Vehicle control method and device, vehicle and storage medium

Abstract

The invention relates to a vehicle control method and device, a vehicle and a storage medium, and the method comprises the steps: obtaining the navigation data, intersection passing data and surrounding environment sensing data of the vehicle in a preset city navigation auxiliary mode; constructing a corresponding environment model based on the surrounding environment sensing data; performing fusion positioning by using the current positioning of the vehicle and the environment model to obtain the actual positioning of the vehicle meeting the preset precision; and determining a target lane of the vehicle based on the actual positioning, the navigation data and the intersection passing data, and controlling the vehicle to drive into the target lane. Therefore, the technical problem that in the prior art, due to the fact that the coverage area of a high-precision map is insufficient, the vehicle is difficult to drive according to the navigation route accurately, and large potential safety hazards exist is solved.

Description

Technical Field

[0001] This application relates to the technical field of vehicle intelligent control, and particularly relates to a control method, device, vehicle and storage medium for a vehicle. Background Art

[0002] The urban pilot assist function can drive the vehicle according to the navigation route. This function often requires the in-vehicle geographic information system to have prior high-precision map data and high-precision positioning capabilities.

[0003] However, in related technologies, there are still some challenges in the practical application of the urban pilot assist function. For example, the coverage area of high-precision maps is insufficient. When the navigation says to turn left at the front intersection, but since the road after the left turn is not covered by the high-precision map, the vehicle will randomly select an optimal lane in front of the intersection, which may be the rightmost lane, and then exit the urban pilot assist function. At this time, the vehicle cannot accurately follow the navigation route, thus affecting the driving accuracy and safety, and urgent improvement is needed. Summary of the Invention

[0004] This application provides a control method, device, vehicle and storage medium for a vehicle to solve the technical problem in related technologies that due to the insufficient coverage area of high-precision maps, it is difficult for the vehicle to accurately follow the navigation route, resulting in a large safety hazard.

[0005] The first aspect of the embodiments of this application provides a control method for a vehicle, including the following steps: In a preset urban pilot assist mode, obtain the navigation data, intersection passing data and surrounding environment perception data of the vehicle; construct a corresponding environment model based on the surrounding environment perception data; perform fusion positioning using the current positioning of the vehicle and the environment model to obtain the actual positioning of the vehicle that meets the preset accuracy; determine the target lane of the vehicle based on the actual positioning, the navigation data and the intersection passing data, and control the vehicle to drive into the target lane.

[0006] Optionally, in an embodiment of the present application, before obtaining the navigation data, intersection passing data, and surrounding environment perception data of the vehicle, it further includes: obtaining the current driving state of the vehicle; when the current driving state is a road driving state and the vehicle is in a navigation-on state, determining whether the vehicle meets a preset data collection condition based on the lane line perception state of the vehicle; if the vehicle meets the preset data collection condition, determining whether the vehicle meets a preset data processing condition based on the vehicle's vehicle control parameters; if the vehicle meets the preset data processing condition, determining whether the vehicle meets a preset mode opening condition based on the current positioning and the navigation data in the navigation state; if the vehicle meets the preset mode opening condition, controlling the vehicle to enter the preset urban pilot assistance mode.

[0007] Optionally, in an embodiment of the present application, the constructing a corresponding environment model based on the surrounding environment perception data includes: preprocessing the surrounding environment perception data to obtain perception data that meets preset processing conditions; processing the perception data to extract multiple objects and road structures in the surrounding environment of the vehicle from the perception data; classifying the objects, and respectively performing static modeling and dynamic modeling based on the classification results and the road structures, and fusing the results of the static modeling and the dynamic modeling to obtain the environment model.

[0008] Optionally, in an embodiment of the present application, it further includes: updating the current positioning of the vehicle, and determining whether the vehicle has entered the target lane based on the updated current positioning; if the vehicle has entered the target lane, generating a vehicle takeover reminder, and after pushing the vehicle takeover reminder to the user for a preset duration, controlling the vehicle to enter a preset takeover control mode.

