to generate a decision planning list when the target heavy-load train arrives at the control point if there is a control point within the signal acquisition range of the target heavy-load train. The fourth module is used to execute automatic driving processing of the target heavy-load train based on the decision plan selected from the decision plan list by the user interface.

9. An electronic device, characterized in that, Including the processor and memory; The memory is used to store programs; The processor executes the program to implement the interactive automatic driving control method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The storage medium stores a program that is executed by a processor to implement the interactive automatic driving control method as described in any one of claims 1-7.

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