Lane changing control method and device for autonomous vehicle

Abstract

This application provides a lane change control method and device for autonomous vehicles, which can comprehensively process the interrelationships between multiple lane change intentions, determine the lane change intention with the highest priority in the current driving environment from multiple lane change intentions as the target lane change intention for the final control of the autonomous vehicle to execute, and fully realize the overall management of multiple lane change intentions, which is conducive to improving the accuracy and rationality of lane change decisions.

Description

Technical Field

[0001] This application relates to the field of autonomous driving technology, and more specifically, to a lane change control method and device for autonomous vehicles. Background Technology

[0002] When autonomous vehicles are driving on public roads, they typically encounter several potential lane-changing decision-making needs, including navigation lane changing, efficiency lane changing (speed-reducing lane changing), and lever lane changing. Navigation lane changing refers to actively selecting a lane to reach the navigation destination by following the navigation path. Efficiency lane changing refers to changing lanes to bypass or overtake obstacles that are obstructing or slowing the vehicle ahead, in order to improve traffic efficiency. Lever lane changing refers to the driver's subjective intention to use a lever command to instruct the autonomous vehicle to change lanes to the left or right.

[0003] Currently, existing autonomous driving decision-making technologies can handle navigation lane changes relatively well, converting navigation path information into reference lines usable for trajectory planning; however, they are relatively weak in handling lane change decisions such as efficiency lane changes and lever lane changes, especially when several lane change intentions intersect and influence each other, they often cannot effectively integrate lane change intentions to form a reasonable and unified lane change decision. Summary of the Invention

[0004] In view of this, this application provides a lane change control method and device for autonomous vehicles, which can comprehensively process the interrelationship between multiple lane change intentions, determine the lane change intention with the highest priority in the current driving environment from multiple lane change intentions as the target lane change intention for the final control of the autonomous vehicle to execute, and fully realize the overall management of multiple lane change intentions, which is conducive to improving the accuracy and rationality of lane change decisions.

[0005] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings.

[0006] In a first aspect, embodiments of this application provide a lane change control method for an autonomous vehicle, applied to a lane change intention manager of an autonomous vehicle, the lane change control method comprising: The information extractor collects various auxiliary decision-making information in the current driving environment; wherein, the various auxiliary decision-making information includes: navigation information, map information, obstacle information and joystick information; The various auxiliary decision-making information is input into multiple lane change decision-makers, and multiple lane change intentions corresponding to the multiple lane change decision-makers are output through the multiple lane change decision-makers; wherein, the multiple lane change intentions include: navigation lane change intentions generated due to changes in navigation information, efficiency lane change intentions generated due to the autonomous vehicle being slowed down, and lever lane change intentions generated due to the joystick information. From the multiple lane change intentions, the lane change intention with the highest priority in the current driving environment is determined as the target lane change intention, and the autonomous vehicle is controlled to execute the target lane change intention.

[0007] Secondly, embodiments of this application provide a lane change control device for an autonomous vehicle, applied to a lane change intention manager of an autonomous vehicle, the lane change control device comprising: The information acquisition module is used to collect various auxiliary decision-making information in the current driving environment through an information extractor; wherein, the various auxiliary decision-making information includes: navigation information, map information, obstacle information and joystick information; The decision generation module is used to input the various auxiliary decision information into multiple lane change decision controllers, and output multiple lane change intentions corresponding to the multiple lane change decision controllers respectively; wherein, the multiple lane change intentions include: navigation lane change intentions generated due to changes in navigation information, efficiency lane change intentions generated due to the autonomous vehicle being slowed down, and lever lane change intentions generated due to the joystick information. The decision management module is used to determine the lane change intention with the highest priority in the current driving environment from the multiple lane change intentions as the target lane change intention, and control the autonomous vehicle to execute the target lane change intention.

[0008] Thirdly, embodiments of this application provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the lane change control method for the above-described autonomous vehicle.

[0009] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program, which, when executed by a processor, performs the steps of the lane-changing control method for the autonomous vehicle described above.

