Automatic driving lane determination method and related equipment

Abstract

The invention provides an automatic driving lane determination method and related equipment. The automatic driving lane determination method is applied to a server, and comprises the following steps: receiving user information of a driver sent by a vehicle, and obtaining behavior information of the driver in a preset time period and driving information of the vehicle in the preset time period according to the user information; analyzing the behavior information and the driving information in a preset time period based on a preset configuration model to obtain multiple pieces of lane configuration information corresponding to the driver; multiple pieces of lane configuration information corresponding to the driver are sent to the vehicle, the multiple pieces of lane configuration information are used for being received by the vehicle, target lane configuration information is determined from the multiple pieces of lane configuration information according to the real-time driving information of the vehicle, and a driving lane of the vehicle in the automatic driving process is determined according to the target lane configuration information. According to the invention, the lane selection of automatic driving can meet the driving habits of different drivers in different driving scenes.

Description

Technical Field

[0001] The present application relates to the field of autonomous driving technology, and in particular to an autonomous driving lane determination method and related equipment. Background Art

[0002] In recent years, with the in-depth application of artificial intelligence technology in the automotive field, the technology of autonomous driving has gradually matured, and the applicable scenarios have also tended to be fully covered. The current autonomous driving functions include automatic lane change, adaptive cruise control, and pilot assisted driving.

[0003] However, most of the current autonomous driving technologies are standardized. Especially in the process of automatic lane changing, when it is necessary to decide the lane in which the vehicle should travel, an averaging method is often adopted, that is, a fixed lane is selected in the same scenario. This cannot adapt to the driver's driving habits, thus reducing the user's autonomous driving experience. Summary of the invention

[0004] In view of the above, it is necessary to propose an autonomous driving lane determination method and related equipment to solve the technical problem that the existing autonomous driving function cannot adapt to the driver's driving habits, thereby reducing the user's autonomous driving experience.

[0005] In a first aspect, the present application provides a method for determining lanes for autonomous driving, which is applied to a server, and the method includes: receiving user information of a driver sent by a vehicle, and obtaining behavior information of the driver within a preset time period and driving information of the vehicle within the preset time period based on the user information; analyzing the behavior information and the driving information within the preset time period based on a preset configuration model to obtain multiple lane configuration information corresponding to the driver; sending the multiple lane configuration information corresponding to the driver to the vehicle, wherein the multiple lane configuration information is used for the vehicle to receive, and determining target lane configuration information from the multiple lane configuration information based on the real-time driving information of the vehicle, and determining the driving lane of the vehicle during the autonomous driving process based on the target lane configuration information.

[0006] In the automatic driving lane determination method of the embodiment of the present application, the server obtains the driver's behavior information within a preset time period and the vehicle's driving information within the preset time period based on the driver's user information sent by the vehicle, and determines the lane configuration information by comprehensively analyzing the behavior information and driving information through the preset configuration model, so that the lane recommended by the automatic driving function can fully consider the actual driving scene and the driver's driving habits, and can meet the needs of different drivers, thereby improving the user's automatic driving experience. The server sends the determined multiple lane configuration information to the vehicle, which is equivalent to providing professional "decision-making assistance" for the automatic driving function, so that the vehicle can make lane selection more easily based on the lane configuration information, making the automatic driving process smoother and optimizing the overall driving experience. Based on this, the present application analyzes the driver's behavior information and the vehicle's driving information through the preset configuration model, and can determine the driver's corresponding lane configuration information from multiple dimensions, so that the lane selection of the automatic driving function can meet the driving habits of different drivers in different driving scenarios, and improve the adaptability and flexibility of the automatic driving function.

[0007] In some embodiments of the present application, the driving information includes a driving state and a driving environment, and the method further includes: evaluating the behavior information according to the driving state and the driving environment to obtain a behavior evaluation result; adjusting the weight of the behavior information according to the behavior evaluation result, so that the preset configuration model updates the multiple lane configuration information corresponding to the driver based on the adjusted weight.

[0008] In some embodiments of the present application, the method further includes: obtaining intervention behavior information of the driver during the automatic driving of the vehicle, so that the preset configuration model updates multiple lane configuration information corresponding to the driver based on the intervention behavior information.

[0009] In some embodiments of the present application, the method further includes: associating multiple lane configuration information corresponding to the driver with the user information.

[0010] In a second aspect, the present application also provides an autonomous driving lane determination method, which is applied to a vehicle, and the method includes: if an instruction to turn on the autonomous driving function is received, obtaining the driver's user information and the real-time driving information of the vehicle; sending the user information to a server, and receiving multiple lane configuration information corresponding to the driver sent by the server, wherein the user information is used for the server to receive, and obtaining the driver's behavior information within a preset time period and the vehicle's driving information within the preset time period based on the user information, and analyzing the behavior information and the driving information within the preset time period based on a preset configuration model to obtain multiple lane configuration information corresponding to the driver; determining the target lane configuration information from the multiple lane configuration information based on the real-time driving information of the vehicle, and determining the driving lane of the vehicle during the autonomous driving process based on the target lane configuration information.

[0011] In the automatic driving lane determination method of the embodiment of the present application, if an instruction to turn on the automatic driving function is received, the user information of the driver and the real-time driving information of the vehicle are obtained, providing a data basis for subsequent lane determination. The user information of the driver is sent to the server, and multiple lane configuration information related to the driver sent by the server is received. These lane configuration information can reflect the driver's driving habits in different driving scenarios. According to the real-time driving information of the vehicle, the most suitable target lane configuration information is selected from the multiple lane configuration information to ensure that the target lane configuration information matches the real-time driving information of the current vehicle, thereby improving the safety and reliability of the automatic driving function. According to the target lane configuration information, the driving lane of the vehicle during the automatic driving process is determined, taking into account the driver's driving habits and the current driving scene, thereby improving the automatic driving experience. Based on this, the present application determines the target lane configuration information by integrating the driver's user information and real-time driving information, so that the lane selection of the automatic driving function can meet the driver's driving habits in different driving scenarios, thereby improving the adaptability and flexibility of the automatic driving function.

[0012] In some embodiments of the present application, each lane configuration information in the multiple lane configuration information includes a driving scene and a recommended lane corresponding to the driving scene, the real-time driving information includes a real-time driving status and a real-time driving environment, and determining the target lane configuration information from the multiple lane configuration information based on the real-time driving information of the vehicle includes: determining the real-time driving scene of the vehicle based on the real-time driving status and the real-time driving environment; and determining the target lane configuration information from the multiple lane configuration information based on the real-time driving scene.

[0013] In some embodiments of the present application, the user information includes the driver's user account.