[0009] Optionally, in an embodiment of the present application, it further includes: receiving a road determination signal from the user; optimizing the environment model based on the road determination signal.

[0010] An embodiment of the second aspect of the present application provides a control device for a vehicle, including: a first acquisition module, configured to obtain navigation data, intersection passing data, and surrounding environment perception data of the vehicle in a preset urban pilot assistance mode; a construction module, configured to construct a corresponding environment model based on the surrounding environment perception data; a fusion module, configured to perform fusion positioning using the current positioning of the vehicle and the environment model to obtain the actual positioning of the vehicle that meets the preset accuracy; a first control module, configured to determine the target lane of the vehicle based on the actual positioning, the navigation data, and the intersection passing data, and control the vehicle to enter the target lane.

[0011] Optionally, in an embodiment of the present application, it further includes: a second acquisition module, configured to acquire the current driving state of the vehicle; a first determination module, configured to determine whether the vehicle meets a preset data acquisition condition based on the lane line perception state of the vehicle when the current driving state is a road driving state and the vehicle is in a navigation-on state; a second determination module, configured to determine whether the vehicle meets a preset data processing condition based on the vehicle's vehicle control parameters when the vehicle meets the preset data acquisition condition; a third determination module, configured to determine whether the vehicle meets a preset mode activation condition based on the current positioning and the navigation data in the navigation state when the vehicle meets the preset data processing condition; a fourth determination module, configured to control the vehicle to enter the preset urban pilot assistance mode when the vehicle meets the preset mode activation condition.

[0012] Optionally, in an embodiment of the present application, the construction module includes: a first processing unit, configured to preprocess the surrounding environment perception data to obtain perception data that meets preset processing conditions; a second processing unit, configured to process the perception data and extract multiple objects and road structures in the surrounding environment of the vehicle from the perception data; a construction unit, configured to classify the objects, and perform static modeling and dynamic modeling based on the classification results and the road structures respectively, and fuse the results of the static modeling and the dynamic modeling to obtain the environment model.

[0013] Optionally, in an embodiment of the present application, it further includes: an update module, configured to update the current positioning of the vehicle and determine whether the vehicle has entered the target lane based on the updated current positioning; a second control module, configured to control the vehicle to exit the preset urban pilot assistance mode when the vehicle has entered the target lane.

[0014] Optionally, in an embodiment of the present application, it further includes: a receiving module, configured to receive a road determination signal from the user; an optimization module, configured to optimize the environment model based on the road determination signal.

[0015] An embodiment of the third aspect of the present application provides a vehicle, including: a memory, a processor, and a computer program stored on the memory and executable on the processor, and the processor executes the program to implement the vehicle control method as described in the above embodiments.

[0016] An embodiment of the fourth aspect of the present application provides a computer-readable storage medium, and the computer-readable storage medium stores computer instructions for causing the computer to execute the vehicle control method as described in the above embodiments.

[0017] A fifth aspect embodiment of the present application provides a computer program product, including a computer program which, when executed, is used to implement the vehicle control method as described above.

[0018] In the embodiment of the present application, in the preset urban pilot assist mode, an environment model can be constructed according to the surrounding environment perception data, and the current positioning of the vehicle and the environment model can be fused for positioning to obtain a high-precision actual positioning. Then, the target lane of the vehicle can be determined by using the actual positioning, navigation data, and intersection passing data, and the vehicle can be controlled to drive into the target lane to avoid the risks caused by insufficient coverage of the high-precision map, improve the driving safety of the urban pilot assist function in this scenario, and help improve the usability of the product in more complex scenarios. Thus, the technical problem in the related art that due to insufficient coverage area of the high-precision map, it is difficult for the vehicle to accurately drive according to the navigation route, resulting in a large potential safety hazard is solved.