[0010] The technical solutions provided by the embodiments of this application may include the following beneficial effects: The lane change control method and apparatus for autonomous vehicles provided in this application embodiment can comprehensively process the interrelationships between multiple lane change intentions, determine the lane change intention with the highest priority in the current driving environment from multiple lane change intentions as the target lane change intention for the final control of the autonomous vehicle to execute, and fully realize the overall management of multiple lane change intentions, which is conducive to improving the accuracy and rationality of lane change decisions. Attached Figure Description

[0011] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1 A schematic flowchart of a lane change control method for an autonomous vehicle provided in an embodiment of this application is shown. Figure 2 This paper shows a schematic diagram of the structure of a lane change control device for an autonomous vehicle provided in an embodiment of this application; Figure 3 This is a schematic diagram of the structure of an electronic device 300 provided in an embodiment of this application. Detailed Implementation

[0013] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. It should be understood that the accompanying drawings in this application are for illustrative and descriptive purposes only and are not intended to limit the scope of protection of this application. Furthermore, it should be understood that the schematic drawings are not drawn to scale. The flowcharts used in this application illustrate operations implemented according to some embodiments of this application. It should be understood that the operations in the flowcharts may not be implemented in sequence, and steps without logical contextual relationships may be reversed or implemented simultaneously. In addition, those skilled in the art, guided by the content of this application, may add one or more other operations to the flowcharts, or remove one or more operations from the flowcharts.

[0014] Furthermore, the described embodiments are merely some, not all, of the embodiments of this application. The components of the embodiments of this application described and illustrated herein can typically be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0015] It should be noted that the term "comprising" will be used in the embodiments of this application to indicate the presence of the features declared thereafter, but does not exclude the addition of other features.

[0016] One embodiment of this application provides a lane change control method for an autonomous vehicle that can be applied to a lane change intention manager for an autonomous vehicle. The lane change intention manager includes an information extractor for information collection and multiple lane change decision-makers for generating various types of lane change intentions.

[0017] To facilitate understanding of the embodiments of this application, a lane change control method and device for an autonomous vehicle provided in this application will be described in detail below.

[0018] Reference Figure 1 As shown, Figure 1 This paper illustrates a flowchart of a lane change control method for an autonomous vehicle according to an embodiment of this application. The lane change control method is applied to a lane change intention manager of the autonomous vehicle, and includes steps S101-S103; specifically: S101 collects various auxiliary decision-making information under the current driving environment through an information extractor.

[0019] Here, in the lane change intention manager, various auxiliary decision-making information in the current driving environment can be collected by the information extractor; among them, the various auxiliary decision-making information includes: navigation information, map information, obstacle information (including but not limited to the position information and speed information of obstacles in front of the autonomous vehicle) and joystick information (i.e. whether the driver's control command for the joystick is received).

[0020] S102, the various auxiliary decision-making information is input into multiple lane change decision-makers respectively, and multiple lane change intentions corresponding to the multiple lane change decision-makers are output through the multiple lane change decision-makers respectively.

[0021] Here, the multiple lane change decision-makers may include, but are not limited to: a navigation lane change decision-maker, an efficiency lane change decision-maker, and a lever lane change decision-maker; wherein, the multiple lane change intentions include: a navigation lane change intention generated due to changes in the navigation information (i.e., the lane change intention output by the aforementioned navigation lane change decision-maker), an efficiency lane change intention generated due to the autonomous vehicle being slowed down (i.e., the lane change intention output by the aforementioned efficiency lane change decision-maker), and a lever lane change intention generated due to the lever information (i.e., the lane change intention output by the aforementioned lever lane change decision-maker).

[0022] It should be noted that since the triggering conditions for different types of lane change intentions are different (for example, efficiency lane change intentions are only generated when the autonomous vehicle is slowed down, while lever lane change intentions are only generated when the driver's control command to the lever is received), this application embodiment does not limit the number and type of specific lane change intentions generated at the current moment in actual autonomous driving scenarios.