[0014] In the third aspect, the present application also provides an autonomous driving lane determination device, which includes: a receiving module, used to receive user information of the driver, and obtain the driver's behavior information within a preset time period and the vehicle's driving information within the preset time period based on the user information, wherein the driving information includes driving status and driving environment; an acquisition module, used to analyze the behavior information and the driving information within the preset time period based on a preset configuration model, and obtain multiple lane configuration information corresponding to the driver; a sending module, used to send the multiple lane configuration information corresponding to the driver to the vehicle.

[0015] In a fourth aspect, the present application also provides an autonomous driving lane determination device, which is applied to a server, and the device includes: a receiving module, which is used to receive user information of the driver sent by the vehicle, and obtain the behavior information of the driver within a preset time period and the driving information of the vehicle within the preset time period based on the user information; an acquisition module, which is used to analyze the behavior information and the driving information within the preset time period based on a preset configuration model, and obtain multiple lane configuration information corresponding to the driver; a sending module, which is used to send the multiple lane configuration information corresponding to the driver to the vehicle, wherein the multiple lane configuration information is used for the vehicle to receive, and the target lane configuration information is determined from the multiple lane configuration information according to the real-time driving information of the vehicle, and the driving lane of the vehicle during the autonomous driving process is determined according to the target lane configuration information.

[0016] In a fifth aspect, the present application also provides an autonomous driving lane determination system, the system comprising a vehicle and a server, the vehicle and the server being communicatively connected; the vehicle is used to: if an instruction to turn on the autonomous driving function is received, obtain the driver's user information and the real-time driving information of the vehicle; send the user information to the server; the server is used to: receive the driver's user information sent by the vehicle, and obtain the driver's behavior information within a preset time period and the driving information of the vehicle within the preset time period based on the user information; analyze the behavior information and the driving information within the preset time period based on a preset configuration model to obtain multiple lane configuration information corresponding to the driver; send the multiple lane configuration information corresponding to the driver to the vehicle; the vehicle is also used to: receive the multiple lane configuration information corresponding to the driver sent by the server; determine the target lane configuration information from the multiple lane configuration information based on the real-time driving information of the vehicle, and determine the driving lane of the vehicle during the autonomous driving process based on the target lane configuration information.

[0017] In a sixth aspect, the present application also provides a server, wherein the electronic device includes a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the steps of the autonomous driving lane determination method described in the above embodiment.

[0018] In a seventh aspect, the present application also provides a vehicle, comprising an electronic device, wherein the electronic device comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the steps of the autonomous driving lane determination method described in the above embodiment. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a structural diagram of an autonomous driving lane determination system provided in one embodiment of the present application.

[0020] Figure 2 It is a flowchart of an automatic driving lane determination method provided in one embodiment of the present application.

[0021] Figure 3 This is a flow chart of an autonomous driving lane determination method provided by another embodiment of the present application.

[0022] Figure 4 It is a schematic diagram of the interactive process of the autonomous driving lane determination method provided in one embodiment of the present application.

[0023] Figure 5 It is a schematic diagram of the functional modules of an autonomous driving lane determination device provided in one embodiment of the present application.

[0024] Figure 6 This is a schematic diagram of the functional modules of an autonomous driving lane determination device provided in yet another embodiment of the present application.

[0025] Figure 7 It is a schematic diagram of the hardware structure of a server provided in one embodiment of the present application.

[0026] Figure 8 It is a schematic diagram of the hardware structure of a vehicle provided in one embodiment of the present application.

[0027] Component Symbols Autonomous Driving Lane Determination System10 Server 1 Memory 11 Processor 12 Vehicle 2 Electronic devices 20 Memory 21 Processor 22 Automatic driving lane determination device 100 Receiving module 110 Acquisition module 120 Transmitting module 130 Automatic driving lane determination device 200 Acquisition module 210 Interaction module 220 Determination module 230 The following specific implementation methods will further illustrate the present application in conjunction with the above-mentioned drawings. DETAILED DESCRIPTION

[0028] The embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present application, and cannot be understood as limiting the present application.

[0029] In the embodiments of the present application, it should be noted that, unless otherwise clearly specified and limited, words such as "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "for example" is intended to present related concepts in a specific way.

[0030] In the description of this application, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection, an electrical connection, or mutual communication; it can be a direct connection, or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0031] In the description of the present application, it should be noted that, unless otherwise expressly specified and limited, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In addition, in the description of the present application, the meaning of "plurality" is two or more, unless otherwise clearly and specifically limited.

[0032] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0033] See also Figure 1 , which is a structural diagram of an autonomous driving lane determination system 10 provided in one embodiment of the present application.

[0034] Specifically, the autonomous driving lane determination system 10 includes a vehicle 2 and a server 1, and the vehicle 2 and the server 1 are communicatively connected.

[0035] Among them, vehicle 2 is used to: if receiving an instruction to turn on the automatic driving function, obtain the user information of the driver and the real-time driving information of vehicle 2; and send the user information to server 1. Server 1 is used to: receive the user information of the driver sent by vehicle 2, and obtain the behavior information of the driver within a preset time period and the driving information of vehicle 2 within the preset time period according to the user information; analyze the behavior information and driving information within the preset time period based on the preset configuration model to obtain multiple lane configuration information corresponding to the driver; and send the multiple lane configuration information corresponding to the driver to vehicle 2. Vehicle 2 is also used to: receive the multiple lane configuration information corresponding to the driver sent by server 1; determine the target lane configuration information from the multiple lane configuration information according to the real-time driving information of vehicle 2, and determine the driving lane of vehicle 2 during the automatic driving process according to the target lane configuration information.

[0036] In some embodiments of the present application, the server 1 can be a cloud server, which can be communicatively connected to multiple vehicles 2, and thereby provide cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, and basic cloud computing services such as artificial intelligence platforms for multiple vehicles 2.

[0037] In some embodiments of the present application, when the driver is driving vehicle 2, vehicle 2 collects the driver's behavior information and the driving information of vehicle 2 in real time through various sensors; then the collected driver's behavior information and vehicle 2's driving information are encoded and packaged respectively; then the encoded and packaged behavior information and driving information are associated with the driver's user information and sent to server 1.

[0038] In some embodiments of the present application, the driving information may include driving status and driving environment. The driving status includes but is not limited to the speed, direction, gear, brake status, throttle status, steering angle, etc. of the vehicle 2. The driving environment includes but is not limited to the traffic conditions, weather conditions, road types, etc. around the vehicle 2.

[0039] In some embodiments of the present application, the user information may include a user account logged in by a driver on the vehicle 2 , and each driver has one user account.

[0040] In some embodiments of the present application, when the collected driving information and the driver's behavior information reach a preset data threshold, the vehicle 2 can send the driving information and the driver's behavior information of the vehicle 2 to the server 1. In other embodiments, the vehicle 2 can also send the collected driving information and the driver's behavior information to the server 1 in real time. The embodiments of the present application do not limit the preset data threshold, and can be set according to actual conditions.