[0019] Additional aspects and advantages of the present application will be given in part in the following description, become apparent in part from the following description, or be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The above and / or additional aspects and advantages of the present application will become apparent and be readily understood from the following description of the embodiments in conjunction with the drawings, where:

[0021] Figure 1 is a flowchart of a vehicle control method according to an embodiment of the present application;

[0022] Figure 2 is a schematic diagram of the principle of a vehicle control method according to an embodiment of the present application;

[0023] Figure 3 is a schematic flowchart of a vehicle control method according to an embodiment of the present application;

[0024] Figure 4 is a schematic structural diagram of a vehicle control device according to an embodiment of the present application;

[0025] Figure 5 is a schematic structural diagram of a vehicle according to an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0026] The embodiments of the present application will be described in detail below. The examples of the embodiments are shown in the drawings, where the same or similar reference numerals denote the same or similar elements or elements with the same or similar functions throughout. The embodiments described below by referring to the drawings are exemplary and are intended to explain the present application, and should not be construed as limiting the present application.

[0027] The following describes a control method, device, vehicle, and storage medium for a vehicle according to an embodiment of the present application. In view of the technical problem in the related art mentioned in the above background art that due to the insufficient coverage area of the high-precision map, it is difficult for the vehicle to accurately drive according to the navigation route, posing a significant safety hazard, the present application provides a control method for a vehicle. In this method, in a preset urban pilot assist mode, an environmental model can be constructed based on the surrounding environment perception data, and the current positioning of the vehicle and the environmental model are fused for positioning to obtain a high-precision actual positioning. Then, the target lane of the vehicle is determined using the actual positioning, navigation data, and intersection passing data, and the vehicle is controlled to drive into the target lane to avoid the risks caused by insufficient high-precision map coverage, improve the driving safety of the urban pilot assist function in this scenario, and help improve the usability of the product in more complex scenarios. Thus, the technical problem in the related art that due to the insufficient coverage area of the high-precision map, it is difficult for the vehicle to accurately drive according to the navigation route, posing a significant safety hazard is solved.

[0028] Specifically, Figure 1 FIG. is a schematic flowchart of a control method for a vehicle provided by an embodiment of the present application.

[0029] As Figure 1 shown, the control method for the vehicle includes the following steps:

[0030] In step S101, in a preset urban pilot assist mode, navigation data, intersection passing data, and surrounding environment perception data of the vehicle are acquired.

[0031] It can be understood that the urban pilot assist function generally refers to providing a series of assisted driving functions for users in a complex urban traffic environment to improve driving safety and comfort. Such functions are generally integrated in the advanced driver assistance system (ADAS), and with the help of various sensors on the vehicle (such as cameras, radars, ultrasonic sensors, etc.) and high-precision map data, the surrounding environment can be accurately perceived, and corresponding decisions and control suggestions can be made accordingly.

[0032] To optimize the deficiency of the urban pilot assist function in the high-precision map coverage, the embodiment of the present application can acquire the navigation data of the vehicle (such as standard definition navigation application data (SD map)), intersection passing data (intersection lane information passing data), and surrounding perception data after entering the urban pilot assist mode.

[0033] Among them, the surrounding perception data can be collected by various sensors of the vehicle.

[0034] Optionally, in an embodiment of the present application, before obtaining the navigation data, intersection passing data, and surrounding environment perception data of the vehicle, it further includes: obtaining the current driving state of the vehicle; when the current driving state is a road driving state and the vehicle is in a navigation-on state, determining whether the vehicle meets the preset data collection conditions based on the vehicle's lane line perception state; if the vehicle meets the preset data collection conditions, determining whether the vehicle meets the preset data processing conditions based on the vehicle's overall vehicle control parameters; if the vehicle meets the preset data processing conditions, determining whether the vehicle meets the preset mode opening conditions based on the navigation data in the current positioning and navigation state; if the vehicle meets the preset mode opening conditions, controlling the vehicle to enter the preset urban pilot assist mode.

