Method for determining drivable area, vehicle control method, device and chip

Abstract

Embodiments of the present disclosure provide a method for determining a drivable area, a vehicle control method, a device, and a chip, which relate to the field of autonomous driving. Among them, the method for determining the drivable area includes: determining a target area according to environmental perception data; determining at least two boundary extension areas corresponding to the target area according to the environmental perception data and multiple area boundary lines of the target area; determining the drivable area corresponding to the target area according to the at least two boundary extension areas; wherein, the drivable area is an area for path planning when the vehicle enters the target area, the environmental perception data includes data detected at multiple moments, and the at least two boundary extension areas include a first boundary extension area locked at a first moment and a second boundary extension area locked at a second moment, and the first moment and the second moment are different moments.

Description

Technical Field

[0001] The present disclosure relates to the field of autonomous driving technology, and more particularly, to a method for determining a drivable area, a vehicle control method, a device, and a chip. Background Art

[0002] In autonomous driving technology, path planning can be performed on a vehicle to control the vehicle to drive into a specific area such as a parking space. In order to perform path planning, environmental detection can be performed on the specific area and its surrounding areas to determine the drivable area, and path planning can be performed within the drivable area to control the vehicle to drive into the specific area. Therefore, how to determine an accurate and reliable drivable area is of great significance for path planning and vehicle control in autonomous driving. Summary of the Invention

[0003] In view of this, embodiments of the present disclosure propose a new technical solution for determining a drivable area.

[0004] According to a first aspect of embodiments of the present disclosure, there is provided a method for determining a drivable area, the method including:

[0005] Determining a target area according to environmental perception data; wherein the target area includes a plurality of area boundary lines;

[0006] Determining at least two boundary extension areas corresponding to the target area according to the environmental perception data and the plurality of area boundary lines of the target area; wherein the environmental perception data includes data detected at a plurality of moments, and the at least two boundary extension areas include a first boundary extension area locked at a first moment and a second boundary extension area locked at a second moment, and the first moment and the second moment are different moments;

[0007] Determining a drivable area corresponding to the target area according to the at least two boundary extension areas; wherein the drivable area is an area for path planning when the vehicle drives into the target area.

[0008] Optionally, the determining at least two boundary extension areas corresponding to the target area according to the environmental perception data and the plurality of area boundary lines of the target area includes:

[0009] Determining an initial range of the boundary extension area according to the plurality of area boundary lines of the target area;

[0010] For each boundary extension area, determining a target range of the boundary extension area according to the environmental perception data and the initial range.

[0011] Optionally, determining the target range of the boundary extension area according to the environmental perception data and the initial range includes:

[0012] Determining candidate reference points and the confidence levels of the candidate reference points at each of multiple moments for the boundary extension area according to the environmental perception data; wherein, the candidate reference points are located within the initial range, and the confidence levels of the candidate reference points of different boundary extension areas are different at at least one moment;

[0013] Based on the candidate reference points and the confidence levels of the candidate reference points at the multiple moments, determining the target reference point and the confidence level of the target reference point of the boundary extension area at the current moment;

[0014] Determining the target range of the boundary extension area according to the target reference point and the confidence level.

[0015] Optionally,

[0016] The confidence levels of the candidate reference points of the first boundary extension area at multiple moments after the first moment are less than the confidence level of the target reference point of the first boundary extension area at the first moment;

[0017] The confidence levels of the candidate reference points of the second boundary extension area at multiple moments after the second moment are less than the confidence level of the target reference point of the second boundary extension area at the second moment.

[0018] Optionally, determining the candidate reference points and the confidence levels of the candidate reference points at each of multiple moments for the boundary extension area according to the environmental perception data includes:

[0019] Determining obstacle information located within the initial range of the boundary extension area according to the environmental perception data;

[0020] Determining the candidate reference points of the boundary extension area according to the obstacle information;

[0021] Determining the confidence level of the candidate reference point according to the coordinates of the candidate reference point and the sensor information of the vehicle.

[0022] Optionally, the obstacle information includes the coordinates of multiple obstacle position points; determining the candidate reference points of the boundary extension area according to the obstacle information includes:

[0023] Taking the minimum distance among the perpendicular distances from multiple said obstacle position points to the boundary line of the target area as the first distance; wherein, the boundary line of the target area is the area boundary line corresponding to the boundary extension area where the obstacle position point is located;

[0024] Take the minimum distance among the vertical distances from multiple obstacle position points to the entry boundary line as the second distance; wherein, the entry boundary line is the boundary line among the multiple area boundary lines that serves as the vehicle entry entrance, and the area boundary line corresponding to the boundary extension area is perpendicular to the entry boundary line.

