Geofence determination method and apparatus, vehicle, medium, and program

Abstract

The application relates to the technical field of vehicles, in particular to a method and device for determining a geofence, a vehicle, a medium and a program, wherein the method comprises the following steps: recognizing a road type corresponding to a current position of a vehicle; acquiring sensing data corresponding to the current position of the vehicle and collected by the vehicle itself; and determining a relative position relationship between the vehicle and the geofence based on the road type and the sensing data. Thus, the problems that a sensor is unstable, the vehicle position information is not accurate, and a high-precision positioning device is relatively expensive in the prior art are solved.

Description

Technical Field

[0001] This application relates to the field of vehicle technology, and in particular to a method, device, vehicle, medium and procedure for determining geofencing. Background Technology

[0002] With the development of autonomous driving technology, increasingly advanced autonomous driving functions are being incorporated into mass-produced vehicles. However, due to legal and regulatory restrictions and safety considerations, these advanced autonomous driving functions need to be limited to fixed areas, requiring prior determination of whether the vehicle is within a designated geofence.

[0003] In existing technologies, there are two main methods for determining whether a vehicle is within a geofence:

[0004] 1) It relies on high-precision maps and high-precision positioning equipment, but there is instability when obtaining vehicle location information through satellite positioning, and the accuracy of vehicle location information cannot be guaranteed. In actual operation, the satellite positioning system may locate on other adjacent roads, which may lead to misjudgment when the vehicle is performing autonomous driving.

[0005] 2) Relying on single-vehicle perception to build maps in advance and store them locally, and then using perception to identify road features for real-time matching, this solution is suitable for high-level autonomous driving solutions in urban areas, but the cost of the system's sensors is also very high, and it is usually point-to-point traffic.

[0006] In summary, existing technologies cannot guarantee the accuracy and stability of vehicle location information, and high-precision equipment is also expensive. Summary of the Invention

[0007] This application provides a method, device, vehicle, medium, and procedure for determining geofencing, in order to solve the problems in related technologies, such as unstable sensors leading to inaccurate vehicle location information acquisition and high cost of high-precision positioning equipment.

[0008] The first aspect of this application provides a method for determining a geofence, comprising the following steps: identifying the road type corresponding to the current location of a vehicle; acquiring sensing data corresponding to the current location collected by the vehicle itself; and determining the relative positional relationship between the vehicle and the geofence based on the road type and the sensing data.

[0009] Optionally, determining the relative position of the vehicle and the geofence based on the road type and the sensing data includes: when the road type is a first road type, identifying the number of lanes, lane width, road curvature radius, and duration of traffic light status, intersection conditions, pedestrian conditions, and other vehicle conditions corresponding to the first road type in the sensing data; if the number of lanes is greater than a first preset number, the lane width is greater than or equal to a first preset threshold, the road curvature radius is greater than or equal to a second preset threshold, the duration of no traffic light detection is greater than or equal to a first preset duration, the duration of no intersection detection is greater than a second preset duration, and the duration of no pedestrian or other vehicle type detection is greater than or equal to a third preset duration, then determining that the vehicle is outside the geofence; otherwise, determining that the vehicle is inside the geofence.

[0010] Optionally, determining the relative position of the vehicle and the geofence based on the road type and the perception data includes: when the road type is a second road type, identifying the subsequent road type of the second road type; if the subsequent road type of the second road type is a first road type, and the duration of driving on the first road type is greater than a fourth preset duration, then determining that the vehicle is outside the geofence.

[0011] Optionally, determining the relative position of the vehicle and the geofence based on the road type and the sensing data includes: determining that the vehicle is within the geofence when the road type is a third road type, or when the road type is a second road type and the distance between the second road type and the preset point is less than a third preset threshold.

[0012] Optionally, after determining the relative positional relationship between the vehicle and the geofence based on the road type and the perception data, the method includes: optimizing the high-precision map based on the relative positional relationship.

[0013] Optionally, after determining the relative positional relationship between the vehicle and the geofence based on the road type and the perception data, the method includes: when the vehicle is within the geofence, controlling the vehicle to automatically activate the driver assistance function; otherwise, deactivating the driver assistance function.