[0035] Before entering the urban pilot assist mode, the embodiment of the present application can first determine whether the vehicle meets the conditions for entering this mode.

[0036] For example, in the embodiment of the present application, when the vehicle's current driving state is driving on the road surface and the navigation is set, and the vehicle can perceive and recognize the road lane lines (i.e., meet the data collection conditions), perform self-diagnosis on the autonomous driving domain controller according to the overall vehicle control parameters, and confirm that there is no problem (i.e., meet the data processing conditions), and when the positioning and HD map information can be matched (i.e., meet the mode opening conditions), control the vehicle to enter the urban pilot assist mode.

[0037] In step S102, a corresponding environment model is constructed based on the surrounding environment perception data.

[0038] Furthermore, the embodiment of the present application can construct an environment model by using the data sensed by multiple sensors, so as to realize high-precision vehicle navigation control without the need for a high-precision map by using the environment model and the vehicle's positioning.

[0039] Optionally, in an embodiment of the present application, constructing a corresponding environment model based on the surrounding environment perception data includes: preprocessing the surrounding environment perception data to obtain perception data that meets the preset processing conditions; processing the perception data to extract multiple objects and road structures in the vehicle's surrounding environment from the perception data; classifying the objects, and respectively performing static modeling and dynamic modeling based on the classification results and the road structure, and fusing the results of the static modeling and the results of the dynamic modeling to obtain the environment model.

[0040] As a possible implementation manner, when constructing the environment model, the following steps may be included:

[0041] Step S1: Data collection and preprocessing. In the embodiments of the present application, data can be collected according to the surrounding environment perception data, such as high-precision three-dimensional point cloud data provided by lidar, visual information captured by cameras, such as lane lines, traffic signs, pedestrians, etc., long-distance targets and their speeds detected by millimeter-wave radars, and close-range obstacle detection data of ultrasonic sensors.

[0042] Preprocess the collected data: Time synchronization: Ensure that data from different sensors are aligned in time; Spatial calibration: Map data from different sensors to a unified coordinate system (such as the vehicle body coordinate system or the world coordinate system); Denoising and filtering: Remove sensor noise (such as outliers in point clouds, blurred parts in images); Data fusion: Combine multi-sensor data to improve perception accuracy (such as Kalman filtering, particle filtering, or multi-modal deep learning).

[0043] Perform object detection and classification based on the processed data. Use deep learning models (such as YOLO, FasterR-CNN, PointNet, etc.) to process sensor data and identify objects in the environment (such as vehicles, pedestrians, bicycles, traffic signs, etc.). Classify the detected targets and estimate their attributes such as position, speed, and direction.

[0044] Perform lane and road structure detection based on the preprocessed data. Based on camera images, use semantic segmentation models (such as DeepLab, SegFormer) to identify lane lines, road boundaries, and drivable areas.

[0045] Distinguish between dynamic and static objects. Utilize time series data (such as consecutive frames of point clouds or images) to distinguish static objects (such as buildings, roadblocks) and dynamic objects (such as moving vehicles, pedestrians).

[0046] Based on the above data, the embodiments of the present application can perform dynamic and static modeling.

[0047] Among them, when performing dynamic modeling, it is necessary to predict the trajectories of dynamic objects and consider the interaction behaviors between dynamic objects.

[0048] After that, the embodiments of the present application can fuse the static model and the dynamic model to obtain a complete environment model.

[0049] In step S103, utilize the current positioning of the vehicle and the environment model for fusion positioning to obtain the actual positioning of the vehicle that meets the preset accuracy.

[0050] As a possible implementation manner, the embodiments of the present application can perform fusion processing on the current positioning converted into coordinates and the results perceived by the camera (environment model) to obtain the position where the vehicle is located, that is, the actual positioning.