[0025] Determine a candidate reference point for the boundary extension area according to the area boundary line, the entry boundary line, the first distance, and the second distance.

[0026] Optionally, the determining the confidence level of the candidate reference point according to the coordinates of the candidate reference point and the sensor information of the vehicle includes:

[0027] Calculate at least one of a distance detection confidence factor, a direction detection confidence factor, a multi-sensor assisted detection confidence factor, and a visibility detection confidence factor for the candidate reference point according to the coordinates of the candidate reference point and the sensor information of the vehicle;

[0028] Determine the confidence level of the candidate reference point according to at least one of the distance detection confidence factor, the direction detection confidence factor, the multi-sensor assisted detection confidence factor, and the visibility detection confidence factor.

[0029] Optionally, the sensors installed on the vehicle include multiple cameras, wherein:

[0030] The distance detection confidence factor is the detection confidence calculated according to the distance from the candidate reference point to the target camera and the maximum sensing distance of the target camera; the target camera is the camera among the multiple cameras installed on the vehicle that can detect the candidate reference point and is the closest in distance;

[0031] The direction detection confidence factor is the detection confidence determined according to the angle between the direction from the candidate reference point to the target camera and the detection center direction of the target camera;

[0032] The multi-sensor assisted detection confidence factor is used to indicate whether the candidate reference point is detected by multiple types of sensors installed on the vehicle;

[0033] The visibility detection confidence factor is used to indicate the proportion of pixels within a specific area range centered on the candidate reference point that can be detected by the cameras installed on the vehicle.

[0034] Optionally, the multiple moments are multiple moments arranged in ascending order of time; the determining the target reference point of the boundary extension area and the confidence level of the target reference point at the current moment based on the candidate reference points at the multiple moments and the confidence levels of the candidate reference points includes:

[0035] In the case where the current moment is the earliest moment among the multiple moments, use the candidate reference point and confidence level of the boundary extension region at the earliest moment as the target reference point and confidence level at the earliest moment; or,

[0036] In the case where the current moment is the i-th moment among the multiple moments except the earliest moment, determine the target reference point and confidence level at the i-th moment according to the candidate reference point and confidence level at the i-th moment and the target reference point and confidence level at the (i - 1)-th moment.

[0037] Optionally, the determining the target reference point and confidence level at the i-th moment according to the candidate reference point and confidence level at the i-th moment and the target reference point and confidence level at the (i - 1)-th moment includes:

[0038] If the confidence level of the candidate reference point at the i-th moment is greater than or equal to the confidence level of the target reference point at the (i - 1)-th moment, then after adjusting the target reference point and confidence level at the (i - 1)-th moment according to the candidate reference point and confidence level at the i-th moment, obtain the target reference point and confidence level at the i-th moment; or,

[0039] If the confidence level of the specific position point at the i-th moment is greater than or equal to the confidence level of the specific position point at the (i - 1)-th moment, then after adjusting the target reference point and confidence level at the (i - 1)-th moment according to the candidate reference point and confidence level at the i-th moment, obtain the target reference point and confidence level at the i-th moment; where the specific position point is the target reference point at the (i - 1)-th moment; or,

[0040] If the confidence level of the candidate reference point at the i-th moment is less than the confidence level of the target reference point at the (i - 1)-th moment and the confidence level of the specific position point at the i-th moment is less than the confidence level of the specific position point at the (i - 1)-th moment, then use the target reference point and confidence level at the (i - 1)-th moment as the target reference point and confidence level at the i-th moment.

[0041] Optionally, the determining the initial range of the boundary extension region according to the multiple region boundary lines of the target region includes:

[0042] Determine the entry boundary line among the multiple region boundary lines according to the current position of the vehicle; where the entry boundary line is the entrance for the vehicle to enter the target region;

[0043] Determine at least two region boundary lines among the other region boundary lines except the entry boundary line, and determine the corresponding boundary extension region and initial range for each of the at least two region boundary lines.

[0044] Optionally, the target region is a target parking space.

[0045] Optionally, the method further includes: displaying, by a display device, the target area and the drivable area corresponding to the target area.

