Method, device, electronic equipment and storage medium for controlling an autonomous vehicle

Abstract

The present disclosure provides a method and device for controlling an autonomous vehicle, an electronic device and a storage medium, and relates to the technical field of autonomous driving, so as to at least solve the technical problem of low intelligent degree and poor user experience in the prior art due to the dependence on manual external operation to help the autonomous vehicle to escape from the trouble. The specific implementation scheme is: receiving a request message from the autonomous vehicle, wherein the request message is triggered by road condition information within a preset range around the autonomous vehicle; generating a control instruction based on the request message, wherein the control instruction is used to control the autonomous vehicle to perform a lane changing operation; and sending the control instruction to the autonomous vehicle, so that the autonomous vehicle performs lane changing based on the control instruction.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of automatic driving, and particularly relates to a method and device for controlling an automatic driving vehicle, an electronic device and a storage medium. BACKGROUND

[0002] With the increase of the number of vehicles, the complexity of the traffic system increases dramatically due to the participation of a large number of vehicles of various types. The automatic driving vehicle is an important participant in the current traffic system. When the automatic driving vehicle makes automatic driving decisions according to the environmental perception data and the preset driving rules, it often encounters the constraint driving dilemma caused by the complex road conditions in the traffic system. For example, in a non-congestion section, because the vehicle detects that there are obstacles in each lane of the current driving direction and the lane line is a solid line, the vehicle is self-constrained to stop driving and falls into a dilemma.

[0003] In the related art, the above constraint driving dilemma of the automatic driving vehicle is mainly solved by manually taking over driving or removing obstacles. However, this method has low intelligence and cannot meet the needs of vehicle automatic driving.

[0004] At present, there is no effective solution to the above problems. SUMMARY

[0005] The present disclosure provides a method and device for controlling an automatic driving vehicle, an electronic device and a storage medium to at least solve the technical problem in the prior art that the intelligence of the automatic driving process is low and the user experience is poor due to the dependence on manual external operation to help the automatic driving vehicle escape from the dilemma.

[0006] According to one aspect of the present disclosure, a method for controlling an automatic driving vehicle is provided, comprising: receiving a request message from the automatic driving vehicle, wherein the request message is triggered by road condition information within a preset range around the automatic driving vehicle, the request message is used to request a driving mode to be executed by the automatic driving vehicle, the road condition information is used to determine the type of lane line and the type of detected object within the preset range, the road condition information satisfies a first preset condition, and the first preset condition comprises: the type of lane line is a preset type lane line and the type of detected object is a preset type object, the preset type lane line is used to constrain the automatic driving vehicle to change lanes during driving, and the preset type object is used to hinder the automatic driving vehicle to continue driving; generating a control instruction based on the request message, wherein the control instruction is used to control the automatic driving vehicle to execute a lane changing operation; and sending the control instruction to the automatic driving vehicle, so that the automatic driving vehicle changes lanes based on the control instruction.

[0007] According to another aspect of this disclosure, a method for controlling an autonomous vehicle is provided, comprising: acquiring road condition information within a preset range around the autonomous vehicle, wherein the road condition information is used to determine the type of lane lines and the type of detection objects within the preset range; in response to the road condition information meeting preset conditions, sending a request message to a server, wherein the preset conditions include: the type of lane line is a preset type lane line and the type of detection object is a preset type object, the preset type lane line is used to constrain the autonomous vehicle from changing lanes during driving, the preset type object is used to prevent the autonomous vehicle from continuing to drive, and the request message is used to request a driving mode to be executed by the autonomous vehicle from the server; receiving a control command corresponding to the request message issued by the server, wherein the control command is used to control the autonomous vehicle to perform a lane-changing driving operation; and performing a lane-changing driving operation based on the control command.

[0008] According to another aspect of this disclosure, an apparatus for controlling an autonomous vehicle is also provided, comprising: a receiving module for receiving a request message from the autonomous vehicle, wherein the request message is triggered by road condition information within a preset range around the autonomous vehicle, the request message is used to request a driving mode to be executed by the autonomous vehicle, the road condition information is used to determine the type of lane lines and the type of detection objects within the preset range, the road condition information satisfies a first preset condition, the first preset condition including: the type of lane line is a preset type lane line and the type of detection object is a preset type object, the preset type lane line is used to constrain the autonomous vehicle from changing lanes during driving, and the preset type object is used to prevent the autonomous vehicle from continuing to drive; a generating module for generating control instructions based on the request message, wherein the control instructions are used to control the autonomous vehicle to perform a lane-changing driving operation; and a control module for sending the control instructions to the autonomous vehicle so that the autonomous vehicle performs a lane-changing driving operation based on the control instructions.

[0009] According to another aspect of this disclosure, an apparatus for controlling an autonomous vehicle is also provided, comprising: an acquisition module for acquiring road condition information within a preset range around the autonomous vehicle, wherein the road condition information is used to determine the type of lane lines and the type of detection objects within the preset range; a sending module for sending a request message to a server in response to the road condition information meeting preset conditions, wherein the preset conditions include: the type of lane line is a preset type lane line and the type of detection object is a preset type object, the preset type lane line is used to constrain the autonomous vehicle from changing lanes during driving, the preset type object is used to prevent the autonomous vehicle from continuing to drive, and the request message is used to request the server to execute a driving mode to be executed by the autonomous vehicle; a receiving module for receiving a control command corresponding to the request message sent by the server, wherein the control command is used to control the autonomous vehicle to perform a lane-changing driving operation; and a control module for performing lane-changing driving based on the control command.

[0010] According to another aspect of this disclosure, an electronic device is also provided, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the method for controlling an autonomous vehicle as proposed in this disclosure.

[0011] According to another aspect of this disclosure, a non-transitory computer-readable storage medium storing computer instructions is also provided, wherein the computer instructions are used to cause a computer to perform the method for controlling an autonomous vehicle as proposed in this disclosure.

[0012] According to another aspect of this disclosure, a computer program product is also provided, including a computer program that is executed by a processor to control an autonomous vehicle as proposed in this disclosure.

[0013] In this disclosure, a request message is received from an autonomous vehicle. The request message is triggered by road condition information within a preset range around the autonomous vehicle. The request message requests a driving mode to be executed by the autonomous vehicle. The road condition information is used to determine the type of lane lines and the type of detection objects within the preset range. The road condition information satisfies a first preset condition, which includes: the type of lane line is a preset type lane line and the type of detection object is a preset type object. The preset type lane line is used to constrain the autonomous vehicle from changing lanes during driving, and the preset type object is used to prevent the autonomous vehicle from continuing to drive. Further, a control command is generated based on the request message. The control command is used to control the autonomous vehicle to perform a lane-changing operation. By sending the control command to the autonomous vehicle, the autonomous vehicle can change lanes based on the control command.

[0014] It is readily understood that the technical solution provided in this disclosure can run on the server side to automatically receive and respond to request messages issued by autonomous vehicles, and generate control commands for the autonomous vehicles to help them change lanes and escape from the predicament of being obstructed from continuing their journey. Therefore, this disclosure achieves the goal of controlling autonomous vehicles to change lanes and escape from the predicament of being obstructed from continuing their journey by automatically generating control commands, thereby improving the level of intelligence in the automatic escape of autonomous vehicles and enhancing the user experience. It solves the technical problem in the prior art where the reliance on manual external operation to help autonomous vehicles escape from predicaments results in a low level of intelligence in the autonomous driving process and a poor user experience.

[0015] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this disclosure, nor is it intended to limit the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0016] The accompanying drawings are provided to better understand this solution and do not constitute a limitation of this disclosure. Wherein:

[0017] Figure 1 This is a schematic diagram of an autonomous vehicle getting out of trouble based on existing technology;

[0018] Figure 2 This is a schematic diagram of another method for autonomous vehicles to get out of trouble based on existing technology;

[0019] Figure 3 This is a hardware structure block diagram of a computer terminal (or mobile device) for implementing a method for controlling an autonomous vehicle according to an embodiment of the present disclosure.

[0020] Figure 4 This is a flowchart of a method for controlling an autonomous vehicle according to an embodiment of the present disclosure;

[0021] Figure 5 This is a schematic diagram of an optional process for controlling an autonomous vehicle according to an embodiment of the present disclosure;

[0022] Figure 6 This is a schematic diagram of an optional lane-changing turning process of an autonomous vehicle according to an embodiment of the present disclosure;

[0023] Figure 7 This is a flowchart of another method for controlling an autonomous vehicle according to an embodiment of this disclosure;

[0024] Figure 8 This is a schematic diagram of an optional airport automated driving vehicle control system according to an embodiment of the present disclosure;

[0025] Figure 9 This is a structural block diagram of a device for controlling an autonomous vehicle according to an embodiment of the present disclosure;

[0026] Figure 10 This is a structural block diagram of another device for controlling an autonomous vehicle according to an embodiment of the present disclosure;

[0027] Figure 11 This is a structural block diagram of an optional device for controlling an autonomous vehicle according to an embodiment of the present disclosure. Detailed Implementation

[0028] The acquisition, storage, and application of user personal information involved in the technical solution disclosed herein comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0029] The exemplary embodiments of this disclosure are described below with reference to the accompanying drawings, including various details of the embodiments to aid understanding, and should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this disclosure. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0030] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this disclosure are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this disclosure described herein can be implemented in orders other than those illustrated or described herein. Furthermore, 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.

[0031] Currently, autonomous vehicles frequently encounter driving dilemmas due to constraints, for the following reasons: Based on various road condition information detected by the autonomous vehicle, when making autonomous driving decisions according to multiple driving constraints, the vehicle may self-impose restrictions and stop, failing to automatically extricate itself from the predicament. For example, when an autonomous vehicle detects an obstacle ahead in its current lane, the original decision would be to change lanes. However, if the vehicle then detects that both lane markings are solid lines, according to traffic rules, the final decision is to stop. In this situation, the autonomous vehicle will remain stationary until the detected road condition information allows for a passable driving decision.