[0023] Specifically, as an optional embodiment, in the above-described navigation lane change decision-maker, the generation of navigation lane change intent can be triggered by the method shown in steps a1-a2 below: Step a1: Input the navigation information and the map information into the navigation lane change decision-maker. Through the navigation lane change decision-maker, determine the navigation lane recommended in the navigation information and the current lane where the autonomous vehicle is located.

[0024] Here, in the navigation lane change decision-maker, multiple lanes in the current driving environment can be determined based on the input map information (e.g., the current driving environment includes three lanes: left, middle, and right); based on the input navigation information, the navigation lane recommended by the navigation information can be determined from the above multiple lanes (e.g., if the navigation information recommends exiting at the highway ramp ahead, then the navigation lane can be determined to be the lane corresponding to exiting at the highway ramp ahead).

[0025] It should be noted that, based on the ability to determine multiple lanes in the current driving environment from the map information, the current lane of the autonomous vehicle can be determined from the multiple lanes according to the vehicle position of the autonomous vehicle and the aforementioned map information (i.e., the current lane is determined based on the vehicle position of the autonomous vehicle and the aforementioned map information).

[0026] Step a2: In response to the inconsistency between the navigation lane and the current lane, the navigation lane change decision-maker outputs the navigation lane change intention.

[0027] Here, when the navigation lane is inconsistent with the current lane of the autonomous vehicle (e.g., the navigation lane is the left lane and the current lane of the autonomous vehicle is the middle lane), the navigation lane change decision-maker can output the navigation lane change intention; wherein, the navigation lane change intention is used to indicate the autonomous vehicle's intention to change lanes from the current lane to the navigation lane (e.g., the navigation lane change intention is to indicate the autonomous vehicle's intention to change lanes from the current lane to the left lane at the navigation point ahead).

[0028] Specifically, as an optional embodiment, in the above-described efficiency lane-changing decision-maker, the generation of efficiency lane-changing intent can be triggered by the method shown in steps b1-b4 below: Step b1: Input the obstacle information and the map information into the efficiency lane change decision-maker. The efficiency lane change decision-maker determines the obstacles ahead of the autonomous vehicle in each of the multiple lanes.

[0029] Here, the multiple lanes refer to multiple lanes in the current driving environment (including the current lane where the autonomous vehicle is located); in the efficiency lane change decision-maker, multiple lanes in the current driving environment can also be determined based on the input map information (e.g., the current driving environment includes three lanes: left, middle, and right).

[0030] Here, obstacle information includes, but is not limited to, the position and speed information of obstacles located in front of the autonomous vehicle in the aforementioned multiple lanes; for example, obstacle information may include: obstacle information in front of the autonomous vehicle in the left lane, obstacle information in front of the autonomous vehicle in the middle lane, and obstacle information in front of the autonomous vehicle in the right lane.

[0031] Based on this, according to the above-mentioned obstacle information and map information, the efficiency lane change decision-maker can determine the obstacles (including but not limited to the position and speed of each obstacle) that the autonomous vehicle is facing in the above-mentioned multiple lanes.

[0032] Step b2: Determine whether the autonomous vehicle is being slowed down based on the obstacle in front of it in the current lane.

[0033] Here, for the obstacle in front of the autonomous vehicle in the current lane (denoted as the target obstacle), the distance between the target obstacle and the autonomous vehicle can be calculated based on the position of the target obstacle. If the calculated distance is less than or equal to the target preset distance threshold, it can be determined that the target obstacle obstructs the autonomous vehicle.

[0034] Here, after determining that the obstacle in front of the target is obstructing the autonomous vehicle, the speed difference between the obstacle and the autonomous vehicle can be calculated based on the speed of the obstacle. If the calculated speed difference is greater than or equal to a preset speed threshold, it can be determined that the autonomous vehicle is being slowed down.

