Decision control method and device for automatic driving vehicle, vehicle-mounted terminal and storage medium

Abstract

The invention relates to the technical field of automatic driving, and provides an automatic driving vehicle decision control method and device, a vehicle-mounted terminal and a storage medium. The method comprises the following steps: acquiring driving state information and vehicle type information of other vehicles around an automatic driving vehicle; wherein the driving state information is used for representing whether other vehicles are in an automatic driving mode or not, and the vehicle type information is used for representing whether other vehicles are vehicles of a specified type or not; according to the driving state information and the vehicle type information, generating a driving decision instruction for interaction between the automatic driving vehicle and other vehicles; and controlling the autonomous vehicle to run according to the driving decision instruction. By adopting the method, different decision control strategies can be adopted according to the characteristics of each surrounding vehicle, so that the high-efficiency requirement and the safety requirement of interactive passing of the automatic driving vehicle and the surrounding vehicles are considered.

Description

Technical Field

[0001] This application relates to the field of autonomous driving technology, and in particular, to a decision control method, device, vehicle-mounted terminal, and storage medium for autonomous driving vehicles. Background Art

[0002] An autonomous driving vehicle, also known as a driverless vehicle, perceives the environment, makes decisions, and controls driving through lidar, cameras, and artificial intelligence algorithms. Currently, the decision control method for autonomous driving vehicles is usually to obtain driving environment information and driving behavior parameters such as the speed and position of surrounding vehicles, and then perform decision analysis on the driving environment information and driving behavior parameters according to a preset decision control algorithm, so as to obtain corresponding vehicle control instructions, and control the autonomous driving vehicle to drive based on the vehicle control instructions. However, the surrounding vehicles of autonomous driving vehicles may vary, and using the same decision control algorithm may not be able to balance the high-efficiency requirements and safety requirements for the interactive passage between autonomous driving vehicles and surrounding vehicles. Summary of the Invention

[0003] In view of this, embodiments of this application provide a decision control method, device, vehicle-mounted terminal, and storage medium for autonomous driving vehicles, which can balance the high-efficiency requirements and safety requirements for the interactive passage between autonomous driving vehicles and surrounding vehicles.

[0004] The first aspect of the embodiments of this application provides a decision control method for autonomous driving vehicles, including:

[0005] Obtain the driving state information and vehicle type information of other vehicles around the autonomous driving vehicle; wherein, the driving state information is used to indicate whether other vehicles are in the autonomous driving mode, and the vehicle type information is used to indicate whether other vehicles are designated type vehicles;

[0006] Generate a driving decision instruction for the autonomous driving vehicle to interact with other vehicles according to the driving state information and vehicle type information;

[0007] Control the autonomous driving vehicle to drive according to the driving decision instruction.

[0008] In the embodiments of this application, for any vehicle around the autonomous driving vehicle, it is possible to identify whether the vehicle is in the autonomous driving mode according to the driving state information of the vehicle, and identify whether the vehicle is a designated type vehicle according to the vehicle type information of the vehicle, and then generate a driving decision instruction for the autonomous driving vehicle to interact with the vehicle according to whether the vehicle is in the autonomous driving mode and whether it is a designated type vehicle. That is, when the autonomous driving vehicle interacts with different surrounding vehicles, different decision control strategies can be adopted according to the characteristics of each surrounding vehicle, so that the high-efficiency requirements and safety requirements for the interactive passage between the autonomous driving vehicle and the surrounding vehicles can be balanced.

[0009] In one implementation manner of the embodiment of the present application, according to the driving state information and vehicle type information, a driving decision instruction for the autonomous vehicle to interact with other vehicles is generated, including:

[0010] Determine the intensity of the passing strategy for the autonomous vehicle to interact with other vehicles according to the driving state information and vehicle type information;

[0011] Generate a driving decision instruction according to the intensity of the passing strategy;

[0012] Among them, the intensity of the passing strategy is used to decide the passing efficiency and passing safety of the autonomous vehicle interacting with other vehicles; if the intensity of the passing strategy is higher, the generated driving decision instruction gives more consideration to the passing efficiency of the autonomous vehicle; if the intensity of the passing strategy is lower, the generated driving decision instruction gives more consideration to the passing safety of the autonomous vehicle.

[0013] In one implementation manner of the embodiment of the present application, determining the intensity of the passing strategy for the autonomous vehicle to interact with other vehicles according to the driving state information and vehicle type information includes:

[0014] Obtain the driving behavior parameters of other vehicles;

[0015] Determine the weight coefficient of the driving behavior parameters according to the driving state information and vehicle type information;

[0016] Calculate a target value according to the driving behavior parameters and the weight coefficient;

[0017] Determine the intensity of the passing strategy according to the target value.