[0032] In existing technologies, the main methods used to address the aforementioned constraints on autonomous driving vehicles are as follows:

[0033] The first method involves manual intervention to extricate the autonomous vehicle from a predicament. Figure 1 This is a schematic diagram of a method for autonomous vehicles to get out of trouble based on existing technology, such as... Figure 1 As shown, when an autonomous vehicle is in a constrained driving predicament (such as when there are scattered obstacles in front of the lane and the lane dividers on both sides of the current lane are solid lines), the accompanying safety personnel will take over the autonomous vehicle and change lanes to detour according to road traffic control rules (such as following the instructions of traffic police).

[0034] The second method involves manually removing obstacles to help the autonomous vehicle get out of trouble. Figure 2This is a schematic diagram of another method for autonomous vehicles to get out of trouble based on existing technology, such as... Figure 2 As shown, when an autonomous vehicle is in a constrained driving predicament (such as when there are scattered obstacles in front of the lane and the lane dividers on both sides are solid lines), the accompanying safety personnel manually remove the obstacles so that the autonomous vehicle can pass.

[0035] However, both of the above-mentioned methods for autonomous vehicles to get out of trouble rely on external human intervention (driving takeover or manual clearing), which has a low level of intelligence and is difficult to meet the needs of autonomous driving.

[0036] In response, this disclosure provides a method for autonomous vehicles to escape from difficult situations without relying on external human intervention, thereby improving the intelligence level of autonomous vehicles and enhancing the user experience.

[0037] According to embodiments of this disclosure, a method for controlling an autonomous vehicle is provided. It should be noted that the steps shown in the flowcharts in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowcharts, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0038] The method embodiments provided in this disclosure can be performed in a mobile terminal, computer terminal, or similar electronic device. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the disclosure described and / or claimed herein. Figure 3 This is a hardware structure block diagram of a computer terminal (or mobile device) for implementing a method for controlling an autonomous vehicle according to an embodiment of the present disclosure.

[0039] like Figure 3As shown, the computer terminal 300 includes a computing unit 301, which can perform various appropriate actions and processes based on a computer program stored in read-only memory (ROM) 302 or a computer program loaded from storage unit 308 into random access memory (RAM) 303. The RAM 303 may also store various programs and data required for the operation of the computer terminal 300. The computing unit 301, ROM 302, and RAM 303 are interconnected via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0040] Multiple components in the computer terminal 300 are connected to the I / O interface 305, including: an input unit 306, such as a keyboard and mouse; an output unit 307, such as various types of displays and speakers; a storage unit 308, such as a hard disk and optical disk; and a communication unit 309, such as a network card, modem, or wireless transceiver. The communication unit 309 allows the computer terminal 300 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0041] The computing unit 301 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 301 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 301 executes the method for controlling an autonomous vehicle described herein. For example, in some embodiments, the method for controlling an autonomous vehicle may be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 308. In some embodiments, part or all of the computer program may be loaded and / or installed on the computer terminal 300 via ROM 302 and / or communication unit 309. When the computer program is loaded into RAM 303 and executed by the computing unit 301, one or more steps of the method for controlling an autonomous vehicle described herein may be performed. Alternatively, in other embodiments, computing unit 301 may be configured to perform methods for controlling autonomous vehicles by any other suitable means (e.g., by means of firmware).

[0042] Various implementations of the systems and techniques described herein can be realized in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard parts (ASSPs), systems-on-chip (SoCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a memory system, at least one input device, and at least one output device, and transferring data and instructions to the memory system, the at least one input device, and the at least one output device.

[0043] It should be noted here that, in some optional embodiments, the above... Figure 3 The electronic device shown may include hardware elements (including circuitry), software elements (including computer code stored on a computer-readable medium), or a combination of both hardware and software elements. It should be noted that... Figure 3 This is only one instance of a specific particular example, and is intended to illustrate the types of components that may exist in the aforementioned electronic devices.

[0044] Under the aforementioned operating environment, this disclosure provides, for example... Figure 4 The method shown can be used to control an autonomous vehicle. Figure 3 The computer terminal or similar electronic device shown is used for execution. Figure 4 This is a flowchart of a method for controlling an autonomous vehicle according to an embodiment of this disclosure. Figure 4 As shown, the method may include the following steps:

[0045] Step S40: Receive a request message from the autonomous vehicle. The request message is triggered by road condition information within a preset range around the autonomous vehicle. The request message is used to request the autonomous vehicle to execute a driving mode. The road condition information is used to determine the type of lane lines and the type of detection objects within the preset range. The road condition information satisfies a first preset condition. The first preset condition includes: the type of lane line is a preset type lane line and the type of detection object is a preset type object. The preset type lane line is used to constrain the autonomous vehicle from changing lanes during driving, and the preset type object is used to hinder the autonomous vehicle from continuing to drive.

[0046] The aforementioned autonomous vehicle is equipped with devices such as video cameras, radar sensors, and laser rangefinders to collect road condition information within a preset range around the vehicle. This preset range is a pre-defined area by technicians that can influence the driving behavior of the autonomous vehicle. The road condition information is used to determine the type of lane markings and the type of detection objects within the preset range.

[0047] The types of lane markings mentioned above include, but are not limited to: center lines of the roadway (such as yellow dashed lines, yellow solid lines, double yellow solid lines, and yellow dashed and solid lines), lane dividing lines (such as white dashed lines and white solid lines), lane edge lines (such as solid line edge lines and dashed line edge lines), stop lines (such as white horizontal solid lines), yield lines (such as white double dashed lines), and pedestrian crossing lines. The types of objects to be detected include, but are not limited to: roads, basic traffic infrastructure, pedestrian obstacles, and other vehicles.

[0048] The aforementioned request message is triggered by road condition information within a preset range around the autonomous vehicle. In other words, when the road condition information collected by the autonomous vehicle meets the first preset condition, the aforementioned request message will be sent. This request message is used to request the server to obtain the currently executed driving mode. The first preset condition includes: the lane line type is a preset type lane line, which is used to restrict the autonomous vehicle from changing lanes during driving; the detected object type is a preset type object, which is used to prevent the autonomous vehicle from continuing to move.

[0049] It is easy to understand that the first preset condition mentioned above is used to characterize that the autonomous vehicle is currently in a constrained driving dilemma. For example, when the lane line type is a solid lane dividing line (i.e., a white solid line) and the detected object type is a pedestrian obstacle (here it is assumed that the lane line is the left and right lane dividing line of the current lane, and the detected object is located in the area in front of the current lane), it means that the autonomous vehicle is constrained to "stop moving" and "not allowed to change lanes", that is, the autonomous vehicle is currently in a constrained driving dilemma.

[0050] The aforementioned server receives the request message sent by the autonomous vehicle and responds to the request message to determine the driving mode to be executed for the autonomous vehicle. The server can be an autonomous vehicle control platform deployed on a server, which can be a standalone server, a distributed server cluster, or a cloud server. The connection between the autonomous vehicle and the server is via an Internet of Things (IoT) protocol (such as MQTT) or a wireless communication network connection, particularly a mobile communication network connection (such as 3G, 4G, 5G, 6G, etc.).

[0051] It is readily understood that, through the aforementioned step S40, when an autonomous vehicle is in a constrained driving predicament, a feasible method for escaping the predicament is to send a request message to a server with which a pre-established communication connection has been established to obtain the driving mode to be executed. In this process, the driving mode decision based on road condition information within a preset range around the autonomous vehicle is completed by the server, as can be further described in the embodiments of this disclosure.

[0052] Step S42: Generate control instructions based on the request message, wherein the control instructions are used to control the autonomous vehicle to perform lane-changing operations;

[0053] When the server receives a request message from the autonomous vehicle, it generates the aforementioned control instructions based on that message. These control instructions are used to control the autonomous vehicle to perform a lane-changing maneuver, which helps the autonomous vehicle escape from a constrained driving situation.

[0054] Specifically, generating control instructions based on request messages also includes other methods and steps, which can be referred to in the further description of the embodiments of this disclosure below, and will not be repeated here.

[0055] Step S44: The control command is sent to the autonomous vehicle so that the autonomous vehicle can change lanes based on the control command.

[0056] In step S44 above, the server sends control commands generated based on the request message from the autonomous vehicle to the autonomous vehicle. The autonomous vehicle receives and automatically executes the control commands sent by the server to change lanes. The aforementioned lane change is a lane-changing detour permitted by road traffic rules (such as temporarily crossing a solid line to detour when an emergency occurs ahead, as permitted by traffic rules).

[0057] It is easy to understand that, compared with the methods provided by the prior art, the method for controlling autonomous vehicles provided in this disclosure can automatically make decisions on the driving mode to be executed by the server through message interaction, without relying on external human operation, thus helping autonomous vehicles to get out of the dilemma of constrained driving. The whole process has a high degree of intelligence and can improve the user experience.

[0058] According to steps 40 to 44 of this disclosure, a request message is received from the autonomous vehicle. The request message is triggered by road condition information within a preset range around the autonomous vehicle. The request message is used to request the autonomous vehicle to perform a driving mode. The road condition information is used to determine the type of lane lines and the type of detection objects within the preset range. The road condition information satisfies a first preset condition, which includes: the type of lane line is a preset type lane line and the type of detection object is a preset type object. The preset type lane line is used to constrain the autonomous vehicle from changing lanes during driving, and the preset type object is used to prevent the autonomous vehicle from continuing to drive. Further, a control command is generated based on the request message. The control command is used to control the autonomous vehicle to perform a lane-changing operation. By sending the control command to the autonomous vehicle, the autonomous vehicle can perform a lane-changing operation based on the control command.