[0041] In some embodiments of the present application, after receiving the behavior information and driving information associated with the driver's user information, the server 1 stores the behavior information and driving information associated with the driver's user information in a preset database.

[0042] In some embodiments of the present application, the preset time period may be within one month or one quarter before the current moment. The embodiments of the present application do not limit the preset time period and may be set according to actual conditions.

[0043] In some embodiments of the present application, the user information of each driver is unique. After determining the multiple lane configuration information corresponding to the driver, the server 1 associates the multiple lane configuration information corresponding to the driver with the user information of the driver.

[0044] In some embodiments of the present application, multiple lane configuration information corresponding to the driver's user information can be decoupled from vehicle 2, that is, multiple lane configuration information associated with the driver's user information can be applied in different vehicles 2, so that vehicle 2 logged in to the same user account can apply multiple lane configuration information associated with the logged in user account after turning on the automatic driving function.

[0045] It should be noted that the above scenario is only an example of an application scenario provided by an embodiment of the present application. The embodiment of the present application does not limit the actual form of the various devices included in the scenario, nor does it limit the interaction method between the devices. In the specific application of the solution, it can be set according to actual needs.

[0046] See also Figure 2 , which is a schematic diagram of the steps of an autonomous driving lane determination method provided in another embodiment of the present application.

[0047] The automatic driving lane determination method provided in the embodiment of the present application can be applied to one or more Figure 1 and Figure 7In the server 1 shown, server 1 is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to microprocessors, application specific integrated circuits (ASICs), programmable gate arrays (FPGAs), digital signal processors (DSPs), embedded devices, etc.

[0048] In some embodiments of the present application, the server 1 can be communicatively connected to devices such as a desktop computer, a notebook, and a handheld computer.

[0049] Specifically, the autonomous driving lane determination method includes the following steps. According to different requirements, the order of some steps in the flowchart can be changed, and some steps can be omitted.

[0050] Step S10, receiving user information of the driver sent by the vehicle, and acquiring the behavior information of the driver within a preset time period and the driving information of the vehicle within the preset time period according to the user information.

[0051] In some embodiments of the present application, the driving information includes driving status and driving environment. The driving status includes but is not limited to the speed, direction, gear, brake status, throttle status, steering angle, etc. of the vehicle 2. The driving environment includes but is not limited to the traffic conditions, weather conditions, road types, etc. around the vehicle 2.

[0052] In some embodiments of the present application, after receiving the user information of the driver, the server 1 obtains the behavior information of the driver and the driving information of the vehicle in the preset time period associated with the user information from the preset database according to the user information. The preset time period may be within one month or one quarter before the current moment. The embodiments of the present application do not limit the preset time period and may be set according to actual conditions.

[0053] Step S11, analyzing the behavior information and driving information within a preset time period based on a preset configuration model to obtain a plurality of lane configuration information corresponding to the driver.

[0054] In some embodiments of the present application, each lane configuration information in the multiple lane configuration information includes a driving scenario of the vehicle 2 and a recommended lane corresponding to the driving scenario.

[0055] In some embodiments of the present application, the training method of the preset configuration model includes: obtaining the driver's historical behavior information and the historical driving information of vehicle 2 associated with the historical behavior information, and marking the historical behavior information and the lane information corresponding to the historical driving information, wherein the historical driving information of vehicle 2 includes the historical driving status and the historical driving environment; constructing a training data set, the training data set includes multiple training samples, each training sample includes the driver's historical behavior information and the historical driving information of vehicle 2 associated with the historical behavior information, and the marked lane information; using the training data set to train the preset model to obtain a trained preset configuration model.

[0056] The preset model may include, but is not limited to, a feedforward neural network (FNN) model, a convolutional neural network (CNN) model, a recurrent neural network (RNN) model, a deep structured semantic model (DSSM), etc. The model structure of the preset model may be selected according to actual needs, and this application does not impose specific restrictions on this.

[0057] Specifically, when training the preset model, the training data can be input into the preset model in batches. The preset model learns the potential relationship between historical driving information, historical behavior information and lane information, and how these relationships are associated with lane information. Through continuous iteration and optimization, the preset model can gradually improve its accuracy in determining lane configuration information.

[0058] Among them, the prediction error of the preset model can be minimized and the generalization ability of the model can be improved by adjusting the internal parameters and structure of the preset model based on optimization methods such as gradient descent and back propagation. When the preset model reaches the preset training conditions on the training information set, it can be considered that the training is completed and the matching model is obtained. For example, the preset training conditions may include but are not limited to the training iteration reaching the preset number of iterations, the model prediction accuracy reaching the preset accuracy threshold, etc.

[0059] In the above implementation, by collecting a large amount of historical behavior information and historical driving information as training samples, the preset model can learn the lane selection rules under various driving scenarios and different driving habits. For example, in a congested urban traffic environment, drivers usually choose lanes with relatively small traffic volume and convenient for flexible lane changes. After learning many such historical samples, the preset model can accurately recommend such a suitable lane configuration when faced with similar input situations, thereby providing reliable lane configuration suggestions for the autonomous driving system, effectively avoiding unreasonable lane selection, and improving driving safety and efficiency. Since the training data set covers different historical behavior information and lane information corresponding to different historical driving information, the trained preset configuration model has a strong generalization ability, and can adapt to complex driving scenarios and meet the driving needs of different users in different situations. Based on the unique historical behavior information of each driver participating in the training, the preset model can capture the driving habits of individual drivers, thereby providing different drivers with personalized lane configuration information that fits their own driving habits, thereby improving the autonomous driving experience.

[0060] In some embodiments of the present application, the autonomous driving lane determination method also includes: the server 1 evaluates the behavior information according to the driving state and driving environment to obtain a behavior evaluation result; adjusts the weight of the behavior information according to the behavior evaluation result, so that the preset configuration model updates multiple lane configuration information corresponding to the driver based on the adjusted weight.

[0061] Specifically, the evaluation rules for evaluating the behavior information by the server 1 according to the driving state and driving environment include but are not limited to the changes in the driving state of the vehicle 2 caused by the driver's behavior information in different dimensions, such as safety factor, comfort experience, energy consumption, degree of damage to components, etc. For example, when the driver turns and changes lanes, if the above-mentioned turning and changing lanes behavior increases the risk of collision or reduces the comfort experience or increases energy consumption or causes damage to components, the weight value of the above-mentioned behavior information will be reduced; if the above-mentioned turning and changing lanes behavior reduces the risk of collision or improves the comfort experience or reduces energy consumption or reduces the degree of damage to components, the weight value of the above-mentioned behavior information will be increased.

