Vehicle Steering Control Method and System Based on Autonomous Driving

Abstract

This invention discloses a vehicle steering control method and system based on autonomous driving, belonging to the field of intelligent control technology. It includes: determining the proportion of the vehicle's autonomous control center as the driving entity based on historical vehicle driving data; when the proportion is less than a threshold, sending a control request to the vehicle administrator; otherwise, calculating the real-time processing index of the vehicle's autonomous control center based on real-time traffic data at the turning intersection; when the real-time processing index is less than a processing threshold, sending a control request to the vehicle administrator; otherwise, acquiring real-time vehicle driving data and combining it with real-time traffic data at the turning intersection to calculate the vehicle's driving entity conversion value; when the turning waiting time is less than the driving entity conversion value, controlling the vehicle's steering through the vehicle's autonomous control center; otherwise, sending a control request to the vehicle administrator. This can reduce the task time of autonomous vehicles, avoid congestion at turning intersections, and solve the problem of long driving times during turning in existing autonomous vehicles.

Description

Technical Field

[0001] This invention relates to the field of intelligent control technology, and in particular to a vehicle steering control method and system based on autonomous driving. Background Technology

[0002] The statements in this section merely refer to the background art related to this invention and do not necessarily constitute prior art.

[0003] Autonomous driving refers to the technology that enables vehicles to achieve autonomous control without direct human intervention, through their own environmental perception, route planning, and vehicle status. It utilizes various sensors and advanced monitoring systems to perceive and obtain dynamic changes in the external environment and the vehicle. Based on technologies such as machine learning, computer vision, and deep learning, autonomous vehicles can operate autonomously and safely.

[0004] However, during the preparation for a turn, based on the safety assessment of autonomous driving, the vehicle may remain stationary for an extended period of time, waiting for the safety event to be resolved before proceeding with the driving operation. This not only increases the time it takes for the autonomous vehicle to complete the task but may also cause congestion at the turning intersection. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a vehicle steering control method, system, electronic device, computer-readable storage medium, and computer program product based on autonomous driving. It comprehensively considers historical data of turning intersections, real-time traffic conditions, and real-time driving conditions of autonomous vehicles to switch the driving subject of autonomous vehicles, thereby reducing the task completion time of autonomous vehicles and lowering the possibility of congestion at turning intersections.

[0006] In a first aspect, the present invention provides a vehicle steering control method based on autonomous driving;

[0007] A vehicle steering control method based on autonomous driving includes:

[0008] Obtain historical vehicle driving data, and determine the percentage of vehicles whose driving is performed by the vehicle self-control center based on the historical vehicle driving data;

[0009] When the proportion is less than the preset proportion threshold, a control request is sent to the vehicle administrator so that the vehicle administrator can control the vehicle to turn; when the proportion is greater than the preset proportion threshold, real-time traffic data of the turning intersection is obtained and the real-time processing index of the vehicle self-control center is calculated when the vehicle arrives at the turning intersection.

[0010] When the real-time processing index is less than the preset processing threshold, a control request is sent to the vehicle administrator so that the vehicle administrator can control the vehicle's steering; when the real-time processing index is greater than the preset processing threshold, real-time vehicle driving data is obtained and combined with real-time turning intersection traffic data to calculate the vehicle's execution subject conversion value.

[0011] When the steering wait time is less than the execution subject's conversion value, the vehicle steering is controlled by the vehicle automation center; otherwise, a control request is sent to the vehicle administrator so that the vehicle administrator can control the vehicle steering.

[0012] In some implementations, determining the proportion of vehicles whose driving entity is the vehicle autonomous control center based on historical vehicle driving data specifically involves: based on historical vehicle driving data, counting the historical data volume of vehicles whose driving entity is the vehicle autonomous control center when they arrive at the turning intersection at the same time, and calculating the proportion.