[0051] In step S104, the target lane of the vehicle is determined based on the actual positioning, navigation data, and intersection passing data, and the vehicle is controlled to drive into the target lane.

[0052] In some embodiments, the fused actual positioning can be compared with the intersection lane information and navigation information provided by the SD map to control the vehicle to drive into the correct lane.

[0053] Optionally, in an embodiment of the present application, it further includes: updating the current positioning of the vehicle, and determining whether the vehicle has driven into the target lane based on the updated current positioning; if the vehicle has driven into the target lane, a vehicle takeover reminder is generated, and after the vehicle takeover reminder is pushed to the user for a preset duration, the vehicle is controlled to enter a preset takeover control mode.

[0054] Further, after controlling the vehicle to enter the correct lane, the embodiment of the present application can remind the user that there is a lack of high-precision map data on the road ahead, and generate a takeover reminder to reduce the probability that the vehicle deviates from the predetermined route under automatic control, so that the vehicle randomly selects a lane before the intersection, improving the functional intelligence and user experience.

[0055] Optionally, in an embodiment of the present application, it further includes: receiving the user's road determination signal; optimizing the environment model based on the road determination signal.

[0056] The embodiment of the present application can also collect the user's evaluation of this assistance after the vehicle has completed driving into the target road, such as whether it has correctly driven into the road of the predetermined route. Thus, according to the evaluation, the embodiment of the present application can determine whether the construction of the environment model is reasonable, and optimize the parameters of the environment model when it fails to correctly enter the predetermined road to achieve adaptive learning.

[0057] Combined Figure 2 and Figure 3 As shown, the working principle of the vehicle control method of the embodiment of the present application is elaborated in detail with an embodiment.

[0058] As Figure 2 shown, it is the system architecture involved in the embodiment of the present application.

[0059] The embodiment of the present application can use the cockpit domain controller to interact with the SD map and the HD map, use the autonomous driving domain controller to interact with perception, positioning, diagnosis, and the HD map, and realize navigation path calculation through the interaction between the cockpit domain controller and the autonomous driving domain controller.

[0060] The specific process can be as Figure 3As shown in the figure, when the user is driving the vehicle on the road and the navigation is set, and the vehicle perception can recognize the road lane lines, and the self-diagnosis of the autonomous driving domain controller has no problems, and the positioning can be matched with the HD map information, the vehicle enters the urban pilot assist mode.

[0061] Step S1: The standard definition navigation application (SD map) transmits the navigation information and intersection lane information to the autonomous driving domain controller through signal transmission.

[0062] Step S2: The autonomous driving domain controller performs fusion processing on the positioning coordinates input by the positioning module and the results (environmental model) sensed by the camera to obtain the actual positioning of the current vehicle.

[0063] Step S3: Compare the fused actual positioning with the intersection lane information and navigation information given by the SD map, and control the vehicle to drive into the correct lane.

[0064] After entering the correct lane, remind the user that there is no high-precision map data ahead, and the function will exit soon. Please take over the vehicle, so as to avoid the situation where there is no high-precision map data ahead and it is impossible to plan the correct driving route, causing the vehicle to randomly select a lane in front of the intersection, and improving the intelligence of the function and the user experience.

[0065] According to the vehicle control method proposed in the embodiment of the present application, in the preset urban pilot assist mode, an environmental model can be constructed based on the surrounding environment perception data, and the current positioning of the vehicle and the environmental model can be used for fusion positioning to obtain a high-precision actual positioning. Then, the target lane of the vehicle can be determined using the actual positioning, navigation data, and intersection passing data, and the vehicle can be controlled to drive into the target lane to avoid the risks caused by insufficient coverage of the high-precision map, improving the driving safety of the urban pilot assist function in this scenario and helping the product to be more usable in more complex scenarios. Thus, the technical problem in the related art that it is difficult for the vehicle to accurately drive according to the navigation route due to insufficient coverage area of the high-precision map, resulting in a large safety hazard, is solved.