[0062] In the above embodiment, by evaluating the behavior information from different dimensions based on the driving state and driving environment, and increasing or decreasing the weight value of the behavior information accordingly, the preset configuration model will recalculate and update the multiple lane configuration information corresponding to the driver based on these adjusted weights in subsequent work. For example, when determining the lane configuration information, the original preset configuration model may give a certain weight to the driver's turning and lane changing behavior information, and recommend the initial lane after comprehensive consideration. However, after the evaluation result of server 1, it is found that the above turning and lane changing behavior increases the safety risk or reduces the comfort experience, thereby reducing the weight value of the above turning and lane changing behavior. Then, when the preset configuration model is run again, the influence of the above turning and lane changing behavior will be weakened, and the target lane that pays more attention to safety or comfort experience will be re-recommended, thereby realizing the dynamic optimization and adjustment of the lane configuration information as the driver's actual behavior performance affects different dimensions, better serving the lane decision in the automatic driving scenario, and optimizing the lane configuration mechanism.

[0063] In some embodiments of the present application, the autonomous driving lane determination method also includes: server 1 obtains the intervention behavior information of the driver of vehicle 2 during the autonomous driving process, so that the preset configuration model updates multiple lane configuration information corresponding to the driver based on the intervention behavior information.

[0064] Specifically, the server 1 monitors and records the driver's intervention behavior information on the control of the vehicle 2 in the automatic driving mode, such as changing lanes, taking over control, adjusting the speed, etc. These intervention behavior information reflects the driver's personal driving habits. As the driver's intervention behavior accumulates, the preset configuration model will continuously update the lane configuration information to better adapt to the driver's personal driving habits.

[0065] In the above embodiment, by considering the driver's intervention behavior information during the automatic driving process, server 1 can provide more personalized lane configuration information, so that the lane configuration information is more in line with the driver's expectations, and can also identify the driver's intervention behavior information in specific situations, which helps the automatic driving function to make adjustments in advance in similar situations, thereby improving driving safety.

[0066] Step S12: sending the multiple lane configuration information corresponding to the driver to the vehicle.

[0067] Among them, multiple lane configuration information is used for vehicle 2 to receive, and target lane configuration information is determined from the multiple lane configuration information according to the real-time driving information of vehicle 2, and the driving lane of vehicle 2 during the automatic driving process is determined according to the target lane configuration information.

[0068] In some embodiments of the present application, the autonomous driving lane determination method further includes: server 1 associating multiple lane configuration information corresponding to the driver with the driver's user information.

[0069] In some embodiments of the present application, user information may include an account used by the driver to log in to vehicle 2. Each driver has an account. Vehicle 2 logged in to the same account can apply multiple lane configuration information associated with the logged in account after turning on the automatic driving function.

[0070] In the above embodiment, the user information of each driver is unique. After determining the multiple lane configuration information corresponding to the driver, the server 1 associates the multiple lane configuration information corresponding to the driver with the driver's user information, so that each driver's user information is associated with the individual's multiple lane configuration information, and then the multiple lane configuration information corresponding to the driver's user information can be decoupled from the vehicle 2, that is, the multiple lane configuration information associated with the driver's user information can be applied to different vehicles 2.

[0071] In the automatic driving lane determination method of the embodiment of the present application, the server 1 obtains the driver's behavior information within the preset time period and the driving information of the vehicle 2 within the preset time period according to the user information of the driver sent by the vehicle 2, and determines the lane configuration information by comprehensively analyzing the multi-dimensional data of the behavior information and the driving information through the preset configuration model, so that the lane recommended by the automatic driving function can fully consider the actual driving scene and the driver's driving habits, and can meet the needs of different drivers, thereby improving the user's automatic driving experience. The server 1 sends the determined multiple lane configuration information to the vehicle 2, which is equivalent to providing professional "decision-making assistance" for the automatic driving function, so that the vehicle 2 can make lane selection more easily based on the lane configuration information, making the automatic driving process smoother and optimizing the overall driving experience. Based on this, the present application analyzes the driver's behavior information and the driving information of the vehicle 2 through the preset configuration model, and can determine the lane configuration information corresponding to the driver from multiple dimensions, so that the lane selection of the automatic driving can meet the driving habits of different drivers in different driving scenes, and improve the adaptability and flexibility of the automatic driving function.

[0072] See also Figure 3 , is a schematic diagram of the steps of an autonomous driving lane determination method provided in one embodiment of the present application.

[0073] The automatic driving lane determination method provided in the embodiment of the present application can be applied to one or more Figure 1 and Figure 8In the vehicle 2 shown, the vehicle 2 includes an electronic device 10, which is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions. Its hardware includes but is not limited to a microprocessor, an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA), a digital processor (DSP), an embedded device, etc.

[0074] In some embodiments of the present application, the electronic device 10 may be an on-board device of the vehicle 2, such as a body control module (BCM), a vehicle control unit (VCU), etc.

[0075] In some embodiments of the present application, the electronic device 10 can be communicatively connected to a desktop computer, a notebook, a PDA, a server 1 or the like.

[0076] In some embodiments of the present application, the electronic device 10 can perform human-computer interaction with the user through a keyboard, a mouse, a remote control, a touch pad, or a voice control device.

[0077] In some embodiments of the present application, the electronic device 10 may also include a network device and / or a client device, wherein the network device includes but is not limited to a single network server, a server group consisting of multiple network servers, and the like.

[0078] In some embodiments of the present application, the network where the electronic device 10 is located includes but is not limited to the Internet, a wide area network, a metropolitan area network, a local area network, a virtual private network (VPN), etc.

[0079] Specifically, the autonomous driving lane determination method includes the following steps. According to different requirements, the order of some steps in the flowchart can be changed, and some steps can be omitted.

[0080] Step S20: If an instruction to start the automatic driving function is received, the driver's user information and the vehicle's real-time driving information are obtained.

[0081] In some embodiments of the present application, the user information may include an account used by the driver to log in to the vehicle 2. Each driver has an account, and each account is associated with the driver's behavior information and the vehicle's driving information.

[0082] In some embodiments of the present application, the real-time driving information of the vehicle 2 includes the real-time driving state and the real-time driving environment. The real-time driving state of the vehicle 2 includes but is not limited to the speed, direction, gear position, brake state, throttle state, steering angle, etc. of the vehicle 2. The real-time driving environment of the vehicle 2 includes but is not limited to the traffic conditions, weather conditions, road types, etc. around the vehicle 2.