[0013] In some implementations, calculating the real-time processing index of the vehicle automation center when a vehicle arrives at a turning intersection based on real-time traffic data at the turning intersection specifically includes:

[0014] Add execution entity labels to real-time traffic data at turning intersections, and extract the real-time number of motor vehicles, real-time pedestrian flow, and real-time number of non-motorized vehicles at turning intersections based on the added execution entity labels;

[0015] Based on the real-time number of motor vehicles, the real-time pedestrian flow, and the real-time number of non-motorized vehicles at the turning intersection, the real-time processing index of the vehicle control center is obtained when a vehicle arrives at the turning intersection.

[0016] In some implementations, before calculating the real-time processing index of the vehicle automation center when a vehicle arrives at a turning intersection based on real-time traffic data at the turning intersection, the following steps are also included:

[0017] Historical road traffic data is acquired and labeled with the executing entity. The number of motor vehicles, pedestrian traffic, and non-motorized vehicles at turning intersections are extracted to construct a training set.

[0018] The optimal weight parameters are determined by iteratively optimizing the preset processing exponential function using the training set.

[0019] In some implementations, combining real-time vehicle driving data with real-time intersection traffic data to calculate the vehicle's execution subject conversion value specifically involves: extracting the timestamps from the real-time vehicle driving data when the vehicle arrives at the pedestrian stop line at the intersection and when the vehicle arrives at the intersection where the green light ends for the second time; and calculating the subject conversion value based on these timestamps.

[0020] In some implementations, the real-time processing index is expressed as:

[0021] ;

[0022] In the formula, This indicates a real-time processing index. This indicates the vehicle quantity processing index. This indicates the passenger flow handling index. This indicates the non-motorized vehicle quantity handling index. , , , Indicates the weighting parameter;

[0023] The vehicle quantity processing index is expressed as follows:

[0024]

[0025] The pedestrian flow handling index is expressed as:

[0026]

[0027] The non-motorized vehicle quantity processing index is expressed as:

[0028] ;

[0029] In the formula, express y The number of vehicles turning at intersections at all times. express y Constantly adjust your position based on pedestrian flow at intersections. express y The number of non-motorized vehicles at intersections should be monitored at all times.

[0030] Secondly, the present invention provides a vehicle steering control system based on unmanned driving;

[0031] A vehicle steering control system based on autonomous driving, comprising:

[0032] The percentage statistics module is configured to: acquire historical vehicle driving data, determine the percentage of the vehicle self-control center that is the driving entity based on the historical vehicle driving data; when the percentage is less than the preset percentage threshold, send a control request to the vehicle administrator so that the vehicle administrator can control the vehicle steering.

[0033] The real-time processing module is configured to: when the proportion is greater than a preset proportion threshold, acquire real-time traffic data at the turning intersection and calculate the real-time processing index of the vehicle control center when the vehicle arrives at the turning intersection; when the real-time processing index is less than a preset processing threshold, send a control request to the vehicle administrator so that the vehicle administrator can control the vehicle to turn.

[0034] The subject conversion module is configured to: when the real-time processing index is greater than the preset processing threshold, acquire real-time vehicle driving data and combine it with real-time turning intersection traffic data to calculate the subject conversion value of the vehicle; when the turning waiting time is less than the subject conversion value, control the vehicle to turn through the vehicle self-control center; otherwise, send a control request to the vehicle administrator so that the vehicle administrator can control the vehicle to turn.

[0035] Thirdly, the present invention provides an electronic device;

[0036] An electronic device includes a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the above-described vehicle steering control method based on autonomous driving.

[0037] Fourthly, the present invention provides a computer-readable storage medium;

[0038] A computer-readable storage medium having a computer program / instructions stored thereon, which, when executed by a processor, implements the steps of the above-described vehicle steering control method based on autonomous driving.

[0039] Fifthly, the present invention provides a computer program product;

[0040] A computer program product includes a computer program / instructions that, when executed by a processor, implement the steps of the above-described vehicle steering control method based on autonomous driving.