[0035] Step b3: In response to the autonomous vehicle being slowed down, based on the obstacles ahead of the autonomous vehicle in the multiple lanes, determine the lane with the least obstruction as the speed lane.

[0036] Here, after determining that the autonomous vehicle is being slowed down, for each lane, the relative distance and speed difference between the autonomous vehicle and the obstacle in front of it can be calculated based on the obstacle in front of it in that lane (refer to the specific implementation of step b2 above, and the repeated parts will not be repeated here). At this time, based on the weight coefficients corresponding to the relative distance and speed difference, the weighted sum can be used as the degree of obstruction in front of the autonomous vehicle in that lane through a weighted calculation method. In this way, the lane with the least degree of obstruction in front can be determined as the speed lane from multiple lanes.

[0037] Step b4: Output the efficiency lane change intention through the efficiency lane change decision-maker.

[0038] Here, when the express lane is not the same as the current lane of the autonomous vehicle (e.g., the express lane is the left lane and the current lane of the autonomous vehicle is the middle lane), the efficiency lane change decision-maker can output the efficiency lane change intention; whereby the efficiency lane change intention is used to indicate the autonomous vehicle's intention to change lanes from the current lane to the express lane.

[0039] Specifically, as an optional embodiment, in the above-mentioned lever lane change decision-maker, the generation of lever lane change intent can be triggered by the method shown in steps c1-c2 below: Step c1: Input the joystick information and the map information into the lever lane change decision-maker. Through the lever lane change decision-maker, determine the target lane indicated in the joystick information and the current lane where the autonomous vehicle is located.

[0040] Here, in the lane change decision controller, multiple lanes in the current driving environment can be determined based on the input map information (e.g., the current driving environment includes three lanes: left, middle, and right). Based on this, the current lane where the autonomous vehicle is located can be determined from the multiple lanes based on the vehicle position of the autonomous vehicle and the aforementioned map information.

[0041] Based on this, according to the input joystick information, the joystick lane change decision-maker can determine the target lane indicated in the joystick information from the multiple lanes mentioned above (for example, if the joystick information is the driver's control instruction to turn left, and the current lane of the autonomous vehicle is the middle lane, then the target lane indicated in the joystick information can be determined to be the left lane).

[0042] Step c2: Output the lane change intention through the lever lane change decision-maker.

[0043] Here, based on the received joystick information, it can be determined that the target lane indicated by the joystick information must be inconsistent with the current lane, so the joystick lane change decision-maker can output the joystick lane change intention; whereby the joystick lane change intention is used to indicate the intention of the autonomous vehicle to change lanes from the current lane to the target lane.

[0044] S103, from the plurality of lane change intentions, determine the lane change intention with the highest priority in the current driving environment as the target lane change intention, and control the autonomous vehicle to execute the target lane change intention.

[0045] Here, since navigation lane change intentions usually have the highest priority, we can first determine whether a navigation lane change intention is among multiple lane change intentions. If a navigation lane change intention is included among multiple lane change intentions, we can determine the target lane change intention using the methods shown in steps d1-d2 below: Step d1: Calculate the target distance between the current vehicle position and the target position of the autonomous vehicle, based on the target position indicated in the navigation lane change intention.

[0046] For example, if the navigation information recommends exiting the highway at the on-ramp or off-ramp ahead, then the navigation lane is the lane corresponding to exiting the highway at the on-ramp or off-ramp, and the target location is the location corresponding to the on-ramp or off-ramp.

[0047] Step d2: In response to the target distance being less than or equal to a preset distance threshold, the navigation lane change intention is determined as the target lane change intention.

[0048] Here, when it is detected that the autonomous vehicle is close to the target location (i.e. the target distance is less than or equal to the preset distance threshold), the navigation lane change intention can be determined as the target lane change intention. Combined with navigation information and map information, the navigation lane change intention is converted into a reference line used by the backend path / speed trajectory planning, thereby controlling the autonomous vehicle to change lanes according to the converted reference line.