[0018] In one implementation manner of the embodiment of the present application, determining the intensity of the passing strategy for the autonomous vehicle to interact with other vehicles according to the driving state information and vehicle type information includes:

[0019] If it is determined according to the driving state information that other vehicles are in the autonomous driving mode, determine that the intensity of the passing strategy for the autonomous vehicle to interact with other vehicles is the first intensity;

[0020] If it is determined according to the driving state information that other vehicles are in the non-autonomous driving mode, determine that the intensity of the passing strategy for the autonomous vehicle to interact with other vehicles is the second intensity, and the second intensity is lower than the first intensity.

[0021] In one implementation manner of the embodiment of the present application, determining the intensity of the passing strategy for the autonomous vehicle to interact with other vehicles according to the driving state information and vehicle type information includes:

[0022] If it is determined that the other vehicle is a specified type of vehicle according to the vehicle type information, then determine that the passing strategy strength for the autonomous vehicle to interact with the other vehicle is the third strength;

[0023] If it is determined that the other vehicle is a non-specified type of vehicle according to the vehicle type information, then determine that the passing strategy strength for the autonomous vehicle to interact with the other vehicle is the fourth strength, and the fourth strength is higher than the third strength.

[0024] In one implementation manner of the embodiments of the present application, according to the passing strategy strength, generate a driving decision instruction, including:

[0025] According to the passing strategy strength, determine the safe distance for the autonomous vehicle to interact with the other vehicle; wherein, the safe distance is inversely proportional to the passing strategy strength;

[0026] Generate a driving decision instruction according to the safe distance, the driving behavior parameters of the autonomous vehicle, and the driving behavior parameters of the other vehicle.

[0027] In one implementation manner of the embodiments of the present application, obtain the driving state information and vehicle type information of other vehicles around the autonomous vehicle, including:

[0028] Receive the driving state information and vehicle type information sent by the other vehicle;

[0029] Or, receive the driving state information and vehicle type information of other vehicles sent by the roadside unit.

[0030] The second aspect of the embodiments of the present application provides an autonomous vehicle decision control device, including:

[0031] An other vehicle information receiving module, configured to obtain the driving state information and vehicle type information of other vehicles around the autonomous vehicle; wherein, the driving state information is used to indicate whether the other vehicle is in the autonomous driving mode, and the vehicle type information is used to indicate whether the other vehicle is a specified type of vehicle;

[0032] A decision instruction generating module, configured to generate a driving decision instruction for the autonomous vehicle to interact with the other vehicle according to the driving state information and vehicle type information;

[0033] A decision control module, configured to control the autonomous vehicle to drive according to the driving decision instruction.

[0034] The third aspect of the embodiments of the present application provides an in-vehicle terminal, including a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, it implements the autonomous vehicle decision control method provided in the first aspect of the embodiments of the present application.

[0035] In the fourth aspect of the embodiments of the present application, a computer-readable storage medium is provided. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, it implements the autonomous driving vehicle decision control method provided in the first aspect of the embodiments of the present application.

[0036] In the fifth aspect of the embodiments of the present application, a computer program product is provided. When the computer program product runs on an in-vehicle terminal, it causes the in-vehicle terminal to execute the autonomous driving vehicle decision control method provided in the first aspect of the embodiments of the present application.

[0037] It can be understood that the beneficial effects of the above second aspect to the fifth aspect can be referred to the relevant descriptions in the first aspect above, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 is a flowchart of an autonomous driving vehicle decision control method provided by an embodiment of the present application;

[0039] Figure 2 is a schematic structural diagram of an autonomous driving vehicle decision control device provided by an embodiment of the present application;

[0040] Figure 3 is a schematic diagram of an in-vehicle terminal provided by an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0041] In the following description, specific details such as specific system structures and technologies are presented for the purpose of illustration rather than limitation, so as to thoroughly understand the embodiments of the present application. However, those skilled in the art should clearly understand that the present application can also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid unnecessary details from interfering with the description of the present application. Additionally, in the description of the specification and the appended claims of the present application, the terms "first", "second", "third", etc. are only used for distinguishing descriptions and cannot be understood as indicating or implying relative importance.

