An automatic driving vehicle control method and device, electronic equipment and storage medium

Abstract

This application relates to an autonomous vehicle control method. When a target vehicle travels to a target road segment and its acquired positioning data becomes abnormal, the method acquires navigation line data from at least one historical vehicle that has traveled through the target road segment. A preset simulation control algorithm is used to simulate the target road segment, resulting in a preset navigation line for the target vehicle to travel through it. The navigation line data is used to calculate the target driving data required for the target vehicle to travel along the preset navigation line. Based on the current driving data and the target driving data, the target vehicle's posture is adjusted to achieve control. This application also allows the autonomous vehicle to acquire control when it loses positioning information, enabling timely acquisition or relinquishment of control when positioning data is temporarily unavailable, thus achieving stable control of the autonomous vehicle.

Description

Technical Field

[0001] This application relates to the field of intelligent vehicles and autonomous vehicles, and in particular to an autonomous vehicle control method, device, electronic device and storage medium. Background Technology

[0002] With the development of vehicle sensors, autonomous vehicle positioning systems are gradually shifting from traditional integrated navigation positioning to multi-sensor fusion positioning. The accuracy of traditional integrated navigation positioning mainly depends on the quality of GNSS (Global Navigation Satellite System) / RTK (Real-time kinematic) signals. In urban canyons, under overpasses, and other road sections where satellite signals are easily interfered with or lost, the positioning accuracy will gradually increase over time, and may even deviate from the lane, failing to meet the positioning needs of autonomous vehicles. This leads to the failure of autonomous vehicle decision-making and planning, and ultimately requires human intervention.

[0003] In related technologies, the positioning information calculated by positioning schemes such as laser SLAM (Simultaneous Localization and Mapping) and visual SLAM can be used as additional observation information to solve the above problems. However, when SLAM or other positioning data is missing for a short period of time, sensor synchronization and calculation delays will reduce the reliability of laser SLAM positioning, further affecting subsequent vehicle control and decision-making.

[0004] Therefore, how to acquire or relinquish control of the vehicle when location data is missing for a short period of time in order to achieve stable control of autonomous vehicles has become an urgent problem to be solved by those skilled in the art. Summary of the Invention

[0005] To address or partially address the problems existing in related technologies, this application provides an autonomous vehicle control method, device, electronic device, and storage medium, which can acquire or relinquish control of vehicle driving in a timely manner when positioning data is missing for a short period of time, so as to achieve stable control of the autonomous vehicle.

[0006] The first aspect of this application provides an autonomous vehicle control method, comprising:

[0007] When the target vehicle travels to the target road segment and the location data obtained by the target vehicle is abnormal, the navigation line data of at least one historical vehicle that traveled through the target road segment is obtained. The target road segment is the road segment that the target vehicle travels through and the location signal received by the target vehicle does not meet the preset strength.

[0008] The target road segment is controlled and simulated using a preset simulation control algorithm to obtain a preset navigation line for the target vehicle to travel through the target road segment;

[0009] The target driving data required for the target vehicle to travel along the preset navigation line is obtained by using the navigation line data for calculation.

[0010] Based on the current driving data of the target vehicle and the target driving data, the position and posture of the target vehicle are adjusted to achieve control of the target vehicle.

[0011] A second aspect of this application provides an autonomous vehicle control device, comprising:

[0012] The first processing module is used to acquire navigation line data of at least one historical vehicle that has traveled through the target road segment when the target vehicle travels to the target road segment and the positioning data acquired by the target vehicle is abnormal. The target road segment is the road segment that the target vehicle travels through and the positioning signal received by the target vehicle does not meet the preset strength.

[0013] The second processing module is used to perform control simulation on the target road segment through a preset simulation control algorithm to obtain a preset navigation line for the target vehicle to travel through the target road segment;

[0014] The third processing module is used to perform calculations using the navigation line data to obtain the target driving data required when the target vehicle travels along the preset navigation line.

[0015] The fourth processing module is used to adjust the position and posture of the target vehicle based on the current driving data and the target driving data, so as to achieve control of the target vehicle.