[0059] It is readily understood that the technical solution provided in this disclosure can run on the server side to automatically receive and respond to request messages issued by autonomous vehicles, and generate control commands for the autonomous vehicles to help them change lanes and escape from the predicament of being obstructed from continuing their journey. Therefore, this disclosure achieves the goal of controlling autonomous vehicles to change lanes and escape from the predicament of being obstructed from continuing their journey by automatically generating control commands, thereby improving the level of intelligence in the automatic escape of autonomous vehicles and enhancing the user experience. It solves the technical problem in the prior art where the reliance on manual external operation to help autonomous vehicles escape from predicaments results in a low level of intelligence in the autonomous driving process and a poor user experience.

[0060] The method for controlling autonomous vehicles described in this disclosure can be applied, but is not limited to, to application scenarios in areas such as urban roads, hospitals, logistics parks, airports, and schools to help autonomous vehicles get out of trouble. The following example, taking an autonomous vehicle trapped in a restricted driving situation at an airport by solid lane dividers and obstacles, further illustrates the technical solution for controlling autonomous vehicles described in this disclosure.

[0061] Figure 5 This is a schematic diagram illustrating an optional process for controlling an autonomous vehicle according to an embodiment of this disclosure. Multiple autonomous vehicles operating in the airport are connected to the airport's autonomous driving control platform (i.e., the server) via connections (such as local area network connections, mobile communication network connections, mobile hotspot connections, Bluetooth connections, etc.). When an autonomous vehicle encounters a constrained driving predicament, it can control the vehicle through... Figure 5 The process of controlling the autonomous vehicle shown demonstrates how the airport's autonomous driving control platform helps the vehicle automatically get out of trouble without the need for external operation by technicians.

[0062] like Figure 5 As shown, when an autonomous vehicle is in a constrained driving predicament, it first summarizes the on-site conditions (usually road condition information within a preset range around the autonomous vehicle). If the current on-site conditions cannot be resolved by the autonomous vehicle itself, it sends an escape signal, which is an escape request message sent to the airport's autonomous driving control platform. Further, the airport's autonomous driving control platform receives the escape signal and processes the corresponding escape request from the autonomous vehicle.

[0063] like Figure 5 As shown, after the autonomous vehicle sends an escape signal, it continuously monitors whether the airport's autonomous driving control platform sends a return signal. When the autonomous vehicle receives a return signal from the airport's autonomous driving control platform, it controls the vehicle to change lanes according to the control instructions in the return signal and executes the corresponding escape procedure. This escape procedure can be determined by the control instructions from candidate programs pre-stored in the autonomous vehicle. This escape procedure is used to control the autonomous vehicle to simultaneously perform other auxiliary operations corresponding to the lane change operation (such as flashing vehicle indicator lights, honking the horn, and detecting whether the vehicle has successfully escaped the constrained driving predicament).

[0064] like Figure 5 As shown, after the autonomous vehicle completes the lane change operation and executes the escape procedure, if it detects that it has successfully escaped the constrained driving predicament and the current road conditions allow the vehicle to return to the default lane before the lane change, it controls the autonomous vehicle to return to the model lane and continue driving.

[0065] It is easy to understand that, through, for example Figure 5 The process of controlling autonomous vehicles shown can help unmanned cargo vehicles (such as baggage handling vehicles and autonomous shuttle buses) automatically escape from driving difficulties in airport scenarios without requiring additional assistance from airport security personnel (such as taking over driving or removing obstacles). This saves airport manpower costs, improves cargo efficiency, and enhances the intelligence of airport vehicles.

[0066] As an optional implementation, step S42 above, which generates control instructions based on the request message, further includes the following method steps:

[0067] Step S421: In response to the request message, obtain the road information, object information of the detected object, and vehicle information of the autonomous vehicle reported by the autonomous vehicle during the driving process.

[0068] Step S422: Obtain lane change information using object information and vehicle information, wherein the lane change information is the minimum road width through which the autonomous vehicle passes a right-angle bend during the lane change process.

[0069] Step S423: Generate control commands based on road information, object information, and lane change information.

[0070] Upon receiving the request message, the server retrieves road information, object information of the detected objects, and vehicle information of the autonomous vehicle based on the message. The road information represents the road data currently in which the autonomous vehicle is located, such as road width and the number of lanes in the same direction. The object information represents the object data of the detected objects currently being monitored by the autonomous vehicle, such as obstacle type and obstacle dimensions (width, height, etc.). The vehicle information represents the vehicle structure data of the autonomous vehicle corresponding to the request message, such as vehicle dimensions (length, width, height, etc.), wheelbase, and maximum steering angle.

[0071] To help autonomous vehicles determine the appropriate driving mode, the server calculates the aforementioned lane change information based on the object and vehicle information reported by the autonomous vehicle, using geometric relationships. This lane change information refers to the narrowest road width the autonomous vehicle passes through during a right-angle turn. Based on this lane change information, it can be determined whether the autonomous vehicle can safely change lanes within its current lane.

[0072] It's easy to understand that the lane change information mentioned above refers to the narrowest road width a right-angle turn will pass through during a lane change. This lane change information can also be understood as the narrowest road width that the autonomous vehicle needs to occupy during a lane change. When making decisions about the driving mode to be executed for an autonomous vehicle, if it is found that the road currently occupied by the autonomous vehicle can provide road space no less than this narrowest road width to support the autonomous vehicle's lane change, then control commands can be generated based on road information, object information, and lane change information.

[0073] Based on the aforementioned road information, object information, and lane change information, control commands are generated. These control commands are used to control the autonomous vehicle to perform lane change operations. It is easy to understand that when the conclusion that a lane change is safe can be reached based on the aforementioned road information, object information, and lane change information, the aforementioned control commands are generated to help the autonomous vehicle escape its current constrained driving predicament.

[0074] As an optional implementation, step S422 above, which uses object information and vehicle information to obtain lane change information, also includes the following method steps:

[0075] Step S4221: Obtain the center position of the detected object from the object information;

[0076] Step S4222: Determine the first parameter, the second parameter, the third parameter, and the fourth parameter based on the center position and vehicle information. The first parameter is the maximum steering angle of the front outer wheel of the autonomous vehicle during the lane change process. The second parameter is the minimum turning radius of the autonomous vehicle during the lane change process. The third parameter is the radius of the innermost moving trajectory of the autonomous vehicle approaching the detection object during the lane change process. The fourth parameter is determined based on the length between the front bumper and the front outer wheel of the autonomous vehicle.

[0077] Step S4223: Obtain lane change information using the first parameter, the second parameter, the third parameter, and the fourth parameter.

[0078] For example, when an autonomous vehicle is trapped in a driving dilemma at an airport due to solid lane dividers on the left and right and obstacles in front, the location and size of the obstacles, as well as the vehicle information, are important factors in determining whether the autonomous vehicle can change lanes to escape the predicament.

[0079] Specifically, the center position of an obstacle (i.e., the object being detected) is obtained by using its object information. For example, the center position of a spherical obstacle can be considered as the center of the sphere or the projection of the center of the sphere onto the ground, while the center position of an irregular obstacle can be considered as the geometric center or the projection of the geometric center onto the ground.

[0080] The first parameter mentioned above is the maximum steering angle of the front outer wheel of the autonomous vehicle during the lane change process. When the vehicle maintains the maximum steering angle of the front outer wheel while turning, it can minimize the road space occupied by the vehicle during the lane change process.

[0081] The second parameter mentioned above is the minimum turning radius of the autonomous vehicle during lane changing. This second parameter can be determined by the maximum steering angle of the front outer wheel.

[0082] The third parameter mentioned above is the radius of the innermost movement trajectory of the autonomous vehicle as it approaches the detected object during the lane change process. This third parameter is used to determine the conditions under which the autonomous vehicle will not collide with the detected object during the lane change process.

[0083] The fourth parameter mentioned above is the difference between the radius of the outermost moving trajectory and the minimum turning radius of the autonomous vehicle during lane changing. The outermost moving trajectory is typically the movement trajectory of the outermost end point of the vehicle's front bumper during lane changing. This fourth parameter is determined based on the length between the front bumper and the outer front wheel of the autonomous vehicle.

[0084] Furthermore, based on the center position of the detected object and vehicle information, the first, second, third, and fourth parameters are determined. Using the first, second, third, and fourth parameters, the minimum road width through which the autonomous vehicle passes a right-angle bend during the lane-changing process is calculated, i.e., the lane-changing information.

[0085] As an optional implementation, step S4222 above, which determines the first parameter, second parameter, third parameter, and fourth parameter based on the center position and vehicle information, further includes the following method steps:

[0086] Step S4311: Obtain the first axle center and the second axle center of the autonomous vehicle from the vehicle information, wherein the first axle center is the front outer wheel axle center of the autonomous vehicle and the second axle center is the rear outer wheel axle center of the autonomous vehicle.

[0087] Step S4312: Determine the first parameter using the center position, the first axis, and the second axis;

[0088] Step S4313: Determine the second, third, and fourth parameters based on the first parameter and vehicle information.

[0089] Taking the scenario of controlling an autonomous vehicle to escape a constrained driving predicament in an airport as an example, the first, second, third, and fourth parameters can be determined by using the vehicle structure information of the autonomous vehicle and the center position of the obstacle in the constrained driving predicament.

[0090] Figure 6 This is a schematic diagram of an optional lane-changing turning process of an automated vehicle according to an embodiment of the present disclosure, such as... Figure 6 As shown, O is the center position of the obstacle, and a Cartesian coordinate system is established with O as the origin. A and B represent the positions of the front wheels of the autonomous vehicle, and C and D represent the positions of the rear wheels. Based on the vehicle structure information of the autonomous vehicle in the airport, the coordinates of the front outer wheel axle center (i.e., the first axle center mentioned above, also the axle center of wheel A) and the coordinates of the rear outer wheel axle center (i.e., the second axle center mentioned above, also the axle center of wheel D) are determined. Furthermore, using the coordinates of the axle centers of wheels A and D, coordinate calculations are performed to determine the maximum steering angle of the front outer wheels of the autonomous vehicle during lane changing (i.e., the first parameter mentioned above, denoted as α). In other words, the first parameter is determined using the position of point O, the first axle center, and the second axle center.