[0046] According to a second aspect of embodiments of the present disclosure, a method for determining a drivable area is provided. The method includes:

[0047] Determining a target area according to environmental perception data; wherein the target area includes a plurality of area boundary lines;

[0048] Determining at least one boundary extension area corresponding to the target area according to the environmental perception data and the plurality of area boundary lines of the target area; wherein the environmental perception data includes data detected at a plurality of moments and used to determine the boundary extension area, and each boundary extension area is determined at a specific moment, and the range of the boundary extension area is not updated after the specific moment;

[0049] Determining the drivable area corresponding to the target area according to the at least one boundary extension area; wherein the drivable area is an area for path planning when a vehicle enters the target area.

[0050] According to a third aspect of embodiments of the present disclosure, a vehicle control method is provided. The method includes:

[0051] Determining a target area and a drivable area corresponding to the target area according to environmental perception data; wherein the target area includes a plurality of area boundary lines;

[0052] In a case where the target area is determined as a target area to be entered by the vehicle, performing path planning within the drivable area, and controlling the vehicle to enter the target area based on the planned path;

[0053] Wherein, the drivable area is an area for path planning when a vehicle enters the target area determined according to at least one boundary extension area; the at least one boundary extension area is at least one boundary extension area corresponding to the target area determined according to the environmental perception data and the plurality of area boundary lines of the target area; wherein the environmental perception data includes data detected at a plurality of moments, and each boundary extension area is determined at a specific moment.

[0054] According to a fourth aspect of embodiments of the present disclosure, an electronic device is provided, including a memory and a processor. The memory is used to store computer instructions, and the processor is used to call the computer instructions from the memory to execute the method according to any one of the first aspect, the second aspect or the third aspect.

[0055] According to a fourth aspect of the embodiments of the present disclosure, a vehicle is provided, including a memory and a processor. The memory is configured to store computer instructions, and the processor is configured to call the computer instructions from the memory to execute the method according to any one of the first aspect, the second aspect, or the third aspect.

[0056] According to a fifth aspect of the embodiments of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the method according to any one of the first aspect, the second aspect, or the third aspect is implemented.

[0057] According to a sixth aspect of the embodiments of the present disclosure, a chip is provided, including a processing unit configured to execute the method according to any one of the first aspect, the second aspect, or the third aspect.

[0058] Based on the method for determining a drivable area provided by the embodiments of the present disclosure, the area around the target area is divided into at least one boundary extension area according to the area boundary line of the target area. During the movement of the vehicle, the target range of each boundary extension area can be determined respectively, and the target range of each boundary extension area can be obtained more flexibly and accurately, so that the drivable area determined according to the boundary extension area is more accurate and reliable, and the safety and reliability of vehicle control are improved.

[0059] Other features and advantages of the present disclosure will become clear through the following detailed description of the exemplary embodiments of the present disclosure with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] The drawings incorporated in the specification and constituting a part of the specification illustrate embodiments of the present disclosure and, together with the description, are used to explain the principles of the present disclosure.

[0061] Figure 1 is a schematic diagram of an intelligent networked system to which the method provided by the embodiments of the present disclosure can be applied.

[0062] Figure 2 is according to Figure 1 A schematic diagram of a vehicle provided according to the illustrated embodiment.

[0063] Figure 3 is a schematic diagram of an obstacle existing in a parking scenario provided by the embodiments of the present disclosure.

[0064] Figure 4 is a flowchart of a method for determining a drivable area provided by the embodiments of the present disclosure.

[0065] Figure 5 is a schematic diagram of a boundary extension area where the target area is a vertical area provided by the embodiments of the present disclosure.

[0066] Figure 6 It is a schematic diagram of a boundary extension area with a target area being a parallel area provided by an embodiment of the present disclosure.

[0067] Figure 7 It is a schematic diagram of the relative position between a vehicle and an obstacle provided by an embodiment of the present disclosure.

[0068] Figure 8 It is a schematic flowchart of a method for determining a drivable area provided by an embodiment of the present disclosure.

[0069] Figure 9 It is a schematic flowchart of a vehicle control method provided by an embodiment of the present disclosure.

[0070] Figure 10 It is a schematic structural diagram of an electronic device provided by an embodiment of the present disclosure. Detailed implementation manners

[0071] Now, various exemplary embodiments of the present disclosure will be described in detail with reference to the accompanying drawings. It should be noted that: Unless otherwise specifically stated, the relative arrangements of components and steps, numerical expressions, and values set forth in these embodiments do not limit the scope of the present disclosure.

[0072] The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way a limitation on the present disclosure or its application or use.

[0073] Technologies, methods, and devices known to those of ordinary skill in the relevant art may not be discussed in detail, but where appropriate, the above-mentioned technologies, methods, and devices should be regarded as part of the specification.