[0041] Compared with the prior art, the beneficial effects of the present invention are:

[0042] The technical solution provided by this invention comprehensively considers road traffic data and vehicle driving data, and utilizes the regularity of traffic conditions in real life, such as morning and evening rush hours, combined with the actual driving situation of autonomous vehicles, to switch the driving execution subject of autonomous vehicles. This not only reduces the task completion time of autonomous vehicles, but also avoids the possibility of congestion at turning intersections. Attached Figure Description

[0043] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0044] Figure 1 A flowchart illustrating the vehicle steering control method based on autonomous driving provided in an embodiment of the present invention;

[0045] Figure 2This is a schematic diagram of the system framework of a vehicle steering control system based on autonomous driving, provided in an embodiment of the present invention. Detailed Implementation

[0046] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0047] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments of the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. Furthermore, it should be understood that the terms “comprising” and “having”, and any variations thereof, are intended to cover non-exclusive inclusion, for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0048] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0049] Example 1

[0050] Existing autonomous vehicles, during turning, suffer from excessively long turning wait times due to the safety driving system's judgment, leading to congestion risks at turning intersections. Therefore, this invention provides a vehicle steering control method based on autonomous driving.

[0051] Next, combined Figure 1 This embodiment discloses a vehicle steering control method based on autonomous driving, which includes the following steps:

[0052] S1. Acquire road traffic data and vehicle driving data and store them in the constructed autonomous vehicle steering storage database.

[0053] In this embodiment, the road traffic data includes the number of motor vehicles, pedestrian flow, number of non-motorized vehicles, and real-time traffic light data at the turning intersection. The vehicle driving data includes the vehicle arrival timestamp, the timestamp of reaching the pedestrian stop line, and the turning waiting time.

[0054] The autonomous vehicle uses radar sensors and onboard cameras to identify the distance between its front wheels and the pedestrian stop line, the timestamp of its arrival at the pedestrian stop line, and the turning waiting time within its sensing range. The GPS system obtains the vehicle arrival timestamp, real-time traffic light data, the number of motor vehicles, pedestrian traffic, and non-motorized vehicles at the intersection. The timestamp of arrival at the pedestrian stop line is generated by the distance between the autonomous vehicle's front wheels and the pedestrian stop line and the real-time time.

[0055] S2. Based on the historical vehicle driving data in the autonomous vehicle steering storage database, determine the proportion of the driving entity that is the vehicle self-control center; when the proportion is less than the preset proportion threshold, send a control request to the vehicle administrator so that the vehicle administrator can control the vehicle steering and update the driving entity data at the same time; when the proportion is greater than the preset proportion threshold, execute S3.

[0056] In this embodiment, the driving entities of the autonomous vehicle include the vehicle administrator and the vehicle automation center. In the autonomous vehicle's steering storage database, the timestamp 't' of the autonomous vehicle arriving at the turning intersection is linked to the driving entity... A It belongs to a one-to-one correspondence, that is .

[0057] In real life, traffic conditions are regular, such as morning rush hour and evening rush hour. Therefore, in this embodiment, the regularity of traffic conditions is utilized by combining historical data. Based on the proportion of the vehicle self-control center as the execution subject at the target turning intersection at the same time, it is preliminarily judged whether the execution subject needs to be changed, thereby further improving the response speed of the autonomous vehicle during driving.

[0058] As one implementation method, S2 specifically includes:

[0059] S201. When vehicles arrive at a turning intersection at the same time, the historical data volume where the driving entity is the vehicle's automatic control center is statistically analyzed, and the percentage is calculated, as follows:

[0060] ;

[0061] In the formula, n represents the number of historical data points where the autonomous vehicle arrives at the turning intersection at time t, and the driving entity is the vehicle's self-control center. N represents the total number of historical data points where the autonomous vehicle arrives at the turning intersection.

[0062] S202. When the proportion of the driving entity being the vehicle control center is less than the proportion threshold, a control request is sent to the administrator, and the driving entity data is updated at the same time; otherwise, S3 is executed.

[0063] For example, if the preset percentage threshold is 0.5, n[i]={2,3,6,8,4}, and N[i]={6,8,9,16,4}, then the percentage of driving entities being the vehicle's autonomous control center is determined. [i]={0.333, 0.375, 0.333, 0.5, 1}, where i represents the i-th example. In the first, second, and third examples, the vehicle directly sends a control request to the administrator and updates the driving entity data. In the fourth and fifth examples, S3 is executed.