[0091] like Figure 6As shown, based on the maximum steering angle α of the front outer wheel and the vehicle structure information of the autonomous vehicle, the minimum turning radius R2 (i.e., the second parameter mentioned above) of the autonomous vehicle during the lane change process is determined by calculation, the radius R1 of the innermost moving trajectory of the autonomous vehicle close to the detection object during the lane change process is determined (i.e., the third parameter mentioned above), and the difference AE between the radius OE of the outermost moving trajectory of the autonomous vehicle during the lane change process and the minimum turning radius R2 (length equal to OA) is determined (i.e., the fourth parameter mentioned above).

[0092] As an optional implementation, step S4222 above, determining the second parameter based on the first parameter and vehicle information, further includes the following method steps:

[0093] Step S4321: Obtain the wheelbase of the autonomous vehicle from the vehicle information;

[0094] Step S4322: Determine the second parameter based on the first parameter and the wheelbase.

[0095] Taking the scenario of controlling an autonomous vehicle to escape a constrained driving predicament at an airport as an example, the wheelbase of the autonomous vehicle is obtained from its vehicle structure information, such as... Figure 6 As shown, the wheelbase of the autonomous vehicle, which is the distance between wheels A and D, is denoted as AD. Based on the maximum steering angle α of the front outer wheels and the wheelbase AD, the minimum turning radius R2 (i.e., the second parameter mentioned above) of the autonomous vehicle during lane changing is determined.

[0096] like Figure 6 As shown, the minimum turning radius R2 is equal to the length of line segment OA. Based on geometric relationships, it is easy to obtain that OA × sinα = AD. That is to say, The second parameter is determined based on the first parameter and the wheelbase.

[0097] As an optional implementation, step S4222 above, determining the third parameter based on the first parameter and vehicle information, further includes the following method steps:

[0098] Step S4331: Obtain the wheelbase and width of the autonomous vehicle from the vehicle information;

[0099] Step S4332: Determine the third parameter based on the first parameter, wheelbase, and vehicle width.

[0100] Taking the scenario of controlling an autonomous vehicle to escape a constrained driving predicament at an airport as an example, the wheelbase and width of the autonomous vehicle are obtained from its structural information, such as... Figure 6As shown, the wheelbase of the autonomous vehicle, which is the distance between wheels A and D, is denoted as AD; the width of the autonomous vehicle, which is the distance between wheels C and D, is denoted as CD. Based on the maximum steering angle α of the front outer wheels, the wheelbase AD, and the width CD, the radius R1 of the innermost movement trajectory of the autonomous vehicle approaching the detection object during lane changing is calculated (i.e., the third parameter mentioned above).

[0101] like Figure 6 As shown, the radius R1 of the innermost movement trajectory of the autonomous vehicle approaching the detection object during lane changing is equal to the length of line segment OC. Based on geometric relationships, it is easy to obtain: OC = OD - CD, where... therefore, In other words, the third parameter is determined based on the first parameter, wheelbase, and vehicle width.

[0102] As an optional implementation, step S4222 above, determining the fourth parameter based on the first parameter and vehicle information, further includes the following method steps:

[0103] Step S43411: Obtain the length between the front bumper and the front outer wheel of the autonomous vehicle from the vehicle information;

[0104] Step S43412: Determine the fourth parameter based on the first parameter and the length between the front bumper and the front outer wheel.

[0105] Taking the scenario of controlling an autonomous vehicle to escape a restrained driving predicament at an airport as an example, the length between the front bumper and the front outer wheel of the autonomous vehicle is obtained from the vehicle's structural information, such as... Figure 6 As shown, the length between the front bumper and the outer front wheel of the autonomous vehicle, which is also the distance between wheel A and point F, is denoted as AF. Based on the maximum steering angle α of the outer front wheel and the length AF between the front bumper and the outer front wheel, the difference AE between the radius of the outermost moving trajectory and the minimum turning radius of the autonomous vehicle during lane changing is determined (i.e., the fourth parameter mentioned above).

[0106] like Figure 6 As shown, points F and E are both points on the outermost moving trajectory of the autonomous vehicle during lane changing, and the length of arc EF is much smaller than the circumference of the outermost moving trajectory. Therefore, line segment EF and OE can be considered to be perpendicular (approximately). At this time, it is easy to obtain from geometric relationships that the size of angle EFA is equal to the maximum steering angle α of the front outer wheel. Furthermore, AF × sinα = AE, that is, the fourth parameter is determined based on the first parameter and the length between the front bumper and the front outer wheel.

[0107] In summary, the first, second, third, and fourth parameters are determined based on the center position of the detected object and the vehicle information of the autonomous vehicle. Further, according to step S4223 above, the first, second, third, and fourth parameters are used to obtain the narrowest road width through which the autonomous vehicle passes a right-angle bend during the lane-changing process, i.e., lane-changing information is obtained.

[0108] like Figure 6 As shown, the narrowest road width through which an autonomous vehicle passes during a right-angle turn in lane-changing is denoted as L. It's easy to understand that, to ensure the safety of the autonomous vehicle during turning, a buffer amount s (e.g., 0.5m) can be set. The radius OE of the outermost movement trajectory of the autonomous vehicle during lane-changing, plus the buffer amount s, is taken as the narrowest road width L through which the autonomous vehicle passes during a right-angle turn in lane-changing; that is, L = OE + s.

[0109] like Figure 6 As shown, based on the maximum steering angle α of the front outer wheel of the autonomous vehicle during the lane change process (i.e., the first parameter mentioned above), the minimum turning radius R2 (i.e., the second parameter mentioned above), the radius R1 of the innermost moving trajectory closest to the detection object (i.e., the third parameter mentioned above), and the difference AE between the radius of the outermost moving trajectory and the minimum turning radius (i.e., the fourth parameter mentioned above), it is easy to obtain the following from the geometric relationship: OE = R2 - R1 + AE. Furthermore, L = R2 - R1 + AE + s, that is, the lane change information L is calculated.

[0110] Specifically, based on the above geometric relationships, it is easy to obtain: In other words, lane change information is obtained through calculation from object information and vehicle information.

[0111] It should be noted that, as Figure 7 The shaded circle in the diagram represents the projection of the circumscribed sphere of the obstacle onto the ground. The circle with radius R2 represents the trajectory of the outermost wheel of the autonomous vehicle as it maintains the maximum steering angle of its outermost wheel during a full turn (passing slowly, without considering slippage or other factors). The circle with radius R3 represents the trajectory of the outermost point of the vehicle as it maintains the maximum steering angle of its outermost wheel during a full turn (passing slowly, without considering slippage or other factors, but considering the buffer amount s).

[0112] Furthermore, based on the lane change information calculation method provided in this embodiment, the calculations for the three types of vehicles actually operating in the airport scenario are as follows:

[0113] Vehicle 1: The maximum steering angle of the front outer wheel is 34°, the minimum turning radius is 6 meters, the wheelbase is 2.7 meters, and the width is 2.07 meters. The calculated minimum road width for vehicle 1 to pass through the right-angle bend during lane change is 4.522 meters. After considering the buffer, the minimum road width is taken as 5 meters.

[0114] Vehicle 2: The maximum steering angle of the front outer wheel is 33.8°, the minimum turning radius is 7.5 meters, the wheelbase is 3.32 meters, and the width is 2.52 meters. The calculation shows that the narrowest road width for Vehicle 2 to pass through the right-angle bend during the lane change process is 6.14 meters. After considering the buffer, the narrowest road width is taken as 6.7 meters.

[0115] Vehicle 3: The maximum steering angle of the front outer wheel is 32°, the minimum turning radius is 8 meters, the wheelbase is 3.8 meters, and the width is 2.38 meters. The calculation shows that the narrowest road width for vehicle 3 to pass through the right-angle bend during the lane change process is 4.9 meters. After considering the buffer, the narrowest road width is taken as 5.4 meters.

[0116] It should be noted that the calculation data for the above three types of vehicles only shows the main data, and the remaining vehicle structural data (such as the length between the front bumper and the front outer wheel) are determined by the vehicle model.

[0117] As an optional implementation, step S423 above, which generates control commands based on road information, object information, and lane change information, further includes the following method steps:

[0118] Step S4231: Obtain the road width of the autonomous vehicle during its driving process from the road information and obtain the object width of the detected object from the object information;

[0119] Step S4232: In response to the road width, object width, and right-angle bend passing through the narrowest road width satisfying the second preset condition, a control command is generated, wherein the second preset condition is used to determine that the current road conditions within the preset range meet the lane change conditions.

[0120] In the above optional implementation, the road width of the autonomous vehicle during its driving process is obtained from the road information. The road width may include the width of the lane where the autonomous vehicle is currently located, the total width of the lanes in the same direction as the autonomous vehicle, and the width from the edge of the detected object (such as an obstacle) to both sides of the road (including the width from the left edge of the detected object to the left side of the road and the width from the right edge of the detected object to the right side of the road).

[0121] In the above optional implementation, when obtaining the object width of the detected object from the object information, in order to avoid possible collisions between the autonomous vehicle and the detected object, the object width can be the width of the widest position of the detected object. Preferably, the object width can be the width of the widest position of the detected object within the vehicle's passage height range (such as the vehicle height plus buffer).

[0122] Furthermore, it is determined that the road width, the object width, and the narrowest road width for right-angle bends calculated by the aforementioned method in this embodiment of the present disclosure satisfy the second preset condition. A control command is generated when the road width, object width, and narrowest road width for right-angle bends satisfy the second preset condition. The second preset condition is used to determine that the current road conditions within a preset range meet the lane-changing conditions. It is readily understood that the current road conditions within a preset range meeting the lane-changing conditions indicate that, based on the road width, the object width, and the narrowest road width for right-angle bends, the available road width provided by the current road conditions within the preset range can support the autonomous vehicle in performing safe lane-changing operations.