[0016] A third aspect of this application provides an electronic device, comprising:

[0017] Processor; and

[0018] The memory stores executable code, which, when executed by the processor, causes the processor to perform the autonomous vehicle control method as described above.

[0019] A fourth aspect of this application provides a computer-readable storage medium having executable code stored thereon, which, when executed by a processor of an electronic device, causes the processor to perform the autonomous vehicle control method as described above.

[0020] The technical solution provided in this application may include the following beneficial effects:

[0021] The technical solution of this application, when a target vehicle travels to a target road segment and the location data acquired by the target vehicle becomes abnormal, acquires navigation line data from at least one historical vehicle that has traveled through the target road segment; performs control simulation on the target road segment using a preset simulation control algorithm to obtain a preset navigation line for the target vehicle to travel through the target road segment; calculates the target driving data required for the target vehicle to travel along the preset navigation line using the navigation line data; and adjusts the target vehicle's posture based on the current driving data and the target driving data to achieve control of the target vehicle. This application acquires control of the autonomous vehicle when it loses location information, then calculates the target driving data required for the target vehicle to travel along the preset navigation line based on the navigation line data, and then controls the target vehicle to continue moving forward based on the target driving data. This is used to acquire or relinquish control of the vehicle in a timely manner when location data is missing for a short period, thereby achieving stable control of the autonomous vehicle.

[0022] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0023] The above and other objects, features and advantages of this application will become more apparent from the more detailed description of exemplary embodiments thereof in conjunction with the accompanying drawings, wherein the same reference numerals generally represent the same components in the exemplary embodiments thereof.

[0024] Figure 1 This is a schematic flowchart illustrating an autonomous vehicle control method according to an embodiment of this application;

[0025] Figure 2 This is another schematic flowchart illustrating the autonomous vehicle control method shown in the embodiments of this application;

[0026] Figure 3 This is a schematic flowchart of another embodiment of the autonomous vehicle control method shown in the embodiments of this application;

[0027] Figure 4 This is a schematic diagram of the structure of an autonomous vehicle control device shown in an embodiment of this application;

[0028] Figure 5 This is a schematic diagram of the structure of an electronic device shown in an embodiment of this application. Detailed Implementation

[0029] Embodiments of this application will now be described in more detail with reference to the accompanying drawings. While embodiments of this application are shown in the drawings, it should be understood that this application may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to make this application more thorough and complete, and to fully convey the scope of this application to those skilled in the art.

[0030] The terminology used in this application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. The singular forms “a,” “the,” and “the” used in this application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any or all possible combinations of one or more of the associated listed items.

[0031] It should be understood that although the terms "first," "second," "third," etc., may be used in this application to describe various information, this information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, without departing from the scope of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0032] To address the control issues of autonomous vehicles when SLAM or other positioning data are unavailable, this application acquires control of the vehicle's movement when the autonomous vehicle loses its positioning information. Then, based on data such as the speed and curvature of the navigation line ahead, it calculates the speed and acceleration information required for the vehicle to travel along the navigation line, thereby controlling the vehicle to continue moving forward.

[0033] The technical solutions of the embodiments of this application are described in detail below with reference to the accompanying drawings.

[0034] Figure 1 This is a flowchart illustrating an autonomous vehicle control method according to an embodiment of this application.

[0035] See Figure 1 This application provides an autonomous vehicle control method, which specifically includes the following steps:

[0036] S101: If the target vehicle travels to the target road segment and the location data obtained by the target vehicle is abnormal, obtain navigation line data of at least one historical vehicle that traveled through the target road segment.

[0037] The target road segment is the road segment that the target vehicle passes through, and the positioning signal received by the target vehicle does not meet the preset strength. Here, the preset strength can be set by the user according to the actual situation. That is, when the positioning signal received by the target vehicle is less than the preset strength, the positioning data is abnormal.

[0038] It should be noted that the method for determining the target road segment includes: obtaining historical navigation information of at least one historical vehicle traveling along the first road segment; and determining the first road segment as the target road segment when at least two historical vehicles are traveling along the first road segment and the positioning signal received by the historical vehicles does not meet the preset strength.