[0049] Specifically, after calculating the target distance between the current vehicle position of the autonomous vehicle and the target position, if the calculated target distance is greater than the aforementioned preset distance threshold, the target lane change intention can be determined according to the method shown in steps e1-e2 below: Step e1: In response to the target distance being greater than a preset distance threshold, the lane change value corresponding to each of the multiple lane change intentions is evaluated and calculated according to the lane change value evaluation method corresponding to each of the multiple lane change intentions.

[0050] Here, the evaluation methods for lane change value are different for different types of lane change intentions. For example, for navigation lane change intentions, the lane change value corresponding to the navigation lane change intention can be evaluated and calculated based on the target distance mentioned above; among them, the smaller the target distance, the greater the lane change value (i.e., the more urgent the lane change need).

[0051] Specifically, for efficient lane change intentions, the lane change value corresponding to the calculated efficient lane change intention can be evaluated by calculating the degree of obstruction in each lane for the autonomous vehicle in step b3 above. The smaller the degree of obstruction in the lane that indicates the need to change in the efficient lane change intention, the greater the lane change value (i.e., the more urgent the lane change need).

[0052] Step e2: From the multiple lane change intentions, determine the lane change intention with the highest lane change value as the target lane change intention.

[0053] It should be noted that in autonomous driving scenarios, since the vehicle control authority is not in the hands of the driver, the lane change intentions initiated by the driver have the lowest priority. Therefore, when executing steps e1-e2, it is usually only necessary to compare the lane change values ​​corresponding to the navigation lane change intention and the efficiency lane change intention respectively. The lane change intention with the highest lane change value is determined as the target lane change intention.

[0054] Specifically, when multiple lane change intentions do not include a navigation lane change intention, the following method can be used as shown in step f1: Step f1: When the navigation lane change intention is not included among the multiple lane change intentions, the efficiency lane change intention is determined as the target lane change intention based on the fact that the efficiency lane change intention has a higher priority than the lever lane change intention in the autonomous driving scenario.

[0055] Here, referring to the relevant explanation at step e2 above, it can be seen that in the autonomous driving scenario, the priority of the efficiency lane change intention is higher than the lever lane change intention. Therefore, if it is determined that there is no navigation lane change intention, the efficiency lane change intention can be directly determined as the target lane change intention.

[0056] The lane change control method for autonomous vehicles provided in this application can comprehensively handle the interrelationships between multiple lane change intentions, determine the lane change intention with the highest priority in the current driving environment from multiple lane change intentions as the target lane change intention for the final control of the autonomous vehicle to execute, and fully realize the overall management of multiple lane change intentions, which is conducive to improving the accuracy and rationality of lane change decisions.

[0057] Based on the same inventive concept, this application also provides a lane change control device for an autonomous vehicle corresponding to the lane change control method of the above-mentioned autonomous vehicle. Since the principle of solving the problem by the lane change control device of the autonomous vehicle in the embodiments of this application is similar to that of the lane change control method of the above-mentioned autonomous vehicle in the embodiments of this application, the implementation of the lane change control device of the autonomous vehicle can refer to the implementation of the lane change control method of the above-mentioned autonomous vehicle, and the repeated parts will not be described again.

[0058] Reference Figure 2 As shown, Figure 2 A schematic diagram of the structure of a lane change control device for an autonomous vehicle provided in an embodiment of this application is shown, wherein the lane change control device for the autonomous vehicle includes: The information acquisition module 201 is used to acquire various auxiliary decision-making information in the current driving environment through an information extractor; wherein, the various auxiliary decision-making information includes: navigation information, map information, obstacle information and joystick information; The decision generation module 202 is used to input the various auxiliary decision information into multiple lane change decision controllers respectively, and output multiple lane change intentions corresponding to the multiple lane change decision controllers respectively; wherein, the multiple lane change intentions include: navigation lane change intentions generated due to changes in navigation information, efficiency lane change intentions generated due to the autonomous vehicle being slowed down, and lever lane change intentions generated due to the joystick information. The decision management module 203 is used to determine the lane change intention with the highest priority in the current driving environment from the multiple lane change intentions as the target lane change intention, and control the autonomous vehicle to execute the target lane change intention.