[0123] In summary, in the method for controlling an autonomous vehicle provided in this disclosure, the server can determine the current road information, object information, and lane change information of the autonomous vehicle based on the request message sent by the autonomous vehicle, thereby making a driving mode decision to be executed for the autonomous vehicle and generating control commands. Once the control commands are issued to the autonomous vehicle, they can help the autonomous vehicle escape from a constrained driving predicament. It is easy to understand that the above-described method for controlling an autonomous vehicle does not rely on external human intervention to help the autonomous vehicle escape from a predicament, demonstrating a high degree of intelligence and improving the user experience.

[0124] As an optional implementation, step S4232 above, generating control instructions, further includes the following method steps:

[0125] Step S4233: Obtain the request identifier, authorization identifier, timestamp, instruction content, and encryption key corresponding to the request message. The request identifier is used to identify that the request message originates from the autonomous vehicle, the authorization identifier is used to authorize the autonomous vehicle to perform the lane change operation, the instruction content is used to determine the execution process of the lane change operation, the timestamp is used to determine the validity period of the lane change operation, and the encryption key is used to encrypt the instruction content.

[0126] Step S4234: Encapsulate the request identifier, authorization identifier, timestamp, instruction content, and encryption key to generate control instructions.

[0127] In the above optional implementation, after the server receives a request message sent by the autonomous vehicle, it obtains the request identifier, authorization identifier, timestamp, instruction content, and encryption key corresponding to the request message. Further, the request identifier, authorization identifier, timestamp, instruction content, and encryption key are encapsulated to generate a control instruction. Thus, the above-mentioned content included in the control instruction can be used to verify the control instruction received by the autonomous vehicle, ensuring the accuracy, safety, and efficiency of the subsequent lane-changing operation performed by the autonomous vehicle according to the control instruction.

[0128] Specifically, the aforementioned request identifier is the request code or vehicle code of the autonomous vehicle, which can be used to identify that the request message originates from the autonomous vehicle. Based on the aforementioned request identifier, the accuracy of information interaction (such as request message interaction and control command interaction) between the autonomous vehicle and the server can be guaranteed.

[0129] Specifically, the aforementioned authorization identifier can be an authorization code pre-issued to the autonomous vehicle by the server (such as the autonomous driving control center). This authorization identifier can be used to authorize the autonomous vehicle to perform lane-changing operations. Based on this authorization identifier, the safety of the autonomous vehicle's lane-changing operation based on control commands can be guaranteed.

[0130] Specifically, the aforementioned instruction content may be instruction content generated according to the method decision-making of the embodiments of this disclosure (i.e., instruction content for controlling the autonomous vehicle to change lanes obtained through request message decision-making). The aforementioned instruction content is used to determine the execution process of the lane-changing operation.

[0131] Specifically, the aforementioned timestamps are used to record the time information of key operations (such as the time when the autonomous vehicle sends a request message, the time when the server receives the request message, the time when the server issues control commands, the expected time when the autonomous vehicle receives the control commands, and the expected time when the autonomous vehicle completes the lane-changing operation). These timestamps are used to determine the timeliness of lane-changing operations. Based on these timestamps, the efficiency of the autonomous vehicle's lane-changing operation based on control commands can be guaranteed. For example, when the server detects that the autonomous vehicle has timed out of receiving control commands or timed out of completing a lane-changing operation, it issues a warning message or stops controlling the autonomous vehicle.

[0132] Specifically, the aforementioned encryption key is used to encrypt the instruction content. The corresponding decryption key is stored in the corresponding autonomous vehicle. After the encryption key is encapsulated into the control instruction and sent to the autonomous vehicle, the autonomous vehicle uses the decryption key to decrypt the instruction content and executes the lane-changing operation according to the instruction content. Based on the above-mentioned encryption process of the instruction content, the security of the control instruction during transmission can be guaranteed, further improving the security of the autonomous vehicle's lane-changing operation based on the control instruction.

[0133] It is readily understood that, compared to existing technologies, the method described in this disclosure, when an autonomous vehicle is in a constrained driving predicament, generates control commands for the autonomous vehicle through the above-described method steps performed by the server, based on the interaction between the autonomous vehicle and the server (such as an autonomous driving control platform), to help the autonomous vehicle perform lane-changing operations and escape the constrained driving predicament. The above-described method for controlling autonomous vehicles does not rely on external manual operation to help the autonomous vehicle escape predicament, resulting in high efficiency, a high degree of intelligence, and a better user experience.

[0134] According to another embodiment of this disclosure, a method for controlling an autonomous vehicle is also provided, which can be performed by the autonomous vehicle. Figure 7 This is a flowchart of another method for controlling an autonomous vehicle according to embodiments of this disclosure. Figure 8 As shown, the method may include the following steps:

[0135] Step S70: Obtain road condition information within a preset range around the autonomous vehicle, wherein the road condition information is used to determine the type of lane lines and the type of detection object within the preset range;

[0136] Step S72: In response to the road condition information meeting the preset conditions, a request message is sent to the server. The preset conditions include: the type of lane line is a preset type lane line and the type of the detected object is a preset type object. The preset type lane line is used to constrain the autonomous vehicle from changing lanes during driving. The preset type object is used to prevent the autonomous vehicle from continuing to drive. The request message is used to request the autonomous vehicle to execute a driving mode from the server.

[0137] Step S74: Receive the control command corresponding to the request message sent by the server, wherein the control command is used to control the autonomous vehicle to perform lane change operation;

[0138] Step S76: Change lanes based on control commands.

[0139] The method for controlling an autonomous vehicle described in this embodiment can be run as a computer program within the autonomous vehicle. The autonomous vehicle is equipped with devices such as video cameras, radar sensors, and laser rangefinders to collect road condition information within a preset range around the vehicle. This preset range is a range pre-defined by a technician that can influence the driving behavior of the autonomous vehicle. The road condition information is used to determine the type of lane markings and the type of detection objects within the preset range.

[0140] The specific implementation process for obtaining road condition information within a preset range around the autonomous vehicle can be any feasible lane line detection and recognition method and detection object recognition method in related technologies, and will not be elaborated here.

[0141] The types of lane markings mentioned above include, but are not limited to: center lines of the roadway (such as yellow dashed lines, yellow solid lines, double yellow solid lines, and yellow dashed and solid lines), lane dividing lines (such as white dashed lines and white solid lines), lane edge lines (such as solid line edge lines and dashed line edge lines), stop lines (such as white horizontal solid lines), yield lines (such as white double dashed lines), and pedestrian crossing lines. The types of objects to be detected include, but are not limited to: roads, basic traffic infrastructure, pedestrian obstacles, and other vehicles.

[0142] Further, it is determined whether the aforementioned road condition information meets preset conditions. When the road condition information meets the preset conditions, a request message is sent to the server (such as an autonomous driving control platform). This request message is used to request the server to obtain the driving mode to be executed. The aforementioned preset conditions include: the type of the lane line is a preset type lane line, which is used to constrain the autonomous vehicle from changing lanes during driving; the type of the detected object is a preset type object, which is used to hinder the autonomous vehicle from continuing to move.

[0143] It is easy to understand that the above-mentioned preset conditions are used to characterize the autonomous vehicle's current driving dilemma. For example, when the lane line type is a solid lane dividing line (i.e., a white solid line) and the detected object type is a pedestrian obstacle (here it is assumed that the lane line is the left and right lane dividing line of the current lane, and the detected object is located in the area in front of the current lane), it means that the autonomous vehicle is constrained to "stop moving" and "not allowed to change lanes", that is, the autonomous vehicle is currently in a driving dilemma.

[0144] The aforementioned server receives the request message sent by the autonomous vehicle and responds to the request message to determine the driving mode to be executed for the autonomous vehicle. The server can be an autonomous vehicle control platform deployed on a server, which can be a standalone server, a distributed server cluster, or a cloud server. The connection between the autonomous vehicle and the server is via an Internet of Things (IoT) protocol (such as MQTT) or a wireless communication network connection, particularly a mobile communication network connection (such as 3G, 4G, 5G, 6G, etc.).

[0145] Furthermore, after sending a request message, the autonomous vehicle listens to the server, receives the control commands corresponding to the request message issued by the server, and changes lanes based on the control commands. The aforementioned lane-changing behavior is a lane-changing detour behavior permitted by road traffic management rules (such as temporarily crossing a solid line to detour when there is an emergency ahead, as permitted by traffic rules).

[0146] It is easy to understand that, compared with the methods provided by the prior art, the method for controlling autonomous vehicles provided in this disclosure can change lanes and drive according to the control instructions returned by the server without relying on external manual operation, through message interaction with the server, thereby escaping the constraints of driving. The whole process has a high degree of intelligence and can improve the user experience.

[0147] According to steps 70 to 76 of this disclosure, road condition information within a preset range around the autonomous vehicle is obtained, wherein the road condition information is used to determine the type of lane lines and the type of detection objects within the preset range; in response to the road condition information meeting preset conditions, a request message is sent to the server, wherein the preset conditions include: the type of lane line is a preset type lane line and the type of detection object is a preset type object, the preset type lane line is used to constrain the autonomous vehicle from changing lanes during driving, the preset type object is used to prevent the autonomous vehicle from continuing to drive, and the request message is used to request the autonomous vehicle from the server to perform a driving mode; a control command corresponding to the request message issued by the server is received, wherein the control command is used to control the autonomous vehicle to perform a lane-changing driving operation; and the lane-changing driving is performed based on the control command.