[0039] In this embodiment, if at least two historical vehicles receive positioning signals weaker than a preset strength when multiple historical vehicles travel to the first road segment, then the first road segment can be identified as the target road segment. In a specific embodiment, determining the target road segment requires obtaining historical data on multiple vehicles traveling on the first road segment to calculate their routes.

[0040] Furthermore, it should be noted that the amount of navigation line data extracted depends on the curvature of the navigation line. If the curvature is large (meaning the navigation line is more curved), a small amount of data can be extracted based on the curvature. If the curvature is small (meaning the navigation line is less curved; if the curvature is 0, the navigation line is straight), more data needs to be extracted.

[0041] S102: The target road segment is controlled and simulated using a preset simulation control algorithm to obtain the preset navigation line for the target vehicle to travel through the target road segment.

[0042] The preset navigation line is the most probable navigation control route obtained through simulation control of the target road segment. It should be noted that using a preset simulation control algorithm to simulate control of target road segments with weak signals is a standard practice for those skilled in the art, and will not be elaborated upon here.

[0043] It should be noted that the preset navigation line for the target road segment is based on the simulation control of the driving route using data from multiple vehicles traveling on the target road segment in the past. The current driving data can be used to update the historical driving data, thereby making the simulation control path more complete and accurate. Furthermore, as much historical data as possible should be acquired to achieve a more complete simulation control.

[0044] S103: Calculate the target driving data required when the target vehicle travels along the preset navigation line using navigation line data.

[0045] In this embodiment of the application, the above-mentioned calculation using navigation line data to obtain the target driving data required for the target vehicle to travel along the preset navigation line includes: obtaining the linear velocity, angular velocity, and curvature of the second road segment of the target road segment driven by multiple historical vehicles; sequentially calculating multiple sets of driving data required for the target vehicle to travel in the second road segment based on the linear velocity and angular velocity; calculating the target speed and target angular velocity required for the second road segment based on the multiple sets of driving data, and then determining the target speed and target angular velocity as the target driving data required for the target vehicle to travel along the preset navigation line.

[0046] In a specific embodiment, the linear velocity and angular velocity required for each segment of the autonomous vehicle are calculated using navigation line data. The average velocity and angular velocity required for that segment of the navigation line are then derived from the linear velocity and angular velocity of each segment. It should be noted that the calculated velocity and angular velocity represent the overall velocity trend of the navigation line ahead, and are the target average values ​​that the autonomous vehicle needs to adjust to.

[0047] S104: Based on the current driving data and target driving data of the target vehicle, adjust the position and posture of the target vehicle to achieve control of the target vehicle.

[0048] In this embodiment of the application, the above-mentioned adjustment of the target vehicle's posture based on the target vehicle's current driving data and target driving data to achieve target vehicle control includes: acquiring the target vehicle's current driving data, which includes the target vehicle's current speed and current angular velocity; and adjusting the target vehicle's acceleration and angular acceleration on the target road segment based on the target vehicle's current speed and current angular velocity, as well as the target speed and target angular velocity of the target route, to achieve target vehicle control.

[0049] This application provides an autonomous vehicle control method. When a target vehicle travels to a target road segment and its acquired positioning data becomes abnormal, the method acquires navigation line data from at least one historical vehicle that has traveled through the target road segment. A preset simulation control algorithm is used to simulate the target road segment, resulting in a preset navigation line for the target vehicle to travel through. The navigation line data is used to calculate the target driving data required for the target vehicle to travel along the preset navigation line. Based on the current driving data and the target driving data, the target vehicle's posture is adjusted to achieve control. This application acquires control of the autonomous vehicle when it loses positioning information, calculates the target driving data required for the target vehicle to travel along the preset navigation line based on the navigation line data, and then controls the target vehicle to continue moving forward based on the target driving data. This allows for timely acquisition or relinquishment of control when positioning data is missing for a short period, thereby achieving stable control of the autonomous vehicle.

[0050] Based on the foregoing embodiments, this application provides an autonomous vehicle control method, referring to... Figure 2 and Figure 3 As shown, the method includes the following steps:

[0051] S201: Based on historical navigation information, determine the target road segment where the positioning signal does not meet the preset strength.