[0059] In an optional implementation, when the multiple auxiliary decision-making information is input into multiple lane-change decision-makers, and multiple lane-change intentions corresponding to the multiple lane-change decision-makers are output through the multiple lane-change decision-makers, the decision generation module 202 is used to: The navigation information and the map information are input into the navigation lane change decision-maker. The navigation lane change decision-maker determines the navigation lane recommended in the navigation information and the current lane where the autonomous vehicle is located. The current lane is determined based on the vehicle position of the autonomous vehicle and the map information. In response to the inconsistency between the navigation lane and the current lane, the navigation lane change decision-maker outputs a navigation lane change intention; wherein, the navigation lane change intention is used to indicate the intention of the autonomous vehicle to change lanes from the current lane to the navigation lane.

[0060] In an optional implementation, when the multiple auxiliary decision-making information is input into multiple lane-change decision-makers, and multiple lane-change intentions corresponding to the multiple lane-change decision-makers are output through the multiple lane-change decision-makers, the decision generation module 202 is used to: The obstacle information and the map information are input into the efficiency lane change decision-maker. The efficiency lane change decision-maker determines the obstacles ahead of the autonomous vehicle in multiple lanes. The multiple lanes refer to multiple lanes in the current driving environment. Based on the obstacle ahead of the autonomous vehicle in the current lane, determine whether the autonomous vehicle is being slowed down; In response to the autonomous vehicle being slowed down, based on the obstacles ahead of the autonomous vehicle in the multiple lanes, the lane with the least obstruction ahead is determined as the speed lane. The efficiency lane change decision-maker outputs an efficiency lane change intention; wherein, the efficiency lane change intention is used to indicate the intention of the autonomous vehicle to change lanes from the current lane to the speed lane.

[0061] In an optional implementation, when the multiple auxiliary decision-making information is input into multiple lane-change decision-makers, and multiple lane-change intentions corresponding to the multiple lane-change decision-makers are output through the multiple lane-change decision-makers, the decision generation module 202 is used to: The joystick information and the map information are input into the lever lane change decision-maker. The lever lane change decision-maker determines the target lane indicated in the joystick information and the current lane where the autonomous vehicle is located. The current lane is determined based on the vehicle position of the autonomous vehicle and the map information. The lane change decision-maker outputs the lane change intention via the lever; wherein, the lane change intention is used to indicate the intention of the autonomous vehicle to change lanes from the current lane to the target lane.

[0062] In an optional implementation, when determining the lane-changing intention with the highest priority in the current driving environment from the plurality of lane-changing intentions as the target lane-changing intention, the decision management module 203 is used to: Based on the target location in the navigation lane change intention that instructs the autonomous vehicle to change lanes, calculate the target distance between the current vehicle position of the autonomous vehicle and the target location; In response to the target distance being less than or equal to a preset distance threshold, the navigation lane change intention is determined as the target lane change intention.

[0063] In an optional implementation, after calculating the target distance between the current vehicle position and the target position of the autonomous vehicle, the decision management module 203 is further configured to: In response to the target distance being greater than a preset distance threshold, the lane change value corresponding to each of the multiple lane change intentions is evaluated and calculated according to the lane change value evaluation method corresponding to each of the multiple lane change intentions. From the multiple lane change intentions, the lane change intention with the highest lane change value is determined as the target lane change intention.

[0064] In an optional implementation, when determining the lane-changing intention with the highest priority in the current driving environment from the plurality of lane-changing intentions as the target lane-changing intention, the decision management module 203 is used to: When the navigation lane change intention is not included among the multiple lane change intentions, the efficiency lane change intention is determined as the target lane change intention because the efficiency lane change intention has a higher priority than the lever lane change intention in the autonomous driving scenario.