[0148] It is readily understood that the technical solution provided in this disclosure can operate on autonomous vehicles. Through message interaction with the server, it can change lanes and escape from constrained driving situations without relying on external human intervention, based on control commands returned by the server. Therefore, this disclosure achieves the goal of controlling autonomous vehicles to change lanes and escape from obstructed driving situations by automatically generating control commands. This improves the intelligence level of autonomous vehicles' automatic escape from difficulties and enhances the user experience. It solves the technical problem in existing technologies where reliance on external human intervention to help autonomous vehicles escape from difficulties results in low intelligence levels and poor user experience in the autonomous driving process.

[0149] The method for controlling autonomous vehicles described in this disclosure can be applied, but is not limited to, to application scenarios in areas such as urban roads, hospitals, logistics parks, airports, and schools to help autonomous vehicles get out of trouble. The following example, taking an autonomous vehicle trapped in a restricted driving situation at an airport by solid lane dividers and obstacles, further illustrates the technical solution for controlling autonomous vehicles described in this disclosure.

[0150] Figure 8 This is a schematic diagram of an optional airport automated driving vehicle control system according to an embodiment of the present disclosure, such as... Figure 8 As shown, the airport autonomous vehicle control system includes an environmental perception and localization section, a decision-making and planning section, and an execution and control section.

[0151] likeFigure 8 As shown, in the environmental perception and positioning section, environmental perception is achieved through cameras, LiDAR, millimeter-wave radar, and ultrasonic radar installed on airport facilities or autonomous vehicles, obtaining road condition information within a preset range around the autonomous vehicle while it is driving within the airport. Multiple positioning methods are used for autonomous vehicle positioning, including satellite positioning, differential positioning, inertial positioning, and sensor positioning. Vehicle-to-everything (V2X) wireless communication technology is used for communication interaction between autonomous vehicles, including vehicle-to-vehicle (V2V) communication and vehicle-to-network (V2N) communication.

[0152] like Figure 8 As shown, in the decision planning section, path planning is performed for autonomous vehicles based on high-precision maps and multiple path planning algorithms, and behavior decisions are made for autonomous vehicles through autonomous driving behavior prediction models and autonomous driving behavior decision models.

[0153] like Figure 8 As shown, in the execution control section, the throttle system, steering system, braking system and shifting system of the autonomous vehicle are controlled through the vehicle data bus (such as the CAN bus).

[0154] The method for controlling autonomous vehicles provided in this disclosure can be operated in, for example, Figure 8 The airport autonomous vehicle control system shown acquires road condition information for the autonomous vehicle through environmental perception and positioning. When the autonomous vehicle is in a constrained driving predicament, it sends a request message to the server (such as the autonomous driving control platform) via V2X.

[0155] In this embodiment of the disclosure, under normal circumstances (when the autonomous vehicle is driving normally), both the autonomous vehicle and the server can perform the following operations: Figure 8 The above-described decision-making and planning operations are shown. However, when the autonomous vehicle is in a constrained driving predicament, it cannot make decisions and plans for itself. In this case, the server makes decisions and plans based on the request messages sent by the autonomous vehicle and generates control commands.

[0156] like Figure 9 As shown, when an autonomous vehicle receives a control command from the server, it controls the throttle system, steering system, braking system, and gear shifting system of the autonomous vehicle through the vehicle data bus (such as the CAN bus) to change lanes and escape from the constraint driving predicament according to the content of the control command.

[0157] As an optional implementation, step S76 above, which involves changing lanes based on control commands, further includes the following method steps:

[0158] Step S761: Parse the control command to obtain the request identifier, authorization identifier, timestamp, encrypted command content and encryption key corresponding to the request message. The request identifier is used to identify that the request message comes from the autonomous vehicle, the authorization identifier is used to authorize the autonomous vehicle to perform the lane change operation, the command content is used to determine the execution process of the lane change operation, the timestamp is used to determine the validity period of the lane change operation, and the encryption key is used to encrypt the command content.

[0159] Step S762: Decrypt the encrypted instruction content using the decryption key corresponding to the encryption key to obtain the instruction content;

[0160] Step S763: In response to the successful verification of the request identifier, authorization identifier and timestamp and the absence of collision risk within the preset range, the vehicle changes from the first lane to the second lane based on the instruction content to bypass the detection object. The first lane is the initial planned driving lane of the autonomous vehicle, and the second lane is the driving lane of the autonomous vehicle after the lane change.

[0161] The aforementioned control commands are encapsulated by the server and sent to the autonomous vehicle. Parsing the control commands yields the request identifier, authorization identifier, timestamp, encrypted command content, and encryption key corresponding to the request message.

[0162] Specifically, the aforementioned request identifier is the request code or vehicle code of the autonomous vehicle, which can be used to identify that the request message originates from the autonomous vehicle. Based on the aforementioned request identifier, the accuracy of information interaction (such as request message interaction and control command interaction) between the autonomous vehicle and the server can be guaranteed.

[0163] Specifically, the aforementioned authorization identifier can be an authorization code pre-issued to the autonomous vehicle by the server (such as the autonomous driving control center). This authorization identifier can be used to authorize the autonomous vehicle to perform lane-changing operations. Based on this authorization identifier, the safety of the autonomous vehicle's lane-changing operation based on control commands can be guaranteed.

[0164] Specifically, the aforementioned instructions can be generated by the server based on the driving mode decision. These instructions are used to determine the execution process of the lane-changing operation.

[0165] Specifically, the aforementioned timestamps are used to record the time information of key operations (such as the time when the autonomous vehicle sends a request message, the time when the server receives the request message, the time when the server issues control commands, the expected time when the autonomous vehicle receives the control commands, and the expected time when the autonomous vehicle completes the lane-changing operation). These timestamps are used to determine the timeliness of lane-changing operations. Based on these timestamps, the efficiency of the autonomous vehicle's lane-changing operation based on control commands can be guaranteed. For example, when the server detects that the autonomous vehicle has timed out of receiving control commands or timed out of completing a lane-changing operation, it issues a warning message or stops controlling the autonomous vehicle.

[0166] Specifically, the aforementioned encryption key is used to encrypt the instruction content. The corresponding decryption key is stored in the autonomous vehicle. The autonomous vehicle uses the decryption key to decrypt the encrypted instruction content to obtain the instruction content. Because the instruction content in the aforementioned control instructions is encrypted, the security of the control instructions during transmission is guaranteed, further enhancing the security of the autonomous vehicle's lane-changing operation based on the control instructions.

[0167] Furthermore, the autonomous vehicle verifies the request identifier, authorization identifier, and timestamp in the control command. If the above request identifier, authorization identifier, and timestamp are all successfully verified and there is no collision risk within the preset range (whether there is a collision risk can be decided by the server or detected by the autonomous vehicle after receiving the command content), it changes from the first lane to the second lane to bypass the detection target based on the command content. The first lane is the initial planned driving lane of the autonomous vehicle, and the second lane is the driving lane of the autonomous vehicle after changing lanes.

[0168] It is easy to understand that by verifying the request identifier, authorization identifier, and timestamp corresponding to the request message contained in the control command, the accuracy, safety, and efficiency of the subsequent lane-changing operation of the autonomous vehicle in accordance with the control command can be guaranteed.

[0169] As an optional implementation, the above method for controlling an autonomous vehicle further includes the following steps:

[0170] Step S781: Detect whether the autonomous vehicle has successfully bypassed the detection target;

[0171] Step S782: In response to the autonomous vehicle successfully bypassing the detected object, it changes back from the second lane to the first lane to continue driving.

[0172] In the above optional implementation, after the autonomous vehicle performs a lane-changing operation according to the control command returned by the server, it is detected whether the autonomous vehicle has successfully bypassed the detection object (i.e., whether the autonomous vehicle has successfully escaped the constrained driving dilemma). If the autonomous vehicle has successfully bypassed the detection object, and the current road conditions allow the vehicle to change back from the second lane to the first lane, it changes back from the second lane to the first lane to continue driving.

[0173] After the autonomous vehicle completes the lane change operation and executes the escape procedure, if it detects that it has successfully escaped the constrained driving predicament and the current road conditions allow the vehicle to return to the default lane before the lane change, it controls the autonomous vehicle to return to the model lane and continue driving.

[0174] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods according to the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this disclosure, in essence, or the part that contributes to the prior art, 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 terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of this disclosure.

[0175] According to another embodiment of this disclosure, an apparatus for controlling an autonomous vehicle is also provided. This apparatus is used to implement the above embodiments and preferred embodiments, and details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that implements a predetermined function. Although the apparatus described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0176] Figure 9 This is a structural block diagram of a device for controlling an autonomous vehicle according to an embodiment of the present disclosure, such as... Figure 10As shown, the device 900 for controlling an autonomous vehicle includes: a receiving module 901, used to receive a request message from the autonomous vehicle, wherein the request message is triggered by road condition information within a preset range around the autonomous vehicle, the request message is used to request the autonomous vehicle to perform a driving mode, the road condition information is used to determine the type of lane lines and the type of detection objects within the preset range, the road condition information satisfies a first preset condition, the first preset condition includes: the type of lane line is a preset type lane line and the type of detection object is a preset type object, the preset type lane line is used to constrain the autonomous vehicle from changing lanes during driving, and the preset type object is used to prevent the autonomous vehicle from continuing to drive; a generating module 902, used to generate control commands based on the request message, wherein the control commands are used to control the autonomous vehicle to perform a lane-changing driving operation; and a control module 903, used to send the control commands to the autonomous vehicle so that the autonomous vehicle can perform lane-changing driving based on the control commands.

[0177] Optionally, the generation module 902 is further configured to: respond to the request message, obtain road information, object information of the detected object, and vehicle information of the autonomous vehicle reported by the autonomous vehicle during the driving process; obtain lane change information using the object information and vehicle information, wherein the lane change information is the narrowest road width through which the autonomous vehicle passes a right-angle bend during the lane change process; and generate control commands based on the road information, object information, and lane change information.