[0052] In this embodiment of the application, the method for determining the target road segment includes: obtaining historical navigation information of at least one historical vehicle traveling along the first road segment; when at least two historical vehicles are traveling along the first road segment and the positioning signal received by the historical vehicles does not meet the preset strength, the first road segment is determined as the target road segment.

[0053] In this embodiment of the application, if at least two historical vehicles receive positioning signals with a strength less than a preset value when multiple historical vehicles travel to the first road segment, then the first road segment can be determined as the target road segment.

[0054] Furthermore, it should be noted that the amount of navigation line data extracted depends on the curvature of the navigation line. If the curvature is large (meaning the navigation line is more curved), a small amount of data can be extracted based on the curvature. If the curvature is small (meaning the navigation line is less curved; if the curvature is 0, the navigation line is straight), more data needs to be extracted.

[0055] S202: Simulate and control the target road segment to obtain the preset navigation line for the target vehicle to travel through the target road segment.

[0056] The preset navigation line is the most probable navigation control route obtained through simulation control of the target road segment. It should be noted that simulation control of target road segments with weak signals is a standard practice for those skilled in the art, and will not be elaborated upon here.

[0057] S203: Determine whether the target vehicle has traveled to the target road segment. If yes, execute S204; otherwise, maintain normal control.

[0058] In this embodiment of the application, determining whether a target vehicle has traveled to a target road segment includes: determining whether the target vehicle has traveled to a target road segment based on the target vehicle's location data; when the target vehicle's location data is within the location data range corresponding to the target road segment, it is determined that the target vehicle has traveled to the target road segment.

[0059] S204: Determine whether the location data acquired by the target vehicle is abnormal. If yes, execute S205; otherwise, proceed with normal control.

[0060] In this embodiment of the application, determining whether the positioning data acquired by the target vehicle is abnormal includes: determining whether the positioning data acquired by the target vehicle meets preset conditions; or determining whether the deviation between the positioning coordinates of the target vehicle and the preset navigation line within a preset time exceeds a preset threshold; if the positioning data of the target vehicle does not meet the preset conditions or the deviation between the positioning coordinates of the target vehicle and the preset navigation line within a preset time exceeds the preset threshold, then it is determined that the positioning data acquired by the target vehicle is abnormal.

[0061] S205: Obtain navigation line data for at least one historical vehicle that has traveled through the target road segment.

[0062] In this embodiment of the application, if at least two historical vehicles receive positioning signals with a strength less than a preset value when multiple historical vehicles travel to the first road segment, then the first road segment can be determined as the target road segment.

[0063] Furthermore, it should be noted that the amount of navigation line data extracted depends on the curvature of the navigation line. If the curvature is large (meaning the navigation line is more curved), a small amount of data can be extracted based on the curvature. If the curvature is small (meaning the navigation line is less curved; if the curvature is 0, the navigation line is straight), more data needs to be extracted.

[0064] S206: Calculate the target driving data required for the target vehicle to travel along the preset navigation line using navigation line data.

[0065] In this embodiment of the application, the above-mentioned calculation using navigation line data to obtain the target driving data required for the target vehicle to travel along the preset navigation line includes: obtaining the linear velocity, angular velocity, and curvature of the second road segment of the target road segment driven by multiple historical vehicles; sequentially calculating multiple sets of driving data required for the target vehicle to travel in the second road segment based on the linear velocity and angular velocity; calculating the target speed and target angular velocity required for the second road segment based on the multiple sets of driving data, and then determining the target speed and target angular velocity as the target driving data required for the target vehicle to travel along the preset navigation line.

[0066] In a specific embodiment, the linear velocity and angular velocity required for each segment of the autonomous vehicle are calculated using navigation line data. The average velocity and angular velocity required for that segment of the navigation line are then derived from the linear velocity and angular velocity of each segment. It should be noted that the calculated velocity and angular velocity represent the overall velocity trend of the navigation line ahead, and are the target average values ​​that the autonomous vehicle needs to adjust to.