[0083] In some embodiments of the present application, the method for determining lanes in an autonomous driving system further includes: the electronic device 10 performs data preprocessing on real-time driving information, wherein the data preprocessing includes data cleaning, data integration, and data conversion. Based on this, key information can be quickly identified and processed, unnecessary calculations can be reduced, and more comprehensive real-time driving information can be obtained, thereby improving the reliability of subsequent lane determination in autonomous driving.

[0084] In some embodiments of the present application, the autonomous driving lane determination method also includes: the electronic device 10 packages and encodes real-time driving information into a preset format, such as a vector, which can improve data processing efficiency and accuracy, thereby improving the accuracy and efficiency of subsequent lane determination.

[0085] Step S21, sending user information to a server, and receiving a plurality of lane configuration information corresponding to the driver sent by the server.

[0086] Among them, the user information is used to be received by server 1, and the driver's behavior information within a preset time period and the driving information of vehicle 2 within a preset time period are obtained based on the user information, and the behavior information and driving information within the preset time period are analyzed based on the preset configuration model to obtain multiple lane configuration information corresponding to the driver.

[0087] In some embodiments of the present application, each lane configuration information in the multiple lane configuration information includes a driving scenario of the vehicle 2 and a recommended lane corresponding to the driving scenario.

[0088] It should be noted that the specific steps of how the server 1 determines the multiple lane configuration information corresponding to the driver based on the driving information of the vehicle 2 within the preset time period and the behavior information of the driver within the preset time period can be referred to above. Figure 3 The embodiments shown will not be described in detail here.

[0089] Step S22, determining target lane configuration information from multiple lane configuration information based on the real-time driving information of the vehicle, and determining the driving lane of the vehicle during the automatic driving process based on the target lane configuration information.

[0090] In some embodiments of the present application, the step of determining target lane configuration information from multiple lane configuration information based on the real-time driving information of vehicle 2 specifically includes: determining the real-time driving scene of vehicle 2 based on the real-time driving status information and the real-time driving environment information; determining the target lane configuration information from multiple lane configuration information based on the driving scene.

[0091] The driving scene may include different scenes, such as a first scene, a second scene, etc. Each driving scene is composed of a driving state of the vehicle 2 and a driving environment of the vehicle 2 .

[0092] Since each lane configuration information includes the driving scenario of the vehicle 2 and the recommended lane corresponding to the driving scenario, the electronic device 10 can determine the target lane configuration information from the multiple lane configuration information according to the real-time driving scenario, that is, the target lane configuration information including the real-time driving scenario.

[0093] In the automatic driving lane determination method of the embodiment of the present application, if an instruction to turn on the automatic driving function is received, the user information of the driver and the real-time driving information of the vehicle 2 are obtained, providing a data basis for the subsequent lane determination. The user information of the driver is sent to the server 1, and multiple lane configuration information related to the driver sent by the server 1 is received. These lane configuration information can reflect the driver's driving habits in different driving scenarios. According to the real-time driving information of the vehicle 2, the most suitable target lane configuration information is selected from the multiple lane configuration information, ensuring that the target lane configuration information matches the real-time driving information of the current vehicle 2, thereby improving the safety and reliability of the automatic driving function. According to the target lane configuration information, the driving lane of the vehicle 2 during the automatic driving process is determined, taking into account the driver's driving habits and the current driving scene, thereby improving the automatic driving experience. Based on this, the present application determines the target lane configuration information by integrating the driver's user information and real-time driving information, so that the lane selection of the automatic driving function can meet the driver's driving habits in different driving scenarios, thereby improving the adaptability and flexibility of the automatic driving function.

[0094] See also Figure 4 , which is an interactive flow chart of an autonomous driving lane determination method provided in one embodiment of the present application.

[0095] Step S30: If the vehicle receives an instruction to turn on the automatic driving function, the driver's user information and the vehicle's real-time driving information are obtained.

[0096] Step S31: The vehicle sends user information to the server.

[0097] Step S32, the server receives the user information of the driver sent by the vehicle, and obtains the behavior information of the driver within a preset time period and the driving information of the vehicle within the preset time period according to the user information.

[0098] Step S33: the server analyzes the behavior information and driving information within a preset time period based on a preset configuration model to obtain a plurality of lane configuration information corresponding to the driver.

[0099] In step S34, the server sends the multiple lane configuration information corresponding to the driver to the vehicle.

[0100] Step S35, the vehicle receives multiple lane configuration information corresponding to the driver sent by the server.

[0101] Step S36: The vehicle determines target lane configuration information from multiple lane configuration information according to the real-time driving information of the vehicle.

[0102] Step S37: The vehicle determines the driving lane of the vehicle during the automatic driving process according to the target lane configuration information.

[0103] See also Figure 5 , which is a schematic diagram of the functional modules of an autonomous driving lane determination device 100 provided in one embodiment of the present application.

[0104] In this embodiment, based on the above Figure 2 The same concept as the autonomous driving lane determination method in the illustrated embodiment, the present application also provides an autonomous driving lane determination device 100, which can be used to execute the above autonomous driving lane determination method. For ease of explanation, the composition diagram of the embodiment of the autonomous driving lane determination device 100 only shows the parts related to the embodiment of the present application. Those skilled in the art can understand that the illustrated structure does not constitute a limitation on the autonomous driving lane determination device 100, and may include more or fewer components than shown in the diagram, or combine certain components, or arrange the components differently.

[0105] Specifically, the automatic driving lane determination device 100 provided in the embodiment of the present application includes a receiving module 110, an acquisition module 120, and a sending module 130. The receiving module 110 is used to receive the user information of the driver sent by the vehicle 1, and obtain the behavior information of the driver within a preset time period and the driving information of the vehicle within the preset time period according to the user information. The acquisition module 120 is used to analyze the behavior information and driving information within the preset time period based on the preset configuration model, and obtain multiple lane configuration information corresponding to the driver. The sending module 130 is used to send the multiple lane configuration information corresponding to the driver to the vehicle, wherein the multiple lane configuration information is used for vehicle 2 to receive, and the target lane configuration information is determined from the multiple lane configuration information according to the real-time driving information of vehicle 2, and the driving lane of vehicle 2 during the automatic driving process is determined according to the target lane configuration information.

[0106] In the automatic driving lane determination device 100 of the embodiment of the present application, the server 1 obtains the driver's behavior information within the preset time period and the driving information of the vehicle 2 within the preset time period according to the user information sent by the vehicle 2, and comprehensively analyzes the behavior information and driving information through the preset configuration model to determine the lane configuration information, so that the lane recommended by the automatic driving function can fully consider the actual driving scene and the driver's driving habits, and can meet the needs of different drivers, thereby improving the user's automatic driving experience. The server 1 sends the determined multiple lane configuration information to the vehicle 2, which is equivalent to providing professional "decision assistance" for the automatic driving function. The vehicle 2 can make lane selection more easily based on the lane configuration information, making the automatic driving process smoother and optimizing the overall driving experience. Based on this, the present application analyzes the driver's behavior information and the driving information of the vehicle 2 through the preset configuration model, and can determine the lane configuration information corresponding to the driver from multiple dimensions, so that the lane selection of the automatic driving can meet the driving habits of different drivers in different driving scenes, and improve the adaptability and flexibility of the automatic driving function.