[0065] Based on the same inventive concept, this application also provides an electronic device corresponding to the above-mentioned lane change control method. Since the principle of solving the problem by the electronic device in the embodiments of this application is similar to that of the above-mentioned lane change control method in the embodiments of this application, the implementation of the electronic device can refer to the implementation of the above-mentioned lane change control method, and the repeated parts will not be described again.

[0066] Figure 3 The present application provides a schematic diagram of the structure of an electronic device 300, including a processor 301, a memory 302, and a bus 303. The memory 302 stores machine-readable instructions executable by the processor 301. When the electronic device runs a lane change control method for an autonomous vehicle as described in the embodiment, the processor 301 communicates with the memory 302 via the bus 303, and the processor 301 executes the machine-readable instructions. When the processor 301 executes the machine-readable instructions, it implements the lane change control method for the autonomous vehicle.

[0067] Specifically, the memory 302 and processor 301 mentioned above can be general-purpose memory and processor, without any specific limitations. When the processor 301 runs the computer program stored in the memory 302, it can execute the lane change control method of the above-mentioned autonomous vehicle.

[0068] Corresponding to the lane change control method for autonomous vehicles in this application, this application embodiment also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, performs the steps of the lane change control method for autonomous vehicles described above.

[0069] Specifically, the storage medium can be a general-purpose storage medium, such as a portable disk or hard disk. When the computer program on the storage medium is run, it can execute the lane change control method of the aforementioned autonomous vehicle.

[0070] In the embodiments provided in this application, it should be understood that the disclosed systems and methods can be implemented in other ways. The system embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and there may be other division methods in actual implementation. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the coupling or direct coupling or communication connection shown or discussed may be through some communication interface; the indirect coupling or communication connection between systems or units may be electrical, mechanical, or other forms.

[0071] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0072] In addition, the functional units in the embodiments provided in this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0073] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0074] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. In addition, the terms "first", "second", "third", etc. are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0075] Finally, it should be noted that the above-described embodiments are merely specific implementations of this application, used to illustrate the technical solutions of this application, and not to limit them. The protection scope of this application is not limited thereto. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some of the technical features, within the scope of the technology disclosed in this application; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application. All should be covered within the protection scope of this application. Therefore, the protection scope of this application should be determined by the protection scope of the claims.

Claims

1. A lane change control method for an autonomous vehicle, characterized in that, A lane change intention manager applied to autonomous vehicles, wherein the lane change control method includes: The information extractor collects various auxiliary decision-making information in the current driving environment; wherein, the various auxiliary decision-making information includes: navigation information, map information, obstacle information and joystick information; The various auxiliary decision-making information is input into multiple lane change decision-makers, and multiple lane change intentions corresponding to the multiple lane change decision-makers are output through the multiple lane change decision-makers; wherein, the multiple lane change intentions include: navigation lane change intentions generated due to changes in navigation information, efficiency lane change intentions generated due to the autonomous vehicle being slowed down, and lever lane change intentions generated due to the joystick information. From the multiple lane change intentions, the lane change intention with the highest priority in the current driving environment is determined as the target lane change intention, and the autonomous vehicle is controlled to execute the target lane change intention.

2. The lane change control method according to claim 1, characterized in that, The step of inputting the various auxiliary decision-making information into multiple lane change decision-makers, and outputting multiple lane change intentions corresponding to the multiple lane change decision-makers, includes: The navigation information and the map information are input into the navigation lane change decision-maker. The navigation lane change decision-maker determines the navigation lane recommended in the navigation information and the current lane where the autonomous vehicle is located. The current lane is determined based on the vehicle position of the autonomous vehicle and the map information. In response to the inconsistency between the navigation lane and the current lane, the navigation lane change decision-maker outputs a navigation lane change intention; wherein, the navigation lane change intention is used to indicate the intention of the autonomous vehicle to change lanes from the current lane to the navigation lane.