[0014] A second aspect of this application provides a geofence determination device, comprising: an identification module for identifying the road type corresponding to the current location of a vehicle; an acquisition module for acquiring perception data corresponding to the current location collected by the vehicle itself; and a determination module for determining the relative positional relationship between the vehicle and the geofence based on the road type and the perception data.

[0015] A third aspect of this application provides a vehicle, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the geofencing determination method as described in the above embodiments.

[0016] A fourth aspect of this application provides a computer-readable storage medium having a computer program stored thereon, which is executed by a processor to implement the geofencing determination method as described in the above embodiments.

[0017] A fifth aspect of this application provides a computer program product, including a computer program, which, when executed, is used to implement the geofencing determination method as described in the above embodiments.

[0018] Therefore, this application has at least the following beneficial effects:

[0019] This application determines the relative position of a vehicle to a geofence by combining the road type corresponding to the vehicle's current location with the perception data collected by the vehicle itself, thereby saving overall costs and improving the accuracy of geofence recognition.

[0020] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0021] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0022] Figure 1 This is a flowchart illustrating a geofence determination method according to an embodiment of this application;

[0023] Figure 2 This is a flowchart of a geofence determination method according to an embodiment of this application;

[0024] Figure 3 This is a flowchart of a geofence determination method according to another embodiment of this application;

[0025] Figure 4 This is a block diagram of a geofencing determination device according to an embodiment of this application;

[0026] Figure 5 This is a structural schematic diagram of a vehicle according to an embodiment of this application. Detailed Implementation

[0027] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0028] In related technology (1), high-precision maps and positioning equipment are used, combined with the detection information from the vehicle's own multi-source sensors, to determine whether the vehicle is within a preset geofence, as follows:

[0029] Steps: 1) Obtain the vehicle's current location through the vehicle positioning device; 2) Calculate the relative positional relationship between the vehicle and the road edge based on the obtained positioning information and high-precision map data, which is regarded as the first relative positional relationship; 3) Obtain the relative positional relationship between the vehicle and the road edge based on the detection information of the vehicle's own sensors, which is regarded as the second relative positional relationship; 4) Determine whether the vehicle is within the geofence by comparing the first relative positional relationship and the second relative positional relationship.

[0030] In related technology (2), a geofencing model is constructed based on machine learning or deep learning. The input is geographical location and environmental information, and the output is the on / off status of vehicle functions, as follows:

[0031] Steps: 1) Obtain geographic location information and environmental information; 2) Input the geographic location information and environmental information into the preset geofence model corresponding to the vehicle to obtain the status of whether the function can be enabled.

[0032] However, the aforementioned existing technologies require high-precision maps and positioning equipment, which are costly and unsuitable for low-cost intelligent driving solutions. They also require data collection in advance for model training and consume significant computing power.

[0033] Therefore, this application determines the actual position of the vehicle by combining the reference data of the high-precision map corresponding to the current position of the vehicle and the perception data of the current position collected by the vehicle itself. It can accurately identify highways and urban expressways, so as to develop a relatively low-hardware-cost autonomous driving solution and also to develop advanced autonomous driving functions.

[0034] The following description, with reference to the accompanying drawings, describes a method, apparatus, vehicle, storage medium, and program product for determining geofencing according to embodiments of this application. Specifically, Figure 1 This is a flowchart illustrating a geofence determination method provided in an embodiment of this application.

[0035] like Figure 1 As shown, the method for determining a geofence includes the following steps:

[0036] In step S101, the road type corresponding to the vehicle's current location is identified.

[0037] It is understood that the embodiments of this application can identify the road type corresponding to the current location of the vehicle, so as to determine the relative positional relationship between the vehicle and the geofence based on reference data and perception data.

[0038] It should be noted that this application can obtain the road type corresponding to the vehicle's current location through satellite positioning systems or navigation maps.

[0039] In step S102, the perception data corresponding to the current position collected by the vehicle itself is obtained.

[0040] It is understood that the embodiments of this application can acquire the perception data corresponding to the current position collected by the vehicle itself through sensors such as cameras and millimeter-wave radar, so as to determine the relative positional relationship between the vehicle and the geofence based on the reference data and perception data.

[0041] In step S103, the actual location of the vehicle is determined to be inside or outside the geofence based on reference data and perception data.

[0042] It is understood that the embodiments of this application determine the relative positional relationship between the vehicle and the geofence based on reference data and perception data, thereby saving overall costs and improving the accuracy of fence recognition.