[0107] See also Figure 6 , which is a schematic diagram of the functional modules of the autonomous driving lane determination device 200 provided in one embodiment of the present application.

[0108] In this embodiment, based on the above Figure 3 The same concept as the autonomous driving lane determination method in the illustrated embodiment, the present application also provides an autonomous driving lane determination device 200, which can be used to execute the above autonomous driving lane determination method. For ease of explanation, the composition diagram of the embodiment of the autonomous driving lane determination device 200 only shows the parts related to the embodiment of the present application. Those skilled in the art can understand that the illustrated structure does not constitute a limitation on the autonomous driving lane determination device 200, and may include more or fewer components than shown in the diagram, or combine certain components, or arrange the components differently.

[0109] Specifically, the automatic driving lane determination device 200 provided in the embodiment of the present application includes an acquisition module 210, an interaction module 220, and a determination module 230. The acquisition module 210 is used to obtain the user information of the driver and the real-time driving information of the vehicle if an instruction to turn on the automatic driving function is received. The interaction module 220 is used to send the user information to the server and receive multiple lane configuration information corresponding to the driver sent by the server, wherein the user information is used for server 1 to receive, and obtain the driver's behavior information within a preset time period and the driving information of vehicle 2 within a preset time period according to the user information, and analyze the behavior information and driving information within the preset time period based on the preset configuration model to obtain multiple lane configuration information corresponding to the driver. The determination module 230 determines the target lane configuration information from the multiple lane configuration information according to the real-time driving information of the vehicle, and determines the driving lane of the vehicle during the automatic driving process according to the target lane configuration information.

[0110] In the automatic driving lane determination device 200 of the embodiment of the present application, if an instruction to turn on the automatic driving function is received, the user information of the driver and the real-time driving information of the vehicle 2 are obtained, providing a data basis for subsequent lane determination. The user information of the driver is sent to the server 1, and multiple lane configuration information related to the driver sent by the server 1 is received. These lane configuration information can reflect the driving habits of the driver in different driving scenarios. According to the real-time driving information of the vehicle 2, the most suitable target lane configuration information is selected from the multiple lane configuration information, ensuring that the target lane configuration information matches the real-time driving information of the current vehicle 2, thereby improving the safety and reliability of the automatic driving function. According to the target lane configuration information, the driving lane of the vehicle 2 during the automatic driving process is determined, taking into account the driving habits of the driver and the current driving scene, thereby improving the automatic driving experience. Based on this, the present application determines the target lane configuration information by integrating the user information and real-time driving information of the driver, so that the lane selection of the automatic driving function can meet the driving habits of the driver in different driving scenes, thereby improving the adaptability and flexibility of the automatic driving function.

[0111] See also Figure 7 , a hardware structure diagram of server 1 is provided for an embodiment of the present application.

[0112] In some embodiments of the present application, the server 1 includes, but is not limited to, a memory 11, a processor 12, and a computer program stored in the memory 11 and executable on the processor 12, such as an autonomous driving lane determination program. When the computer program is executed by the processor, an autonomous driving lane determination method as in the above-mentioned embodiment is implemented.

[0113] The server 1 may be a body control module (BCM), a vehicle control unit (VCU), or the like.

[0114] Figure 7 Only the server 1 having the memory 11 and the processor 12 is shown, and those skilled in the art can understand that Figure 7 The structure shown does not constitute a limitation on the server 1, and may include fewer or more components than shown in the figure, or combine certain components, or arrange the components differently.

[0115] The memory 11 in the server 1 stores multiple computer-readable instructions to implement an automatic driving lane determination method, and the processor 12 can execute multiple instructions to achieve: receiving user information of the driver sent by the vehicle 2, and obtaining the driver's behavior information within a preset time period and the driving information of the vehicle 2 within the preset time period based on the user information; analyzing the behavior information and driving information within the preset time period based on a preset configuration model to obtain multiple lane configuration information corresponding to the driver; sending the multiple lane configuration information corresponding to the driver to the vehicle 2, wherein the multiple lane configuration information is used for the vehicle 2 to receive, and determining the target lane configuration information from the multiple lane configuration information according to the real-time driving information of the vehicle 2, and determining the driving lane of the vehicle 2 during the automatic driving process according to the target lane configuration information.

[0116] Specifically, the specific implementation method of the processor 12 for the above instructions can refer to Figure 2 The description of the relevant steps in the corresponding embodiments will not be repeated here.

[0117] Those skilled in the art will appreciate that the schematic diagram is merely an example of the server 1 and does not constitute a limitation on the server 1. The server 1 may be a bus-type structure or a star-type structure. The server 1 may also include more or less other hardware or software than shown in the diagram, or a different arrangement of components. For example, the server 1 may also include input and output devices, network access devices, etc.

[0118] It should be noted that server 1 is only an example, and other existing or future electronic products that are suitable for this application should also be included in the protection scope of this application and included here by reference.

[0119] Among them, the memory 11 includes at least one type of computer-readable storage medium, and the computer-readable storage medium can be non-volatile or volatile. Computer-readable storage media include flash memory, mobile hard disk, multimedia card, card-type memory (such as SD memory, DX memory, etc.), magnetic memory, disk, optical disk, etc. In some embodiments, the memory 11 can be an internal storage unit of the server 1, such as a mobile hard disk of the server 1. In other embodiments, the memory 11 can also be an external storage device of the server 1, such as a plug-in mobile hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (FlashCard), etc. equipped on the server 1. The memory 11 can not only be used to store application software and various types of information installed on the server 1, such as a code of an automatic driving lane determination program, but also can be used to temporarily store information that has been output or is to be output.

[0120] In some embodiments, the processor 12 may be composed of an integrated circuit, for example, a single packaged integrated circuit, or a plurality of packaged integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and combinations of various control chips. The processor 12 is the control core (Control Unit) of the server 1, and uses various interfaces and lines to connect the various components of the entire server 1, and executes various functions and processes information of the server 1 by running or executing programs or modules stored in the memory 11 (for example, executing an automatic driving lane determination program, etc.), and calling information stored in the memory 11.

[0121] The processor 12 executes the operating system of the server 1 and various installed applications. The processor 12 executes the application to implement the steps in each of the above-mentioned embodiments of the automatic driving lane determination method, for example Figure 2 Steps shown.