[0064] S3. Calculate the real-time processing index of the vehicle control center when the vehicle arrives at the turning intersection based on the real-time traffic data of the turning intersection in the autonomous vehicle turning storage database. When the real-time processing index is less than the preset processing threshold, send a control request to the vehicle administrator so that the vehicle administrator can control the vehicle turning. When the real-time processing index is greater than the preset processing threshold, execute S4.

[0065] In various traffic conditions, such as heavy traffic, high pedestrian and non-motorized vehicle traffic at intersections, autonomous vehicles may experience a decrease in processing capacity, making it difficult to perform timely turning maneuvers. Therefore, in this embodiment, real-time driving conditions are assessed by comprehensively considering both traffic and pedestrian flow. When the autonomous vehicle's processing capacity decreases, a vehicle administrator takes control of the vehicle.

[0066] As one implementation method, S3 specifically includes:

[0067] S301, Add execution entity labels to real-time turning intersection traffic data.

[0068] In this embodiment, the real-time turning intersection traffic dataset with the attached execution subject label is represented as follows: ;

[0069] in, Indicates in y The entity responsible for implementing the timetable for vehicles is a , express y The value of the number of vehicles turning at the intersection at any given time. express y Constantly check the pedestrian flow at the intersection. express y The value of the number of non-motorized vehicles turning at the intersection at any given time.

[0070] The real-time turning intersection traffic dataset includes traffic data from multiple autonomous vehicles. By attaching execution entity labels to the turning intersection traffic dataset, it is easy to query the execution entity attribution for traffic conditions at different turning intersections, which is beneficial for calculating the processing index of the vehicle autonomous control center when a vehicle arrives at a turning intersection.

[0071] S302. Based on the additional execution subject label, extract the real-time number of motor vehicles, real-time pedestrian flow, and real-time number of non-motor vehicles at the turning intersection when the execution subject is the vehicle self-control center.

[0072] Based on the real-time number of motor vehicles, pedestrian traffic, and non-motorized vehicles at the turning intersection, a real-time processing index for the vehicle automation center is obtained when a vehicle arrives at the turning intersection. The real-time processing index is expressed as:

[0073] ;

[0074] In the formula, This indicates a real-time processing index. This indicates the vehicle quantity processing index. This indicates the passenger flow handling index. This indicates the non-motorized vehicle quantity handling index. , , , Indicates the weighting parameter;

[0075] The vehicle quantity processing index is expressed as follows:

[0076]

[0077] The pedestrian flow handling index is expressed as:

[0078]

[0079] The non-motorized vehicle quantity processing index is expressed as:

[0080] ;

[0081] In the formula, express y The number of vehicles turning at intersections at all times. express y Constantly adjust your position based on pedestrian flow at intersections. express y The number of non-motorized vehicles at intersections should be monitored at all times.

[0082] S303. When the real-time processing index is less than the preset processing threshold, a control request is sent to the vehicle administrator so that the vehicle administrator can control the vehicle steering; when the real-time processing index is greater than the preset processing threshold, S4 is executed.

[0083] As one implementation method, before calculating the real-time processing index of the vehicle automation center when a vehicle arrives at the turning intersection based on real-time traffic data at the turning intersection, the method further includes:

[0084] (1) Obtain historical road traffic data and attach execution entity labels, extract the number of motor vehicles, pedestrian traffic and non-motor vehicles at turning intersections, and construct a training set.

[0085] (2) Iteratively optimize the preset processing exponential function using the training set and continuously update it. This continues until the optimal weight parameters are found.

[0086] Specifically, the training set also records K processing exponential functions, and the th... f The processing exponential function is denoted as , , , and These are the preset processing exponential functions. The weight parameters, , and All are independent variables of the sample.

[0087] Will , and As independent variables of the sample, they were substituted into... , and By processing exponential functions Calculation yields the first f One real-time processing index; Let f = f + 1 The real-time processing index was iteratively calculated, yielding a total of K A set of real-time processing indices are used to select the processing index function corresponding to the smallest real-time processing index. and lock the weight parameters , , and As the optimal weight parameter.