[0042] With the development of autonomous driving technology, autonomous vehicles are being used more and more widely. When an autonomous vehicle is driving on the road, the driving states and intentions of surrounding vehicles usually cannot be accurately estimated in real time. Especially for non-autonomous vehicles, different drivers have different driving habits, resulting in different driving behaviors, making it even more difficult to predict their driving intentions. Currently, the decision-making control algorithms adopted by autonomous vehicles for different surrounding vehicles are roughly the same, and may not be able to balance the efficiency requirements and safety requirements for the interactive passage between autonomous vehicles and surrounding vehicles. In view of this, the embodiments of the present application provide a decision-making control method, device, on-vehicle terminal, and storage medium for autonomous vehicles, which can balance the efficiency requirements and safety requirements for the interactive passage between autonomous vehicles and surrounding vehicles. For more specific technical implementation details of the embodiments of the present application, please refer to the method embodiments described below.

[0043] It should be understood that the execution entities of the method embodiments of the present application are various types of terminal devices or servers. For example, it can be a mobile phone, a tablet computer, a wearable device, an on-vehicle terminal, an augmented reality (AR) / virtual reality (VR) device, a laptop computer, an ultra-mobile personal computer (UMPC), a netbook, a personal digital assistant (PDA), a large-screen TV, and so on. The embodiments of the present application do not impose any restrictions on the specific types of the terminal devices and servers.

[0044] Please refer to Figure 1 , which shows a decision-making control method for an autonomous vehicle provided by an embodiment of the present application, including:

[0045] 101. Obtain the driving state information and vehicle type information of other vehicles around the autonomous vehicle;

[0046] The execution entity of the method embodiment of the present application can be the on-vehicle terminal of the autonomous vehicle. The on-vehicle terminal can obtain relevant information of surrounding vehicles for decision-making analysis, so as to obtain corresponding driving decision instructions, and control the autonomous vehicle to drive based on the driving decision instructions. The following content will specifically illustrate how to perform decision-making analysis based on the surrounding vehicle information to obtain corresponding driving decision instructions.

[0047] First, obtain the driving status information and vehicle type information of other vehicles around the autonomous vehicle. Among them, the other vehicles around can be all vehicles within a certain range around the autonomous vehicle. The driving status information of each other vehicle is respectively used to indicate whether the corresponding vehicle is in the autonomous driving mode, which can specifically include two categories: autonomous driving mode and non-autonomous driving mode. The vehicle type information of each other vehicle is respectively used to indicate whether the corresponding vehicle is a specified type of vehicle, which can specifically include two categories: specified type of vehicle and non-specified type of vehicle. As an example, the specified type of vehicle can include, but is not limited to: large trucks, large buses, trailers, oil tank trucks, ambulances, fire trucks, and other special duty vehicles. The non-specified type of vehicle can include other vehicles except the specified type of vehicle, such as ordinary cars and small trucks, and so on. The on-vehicle terminal of the autonomous vehicle can receive the driving status information and vehicle type information of each other vehicle around through wireless communication.

[0048] In one implementation manner of the embodiment of the present application, obtaining the driving status information and vehicle type information of other vehicles around the autonomous vehicle includes:

[0049] Receive the driving status information and vehicle type information sent by other vehicles.

[0050] Each vehicle in the road scene can broadcast its own vehicle information to nearby vehicles. For example, it can send its own driving status information and vehicle type information to all vehicles within a certain range around through a 5G network or other means. In this way, the on-vehicle terminal of the autonomous vehicle can receive the driving status information and vehicle type information sent by each other vehicle around through a 5G network or other means.

[0051] In another implementation manner of the embodiment of the present application, obtaining the driving status information and vehicle type information of other vehicles around the autonomous vehicle includes:

[0052] Receive the driving status information and vehicle type information of other vehicles sent by the roadside unit.

[0053] In another implementation manner, the driving status information and vehicle type information of each other vehicle around the autonomous vehicle can be detected by the roadside unit, and then the roadside unit sends the driving status information and vehicle type information of each other vehicle to the on-vehicle terminal of the autonomous vehicle through communication technologies such as V2X.

[0054] 102. Generate a driving decision instruction for the autonomous vehicle to interact with other vehicles according to the driving status information and vehicle type information;

[0055] After the in-vehicle terminal of an autonomous vehicle receives the driving state information and vehicle type information of other surrounding vehicles, it can generate a driving decision instruction for the autonomous vehicle to interact with other surrounding vehicles based on these two parts of information. Among them, for surrounding vehicles with different driving states and different vehicle types, the autonomous vehicle can adopt different decision control strategies when interacting with them, so as to balance the safety and efficiency of vehicle passage.