[0074] In all the examples shown and discussed here, any specific value should be construed as merely exemplary and not as a limitation. Therefore, other examples of the exemplary embodiments may have different values.

[0075] It should be noted that: Similar reference numerals and letters denote similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further discussed in subsequent drawings.

[0076] First, the application scenarios of the embodiments of the present disclosure will be described.

[0077] Figure 1 It is a schematic diagram of an intelligent networked system 100 to which the method provided by the embodiments of the present disclosure can be applied. As Figure 1 shown, the intelligent networked system 100 may include: a vehicle 101, a server 102, and a user terminal 103.

[0078] In some examples, vehicle 101 can be a vehicle with autonomous driving capabilities. Among them, autonomous driving is also known as driverless or intelligent driving. A vehicle with autonomous driving capabilities can perform driving tasks such as environmental perception, decision-making and planning, and control execution. The levels of autonomous driving can refer to the automotive intelligence classification standard formulated by the Society of Automotive Engineers (SAE). For example, level L0 is manual driving, L1 is assisted driving, L2 is partial autonomous driving, L3 is conditional autonomous driving, L4 is highly autonomous driving, and L5 is fully autonomous driving. The above classification methods for autonomous driving levels are only for example, and the present disclosure embodiments do not limit the classification criteria and levels of autonomous driving.

[0079] In some examples, server 102 can be a single server or a distributed server cluster composed of multiple servers. Its deployment method can include a local server or a cloud server. The server 102 can communicate with vehicle 101 and / or user terminal 103 based on a communication network, and provide various services for vehicle 101 and / or user terminal 103. For example, the server can receive the perception data sent by the vehicle and provide services such as high-precision maps, data analysis, and decision-making and planning for the vehicle. Another example is that the server can receive query instructions or control instructions sent by the user terminal and provide corresponding services for the user.

[0080] In some examples, user terminal 103 can be any form of electronic device that provides services for users, such as a personal computer, a laptop, a smart tablet, a smart phone, a smart wearable device, etc. Users can interact with the vehicle or the server through the human-machine interaction terminal configured on vehicle 101, or can also interact with the vehicle or the server through user terminal 103. For example, query the status and / or parameters of the vehicle through the user terminal, or control the vehicle to execute set tasks and / or modify configuration parameters, etc.; among them, the user terminal runs an application program based on this intelligent network connection system to achieve interaction with the vehicle or the server. The application program can be a local application, a web application or a small program, etc., which is not limited here.

[0081] In some examples, the above application program running on the user terminal can provide authentication or authorization services for users. Users who have successfully authenticated and been granted corresponding permissions can query and / or control the vehicle within the granted permissions.

[0082] Between the vehicle 101, the server 102, and the user terminal 103, communication can be carried out through the communication link provided by the communication network 104. The communication network 104 can include one or more networks of any type. For example, the communication network 104 can include the Internet, local area network (LAN), wide area network (WAN), virtual private network (VPN), public switched telephone network (PSTN), satellite communication network, Wi-Fi, 2G, 3G, 4G, 5G, 6G, NB-IoT, eMTC, infrared, Bluetooth, NFC, or other networks that provide communication, or a combination of multiple of the above networks. The communication networks between the vehicle 101 and the server 102, between the user terminal 103 and the server 102, and between the user terminal 103 and the vehicle 101 can be the same or different.

[0083] It should be noted that Figure 1 The structure of the intelligent connected vehicle system 100 shown in

[0084] Figure 2 is provided according to Figure 1 the schematic diagram of a vehicle 101 in the shown embodiment. As Figure 2 shown, the vehicle 101 can include a sensing component 1011, a computing platform 1012, an execution component 1013, etc. Among them, the sensing component 1011, the computing platform 1012, and the execution component 1013 can be connected through a bus or other means.

[0085] In some examples, the sensing component 1011 can be used to collect information about the vehicle itself or the outside. The sensing component 1011 can include at least one of a vision sensing unit, a radar, a positioning and navigation unit, an inertial measurement unit (IMU), or other sensing units. Among them, the vision sensor unit can include one or more cameras, the radar can include at least one of a lidar, a millimeter-wave radar, an ultrasonic radar, or other radars, and the positioning and navigation unit can include at least one of a GPS system, a Beidou system, or other global positioning systems.