[0122] Exemplarily, the computer program may be divided into one or more modules / units, one or more modules / units are stored in the memory 11, and executed by the processor 12 to complete the present application. One or more modules / units may be a series of computer-readable instruction segments capable of completing specific functions, and the instruction segments are used to describe the execution process of the computer program in the server 1. For example, the computer program may be divided into a receiving module 110, an acquiring module 120, and a sending module 130.

[0123] The above-mentioned integrated unit implemented in the form of a software function module can be stored in a computer-readable storage medium. The above-mentioned software function module is stored in a storage medium, including a number of instructions for enabling a computer device (which can be a personal computer, a computer device or a network device, etc.) or a processor to execute a part of an automatic driving lane determination method in each embodiment of the present application.

[0124] If the module / unit integrated in the server 1 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 this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, and can also instruct the relevant hardware devices to complete through a computer program. The computer program can be stored in a computer-readable storage medium, and when the computer program is executed by the processor, the steps of each of the above-mentioned method embodiments can be implemented.

[0125] The computer program includes computer program code, which may be in source code form, object code form, executable file or some intermediate form, etc. Computer readable media may include: any entity or device capable of carrying computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory and other memory, etc.

[0126] Furthermore, the computer-readable storage medium may mainly include a program storage area and an information storage area, wherein the program storage area may store an operating system, an application required for at least one function, etc.; the information storage area may store information created according to the use of the blockchain node, etc.

[0127] The bus can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus can be divided into an address bus, an information bus, a control bus, etc. Figure 7 Only one arrow is used in the figure, but it does not mean that there is only one bus or one type of bus. The bus is configured to realize the connection and communication between the memory 11 and at least one processor 12, etc.

[0128] See also Figure 8 , a schematic diagram of the hardware structure of vehicle 2 is provided for one embodiment of the present application.

[0129] In some embodiments of the present application, the vehicle 2 may include an electronic device 20, which includes but is not limited to a memory 21, a processor 22, and a computer program stored in the memory 21 and executable on the processor 22, such as an autonomous driving lane determination program. When the computer program is executed by the processor, an autonomous driving lane determination method as in the above-mentioned embodiment is implemented.

[0130] The electronic device 20 may be a body control module (BCM), a vehicle control unit (VCU), or the like.

[0131] Figure 8 Only the electronic device 20 having the memory 21 and the processor 22 is shown, and those skilled in the art can understand that Figure 8 The structure shown does not constitute a limitation on the electronic device 20 , and the electronic device 20 may include fewer or more components than shown in the figure, or combine some components, or arrange the components differently.

[0132] The memory 21 in the electronic device 20 stores multiple computer-readable instructions to implement an automatic driving lane determination method, and the processor 22 can execute multiple instructions to achieve: if an instruction to turn on the automatic driving function is received, the driver's user information and the real-time driving information of the vehicle 2 are obtained; the user information is sent to the server 1, and the multiple lane configuration information corresponding to the driver sent by the server 1 is received, wherein the user information is used for the server 1 to receive, and the driver's behavior information within a preset time period and the driving information of the vehicle 2 within the preset time period are obtained according to the user information, and the behavior information and driving information within the preset time period are analyzed based on the preset configuration model to obtain the multiple lane configuration information corresponding to the driver; the target lane configuration information is determined from the multiple lane configuration information according to the real-time driving information of the vehicle 2, and the driving lane of the vehicle 2 during the automatic driving process is determined according to the target lane configuration information.

[0133] Specifically, the specific implementation method of the processor 22 for the above instructions can refer to Figure 3 The description of the relevant steps in the corresponding embodiments will not be repeated here.

[0134] Those skilled in the art will appreciate that the schematic diagram is merely an example of the electronic device 20 and does not constitute a limitation on the electronic device 20. The electronic device 20 may be a bus structure or a star structure. The electronic device 20 may also include more or less other hardware or software than shown in the diagram, or a different arrangement of components. For example, the electronic device 20 may also include input and output devices, network access devices, etc.

[0135] It should be noted that the electronic device 20 is only an example, and other existing or future electronic products that are suitable for the present application should also be included in the protection scope of the present application and included here by reference.

[0136] Among them, the memory 21 includes at least one type of computer-readable storage medium, and the computer-readable storage medium can be non-volatile or volatile. Computer-readable storage media include flash memory, mobile hard disk, multimedia card, card-type memory (such as SD memory, DX memory, etc.), magnetic memory, disk, optical disk, etc. In some embodiments, the memory 21 can be an internal storage unit of the electronic device 20, such as a mobile hard disk of the electronic device 20. In other embodiments, the memory 21 can also be an external storage device of the electronic device 20, such as a plug-in mobile hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device 20. The memory 21 can not only be used to store application software and various types of information installed in the electronic device 20, such as a code of an automatic driving lane determination program, but also can be used to temporarily store information that has been output or is to be output.

[0137] In some embodiments, the processor 22 may be composed of an integrated circuit, for example, a single packaged integrated circuit, or a plurality of packaged integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and a combination of various control chips. The processor 22 is the control core (Control Unit) of the electronic device 20, and uses various interfaces and lines to connect the various components of the entire electronic device 20, and executes various functions and processes information of the electronic device 20 by running or executing programs or modules stored in the memory 21 (for example, executing an automatic driving lane determination program, etc.), and calling the information stored in the memory 21.

[0138] The processor 22 executes the operating system of the electronic device 20 and various installed applications. The processor 22 executes the application to implement the steps in each of the above-mentioned embodiments of the automatic driving lane determination method, for example Figure 3 Steps shown.

[0139] Exemplarily, the computer program may be divided into one or more modules / units, one or more modules / units are stored in the memory 21, and executed by the processor 22 to complete the present application. One or more modules / units may be a series of computer-readable instruction segments capable of completing specific functions, and the instruction segments are used to describe the execution process of the computer program in the electronic device 20. For example, the computer program may be divided into an acquisition module 220, an interaction module 220, and a determination module 230.

[0140] The above-mentioned integrated unit implemented in the form of a software function module can be stored in a computer-readable storage medium. The above-mentioned software function module is stored in a storage medium, including a number of instructions for enabling a computer device (which can be a personal computer, a computer device or a network device, etc.) or a processor to execute a part of an automatic driving lane determination method in each embodiment of the present application.

[0141] If the module / unit integrated in the electronic device 20 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 this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, and can also instruct the relevant hardware devices to complete through a computer program. The computer program can be stored in a computer-readable storage medium, and when the computer program is executed by the processor, the steps of each of the above-mentioned method embodiments can be implemented.

[0142] The computer program includes computer program code, which may be in source code form, object code form, executable file or some intermediate form, etc. Computer readable media may include: any entity or device capable of carrying computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory and other memory, etc.