[0056] In one implementation manner of the embodiment of the present application, generating a driving decision instruction for the autonomous vehicle to interact with other vehicles according to the driving state information and vehicle type information includes:

[0057] (1) Determine the intensity of the passing strategy for the autonomous vehicle to interact with other vehicles according to the driving state information and vehicle type information;

[0058] (2) Generate a driving decision instruction according to the intensity of the passing strategy.

[0059] When generating a driving decision instruction, the intensity of the passing strategy for the autonomous vehicle to interact with other vehicles can be determined first according to the driving state information and vehicle type information. Among them, the intensity of the passing strategy is used to decide the passing efficiency and passing safety of the autonomous vehicle interacting with other vehicles; if the intensity of the passing strategy is higher, the generated driving decision instruction will consider the passing efficiency of the autonomous vehicle more; if the intensity of the passing strategy is lower, the generated driving decision instruction will consider the passing safety of the autonomous vehicle more. After determining the intensity of the passing strategy, the corresponding driving decision instruction can be generated according to this intensity of the passing strategy. For example, if the intensity of the passing strategy is relatively high, it means that the autonomous vehicle pays attention to passing efficiency during interaction. At this time, the enthusiasm for seizing the right of way can be increased, the safety distance from other vehicles can be appropriately reduced, and when it is possible to yield and possible not to yield, it is more inclined to choose not to yield, so as to effectively improve the passing efficiency of the autonomous vehicle when meeting other vehicles. If the intensity of the passing strategy is relatively low, it means that the autonomous vehicle pays attention to passing safety during interaction. At this time, the enthusiasm for seizing the right of way can be reduced, the safety distance from other vehicles can be appropriately increased, and when it is possible to yield and possible not to yield, it is more inclined to choose to yield, so as to effectively improve the passing safety of the autonomous vehicle when meeting other vehicles.

[0060] In one implementation manner of the embodiment of the present application, determining the intensity of the passing strategy for the autonomous vehicle to interact with other vehicles according to the driving state information and vehicle type information includes:

[0061] (1) Obtain the driving behavior parameters of other vehicles;

[0062] (2) Determine the weight coefficient of the driving behavior parameters according to the driving state information and vehicle type information;

[0063] (3) Calculate a target value based on the driving behavior parameters and weight coefficients.

[0064] (4) Determine the intensity of the passing strategy according to the target value.

[0065] Specifically, when determining the intensity of the passing strategy for the autonomous vehicle to interact with other vehicles, the driving behavior parameters of other vehicles can be obtained first. Here, the driving behavior parameters can include, but are not limited to: the position, speed, relative speed (relative to the autonomous vehicle), relative position (relative to the autonomous vehicle), and heading angle of the vehicle, etc. Similar to the acquisition methods of driving state information and vehicle type information, the driving behavior parameters of each other vehicle can also be sent to the autonomous vehicle by each other vehicle separately, or sent to the autonomous vehicle by the roadside unit. Then, according to the driving state information and vehicle type information, determine the weight coefficients of the driving behavior parameters. For example, according to the driving state information and vehicle type information, the weight coefficients of the position, speed, relative speed, and relative position can be set respectively. Next, calculate the target value based on the driving behavior parameters and weight coefficients. Here, the target value can be calculated by the method of weighted summation. Finally, determine the corresponding passing strategy intensity according to the calculated target value. For example, if the target value is larger, a lower passing strategy intensity can be determined. In actual operation, a strategy intensity decision model Q can be set, which is specifically expressed as Q(s[i], c[i], f[i]), where s[i] represents the driving state information, c[i] represents the vehicle type information, and f[i] represents the driving behavior parameters. After inputting s[i], c[i], and f[i] into the strategy intensity decision model Q for processing, the corresponding passing strategy intensity can be output. Obviously, for each other vehicle around the autonomous vehicle, a corresponding passing strategy intensity can be determined in the above manner, that is, when the autonomous vehicle interacts with different other vehicles around, different passing strategy intensities can be adopted respectively.

[0066] In an implementation manner of the embodiment of the present application, determining the intensity of the passing strategy for the autonomous vehicle to interact with other vehicles according to the driving state information and vehicle type information includes:

[0067] (1) If it is determined according to the driving state information that the other vehicle is in the autonomous driving mode, determine that the intensity of the passing strategy for the autonomous vehicle to interact with the other vehicle is the first intensity;

[0068] (2) If it is determined according to the driving state information that the other vehicle is in the non - autonomous driving mode, determine that the intensity of the passing strategy for the autonomous vehicle to interact with the other vehicle is the second intensity, and the second intensity is lower than the first intensity.