[0043] In this embodiment of the application, the relative positional relationship between the vehicle and the geofence is determined based on the road type and perception data, including: when the road type is a first road type, the number of lanes, lane width, road curvature radius, and duration of traffic light status, intersection conditions, pedestrian conditions, and other vehicle conditions corresponding to the first road type are identified in the perception data; if the number of lanes is greater than a first preset number, the lane width is greater than or equal to a first preset threshold, the road curvature radius is greater than or equal to a second preset threshold, the duration of no traffic light identification is greater than or equal to a first preset duration, the duration of no intersection identification is greater than a second preset duration, and the duration of no pedestrian or other vehicle types identification is greater than or equal to a third preset duration, then the vehicle is determined to be outside the geofence; otherwise, the vehicle is determined to be inside the geofence.

[0044] The first preset quantity can be 3, the first preset threshold can be 3.5m, the second preset threshold can be 200m, the first preset duration can be 20min, the second preset duration can be 20min, and the third preset duration can be 20min. These settings can be made according to actual needs and are not specifically limited.

[0045] It is understood that the embodiments of this application can determine whether a vehicle is inside or outside a geofence based on the duration of perception data such as the number of lanes, lane width, road curvature radius, traffic light status, intersection conditions, pedestrian conditions, and other vehicle conditions on highways, thereby improving the accuracy of identification in different scenarios.

[0046] In this embodiment of the application, the relative positional relationship between the vehicle and the geofence is determined based on the road type and perception data, including: when the road type is the second road type, the subsequent road type of the second road type is identified; if the subsequent road type of the second road type is the first road type, and the duration of driving on the first road type is greater than a fourth preset duration, the vehicle is determined to be outside the geofence.

[0047] The fourth preset duration can be 1 hour, and can be set according to actual needs without specific limitations.

[0048] It is understood that in this embodiment of the application, if the road type is a ramp, the subsequent road type of the ramp is identified. If the subsequent road type of the ramp is a highway, and the duration of driving on the first road type is greater than the fourth preset duration, the vehicle is determined to be outside the geofence, thereby improving the accuracy of identification.

[0049] In this embodiment of the application, the relative positional relationship between the vehicle and the geofence is determined based on the road type and perception data, including: when the road type is a third road type, or when the road type is a second road type and the distance between the second road type and the preset point is less than a third preset threshold, the vehicle is determined to be within the geofence.

[0050] The third preset threshold can be 1km, and can be set according to actual needs without specific limitations.

[0051] It is understood that, in this application embodiment, when the road type is neither a highway nor a ramp, and the road type is a second road type and the distance between the second road type and the preset point is less than a third preset threshold, the vehicle is determined to be within the geofence, thereby improving the accuracy of identification.

[0052] It should be noted that the first road type is expressway, the second road type is ramp, and the third road type is non-expressway and non-ramp road. The preset point can be a toll station, without specific restrictions.

[0053] In this embodiment of the application, after determining the relative positional relationship between the vehicle and the geofence based on the road type and perception data, the process includes: optimizing the high-precision map based on the relative positional relationship.

[0054] It is understood that the embodiments of this application can optimize high-precision maps based on relative positional relationships, thereby improving the accuracy of high-precision maps.

[0055] In this embodiment of the application, after determining the relative positional relationship between the vehicle and the geofence based on road type and perception data, the method includes: when the vehicle is within the geofence, controlling the vehicle to automatically activate the assisted driving function; otherwise, deactivating the assisted driving function.

[0056] It is understood that, according to the embodiments of this application, the vehicle can be controlled to automatically activate the assisted driving function when it is within the geofence, and otherwise the assisted driving function can be deactivated, so as to limit the operating range of the advanced autonomous driving function and improve system safety.

[0057] According to the geofence determination method proposed in this application, the relative positional relationship between the vehicle and the geofence is determined by the road type corresponding to the vehicle's current location and the perception data corresponding to the vehicle's current location collected by the vehicle itself, which saves overall costs and improves the accuracy of fence recognition.

[0058] The following will combine Figure 2 and Figure 3 The method for determining geofencing in this application is described in detail below:

[0059] Step 1: Receive vehicle location information provided by the navigation map.

[0060] The location information provided by the navigation map refers to the type of road the vehicle is on.