[0067] S207: Based on the current driving data and target driving data of the target vehicle, adjust the position and posture of the target vehicle to achieve control of the target vehicle.

[0068] In this embodiment of the application, the above-mentioned adjustment of the target vehicle's posture based on the target vehicle's current driving data and target driving data to achieve target vehicle control includes: acquiring the target vehicle's current driving data, which includes the target vehicle's current speed and current angular velocity; and adjusting the target vehicle's acceleration and angular acceleration on the target road segment based on the target vehicle's current speed and current angular velocity, as well as the target speed and target angular velocity of the target route, to achieve target vehicle control.

[0069] S208: Determine whether the target vehicle can obtain location data. If not, return to execute S206; if yes, proceed with normal control.

[0070] In this embodiment, the target vehicle will travel along the trend of the preset navigation line. If the positioning data is restored during this process, control will be handed over; otherwise, S206-S208 will continue to be executed until the positioning data is restored.

[0071] It should be noted that this method is applicable when location data is missing for a short period of time. If location data is missing for an extended period of time, manual intervention should be implemented to prevent accidents.

[0072] The embodiments of this application compensate for the control of autonomous vehicles when the positioning data of the simulation platform is missing for a short period of time. They use motion trends to give the direction of adjustment of the target vehicle and combine historical navigation data to increase the stability of control.

[0073] Corresponding to the aforementioned application function implementation method embodiments, this application also provides an autonomous driving vehicle control device, electronic device, and corresponding embodiments.

[0074] Figure 4 This is a schematic diagram of the structure of an autonomous vehicle control device shown in an embodiment of this application.

[0075] See Figure 4 An autonomous vehicle control unit includes a first processing module 401, a second processing module 402, a third processing module 403, and a fourth processing module 404.

[0076] The first processing module 401 is used to acquire navigation line data of at least one historical vehicle that has traveled through the target road segment when the target vehicle travels to the target road segment and the positioning data acquired by the target vehicle is abnormal. The target road segment is the road segment that the target vehicle travels through and the positioning signal received by the target vehicle does not meet the preset strength.

[0077] The second processing module 402 is used to perform control simulation on the target road segment through a preset simulation control algorithm to obtain a preset navigation line for the target vehicle to travel through the target road segment.

[0078] The third processing module 403 is used to calculate using the navigation line data to obtain the target driving data required when the target vehicle travels along the preset navigation line.

[0079] The fourth processing module 404 is used to adjust the position and posture of the target vehicle based on the current driving data of the target vehicle and the target driving data, so as to realize the control of the target vehicle.

[0080] The first processing module 401 is further configured to:

[0081] Obtain historical navigation information of at least one of the historical vehicles traveling along the first road segment;

[0082] When at least two of the historical vehicles are traveling on the first road segment, and the positioning signal received by the historical vehicles does not meet the preset strength, the first road segment is determined to be the target road segment.

[0083] Furthermore, the device also includes: a first judgment module, used to determine whether the target vehicle has traveled to the target road segment;

[0084] The first judgment module is specifically used for:

[0085] Based on the location data of the target vehicle, determine whether the target vehicle has traveled to the target road segment;

[0086] When the location data of the target vehicle is within the location data range corresponding to the target road segment, it is determined that the target vehicle has traveled to the target road segment.

[0087] Furthermore, the device also includes a second judgment module, used to judge whether the positioning data acquired by the target vehicle is abnormal;

[0088] The second judgment module is specifically used for:

[0089] Determine whether the positioning data acquired by the target vehicle meets the preset conditions; or determine whether the deviation between the SLAM positioning navigation line and the preset navigation line exceeds a preset threshold.

[0090] If the positioning data of the target vehicle does not meet the preset conditions or the deviation between the SLAM positioning navigation line and the preset navigation line exceeds the preset threshold, then it is determined that the positioning data acquired by the target vehicle is abnormal.

[0091] Furthermore, the third processing module 403 is specifically used for:

[0092] Obtain the linear velocity, angular velocity, and curvature of the second segment of the target road section for multiple historical vehicles traveling in the target road section;

[0093] The target vehicle is required to travel on the second road segment based on the linear velocity and the angular velocity in sequence.