[0066] Next, refer to the accompanying drawings to describe the vehicle control device according to the embodiment of the present application.

[0067] Figure 4 It is a block diagram of the vehicle control device according to the embodiment of the present application.

[0068] As Figure 4 shown, the vehicle control device 10 includes: a first acquisition module 100, a construction module 200, a fusion module 300, and a first control module 400.

[0069] Specifically, the first acquisition module 100 is used to acquire the navigation data, intersection passing data, and surrounding environment perception data of the vehicle in the preset urban pilot assist mode.

[0070] The building module 200 is configured to build a corresponding environment model based on the surrounding environment perception data.

[0071] The fusion module 300 is configured to perform fusion positioning by using the current positioning of the vehicle and the environment model, so as to obtain the actual positioning of the vehicle that meets the preset accuracy.

[0072] The first control module 400 is configured to determine the target lane of the vehicle based on the actual positioning, navigation data, and intersection passing data, and control the vehicle to drive into the target lane.

[0073] Optionally, in an embodiment of the present application, the control device 10 of the vehicle further includes: a second acquisition module, a first judgment module, a second judgment module, a third judgment module, and a fourth judgment module.

[0074] Wherein, the second acquisition module is configured to acquire the current driving state of the vehicle.

[0075] The first judgment module is configured to judge whether the vehicle meets the preset data acquisition condition based on the lane line perception state of the vehicle when the current driving state is a road driving state and the vehicle is in a navigation-on state.

[0076] The second judgment module is configured to judge whether the vehicle meets the preset data processing condition based on the vehicle's whole vehicle control parameters when the vehicle meets the preset data acquisition condition.

[0077] The third judgment module is configured to judge whether the vehicle meets the preset mode opening condition based on the navigation data in the current positioning and navigation state when the vehicle meets the preset data processing condition.

[0078] The fourth judgment module is configured to control the vehicle to enter the preset urban pilot assistance mode when the vehicle meets the preset mode opening condition.

[0079] Optionally, in an embodiment of the present application, the building module 200 includes: a first processing unit, a second processing unit, and a building unit.

[0080] Wherein, the first processing unit is configured to preprocess the surrounding environment perception data to obtain perception data that meets the preset processing condition.

[0081] The second processing unit is configured to process the perception data and extract multiple objects and road structures in the surrounding environment of the vehicle from the perception data.

[0082] The building unit is configured to classify the objects, perform static modeling and dynamic modeling respectively based on the classification result and the road structure, and fuse the results of the static modeling and the dynamic modeling to obtain the environment model.

[0083] Optionally, in an embodiment of the present application, the control device 10 of the vehicle further includes: an update module and a second control module.

[0084] Wherein, the update module is configured to update the current position of the vehicle, and determine whether the vehicle has entered the target lane based on the updated current position.

[0085] The second control module is configured to generate a vehicle takeover reminder when the vehicle has entered the target lane, and control the vehicle to enter a preset takeover control mode after pushing the vehicle takeover reminder to the user for a preset duration.

[0086] Optionally, in an embodiment of the present application, the control device 10 of the vehicle further includes: a receiving module and an optimization module.

[0087] Wherein, the receiving module is configured to receive the road determination signal of the user.

[0088] The optimization module is configured to optimize the environment model based on the road determination signal.

[0089] It should be noted that the foregoing explanation of the vehicle control method embodiment also applies to the vehicle control device of this embodiment, and will not be elaborated here.

[0090] The vehicle control device provided according to the embodiments of the present application can construct a corresponding environment model according to the surrounding environment perception data in the preset urban pilot assist mode, and perform fusion positioning using the current position of the vehicle and the environment model to obtain a high-precision actual position. Then, the target lane of the vehicle is determined using the actual position, navigation data, and intersection passing data, and the vehicle is controlled to enter the target lane to avoid the risks caused by insufficient coverage of the high-precision map, improve the driving safety of the urban pilot assist function in this scenario, and help improve the usability of the product in more complex scenarios. Thus, the technical problem in the related art that it is difficult for the vehicle to accurately follow the navigation route due to insufficient coverage of the high-precision map area, resulting in a large potential safety hazard, is solved.