[0061] Step 2, Obtain vehicle perception and recognition information

[0062] The perception and recognition information includes, but is not limited to, the number of lanes, lane width, road curvature radius, traffic lights, intersections, pedestrians, and two-wheeled vehicles.

[0063] Step 3, combining location information and perception recognition information, the basic method for determining whether a vehicle is within a preset geofence is as follows. It should be noted that this application uses highways or non-highways as a specific implementation for illustration. Extensions based on this basic method, such as geofence determination methods for elevated roads and tunnels, can all be determined using the method of this application, and therefore are all within the scope of protection of this application:

[0064] 1) By default, the vehicle is outside the geofence when powered on;

[0065] 2) The current vehicle is outside the geofence and meets any of the following conditions:

[0066] ① The road type is a highway with the number of lanes ≥ Num1, the lane width ≥ Width1, the road curvature radius ≥ Radius1, and no traffic lights, intersections, pedestrians, or two-wheeled vehicles are detected, and the duration is ≥ T1.

[0067] ② If the road type is a ramp (including expressway interchanges JCT) and the subsequent road type after the ramp is an expressway, and the duration is ≥ T2, then the vehicle is considered to be within the geofence.

[0068] 3) The vehicle is currently within a geofence, meeting any one of the following conditions.

[0069] ① The road type is neither a highway nor a ramp.

[0070] ② The road type is a highway, but the following situations occur: number of lanes < Num2 or lane width < Width2 or road curvature radius < Radius2 or traffic lights are detected or intersections are detected or pedestrians and two-wheeled vehicles are detected.

[0071] ③ If the road type is a ramp (including JCT expressway interchange) and the distance to the toll station ahead is ≤ length, then the vehicle is considered to be outside the geofence.

[0072] In summary, this application presents an autonomous driving solution based on relatively low hardware costs that can accurately identify highways and urban expressways, thereby limiting the operating range of advanced autonomous driving functions and improving system safety.

[0073] Next, the geofencing determination device according to an embodiment of this application is described with reference to the accompanying drawings.

[0074] Figure 4 This is a block diagram of a geofence determination device according to an embodiment of this application.

[0075] like Figure 4 As shown, the geofence determination device 10 includes: an identification module 100, an acquisition module 200, and a determination module 300.

[0076] The identification module 100 is used to identify the road type corresponding to the current location of the vehicle; the acquisition module 200 is used to acquire the perception data corresponding to the current location collected by the vehicle itself; and the determination module 300 is used to determine the relative positional relationship between the vehicle and the geofence based on the reference data and the perception data.

[0077] It should be noted that the explanation of the above-described method for determining geofence also applies to the geofence determination device of this embodiment, and will not be repeated here.

[0078] According to the geofence determination device proposed in the embodiments of this application, the relative positional relationship between the vehicle and the geofence is determined by the road type corresponding to the current position of the vehicle and the perception data corresponding to the current position collected by the vehicle itself, which saves the overall cost and improves the accuracy of fence recognition.

[0079] Figure 5A schematic diagram of the structure of a vehicle provided in an embodiment of this application. The vehicle may include:

[0080] The memory 501, the processor 502, and the computer program stored on the memory 501 and capable of running on the processor 502.

[0081] When the processor 502 executes the program, it implements the geofence determination method provided in the above embodiments.

[0082] Furthermore, the vehicle also includes:

[0083] Communication interface 503 is used for communication between memory 501 and processor 502.

[0084] The memory 501 is used to store computer programs that can run on the processor 502.

[0085] The memory 501 may include high-speed RAM (Random Access Memory) memory, and may also include non-volatile memory, such as at least one disk storage.

[0086] If the memory 501, processor 502, and communication interface 503 are implemented independently, then the communication interface 503, memory 501, and processor 502 can be interconnected via a bus to complete communication between them. The bus can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 5 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0087] Optionally, in a specific implementation, if the memory 501, processor 502, and communication interface 503 are integrated on a single chip, then the memory 501, processor 502, and communication interface 503 can communicate with each other through an internal interface.

[0088] The processor 502 may be a CPU (Central Processing Unit), an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiments of this application.

[0089] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the above-described geofence determination method.

[0090] This application also provides a computer program product, which, when executed, is used to implement the geofence determination method as described in the above embodiments.