[0088] S4. Based on real-time vehicle driving data and real-time traffic data at turning intersections, calculate the vehicle's execution entity conversion value; when the turning wait time is less than the execution entity conversion value, control the vehicle's steering through the vehicle automation center; otherwise, send a control request to the vehicle administrator so that the vehicle administrator can control the vehicle's steering. Specifically, this includes:

[0089] S401. Extract the timestamps of vehicles arriving at the pedestrian stop line at the turning intersection and the timestamps of vehicles arriving at the turning intersection where the second green light ends from the real-time vehicle driving data. Calculate the main conversion value based on these timestamps. The main conversion value is expressed as follows:

[0090] TP = T2 - T1 ;

[0091] Among them, TP represents the execution subject conversion value, T1 represents the timestamp when the vehicle arrives at the stop line of the sidewalk at the turning intersection, T2 represents the timestamp when the vehicle arrives at the end of the second green light at the turning intersection.

[0092] S402. Obtain the turning waiting duration in the real-time road traffic data. When the turning waiting duration YP > TP or YP = TP, the vehicle directly sends a control request to the vehicle administrator and updates the execution driving subject data at the same time; otherwise, the vehicle continues to be controlled by the vehicle automatic control center until the vehicle turns successfully, and the execution driving subject data is updated.

[0093] Exemplarily, the timestamp T1 when the vehicle arrives at the stop line of the turning intersection is 08:00:00, the timestamp T2 when the vehicle arrives at the end of the second green light at the turning intersection is 8:01:06, and the actual waiting duration YP of the vehicle at the stop line is 1 minute and 7 seconds. Then the execution subject conversion value TP = T2 - T1 = 1 minute and 6 seconds. Then YP > TP, that is, the vehicle directly sends a control request to the vehicle administrator and updates the execution driving subject data at the same time.

[0094] The timestamp T1 when the vehicle arrives at the stop line of the turning intersection is 10:00:00, the timestamp T2 when the vehicle arrives at the end of the second green light at the turning intersection is 10:01:06, and the actual waiting duration YP of the vehicle at the stop line is 1 minute and 0 seconds. Then the execution subject conversion value TP = T2 - T1 = 1 minute and 6 seconds. Then YP < TP, that is, the vehicle continues to be controlled by the vehicle automatic control center until the vehicle turns successfully, and the execution driving subject data is updated.

[0095] The timestamp T1 when the vehicle arrives at the stop line of the turning intersection is 18:00:00, the timestamp T2 when the vehicle arrives at the end of the second green light at the turning intersection is 18:02:15, and the actual waiting duration YP of the vehicle at the stop line is 2 minutes and 15 seconds. Then the execution subject conversion value TP = T2 - T1 = 2 minutes and 15 seconds. Then YP = TP, that is, the vehicle directly sends a control request to the vehicle administrator and updates the execution driving subject data at the same time.

[0096] Embodiment 2

[0097] Combined with Figure 2 , this embodiment discloses a vehicle steering control system based on driverless driving, including:

[0098] A proportion statistics module, configured to: obtain historical vehicle driving data, determine the proportion of the execution driving subject being the vehicle automatic control center according to the historical vehicle driving data; when the proportion is less than a preset proportion threshold, send a control request to the vehicle administrator to enable the vehicle administrator to control the vehicle to turn;

[0099] The real-time processing module is configured to: when the proportion is greater than a preset proportion threshold, acquire real-time traffic data at the turning intersection and calculate the real-time processing index of the vehicle control center when the vehicle arrives at the turning intersection; when the real-time processing index is less than a preset processing threshold, send a control request to the vehicle administrator so that the vehicle administrator can control the vehicle to turn.

[0100] The subject conversion module is configured to: when the real-time processing index is greater than the preset processing threshold, acquire real-time vehicle driving data and combine it with real-time turning intersection traffic data to calculate the subject conversion value of the vehicle; when the turning waiting time is less than the subject conversion value, control the vehicle to turn through the vehicle self-control center; otherwise, send a control request to the vehicle administrator so that the vehicle administrator can control the vehicle to turn.