[0143] Furthermore, the computer-readable storage medium may mainly include a program storage area and an information storage area, wherein the program storage area may store an operating system, an application required for at least one function, etc.; the information storage area may store information created according to the use of the blockchain node, etc.

[0144] The bus can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus can be divided into an address bus, an information bus, a control bus, etc. Figure 8 Only one arrow is used in the figure, but it does not mean that there is only one bus or one type of bus. The bus is configured to realize the connection and communication between the memory 21 and at least one processor 22, etc.

[0145] In the several embodiments provided in this application, it should be understood that the disclosed methods and devices can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of modules is only a logical function division, and there may be other division methods in actual implementation.

[0146] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0147] In addition, each functional module in each embodiment of the present 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. The above integrated unit can be implemented in the form of hardware or in the form of hardware plus software functional modules.

[0148] In addition, it is obvious that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices stated in the specification can also be implemented by one unit or device through software or hardware. The words first, second, etc. are used to indicate names, and do not indicate any specific order.

[0149] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present application and are not intended to limit it. Although the present application has been described in detail with reference to the preferred embodiments, a person of ordinary skill in the art should understand that the technical solution of the present application may be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present application.

Claims

1. A method for determining lanes for autonomous driving, applied to a server, characterized in that: The method comprises: Receiving user information of a driver sent by a vehicle, and acquiring behavior information of the driver within a preset time period and driving information of the vehicle within the preset time period according to the user information; Analyzing the behavior information and the driving information within the preset time period based on a preset configuration model to obtain a plurality of lane configuration information corresponding to the driver; The multiple lane configuration information corresponding to the driver is sent to the vehicle, wherein the multiple lane configuration information is used for the vehicle to receive, and target lane configuration information is determined from the multiple lane configuration information according to the real-time driving information of the vehicle, and the driving lane of the vehicle during the automatic driving process is determined according to the target lane configuration information.

2. The method for determining an automatic driving lane according to claim 1, wherein: The driving information includes a driving state and a driving environment, and the method further includes: Evaluate the behavior information according to the driving state and the driving environment to obtain a behavior evaluation result; The weight of the behavior information is adjusted according to the behavior evaluation result, so that the preset configuration model updates the multiple lane configuration information corresponding to the driver based on the adjusted weight.

3. The automatic driving lane determination method according to claim 2, characterized in that: The method further comprises: The driver's intervention behavior information during the automatic driving of the vehicle is obtained, so that the preset configuration model updates multiple lane configuration information corresponding to the driver based on the intervention behavior information.

4. The method for determining an automatic driving lane according to claim 2, wherein: The method further comprises: The plurality of lane configuration information corresponding to the driver is associated with the user information of the driver.

5. An automatic driving lane determination method, applied to a vehicle, characterized in that: The method comprises: If a command to start the automatic driving function is received, obtaining the user information of the driver and the real-time driving information of the vehicle; Sending the user information to a server, and receiving a plurality of lane configuration information corresponding to the driver sent by the server, wherein the user information is used for the server to receive, and obtaining the behavior information of the driver within a preset time period and the driving information of the vehicle within the preset time period according to the user information, and analyzing the behavior information and the driving information within the preset time period based on a preset configuration model to obtain the plurality of lane configuration information corresponding to the driver; Target lane configuration information is determined from the multiple lane configuration information according to the real-time driving information of the vehicle, and the driving lane of the vehicle during the automatic driving process is determined according to the target lane configuration information.

6. The method for determining an automatic driving lane according to claim 5, wherein: Each lane configuration information of the plurality of lane configuration information includes a driving scenario and a recommended lane corresponding to the driving scenario, the real-time driving information includes a real-time driving state and a real-time driving environment, and determining the target lane configuration information from the plurality of lane configuration information according to the real-time driving information of the vehicle includes: Determining a real-time driving scenario of the vehicle according to the real-time driving state and the real-time driving environment; The target lane configuration information is determined from the plurality of lane configuration information based on the real-time driving scenario.

7. The automatic driving lane determination method according to claim 5, characterized in that: The user information includes the driver's user account.

8. An automatic driving lane determination device, applied to a server, characterized in that: The device comprises: A receiving module, used to receive user information of a driver sent by a vehicle, and obtain behavior information of the driver within a preset time period and driving information of the vehicle within the preset time period according to the user information; an acquisition module, configured to analyze the behavior information and the driving information within the preset time period based on a preset configuration model, and obtain a plurality of lane configuration information corresponding to the driver; A sending module is used to send multiple lane configuration information corresponding to the driver to the vehicle, wherein the multiple lane configuration information is used for the vehicle to receive, and target lane configuration information is determined from the multiple lane configuration information according to the real-time driving information of the vehicle, and the driving lane of the vehicle during the automatic driving process is determined according to the target lane configuration information.

9. An automatic driving lane determination device, applied to a vehicle, characterized in that: The device comprises: An acquisition module, configured to acquire the user information of the driver and the real-time driving information of the vehicle if an instruction to start the automatic driving function is received; an interaction module, configured to send the user information to a server, and receive a plurality of lane configuration information corresponding to the driver sent by the server, wherein the user information is received by the server, and the behavior information of the driver within a preset time period and the driving information of the vehicle within the preset time period are obtained according to the user information, and the behavior information and the driving information within the preset time period are analyzed based on a preset configuration model to obtain the plurality of lane configuration information corresponding to the driver; A determination module is used to determine target lane configuration information from the multiple lane configuration information according to the real-time driving information of the vehicle, and determine the driving lane of the vehicle during the automatic driving process according to the target lane configuration information.

10. An automatic driving lane determination system, characterized in that: The system includes a vehicle and a server, wherein the vehicle and the server are communicatively connected; The vehicle is used to: if receiving an instruction to start the automatic driving function, obtain the user information of the driver and the real-time driving information of the vehicle; and send the user information to the server; The server is used to: receive user information of the driver sent by the vehicle, and obtain the behavior information of the driver within a preset time period and the driving information of the vehicle within the preset time period according to the user information; Analyzing the behavior information and the driving information within the preset time period based on a preset configuration model to obtain a plurality of lane configuration information corresponding to the driver; sending a plurality of lane configuration information corresponding to the driver to the vehicle; The vehicle is also used to: receive multiple lane configuration information corresponding to the driver sent by the server; determine target lane configuration information from the multiple lane configuration information according to the real-time driving information of the vehicle, and determine the driving lane of the vehicle during the automatic driving process according to the target lane configuration information.

11. A server, characterized in that: The server includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, the automatic driving lane determination method as described in any one of claims 1 to 4 is implemented.

12. A vehicle, characterized in that: The vehicle includes an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the autonomous driving lane determination method as described in any one of claims 5 to 7.

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