[0091] Figure 5 The following is a schematic structural diagram of the vehicle provided by the embodiments of the present application. The vehicle may include:

[0092] A memory 501, a processor 502, and a computer program stored on the memory 501 and executable on the processor 502.

[0093] When the processor 502 executes the program, it implements the vehicle control method provided in the above embodiments.

[0094] Furthermore, the vehicle further includes:

[0095] A communication interface 503 for communication between the memory 501 and the processor 502.

[0096] A memory 501 for storing a computer program that can run on the processor 502.

[0097] The memory 501 may include high-speed RAM memory and may also include non-volatile memory, such as at least one disk memory.

[0098] If the memory 501, the processor 502, and the communication interface 503 are implemented independently, the communication interface 503, the memory 501, and the processor 502 can be interconnected through a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For the sake of representation, Figure 5 only a thick line is used to represent it in the figure, but it does not mean that there is only one bus or one type of bus.

[0099] Optionally, in a specific implementation, if the memory 501, the processor 502, and the communication interface 503 are integrated on a single chip, the memory 501, the processor 502, and the communication interface 503 can communicate with each other through an internal interface.

[0100] The processor 502 may be a Central Processing Unit (CPU), or an Application Specific Integrated Circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.

[0101] This embodiment also provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the control method of the vehicle as described above is implemented.

[0102] This application embodiment also provides a computer program product, including a computer program, and when the computer program is executed by a processor, the control method of the vehicle provided by the embodiments of the present invention is implemented.

[0103] In the description of this specification, the descriptions referring to terms such as "one embodiment", "some embodiments", "examples", "specific examples", or "some examples", etc., mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of this application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in any one or N embodiments or examples in a suitable manner. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0104] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include at least one of such features. In the description of this application, the meaning of "N" is at least two, such as two, three, etc., unless otherwise specifically defined.

[0105] Any process or method description depicted in a flowchart or otherwise described herein can be understood to represent a module, segment, or portion of code including one or N executable instructions for implementing a customized logic function or process, and the scope of the preferred embodiments of this application includes additional implementations, where the functions can be executed in a substantially simultaneous manner or in a reverse order according to the functions involved, rather than in the order shown or discussed, which should be understood by those skilled in the art to which the embodiments of this application pertain.

[0106] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a definite sequence list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device), or used in conjunction with these instruction execution systems, apparatuses, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device. More specific examples (non-exhaustive list) of computer-readable media include the following: an electrical connection portion (electronic device) having one or N wirings, a portable computer diskette (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically by optically scanning the paper or other media, followed by editing, interpretation, or otherwise processing as appropriate, and then storing it in a computer memory.

[0107] It should be understood that various parts of the present application can be implemented by hardware, software, firmware, or a combination thereof. In the above-described embodiments, the N steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application-specific integrated circuits having suitable combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc.

[0108] Those of ordinary skill in the art of this technology can understand that all or part of the steps carried by the methods of the above-described embodiments can be completed by instructing relevant hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiments.

[0109] In addition, each functional unit in various embodiments of the present application may be integrated into one processing module, may exist separately as individual physical units, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. When the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0110] The above-mentioned storage medium may be a read-only memory, a magnetic disk, an optical disc, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present application. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present application.

Claims

1. A control method for a vehicle, characterized in that, Including the following steps: Under the preset urban pilot assist mode, obtain the vehicle's navigation data, intersection passing data, and surrounding environment perception data; Based on the surrounding environment perception data, construct a corresponding environment model; Use the vehicle's current positioning and the environment model for integrated positioning to obtain the vehicle's actual positioning that meets the preset accuracy; Based on the actual positioning, the navigation data, and the intersection passing data, determine the vehicle's target lane and control the vehicle to drive into the target lane.