[0069] In the embodiments of the present application, different passing strategy intensities can be set according to different driving state information. Specifically, according to the driving state information, it can be determined whether other surrounding vehicles are in the autonomous driving mode. If other vehicles are in the autonomous driving mode, the passing strategy intensity for the autonomous driving vehicle to interact with other vehicles can be determined as a relatively high first intensity; if other vehicles are in the non-autonomous driving mode, the passing strategy intensity for the autonomous driving vehicle to interact with other vehicles can be determined as a relatively low second intensity. The reason for such a setting is that if other vehicles are in the autonomous driving mode, their decision control algorithms usually have safety guarantee strategies. Therefore, such other vehicles will pay more attention to avoidance and there will be no situation of deliberately speeding and seizing the right of way. Therefore, when the autonomous driving vehicle interacts with such other vehicles, on the premise of ensuring basic safety, the passing strategy intensity can be appropriately increased to actively obtain the right of way to improve the passing efficiency. On the contrary, if other vehicles are in the non-autonomous driving mode, considering that the personalities and driving habits of each driver are different, it is difficult to predict whether such other vehicles have dangerous driving behaviors such as lane-changing and speeding. Therefore, when the autonomous driving vehicle interacts with such other vehicles, the driving safety needs to be considered as the key point, and the passing strategy intensity can be appropriately reduced to try to avoid when interacting with such other vehicles to prevent dangerous situations such as collisions.

[0070] In an implementation manner of the embodiments of the present application, determining the passing strategy intensity for the autonomous driving vehicle to interact with other vehicles according to the driving state information and vehicle type information includes:

[0071] (1) If it is determined according to the vehicle type information that the other vehicle is a specified type of vehicle, then determine the passing strategy intensity for the autonomous driving vehicle to interact with the other vehicle as a third intensity;

[0072] (2) If it is determined according to the vehicle type information that the other vehicle is a non-specified type of vehicle, then determine the passing strategy intensity for the autonomous driving vehicle to interact with the other vehicle as a fourth intensity, and the fourth intensity is higher than the third intensity.

[0073] In the embodiments of the present application, different passing strategy intensities can be set according to different vehicle type information. Specifically, according to the vehicle type information, it can be determined whether other surrounding vehicles are specified type vehicles. If other vehicles are specified type vehicles, the passing strategy intensity for the autonomous vehicle to interact with other vehicles can be determined as the lower third intensity; if other vehicles are not specified type vehicles, the passing strategy intensity for the autonomous vehicle to interact with other vehicles can be determined as the higher fourth intensity. The reason for such a setting is as follows: If other vehicles are specified type vehicles such as large trucks or fire trucks, the autonomous vehicle should consider taking the initiative to avoid when interacting with such other vehicles. On the one hand, staying away from large vehicles can improve its own driving safety, and on the other hand, it can also provide priority passing conditions for special duty vehicles. On the contrary, if other vehicles are not specified type vehicles, the autonomous vehicle does not necessarily adopt the initiative to avoid strategy when interacting with such other vehicles. At this time, a relatively high passing strategy intensity can be appropriately selected to improve the passing efficiency of the autonomous vehicle.

[0074] In one implementation manner of the embodiments of the present application, according to the passing strategy intensity, a driving decision instruction is generated, including:

[0075] (1) According to the passing strategy intensity, determine the safety distance for the autonomous vehicle to interact with other vehicles; wherein, the safety distance is inversely proportional to the passing strategy intensity;

[0076] (2) Generate a driving decision instruction according to the safety distance, the driving behavior parameters of the autonomous vehicle, and the driving behavior parameters of other vehicles.

[0077] After determining the passing strategy intensity for the autonomous vehicle to interact with other surrounding vehicles in the manner described above, a corresponding driving decision instruction can be generated according to the determined passing strategy intensity. Specifically, the passing strategy intensity can be used to determine the safety distance for the autonomous vehicle to interact with other vehicles. If the passing strategy intensity is higher, the safety distance is shorter; conversely, if the passing strategy intensity is lower, the safety distance is longer. After determining the safety distance based on the passing strategy intensity, combined with the driving behavior parameters of the autonomous vehicle and other surrounding vehicles, such as parameters like vehicle speed, position, and heading angle, for analysis, and finally a corresponding driving decision instruction is generated.

[0078] 103. Control the autonomous vehicle to drive according to the driving decision instruction.