[0091] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0092] Furthermore, 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 number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0093] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0094] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or more of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (FPGAs), field-programmable gate arrays (FPGAs), etc.

[0095] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0096] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. A method of determining a geo-fence, characterized by, The method comprises the following steps: identifying a road type corresponding to a current position of a vehicle; acquiring perception data corresponding to the current position collected by the vehicle itself; determining a relative position relationship between the vehicle and a geofence based on the road type and the perception data; wherein the determining the relative position relationship between the vehicle and the geofence based on the road type and the perception data comprises: when the road type is a first road type, identifying a number of lanes, a lane width, a road curvature radius, and a duration of a red light state, an intersection situation, a pedestrian situation, and other vehicle situations corresponding to the first road type in the perception data; if the number of lanes is greater than a first preset number, the lane width is greater than or equal to a first preset threshold, the road curvature radius is greater than or equal to a second preset threshold, a duration of not identifying the red light state is greater than or equal to a first preset duration, a duration of not identifying the intersection is greater than a second preset duration, and a duration of not identifying the pedestrian and other vehicle types is greater than or equal to a third preset duration, it is determined that the vehicle is outside the geofence, otherwise it is determined that the vehicle is inside the geofence; wherein the determining the relative position relationship between the vehicle and the geofence based on the road type and the perception data comprises: when the road type is a second road type, identifying a subsequent road type of the second road type; if the subsequent road type of the second road type is the first road type, and a duration of driving on the first road type is greater than a fourth preset duration, it is determined that the vehicle is outside the geofence; wherein the determining the relative position relationship between the vehicle and the geofence based on the road type and the perception data comprises: when the road type is a third road type, or when the road type is the second road type and a distance between the second road type and a preset point is less than a third preset threshold, it is determined that the vehicle is inside the geofence.

2. The determination method of a geofence according to claim 1, characterized in that, After the determining the relative position relationship between the vehicle and the geofence based on the road type and the perception data, the method comprises: optimizing a high-definition map based on the relative position relationship.

3. The determination method of a geo-fence according to claim 1, wherein, After the determining the relative position relationship between the vehicle and the geofence based on the road type and the perception data, the method comprises: when the vehicle is inside the geofence, controlling the vehicle to automatically start an auxiliary driving function, otherwise, turning off the auxiliary driving function.

4. A determination device of a geofence, characterized by, The method comprises: an identifying module configured to identify a road type corresponding to a current position of a vehicle; an acquiring module configured to acquire perception data corresponding to the current position collected by the vehicle itself; determining a relative position relationship between the vehicle and the geo-fence based on the road type and the perception data, wherein the determining the relative position relationship between the vehicle and the geo-fence based on the road type and the perception data comprises: when the road type is a first road type, identifying a number of lanes, a lane width, a road curvature radius, and a duration of a red light state, an intersection situation, a pedestrian situation, and other vehicle situations corresponding to the first road type in the perception data; and if the number of lanes is greater than a first preset number, the lane width is greater than or equal to a first preset threshold, the road curvature radius is greater than or equal to a second preset threshold, a duration of the red light state is greater than or equal to a first preset time, a duration of the intersection situation is greater than a second preset time, and a duration of the pedestrian situation and the other vehicle situations is greater than or equal to a third preset time, determining that the vehicle is outside the geo-fence, otherwise, determining that the vehicle is inside the geo-fence; wherein the determining the relative position relationship between the vehicle and the geo-fence based on the road type and the perception data comprises: when the road type is a second road type, identifying a subsequent road type of the second road type; and if the subsequent road type of the second road type is the first road type and a duration of driving on the first road type is greater than a fourth preset time, determining that the vehicle is outside the geo-fence. wherein the determining the relative position relationship between the vehicle and the geo-fence based on the road type and the perception data comprises: when the road type is a third road type, or when the road type is the second road type and a distance between the second road type and a preset point is less than a third preset threshold, determining that the vehicle is inside the geo-fence.

5. A vehicle characterized by comprising: comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the computer program to implement the method for determining the geo-fence according to any one of claims 1-3.

6. A computer readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the method for determining the geo-fence according to any one of claims 1-3.

7. A computer program product, characterised in that, The computer program is executed to implement the method for determining the geo-fence according to any one of claims 1-3.

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