[0178] Optionally, the generation module 902 is further configured to: obtain the center position of the detected object from the object information; determine a first parameter, a second parameter, a third parameter, and a fourth parameter based on the center position and vehicle information, wherein the first parameter is the maximum steering angle of the front outer wheel of the autonomous vehicle during the lane change process, the second parameter is the minimum turning radius of the autonomous vehicle during the lane change process, the third parameter is the radius of the innermost moving trajectory of the autonomous vehicle approaching the detected object during the lane change process, and the fourth parameter is determined based on the length between the front bumper and the front outer wheel of the autonomous vehicle; and obtain lane change information using the first parameter, the second parameter, the third parameter, and the fourth parameter.

[0179] Optionally, the generation module 902 is further configured to: obtain the first axle center and the second axle center of the autonomous vehicle from the vehicle information, wherein the first axle center is the front outer wheel axle center of the autonomous vehicle and the second axle center is the rear outer wheel axle center of the autonomous vehicle; determine the first parameter using the center position, the first axle center and the second axle center; and determine the second parameter, the third parameter and the fourth parameter based on the first parameter and the vehicle information.

[0180] Optionally, the generation module 902 is further configured to: obtain the wheelbase of the autonomous vehicle from the vehicle information; and determine the second parameter based on the first parameter and the wheelbase.

[0181] Optionally, the generation module 902 is further configured to: obtain the wheelbase and width of the autonomous vehicle from the vehicle information; and determine the third parameter based on the first parameter, the wheelbase, and the width.

[0182] Optionally, the generation module 902 is further configured to: obtain the length between the front bumper and the front outer wheel of the autonomous vehicle from the vehicle information; and determine the fourth parameter based on the first parameter and the length between the front bumper and the front outer wheel.

[0183] Optionally, the generation module 902 is further configured to: obtain the road width of the autonomous vehicle during its driving process from the road information and the object width of the detected object from the object information; and generate a control command in response to the road width, object width, and right-angle bend passing through the narrowest road width satisfying a second preset condition, wherein the second preset condition is used to determine that the current road conditions within the preset range meet the lane change conditions.

[0184] Optionally, the generation module 902 is further configured to: obtain the request identifier, authorization identifier, timestamp, instruction content, and encryption key corresponding to the request message, wherein the request identifier is used to identify that the request message originates from an autonomous vehicle, the authorization identifier is used to authorize the autonomous vehicle to perform a lane-changing operation, the instruction content is used to determine the execution process of the lane-changing operation, the timestamp is used to determine the validity period of the lane-changing operation, and the encryption key is used to encrypt the instruction content; and encapsulate the request identifier, authorization identifier, timestamp, instruction content, and encryption key to generate a control instruction.

[0185] According to another embodiment of this disclosure, another device for controlling an autonomous vehicle is also provided, which is used to implement the above embodiments and preferred embodiments, and will not be repeated hereafter.

[0186] Figure 10 This is a structural block diagram of another device for controlling an autonomous vehicle according to an embodiment of the present disclosure, such as... Figure 11As shown, the device 1000 for controlling an autonomous vehicle includes: an acquisition module 1001, used to acquire road condition information within a preset range around the autonomous vehicle, wherein the road condition information is used to determine the type of lane lines and the type of detection objects within the preset range; a sending module 1002, used to send a request message to a server in response to the road condition information meeting preset conditions, wherein the preset conditions include: the type of lane line is a preset type lane line and the type of detection object is a preset type object, the preset type lane line is used to constrain the autonomous vehicle from changing lanes during driving, the preset type object is used to prevent the autonomous vehicle from continuing to drive, and the request message is used to request the server to execute a driving mode for the autonomous vehicle; a receiving module 1003, used to receive the control command corresponding to the request message sent by the server, wherein the control command is used to control the autonomous vehicle to perform a lane-changing driving operation; and a control module 1004, used to perform lane-changing driving based on the control command.

[0187] Optionally, the control module 1004 is further configured to: parse the control command to obtain a request identifier, authorization identifier, timestamp, encrypted command content, and encryption key corresponding to the request message, wherein the request identifier is used to identify that the request message originates from the autonomous vehicle, the authorization identifier is used to authorize the autonomous vehicle to perform a lane-changing operation, the command content is used to determine the execution process of the lane-changing operation, the timestamp is used to determine the validity period of the lane-changing operation, and the encryption key is used to encrypt the command content; decrypt the encrypted command content using the decryption key corresponding to the encryption key to obtain the command content; and, in response to the successful verification of the request identifier, authorization identifier, and timestamp and the absence of collision risk within a preset range, change lanes from the first lane to the second lane based on the command content to bypass the detection target, wherein the first lane is the initially planned driving lane of the autonomous vehicle, and the second lane is the driving lane of the autonomous vehicle after the lane change.

[0188] Optionally, Figure 11 This is a structural block diagram of an optional device for controlling an autonomous vehicle according to an embodiment of this disclosure, such as... Figure 10 As shown, the device 1000 for controlling autonomous vehicles includes, in addition to, ​ In addition to all the modules shown, it also includes: a detection module 1005, used to detect whether the autonomous vehicle has successfully bypassed the detection object; in response to the autonomous vehicle successfully bypassing the detection object, it changes from the second lane back to the first lane to continue driving.

[0189] It should be noted that the above modules can be implemented by software or hardware. For the latter, they can be implemented in the following ways, but are not limited to: all the above modules are located in the same processor; or, the above modules are located in different processors in any combination.

[0190] According to another embodiment of this disclosure, an electronic device is also provided, including at least one processor and a memory communicatively connected to the at least one processor, the memory storing instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the steps in any of the above method embodiments.

[0191] Optionally, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor and the input / output device is connected to the processor.

[0192] Optionally, in this embodiment, the processor can be configured to perform the following steps via a computer program:

[0193] Step S1: Receive a request message from the autonomous vehicle. The request message is triggered by road condition information within a preset range around the autonomous vehicle. The request message is used to request the autonomous vehicle to execute a driving mode. The road condition information is used to determine the type of lane lines and the type of detection objects within the preset range. The road condition information satisfies a first preset condition. The first preset condition includes: the type of lane line is a preset type lane line and the type of detection object is a preset type object. The preset type lane line is used to constrain the autonomous vehicle from changing lanes during driving, and the preset type object is used to prevent the autonomous vehicle from continuing to drive.

[0194] Step S2: Generate control instructions based on the request message, wherein the control instructions are used to control the autonomous vehicle to perform lane-changing operations;

[0195] Step S3: Send control commands to the autonomous vehicle so that the autonomous vehicle can change lanes based on the control commands.

[0196] Optionally, specific examples in this embodiment can refer to the examples described in the above embodiments and optional implementations, and will not be repeated here.

[0197] According to another embodiment of this disclosure, this disclosure also provides a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are configured to perform the steps in any of the above method embodiments at runtime.

[0198] Optionally, in this embodiment, the aforementioned non-transitory computer-readable storage medium may be configured to store a computer program for causing a computer to perform the following steps:

[0199] Step S1: Receive a request message from the autonomous vehicle. The request message is triggered by road condition information within a preset range around the autonomous vehicle. The request message is used to request the autonomous vehicle to execute a driving mode. The road condition information is used to determine the type of lane lines and the type of detection objects within the preset range. The road condition information satisfies a first preset condition. The first preset condition includes: the type of lane line is a preset type lane line and the type of detection object is a preset type object. The preset type lane line is used to constrain the autonomous vehicle from changing lanes during driving, and the preset type object is used to prevent the autonomous vehicle from continuing to drive.

[0200] Step S2: Generate control instructions based on the request message, wherein the control instructions are used to control the autonomous vehicle to perform lane-changing operations;

[0201] Step S3: Send control commands to the autonomous vehicle so that the autonomous vehicle can change lanes based on the control commands.

[0202] Optionally, in this embodiment, the aforementioned non-transitory computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any suitable combination thereof. More specific examples of readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, RAM, ROM, erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0203] According to another embodiment of this disclosure, a computer program product is also provided, including a computer program that, when executed by a processor, implements the above-described method for controlling an autonomous vehicle.

[0204] It should be noted that the program code used to implement the method for controlling an autonomous vehicle according to this disclosure can be written in any combination of one or more programming languages. This program code can be provided to the processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing device, such that when executed by the processor or controller, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The program code can be executed entirely on the machine, partially on the machine, as a standalone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0205] In the several embodiments provided in this disclosure, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0206] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a cathode ray tube (CRT) or liquid crystal display (LCD) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0207] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0208] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact via communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other. Servers can be cloud servers, servers in distributed systems, or servers incorporating blockchain technology.

[0209] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and this is not limited herein.

[0210] The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A method for controlling an autonomous vehicle, comprising: A request message is received from an autonomous vehicle, wherein the request message is triggered by road condition information within a preset range around the autonomous vehicle, the request message is used to request the autonomous vehicle to execute a driving mode, the road condition information is used to determine the type of lane lines and the type of detection objects within the preset range, the road condition information satisfies a first preset condition, the first preset condition includes: the type of lane line is a preset type lane line and the type of detection object is a preset type object, the preset type lane line is used to constrain the autonomous vehicle from changing lanes during driving, and the preset type object is used to prevent the autonomous vehicle from continuing to drive. Based on the request message, a control command is generated, wherein the control command is used to control the autonomous vehicle to perform a lane-changing operation; The control command is sent to the autonomous vehicle so that the autonomous vehicle can change lanes based on the control command, thereby enabling the autonomous vehicle to get out of trouble without relying on external human intervention; Generating the control command based on the request message includes: In response to the request message, obtain the road information during the driving process reported by the autonomous vehicle, the object information of the detected object, and the vehicle information of the autonomous vehicle. Lane change information is obtained using the object information and the vehicle information, wherein the lane change information is the narrowest road width through which the autonomous vehicle passes a right-angle bend during the lane change process; The control command is generated based on the road information, the object information, and the lane change information; Obtaining the lane change information using the object information and the vehicle information includes: Obtain the center position of the detected object from the object information; Based on the center position and the vehicle information, a first parameter, a second parameter, a third parameter, and a fourth parameter are determined. The first parameter is the maximum steering angle of the front outer wheel of the autonomous vehicle during the lane change process. The second parameter is the minimum turning radius of the autonomous vehicle during the lane change process. The third parameter is the radius of the innermost moving trajectory of the autonomous vehicle closest to the detected object during the lane change process. The fourth parameter is determined based on the length between the front bumper and the front outer wheel of the autonomous vehicle. The lane change information is obtained using the first parameter, the second parameter, the third parameter, and the fourth parameter.