[0079] After the in-vehicle terminal generates a driving decision instruction, it can control the autonomous vehicle to drive according to the driving decision instruction. Since the driving decision instruction is determined based on the intensity of the passing strategy for the interaction between the autonomous vehicle and other surrounding vehicles, and different passing strategy intensities can be adopted for different other vehicles, the autonomous vehicle may perform different driving actions when facing different surrounding vehicles during driving. For example, if the vehicle interacting with the autonomous vehicle is an ordinary car, the autonomous vehicle may not fully avoid it and will also seize the right of way to improve the passing efficiency while ensuring safety. Another example is that if the vehicle interacting with the autonomous vehicle is an ambulance or a large truck, the autonomous vehicle will consider changing lanes to give way, and at this time, more attention is paid to the safety of vehicle passing, and so on.

[0080] In the embodiment of the present application, for any vehicle around the autonomous vehicle, it is possible to identify whether the vehicle is in the autonomous driving mode according to the driving state information of the vehicle, and identify whether the vehicle is a specified type of vehicle according to the vehicle type information of the vehicle. Then, according to whether the vehicle is in the autonomous driving mode and whether it is a specified type of vehicle, a driving decision instruction for the autonomous vehicle to interact with the vehicle is generated. That is, when the autonomous vehicle interacts with different surrounding vehicles, different decision control strategies can be adopted according to the characteristics of each surrounding vehicle, so as to balance the high-efficiency requirements and safety requirements for the interaction and passing of the autonomous vehicle and the surrounding vehicles.

[0081] In summary, by adopting the autonomous vehicle decision control method provided in the embodiment of the present application, the autonomous vehicle can adaptively adjust the decision control strategy according to the driving state and vehicle type of the surrounding vehicles, so as to balance the high efficiency and safety of vehicle interaction and passing.

[0082] It should be understood that the magnitudes of the sequence numbers of the steps in the above various embodiments do not mean the order of execution. The execution order of each process should be determined according to its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0083] The above mainly describes an autonomous vehicle decision control method. Next, an autonomous vehicle decision control device will be described.

[0084] Please refer to Figure 2 , an embodiment of an autonomous vehicle decision control device in the embodiment of the present application includes:

[0085] An other vehicle information receiving module 201, configured to obtain the driving state information and vehicle type information of other vehicles around the autonomous vehicle; wherein, the driving state information is used to indicate whether the other vehicle is in the autonomous driving mode, and the vehicle type information is used to indicate whether the other vehicle is a specified type of vehicle;

[0086] The decision instruction generation module 202 is configured to generate a driving decision instruction for the autonomous vehicle to interact with other vehicles according to the driving state information and the vehicle type information;

[0087] The decision control module 203 is configured to control the driving of the autonomous vehicle according to the driving decision instruction.

[0088] In an implementation manner of the embodiment of the present application, the decision instruction generation module includes:

[0089] The passing strategy strength determination unit is configured to determine the passing strategy strength for the autonomous vehicle to interact with other vehicles according to the driving state information and the vehicle type information;

[0090] The decision instruction generation unit is configured to generate a driving decision instruction according to the passing strategy strength;

[0091] Wherein, the passing strategy strength is used to determine the passing efficiency and passing safety for the autonomous vehicle to interact with other vehicles; if the passing strategy strength is higher, the generated driving decision instruction gives more consideration to the passing efficiency of the autonomous vehicle; if the passing strategy strength is lower, the generated driving decision instruction gives more consideration to the passing safety of the autonomous vehicle.

[0092] In an implementation manner of the embodiment of the present application, the passing strategy strength determination unit includes:

[0093] The driving behavior parameter acquisition sub-unit is configured to acquire the driving behavior parameters of other vehicles;

[0094] The weight coefficient determination sub-unit is configured to determine the weight coefficient of the driving behavior parameters according to the driving state information and the vehicle type information;

[0095] The target value calculation sub-unit is configured to calculate a target value according to the driving behavior parameters and the weight coefficient;

[0096] The first passing strategy strength determination sub-unit is configured to determine the passing strategy strength according to the target value.

[0097] In an implementation manner of the embodiment of the present application, the passing strategy strength determination unit includes:

[0098] The second passing strategy strength determination sub-unit is configured to determine that the passing strategy strength for the autonomous vehicle to interact with other vehicles is the first strength if it is determined according to the driving state information that the other vehicle is in the autonomous driving mode;

[0099] A third passing strategy intensity determining subunit, configured to determine that the passing strategy intensity for the autonomous driving vehicle to interact with other vehicles is a second intensity if it is determined according to the driving state information that the other vehicles are in a non-autonomous driving mode, and the second intensity is lower than the first intensity.