[0094] Based on the multiple sets of driving data, the target speed and target angular velocity required for the second road segment are calculated, and the target speed and target angular velocity are determined to be the target driving data required for the target vehicle to travel along the preset navigation line.

[0095] Furthermore, the fourth processing module 404 is specifically used for:

[0096] Obtain the current driving data of the target vehicle, including the current speed and current angular velocity of the target vehicle;

[0097] Based on the current speed and angular velocity of the target vehicle and the target speed and angular velocity of the target route, the acceleration and angular acceleration of the target vehicle traveling on the target road segment are adjusted to achieve control of the target vehicle.

[0098] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated further here.

[0099] Figure 5 This is a schematic diagram of the structure of an electronic device shown in an embodiment of this application.

[0100] See Figure 5 The electronic device 500 includes a memory 501 and a processor 502.

[0101] Processor 502 can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor.

[0102] Memory 501 may include various types of storage units, such as system memory, read-only memory (ROM), and permanent storage devices. ROM may store static data or instructions required by processor 502 or other modules of the computer. Permanent storage devices may be read-write storage devices. Permanent storage devices may be non-volatile storage devices that retain stored instructions and data even when the computer is powered off. In some embodiments, permanent storage devices use mass storage devices (e.g., magnetic or optical disks, flash memory) as permanent storage devices. In other embodiments, permanent storage devices may be removable storage devices (e.g., floppy disks, optical drives). System memory may be a read-write storage device or a volatile read-write storage device, such as dynamic random access memory. System memory may store some or all of the instructions and data required by the processor during operation. Furthermore, memory 501 may include any combination of computer-readable storage media, including various types of semiconductor memory chips (e.g., DRAM, SRAM, SDRAM, flash memory, programmable read-only memory), and disks and / or optical disks may also be used. In some implementations, memory 501 may include a removable storage device that is readable and / or writable, such as a laser disc (CD), a read-only digital multifunction optical disc (e.g., DVD-ROM, dual-layer DVD-ROM), a read-only Blu-ray disc, an ultra-high density optical disc, a flash memory card (e.g., SD card, mini SD card, Micro-SD card, etc.), a magnetic floppy disk, etc. Computer-readable storage media do not contain carrier waves or transient electronic signals transmitted wirelessly or via wired connections.

[0103] The memory 501 stores executable code, which, when processed by the processor 502, can cause the processor 502 to execute part or all of the methods described above.

[0104] Furthermore, the method according to this application can also be implemented as a computer program or computer program product, which includes computer program code instructions for performing some or all of the steps in the method described above.

[0105] Alternatively, this application may be implemented as a computer-readable storage medium (or a non-transitory machine-readable storage medium or a machine-readable storage medium) storing executable code (or computer program or computer instruction code) thereon, which, when executed by a processor of an electronic device (or server, etc.), causes the processor to perform part or all of the steps of the methods described above according to this application.

[0106] The various embodiments of this application have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen to best explain the principles, practical application, or improvement of the technology in the market, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A method for controlling an autonomous vehicle, characterized in that, include: When the target vehicle travels to the target road segment and the location data obtained by the target vehicle is abnormal, the navigation line data of at least one historical vehicle that traveled through the target road segment is obtained. The target road segment is the road segment that the target vehicle travels through and the location signal received by the target vehicle does not meet the preset strength. The target road segment is controlled and simulated using a preset simulation control algorithm to obtain a preset navigation line for the target vehicle to travel through the target road segment; The target driving data required for the target vehicle to travel along the preset navigation line is obtained by using the navigation line data for calculation. Based on the current driving data of the target vehicle and the target driving data, the position and posture of the target vehicle are adjusted to achieve control of the target vehicle; The step of calculating the target driving data required for the target vehicle to travel along the preset navigation line using the navigation line data includes: Obtain the linear velocity, angular velocity, and curvature of the second segment of the target road section for multiple historical vehicles traveling in the target road section; The target vehicle is required to travel on the second road segment based on the linear velocity and the angular velocity in sequence. Based on the multiple sets of driving data, the target speed and target angular velocity required for the second road segment are calculated, and the target speed and target angular velocity are determined to be the target driving data required for the target vehicle to travel along the preset navigation line.