[0101] It should be noted that the aforementioned percentage statistics module, real-time processing module, and subject conversion module correspond to the steps in Embodiment 1. The examples and application scenarios implemented by these modules and their corresponding steps are the same, but they are not limited to the content disclosed in Embodiment 1. It should also be noted that these modules, as part of the system, can be executed in a computer system, such as a set of computer-executable instructions.

[0102] Example 3

[0103] Embodiment 3 of the present invention provides an electronic device, including a memory and a processor, as well as computer instructions stored in the memory and running on the processor. When the computer instructions are executed by the processor, they complete the steps of the above-mentioned vehicle steering control method based on autonomous driving.

[0104] Example 4

[0105] Embodiment 4 of the present invention provides a computer-readable storage medium for storing computer instructions, which, when executed by a processor, complete the steps of the above-described vehicle steering control method based on autonomous driving.

[0106] Example 5

[0107] Embodiment 5 of the present invention provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the steps of the above-described vehicle steering control method based on autonomous driving.

[0108] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0109] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0110] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment, whereby a series of operational steps are performed to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0111] The descriptions of each embodiment in the above embodiments have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0112] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A vehicle steering control method based on autonomous driving, characterized in that, include: Obtain historical vehicle driving data, and determine the percentage of vehicles whose driving is performed by the vehicle self-control center based on the historical vehicle driving data; When the proportion is less than the preset proportion threshold, a control request is sent to the vehicle administrator so that the vehicle administrator can control the vehicle to turn; when the proportion is greater than the preset proportion threshold, real-time traffic data of the turning intersection is obtained and the real-time processing index of the vehicle self-control center is calculated when the vehicle arrives at the turning intersection. When the real-time processing index is less than the preset processing threshold, a control request is sent to the vehicle administrator so that the vehicle administrator can control the vehicle steering. When the real-time processing index exceeds the preset processing threshold, real-time vehicle driving data is acquired and combined with real-time traffic data at turning intersections to calculate the vehicle's execution subject conversion value. When the steering wait time is less than the execution subject's conversion value, the vehicle steering is controlled by the vehicle automation center; otherwise, a control request is sent to the vehicle administrator so that the vehicle administrator can control the vehicle steering. The real-time processing index of the vehicle automation center when a vehicle arrives at a turning intersection, calculated based on real-time traffic data at the turning intersection, specifically includes: Add execution entity labels to real-time traffic data at turning intersections, and extract the real-time number of motor vehicles, real-time pedestrian flow, and real-time number of non-motorized vehicles at turning intersections based on the added execution entity labels; Based on the real-time number of motor vehicles, the real-time pedestrian flow, and the real-time number of non-motorized vehicles at the turning intersection, the real-time processing index of the vehicle control center is obtained when a vehicle arrives at the turning intersection. Before calculating the real-time processing index of the vehicle automation center when a vehicle arrives at a turning intersection based on real-time traffic data at the turning intersection, the following steps are also included: Historical road traffic data is acquired and labeled with the executing entity. The number of motor vehicles, pedestrian traffic, and non-motorized vehicles at turning intersections are extracted to construct a training set. The optimal weight parameters are determined by iteratively optimizing the pre-defined processing exponential function using the training set. The calculation of the vehicle's execution subject conversion value is specifically achieved by combining real-time vehicle driving data with real-time intersection traffic data: extracting the timestamps of the vehicle arriving at the pedestrian stop line at the intersection and the timestamps of the vehicle arriving at the intersection where the green light ends for the second time from the real-time vehicle driving data; and calculating the subject conversion value based on the timestamps of the vehicle arriving at the pedestrian stop line at the intersection and the timestamps of the vehicle arriving at the intersection where the green light ends for the second time. The real-time processing index is expressed as: ； In the formula, This indicates a real-time processing index. This indicates the vehicle quantity processing index. This indicates the passenger flow handling index. This indicates the non-motorized vehicle quantity handling index. , , , Indicates the weighting parameter; The vehicle quantity processing index is expressed as follows: The pedestrian flow handling index is expressed as: The non-motorized vehicle quantity processing index is expressed as: ； In the formula, express y The number of vehicles turning at intersections at all times. express y Constantly adjust your position based on pedestrian flow at intersections. express y The number of non-motorized vehicles at intersections should be monitored at all times.