[0100] In an implementation manner of the embodiment of the present application, the passing strategy intensity determining unit includes:

[0101] A fourth passing strategy intensity determining subunit, configured to determine that the passing strategy intensity for the autonomous driving vehicle to interact with other vehicles is a third intensity if it is determined according to the vehicle type information that the other vehicles are specified type vehicles;

[0102] A fifth passing strategy intensity determining subunit, configured to determine that the passing strategy intensity for the autonomous driving vehicle to interact with other vehicles is a fourth intensity if it is determined according to the vehicle type information that the other vehicles are non-specified type vehicles, and the fourth intensity is higher than the third intensity.

[0103] In an implementation manner of the embodiment of the present application, the decision instruction generating unit includes:

[0104] A safety distance determining subunit, configured to determine the safety distance for the autonomous driving vehicle to interact with other vehicles according to the passing strategy intensity; wherein, the safety distance is inversely proportional to the passing strategy intensity;

[0105] A decision instruction generating subunit, configured to generate a driving decision instruction according to the safety distance, the driving behavior parameters of the autonomous driving vehicle, and the driving behavior parameters of the other vehicles.

[0106] In an implementation manner of the embodiment of the present application, the other vehicle information receiving module includes:

[0107] A first receiving unit, configured to receive the driving state information and vehicle type information sent by other vehicles;

[0108] A second receiving unit, configured to receive the driving state information and vehicle type information of other vehicles sent by the roadside unit.

[0109] The embodiment of the present application further provides a computer-readable storage medium, where the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, it implements the autonomous driving vehicle decision control method represented by any of the above embodiments.

[0110] The embodiment of the present application further provides a computer program product, and when the computer program product runs on an in-vehicle terminal, it causes the in-vehicle terminal to execute the autonomous driving vehicle decision control method represented by any of the above embodiments.

[0111] Figure 3It is a schematic diagram of a vehicle-mounted terminal provided by an embodiment of the present application. As Figure 3 shown, the vehicle-mounted terminal 3 of this embodiment includes: a processor 30, a memory 31, and a computer program 32 stored in the memory 31 and executable on the processor 30. When the processor 30 executes the computer program 32, it implements the steps in the embodiments of the above-mentioned various decision-making control methods for autonomous vehicles, such as Figure 1 the steps 101 to 103 shown. Alternatively, when the processor 30 executes the computer program 32, it implements the functions of each module / unit in the above-mentioned device embodiments, such as Figure 2 the functions of the modules 201 to 203 shown.

[0112] The computer program 32 can be divided into one or more modules / units. The one or more modules / units are stored in the memory 31 and executed by the processor 30 to complete the present application. The one or more modules / units can be a series of computer program instruction segments capable of performing specific functions, and these instruction segments are used to describe the execution process of the computer program 32 in the vehicle-mounted terminal 3.

[0113] The so-called processor 30 may be a central processing unit (CPU), or may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0114] The memory 31 may be an internal storage unit of the vehicle-mounted terminal 3, such as the hard disk or memory of the vehicle-mounted terminal 3. The memory 31 may also be an external storage device of the vehicle-mounted terminal 3, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the vehicle-mounted terminal 3. Further, the memory 31 may also include both the internal storage unit and the external storage device of the vehicle-mounted terminal 3. The memory 31 is used to store the computer program and other programs and data required by the vehicle-mounted terminal. The memory 31 may also be used to temporarily store data that has been output or will be output.

[0115] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the above-mentioned division of each functional unit and module is used as an example. In actual applications, the above functions can be allocated to different functional units and modules as needed, that is, the internal structure of the device is divided into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiment can be integrated into a processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above integrated unit can be implemented in the form of hardware or in the form of a software functional unit. In addition, the specific names of each functional unit and module are only for the convenience of mutual distinction and do not limit the protection scope of this application. The specific working processes of the units and modules in the above system can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0116] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described system, device, and unit can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0117] In the above embodiments, the descriptions of the respective embodiments have their own emphases. For parts not detailed or recorded in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0118] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or by a combination of computer software and electronic hardware. Whether these functions are executed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0119] In the embodiments provided in this application, it should be understood that the disclosed device and method can be implemented in other ways. For example, the above-described system embodiment is only illustrative. For example, the division of the module or unit is only a logical function division. In actual implementation, there can be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection to each other can be through some interfaces. The indirect coupling or communication connection of the device or unit can be in an electrical, mechanical or other form.

[0120] The unit described as a separation component may or may not be physically separated. The component shown as a unit may or may not be a physical unit, that is, it may be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiments of the present application.