2. The method according to claim 1, wherein, Determining the first parameter, the second parameter, the third parameter, and the fourth parameter based on the center location and the vehicle information includes: The first axle center and the second axle center of the autonomous vehicle are obtained from the vehicle information, wherein the first axle center is the front outer wheel axle center of the autonomous vehicle, and the second axle center is the rear outer wheel axle center of the autonomous vehicle; The first parameter is determined using the center position, the first axis, and the second axis. The second parameter, the third parameter, and the fourth parameter are determined based on the first parameter and the vehicle information.

3. The method according to claim 2, wherein, Determining the second parameter based on the first parameter and the vehicle information includes: The wheelbase of the autonomous vehicle is obtained from the vehicle information; The second parameter is determined based on the first parameter and the wheelbase.

4. The method according to claim 2, wherein, Determining the third parameter based on the first parameter and the vehicle information includes: The wheelbase and width of the autonomous vehicle are obtained from the vehicle information; The third parameter is determined based on the first parameter, the wheelbase, and the vehicle width.

5. The method according to claim 2, wherein, Determining the fourth parameter based on the first parameter and the vehicle information includes: The length between the front bumper and the front outer wheel of the autonomous vehicle is obtained from the vehicle information. The fourth parameter is determined based on the first parameter and the length between the front bumper and the front outer wheel.

6. The method according to claim 1, wherein, Based on the road information, the object information, and the lane change information, generating the control command includes: The road width of the autonomous vehicle during its driving process is obtained from the road information, and the object width of the detected object is obtained from the object information; In response to the road width, the object width, and the right-angle bend passing through the narrowest road width satisfying a second preset condition, the control command is generated, wherein the second preset condition is used to determine that the current road conditions within the preset range meet the lane change conditions.

7. The method according to claim 6, wherein, Generating the control commands includes: Obtain the request identifier, authorization identifier, timestamp, instruction content, and encryption key corresponding to the request message, wherein the request identifier is used to identify that the request message originates from the autonomous vehicle, the authorization identifier is used to authorize the autonomous vehicle to perform a lane change operation, the instruction content is used to determine the execution process of the lane change operation, the timestamp is used to determine the time limit of the lane change operation, and the encryption key is used to encrypt the instruction content; The request identifier, the authorization identifier, the timestamp, the instruction content, and the encryption key are encapsulated to generate the control instruction.

8. A method for controlling an autonomous vehicle, comprising: Acquire road condition information within a preset range around the autonomous vehicle, wherein the road condition information is used to determine the type of lane lines and the type of detection object within the preset range; In response to the road condition information meeting preset conditions, a request message is sent to the server. The preset conditions include: the type of the lane line is a preset type lane line and the type of the detected object is a preset type object. The preset type lane line is used to constrain the autonomous vehicle from changing lanes during driving, and the preset type object is used to prevent the autonomous vehicle from continuing to drive. The request message is used to request the autonomous vehicle to execute a driving mode from the server. The system receives a control instruction corresponding to the request message sent by the server, wherein the control instruction is used to control the autonomous vehicle to perform a lane-changing operation. The vehicle changes lanes based on the control commands, enabling it to get out of trouble without relying on manual external operation. The control command is generated based on the road information, object information and lane change information reported by the autonomous vehicle during the driving process. The lane change information is obtained using the object information and the vehicle information of the autonomous vehicle. The lane change information is the narrowest road width through which the autonomous vehicle makes a right-angle turn during the lane change process. The lane change information is obtained using a first parameter, a second parameter, a third parameter, and a fourth parameter. The first parameter, the second parameter, the third parameter, and the fourth parameter are determined based on the center position of the detected object and the vehicle information. The center position is obtained from the object information. The first parameter is the maximum steering angle of the front outer wheel of the autonomous vehicle during the lane change process. The second parameter is the minimum turning radius of the autonomous vehicle during the lane change process. The third parameter is the radius of the innermost moving trajectory of the autonomous vehicle closest to the detected object during the lane change process. The fourth parameter is determined based on the length between the front bumper and the front outer wheel of the autonomous vehicle.

9. The method according to claim 8, wherein, Changing lanes based on the control command includes: The control command is parsed to obtain the request identifier, authorization identifier, timestamp, encrypted command content, and encryption key corresponding to the request message. The request identifier is used to identify that the request message originates from the autonomous vehicle, the authorization identifier is used to authorize the autonomous vehicle to perform a lane change operation, the command content is used to determine the execution process of the lane change operation, the timestamp is used to determine the time limit of the lane change operation, and the encryption key is used to encrypt the command content. The encrypted instruction content is decrypted using the decryption key corresponding to the encryption key to obtain the instruction content; In response to the successful verification of the request identifier, authorization identifier, and timestamp, and the absence of collision risk within the preset range, the vehicle changes lanes from the first lane to the second lane based on the instruction content to bypass the detection target. The first lane is the initial planned driving lane of the autonomous vehicle, and the second lane is the driving lane of the autonomous vehicle after the lane change.

10. The method according to claim 9, wherein, The method further includes: Detect whether the autonomous vehicle has successfully bypassed the detection target; In response to the autonomous vehicle successfully bypassing the detected object, it changes back from the second lane to the first lane to continue driving.

11. An apparatus for controlling an autonomous vehicle, comprising: A receiving module is configured to receive a request message from an autonomous vehicle, wherein the request message is triggered by road condition information within a preset range around the autonomous vehicle, the request message is used to request the autonomous vehicle to execute a driving mode, the road condition information is used to determine the type of lane lines and the type of detection objects within the preset range, and the road condition information satisfies a first preset condition, the first preset condition including: the type of lane line is a preset type lane line and the type of detection object is a preset type object, the preset type lane line is used to constrain the autonomous vehicle from changing lanes during driving, and the preset type object is used to prevent the autonomous vehicle from continuing to drive. A generation module is used to generate control instructions based on the request message, wherein the control instructions are used to control the autonomous vehicle to perform a lane-changing operation; The control module is used to send the control commands to the autonomous vehicle so that the autonomous vehicle can change lanes based on the control commands, thereby enabling the autonomous vehicle to get out of trouble without relying on external human intervention. The generation module is further configured to respond to the request message by acquiring road information reported by the autonomous vehicle during its driving process, object information of the detected object, and vehicle information of the autonomous vehicle; using the object information and the vehicle information to acquire lane change information, wherein the lane change information is the narrowest road width through which the autonomous vehicle passes a right-angle bend during the lane change process; and generating the control command based on the road information, the object information, and the lane change information. The generation module is further configured to obtain the center position of the detected object from the object information; determine a first parameter, a second parameter, a third parameter, and a fourth parameter based on the center position and the vehicle information, wherein the first parameter is the maximum steering angle of the front outer wheel of the autonomous vehicle during the lane change process, the second parameter is the minimum turning radius of the autonomous vehicle during the lane change process, the third parameter is the radius of the innermost moving trajectory of the autonomous vehicle approaching the detected object during the lane change process, and the fourth parameter is determined based on the length between the front bumper and the front outer wheel of the autonomous vehicle; and obtain the lane change information using the first parameter, the second parameter, the third parameter, and the fourth parameter.

12. An apparatus for controlling an autonomous vehicle, comprising: The acquisition module is used to acquire road condition information within a preset range around the autonomous vehicle, wherein the road condition information is used to determine the type of lane lines and the type of detection object within the preset range; The sending module is used to send a request message to the server in response to the road condition information meeting preset conditions. The preset conditions include: the type of the lane line is a preset type lane line and the type of the detected object is a preset type object. The preset type lane line is used to constrain the autonomous vehicle from changing lanes during driving. The preset type object is used to prevent the autonomous vehicle from continuing to drive. The request message is used to request the server for the driving mode to be executed by the autonomous vehicle. A receiving module is used to receive a control instruction corresponding to the request message sent by the server, wherein the control instruction is used to control the autonomous vehicle to perform a lane-changing operation; The control module is used to change lanes based on the control commands, so as to help the autonomous vehicle get out of trouble without relying on external human intervention; The control command is generated based on the road information, object information and lane change information reported by the autonomous vehicle during the driving process. The lane change information is obtained using the object information and the vehicle information of the autonomous vehicle. The lane change information is the narrowest road width through which the autonomous vehicle makes a right-angle turn during the lane change process. The lane change information is obtained using a first parameter, a second parameter, a third parameter, and a fourth parameter. The first parameter, the second parameter, the third parameter, and the fourth parameter are determined based on the center position of the detected object and the vehicle information. The center position is obtained from the object information. The first parameter is the maximum steering angle of the front outer wheel of the autonomous vehicle during the lane change process. The second parameter is the minimum turning radius of the autonomous vehicle during the lane change process. The third parameter is the radius of the innermost moving trajectory of the autonomous vehicle closest to the detected object during the lane change process. The fourth parameter is determined based on the length between the front bumper and the front outer wheel of the autonomous vehicle.

13. An electronic device, comprising: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores instructions executable by the at least one processor, which, when executed by the at least one processor, enables the at least one processor to perform the method of any one of claims 1-10.

14. A non-transitory computer-readable storage medium storing computer instructions, wherein, The computer instructions are used to cause the computer to perform the method according to any one of claims 1-10.

15. A computer program product comprising a computer program that, when executed by a processor, implements the method according to any one of claims 1-10.

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