2. The method according to claim 1, characterized in that, The method for determining the target road segment includes: Obtain historical navigation information of at least one of the historical vehicles traveling along the first road segment; When at least two of the historical vehicles are traveling on the first road segment, and the positioning signal received by the historical vehicles does not meet the preset strength, the first road segment is determined to be the target road segment.

3. The method according to claim 1, characterized in that, Before acquiring navigation line data showing the passage of at least one historical vehicle through the target road segment, the method further includes: Determine whether the target vehicle has traveled to the target road segment; The step of determining whether the target vehicle has traveled to the target road segment includes: Based on the location data of the target vehicle, determine whether the target vehicle has traveled to the target road segment; When the location data of the target vehicle is within the location data range corresponding to the target road segment, it is determined that the target vehicle has traveled to the target road segment.

4. The method according to claim 1, characterized in that, Before acquiring navigation line data showing the passage of at least one historical vehicle through the target road segment, the method further includes: Determine whether the location data acquired by the target vehicle is abnormal; The step of determining whether the location data acquired by the target vehicle is abnormal includes: Determine whether the location data acquired by the target vehicle meets preset conditions; or determine whether the deviation between the location coordinates of the target vehicle and the preset navigation line within a preset time exceeds a preset threshold. If the positioning data of the target vehicle does not meet the preset conditions or the deviation between the positioning coordinates of the target vehicle and the preset navigation line within a preset time exceeds the preset threshold, then it is determined that the positioning data acquired by the target vehicle is abnormal.

5. The method according to claim 4, characterized in that, The step of adjusting the position and orientation of the target vehicle based on its current driving data and target driving data to achieve control of the target vehicle includes: Obtain the current driving data of the target vehicle, including the current speed and current angular velocity of the target vehicle; Based on the target vehicle's current speed and angular velocity, as well as the target speed and angular velocity of the target route, the acceleration and angular acceleration of the target vehicle traveling on the target road segment are adjusted to achieve control of the target vehicle.

6. An autonomous vehicle control device, characterized in that, include: The first processing module is used to acquire navigation line data of at least one historical vehicle that has traveled through the target road segment when the target vehicle travels to the target road segment and the positioning data acquired by the target vehicle is abnormal. The target road segment is the road segment that the target vehicle travels through and the positioning signal received by the target vehicle does not meet the preset strength. The second processing module is used to perform control simulation on the target road segment through a preset simulation control algorithm to obtain a preset navigation line for the target vehicle to travel through the target road segment; The third processing module is used to calculate using the navigation line data to obtain the target driving data required when the target vehicle travels along the preset navigation line. The fourth processing module is used to adjust the position and posture of the target vehicle based on the current driving data of the target vehicle and the target driving data, so as to realize the control of the target vehicle; The third processing module is specifically used to obtain the linear velocity, angular velocity, and curvature of the second road segment of the target road segment for multiple historical vehicles; and to calculate multiple sets of driving data required for the target vehicle to travel in the second road segment based on the linear velocity and the angular velocity. Based on the multiple sets of driving data, the target speed and target angular velocity required for the second road segment are calculated, and the target speed and target angular velocity are determined to be the target driving data required for the target vehicle to travel along the preset navigation line.

7. The apparatus according to claim 6, characterized in that, The first processing module is also used for: Obtain historical navigation information of at least one of the historical vehicles traveling along the first road segment; When at least two of the historical vehicles are traveling on the first road segment, and the positioning signal received by the historical vehicles does not meet the preset strength, the first road segment is determined to be the target road segment.

8. An electronic device, characterized in that, include: processor; as well as A memory having executable code stored thereon, which, when executed by the processor, causes the processor to perform the autonomous vehicle control method as described in any one of claims 1-5.

9. A computer-readable storage medium, characterized in that, It stores executable code that, when executed by the processor of an electronic device, causes the processor to perform the autonomous vehicle control method as described in any one of claims 1-5.

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