[0121] In addition, each functional unit in various embodiments of the present application may be integrated in a processing unit, may exist separately as individual physical units, or two or more units may be integrated in one unit. The above integrated units can be implemented in the form of hardware or in the form of software functional units.

[0122] If the above integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such understanding, all or part of the processes in the above embodiments of the present application can also be completed by instructing relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disc, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal, and software distribution medium, etc. It should be noted that the content included in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0123] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A decision-making control method for an autonomous vehicle, characterized in that, Including: Obtain the driving state information and vehicle type information of other vehicles around the autonomous vehicle; wherein, the driving state information is used to indicate whether the other vehicle is in the autonomous driving mode, and the vehicle type information is used to indicate whether the other vehicle is a specified type of vehicle; Generate a driving decision instruction for the autonomous vehicle to interact with the other vehicle according to the driving state information and the vehicle type information; Control the autonomous vehicle to drive according to the driving decision instruction.

2. The method according to claim 1, wherein, The generating a driving decision instruction for the autonomous vehicle to interact with the other vehicle according to the driving state information and the vehicle type information includes: Determine the passing strategy strength for the autonomous vehicle to interact with the other vehicle according to the driving state information and the vehicle type information; Generate the driving decision instruction according to the passing strategy strength; Wherein, the passing strategy strength is used to decide the passing efficiency and passing safety for the autonomous vehicle to interact with the other vehicle; if the passing strategy strength is higher, the generated driving decision instruction gives more consideration to the passing efficiency of the autonomous vehicle; if the passing strategy strength is lower, the generated driving decision instruction gives more consideration to the passing safety of the autonomous vehicle.

3. The method according to claim 2, wherein The determining the passing strategy strength for the autonomous vehicle to interact with the other vehicle according to the driving state information and the vehicle type information includes: Obtain the driving behavior parameters of the other vehicle; Determine the weight coefficient of the driving behavior parameters according to the driving state information and the vehicle type information; Calculate a target value according to the driving behavior parameters and the weight coefficient; Determine the passing strategy strength according to the target value.

4. The method according to claim 2, wherein The determining the passing strategy strength for the autonomous vehicle to interact with the other vehicle according to the driving state information and the vehicle type information includes: If it is determined according to the driving state information that the other vehicle is in the autonomous driving mode, determine that the passing strategy strength for the autonomous vehicle to interact with the other vehicle is the first strength; If it is determined according to the driving state information that the other vehicle is in the non-autonomous driving mode, determine that the passing strategy strength for the autonomous vehicle to interact with the other vehicle is the second strength, and the second strength is lower than the first strength.

5. The method according to claim 2, characterized in that The determining the passing strategy strength for the autonomous vehicle to interact with the other vehicle according to the driving state information and the vehicle type information includes: If it is determined according to the vehicle type information that the other vehicle is a specified type of vehicle, determine that the passing strategy strength for the autonomous vehicle to interact with the other vehicle is the third strength; If it is determined according to the vehicle type information that the other vehicle is a non-specified type of vehicle, determine that the passing strategy strength for the autonomous vehicle to interact with the other vehicle is the fourth strength, and the fourth strength is higher than the third strength.

6. The method according to claim 2, wherein The generating the driving decision instruction according to the passing strategy strength includes: Determine a safety distance for the autonomous vehicle to interact with other vehicles according to the traffic policy strength; wherein, the safety distance is inversely proportional to the traffic policy strength; Generate the driving decision instruction according to the safety distance, the driving behavior parameters of the autonomous vehicle, and the driving behavior parameters of the other vehicle.

7. The method according to any one of claims 1 to 6, characterized in that The obtaining of the driving state information and vehicle type information of other vehicles around the autonomous vehicle includes: Receiving the driving state information and the vehicle type information sent by the other vehicle; Or, receiving the driving state information and the vehicle type information of the other vehicle sent by the roadside unit.

8. An autonomous vehicle decision-making and control device, characterized in that, Including: An other vehicle information receiving module, configured to obtain the driving state information and vehicle type information of other vehicles around the autonomous vehicle; wherein, the driving state information is used to indicate whether the other vehicle is in an autonomous driving mode, and the vehicle type information is used to indicate whether the other vehicle is a specified type of vehicle; A decision instruction generation module, configured to generate a driving decision instruction for the autonomous vehicle to interact with the other vehicle according to the driving state information and the vehicle type information; A decision control module, configured to control the autonomous vehicle to drive according to the driving decision instruction.

9. An in-vehicle terminal, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the autonomous vehicle decision control method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the autonomous vehicle decision control method according to any one of claims 1 to 7.

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