2. The vehicle steering control method based on autonomous driving as described in claim 1, characterized in that, The specific method for determining the proportion of vehicles whose driving entity is the vehicle autonomous control center based on historical vehicle driving data is as follows: based on historical vehicle driving data, statistically analyze the historical data volume of vehicles whose driving entity is the vehicle autonomous control center when they arrive at the turning intersection at the same time and calculate the proportion.

3. A vehicle steering control system based on autonomous driving, characterized in that, include: The percentage statistics module is configured to: obtain historical vehicle driving data, and determine the percentage of the vehicle self-control center that performs the driving action based on the historical vehicle driving data; When the percentage is less than the preset percentage threshold, a control request is sent to the vehicle administrator so that the vehicle administrator can control the vehicle's steering. The real-time processing module is configured to: when the proportion is greater than a preset proportion threshold, acquire real-time traffic data at the turning intersection and calculate the real-time processing index of the vehicle control center when the vehicle arrives at the turning intersection; when the real-time processing index is less than a preset processing threshold, send a control request to the vehicle administrator so that the vehicle administrator can control the vehicle to turn. The subject conversion module is configured to: when the real-time processing index is greater than the preset processing threshold, acquire real-time vehicle driving data and combine it with real-time intersection traffic data to calculate the subject conversion value of the vehicle; when the turning waiting time is less than the subject conversion value, control the vehicle to turn through the vehicle self-control center; otherwise, send a control request to the vehicle administrator so that the vehicle administrator can control the vehicle to turn. The real-time processing index of the vehicle automation center when a vehicle arrives at a turning intersection, calculated based on real-time traffic data at the turning intersection, specifically includes: Add execution entity labels to real-time traffic data at turning intersections, and extract the real-time number of motor vehicles, real-time pedestrian flow, and real-time number of non-motorized vehicles at turning intersections based on the added execution entity labels; Based on the real-time number of motor vehicles, the real-time pedestrian flow, and the real-time number of non-motorized vehicles at the turning intersection, the real-time processing index of the vehicle control center is obtained when a vehicle arrives at the turning intersection. Before calculating the real-time processing index of the vehicle automation center when a vehicle arrives at a turning intersection based on real-time traffic data at the turning intersection, the following steps are also included: Historical road traffic data is acquired and labeled with the executing entity. The number of motor vehicles, pedestrian traffic, and non-motorized vehicles at turning intersections are extracted to construct a training set. The optimal weight parameters are determined by iteratively optimizing the pre-defined processing exponential function using the training set. The calculation of the vehicle's execution subject conversion value is specifically achieved by combining real-time vehicle driving data with real-time intersection traffic data: extracting the timestamps of the vehicle arriving at the pedestrian stop line at the intersection and the timestamps of the vehicle arriving at the intersection where the green light ends for the second time from the real-time vehicle driving data; and calculating the subject conversion value based on the timestamps of the vehicle arriving at the pedestrian stop line at the intersection and the timestamps of the vehicle arriving at the intersection where the green light ends for the second time. The real-time processing index is expressed as: ； In the formula, This indicates a real-time processing index. This indicates the vehicle quantity processing index. This indicates the passenger flow handling index. This indicates the non-motorized vehicle quantity handling index. , , , Indicates the weighting parameter; The vehicle quantity processing index is expressed as follows: The pedestrian flow handling index is expressed as: The non-motorized vehicle quantity processing index is expressed as: ； In the formula, express y The number of vehicles turning at intersections at all times. express y Constantly adjust your position based on pedestrian flow at intersections. express y The number of non-motorized vehicles at intersections should be monitored at all times.

4. An electronic device comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the steps of the vehicle steering control method based on autonomous driving as described in any one of claims 1-2.

5. A computer-readable storage medium having a computer program / instructions stored thereon, characterized in that, When the computer program / instructions are executed by the processor, they implement the steps of the vehicle steering control method based on any one of claims 1-2.

6. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instructions are executed by the processor, they implement the steps of the vehicle steering control method based on any one of claims 1-2.

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