2. The method according to claim 1, wherein Before obtaining the vehicle's navigation data, intersection passing data, and surrounding environment perception data, it further includes: Obtain the vehicle's current driving state; When the current driving state is a road driving state and the vehicle is in a navigation-on state, determine whether the vehicle meets the preset data collection conditions based on the vehicle's lane line perception state; If the vehicle meets the preset data collection conditions, determine whether the vehicle meets the preset data processing conditions based on the vehicle's vehicle control parameters; If the vehicle meets the preset data processing conditions, determine whether the vehicle meets the preset mode activation conditions based on the current positioning and the navigation data in the navigation state; If the vehicle meets the preset mode activation conditions, control the vehicle to enter the preset urban pilot assist mode.

3. The method according to claim 1, wherein The constructing the corresponding environment model based on the surrounding environment perception data includes: Preprocess the surrounding environment perception data to obtain perception data that meets the preset processing conditions; Process the perception data to extract multiple objects and road structures in the vehicle's surrounding environment from the perception data; Classify the objects, and respectively perform static modeling and dynamic modeling based on the classification results and the road structure, and fuse the results of the static modeling and the dynamic modeling to obtain the environment model.

4. The method according to claim 1, wherein It further includes: Update the vehicle's current positioning, and based on the updated current positioning, determine whether the vehicle has driven into the target lane; If the vehicle has driven into the target lane, generate a vehicle takeover reminder, and after pushing the vehicle takeover reminder to the user for a preset duration, control the vehicle to enter the preset takeover control mode.

5. The method according to claim 1, characterized in that, It further includes: Receive the user's road determination signal; Optimize the environment model based on the road determination signal.

6. A control device for a vehicle, characterized in that, It includes: An acquisition module for obtaining the vehicle's navigation data, intersection passing data, and surrounding environment perception data under the preset urban pilot assist mode; A construction module for constructing a corresponding environment model based on the surrounding environment perception data; A fusion module for using the vehicle's current positioning and the environment model for integrated positioning to obtain the vehicle's actual positioning that meets the preset accuracy; A control module for determining the vehicle's target lane based on the actual positioning, the navigation data, and the intersection passing data, and controlling the vehicle to drive into the target lane.

7. The device according to claim 6, characterized in that, It further includes: A second acquisition module for obtaining the vehicle's current driving state; The first judgment module is used to judge whether the vehicle meets the preset data acquisition condition based on the lane line perception state of the vehicle when the current driving state is the road driving state and the vehicle is in the navigation-on state; The second judgment module is used to judge whether the vehicle meets the preset data processing condition based on the vehicle's overall vehicle control parameters when the vehicle meets the preset data acquisition condition; The third judgment module is used to judge whether the vehicle meets the preset mode activation condition based on the current positioning and the navigation data in the navigation state when the vehicle meets the preset data processing condition; The fourth judgment module is used to control the vehicle to enter the preset urban pilot assistance mode when the vehicle meets the preset mode activation condition.

8. The device according to claim 6, wherein The construction module includes: The first processing unit is used to preprocess the surrounding environment perception data to obtain perception data that meets the preset processing conditions; The second processing unit is used to process the perception data and extract multiple objects and road structures in the surrounding environment of the vehicle from the perception data; The construction unit is used to classify the objects, perform static modeling and dynamic modeling respectively based on the classification results and the road structure, and fuse the results of the static modeling and the dynamic modeling to obtain the environment model.

9. A vehicle, characterized in that, It includes: A memory, a processor, and a computer program stored on the memory and executable on the processor, and the processor executes the program to implement the vehicle control method according to any one of claims 1-5.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to be used to implement the vehicle control method according to any one of claims 1-5.

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