3. The lane change control method according to claim 1, characterized in that, The step of inputting the various auxiliary decision-making information into multiple lane change decision-makers, and outputting multiple lane change intentions corresponding to the multiple lane change decision-makers, includes: The obstacle information and the map information are input into the efficiency lane change decision-maker. The efficiency lane change decision-maker determines the obstacles ahead of the autonomous vehicle in multiple lanes. The multiple lanes refer to multiple lanes in the current driving environment. Based on the obstacle ahead of the autonomous vehicle in the current lane, determine whether the autonomous vehicle is being slowed down; In response to the autonomous vehicle being slowed down, based on the obstacles ahead of the autonomous vehicle in the multiple lanes, the lane with the least obstruction ahead is determined as the speed lane. The efficiency lane change decision-maker outputs an efficiency lane change intention; wherein, the efficiency lane change intention is used to indicate the intention of the autonomous vehicle to change lanes from the current lane to the speed lane.

4. The lane change control method according to claim 1, characterized in that, The step of inputting the various auxiliary decision-making information into multiple lane change decision-makers, and outputting multiple lane change intentions corresponding to the multiple lane change decision-makers, includes: The joystick information and the map information are input into the lever lane change decision-maker. The lever lane change decision-maker determines the target lane indicated in the joystick information and the current lane where the autonomous vehicle is located. The current lane is determined based on the vehicle position of the autonomous vehicle and the map information. The lane change decision-maker outputs the lane change intention via the lever; wherein, the lane change intention is used to indicate the intention of the autonomous vehicle to change lanes from the current lane to the target lane.

5. The lane change control method according to claim 1, characterized in that, The step of determining the lane-change intention with the highest priority in the current driving environment from the plurality of lane-change intentions includes: Based on the target location in the navigation lane change intention that instructs the autonomous vehicle to change lanes, calculate the target distance between the current vehicle position of the autonomous vehicle and the target location; In response to the target distance being less than or equal to a preset distance threshold, the navigation lane change intention is determined as the target lane change intention.

6. The lane change control method according to claim 5, characterized in that, After calculating the target distance between the current vehicle position and the target position of the autonomous vehicle, the lane change control method further includes: In response to the target distance being greater than a preset distance threshold, the lane change value corresponding to each of the multiple lane change intentions is evaluated and calculated according to the lane change value evaluation method corresponding to each of the multiple lane change intentions. From the multiple lane change intentions, the lane change intention with the highest lane change value is determined as the target lane change intention.

7. The lane change control method according to claim 1, characterized in that, The step of determining the lane-change intention with the highest priority in the current driving environment from the plurality of lane-change intentions includes: When the navigation lane change intention is not included among the multiple lane change intentions, the efficiency lane change intention is determined as the target lane change intention because the efficiency lane change intention has a higher priority than the lever lane change intention in the autonomous driving scenario.

8. A lane change control device for an autonomous vehicle, characterized in that, A lane change intention manager for use in autonomous vehicles, the lane change control device comprising: The information acquisition module is used to collect various auxiliary decision-making information in the current driving environment through an information extractor; wherein, the various auxiliary decision-making information includes: navigation information, map information, obstacle information and joystick information; The decision generation module is used to input the various auxiliary decision information into multiple lane change decision controllers, and output multiple lane change intentions corresponding to the multiple lane change decision controllers respectively; wherein, the multiple lane change intentions include: navigation lane change intentions generated due to changes in navigation information, efficiency lane change intentions generated due to the autonomous vehicle being slowed down, and lever lane change intentions generated due to the joystick information. The decision management module is used to determine the lane change intention with the highest priority in the current driving environment from the multiple lane change intentions as the target lane change intention, and control the autonomous vehicle to execute the target lane change intention.

9. An electronic device, characterized in that, include: The device includes a processor, a memory, and a bus. The memory stores machine-readable instructions executable by the processor. When the electronic device is running, the processor communicates with the memory via the bus. When the machine-readable instructions are executed by the processor, they perform the steps of the lane change control method for an autonomous vehicle as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, performs the steps of the lane change control method for an autonomous vehicle as described in any one of claims 1 to 7.

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