[0086] In some examples, the computing platform 1012 may include a device with computing capabilities, which is used to process the sensed information collected by the sensing component 1011 to obtain control information, and send corresponding control instructions to the execution component 1013, so that the execution component 1013 performs corresponding actions, thereby realizing the control of the vehicle 101. Exemplarily, the computing platform 1012 may perform behaviors such as simultaneous localization and mapping (SLAM), path planning, and behavior decision-making on the vehicle, thereby realizing the autonomous control of the vehicle. The computing platform 1012 may include at least one processor and at least one memory. Each processor may execute the instructions stored in the memory alone or jointly to implement the method provided by the embodiments of the present disclosure. The processor in the embodiments of the present disclosure may include at least one of a central processing unit (CPU), a graphic process unit (GPU), a neural-network processing unit (NPU), a tensor processing unit (TPU), a data processing unit (DPU), a digital signal processor (DSP), a field programmable gate array (FPGA), a system on chip (SOC), an application specific integrated circuit (ASIC), a microcontroller unit (MCU), or other processors. The memory may be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, a magnetic disk, or an optical disk. In addition to storing instructions, the memory may also store data, such as high-precision maps, path information, the position, direction, speed, etc. of the vehicle. The data stored in the memory can be acquired and used by the processor.

[0087] In some examples, the computing platform of the vehicle may execute computing tasks independently or communicate with the server to complete computing tasks. For example, the computing platform of the vehicle may cooperate with the server to complete corresponding computing tasks.

[0088] The computing platform 1012 may be disposed in the vehicle 101, and part or all of the computing platform 1012 may also be disposed in the server corresponding to the vehicle. For example, functions with higher real-time requirements of the computing platform 1012 are disposed in the vehicle, and another part of functions with lower real-time requirements are disposed in the server corresponding to the vehicle.

[0089] In some examples, the execution component 1013 is configured to perform corresponding actions based on the control of the computing platform 1012, so that the vehicle 101 completes a movement task. The execution component 1013 may include, for example, a power component, a braking component, a transmission component, a steering component, etc.

[0090] It should be noted that Figure 2 The structure of the vehicle 101 shown in [[ ]] is only schematic. The vehicle in the embodiments of the present disclosure is not limited to the above structure, and may include more or fewer components according to needs, and the devices may also be combined or split. For example, the vehicle may not include the above computing platform. For another example, the vehicle may further include a communication component, an interface component, a multimedia component, an input component, an output component, etc.

[0091] In the field of autonomous driving technology, path planning can be performed on a vehicle to control the vehicle to drive into a specific area such as a parking space. In order to perform path planning, environmental detection can be performed on the specific area and its surrounding areas to determine a drivable area, and path planning can be performed within the drivable area to control the vehicle to drive into the specific area. Therefore, how to determine an accurate and reliable drivable area is of great significance to the path planning and vehicle control of autonomous driving.

[0092] Taking the automatic parking function as an example, it is described as follows: In the related art, the target parking space and its surrounding areas for parking can be used as a whole for obstacle detection, and the detected obstacle information can be directly used to determine an overall drivable space area. However, due to the large variety and complexity of obstacle types in the parking environment, and during the movement of the vehicle, the detection accuracy of obstacles will change with the position and detection angle of the sensors installed on the vehicle, resulting in a low accuracy of directly using the obstacle information to determine an overall drivable space area.

[0093] Figure 3 is a schematic diagram showing the presence of obstacles in a parking scenario provided by an embodiment of the present disclosure. As Figure 3As shown in the figure, there are a first obstacle 1 and a second obstacle 2 on the left side of the target area 31 (such as a target parking space), and a third obstacle 3 on the right side. When the target vehicle is at the first position 301 close to the left side of the target parking space in the figure, since the target vehicle is closer to the first obstacle 1 and the second obstacle 2, therefore, based on the information of the first obstacle 1 and the second obstacle 2 detected by the vehicle's sensors, the confidence level is relatively high, while the confidence level of the information of the third obstacle 3 detected is relatively low; when the target vehicle travels to the second position 302 close to the right side of the target parking space, since the target vehicle is far from the first obstacle 1 and the second obstacle 2 and is closer to the third obstacle 3, therefore, based on the information of the first obstacle 1 and the second obstacle 2 detected by the vehicle's sensors, the confidence level decreases, while the confidence level of the information of the third obstacle 3 detected increases. Therefore, during the movement of the vehicle, the detection accuracy of the obstacle changes with the position and detection angle of the sensors installed on the vehicle, resulting in a relatively low accuracy of directly using the obstacle information to determine an overall drivable space area, which affects the safety and reliability of parking. As Figure 3 shown, the target area may include a plurality of area boundary lines, such as Figure 3 shown, the target area may include area boundary lines in the front, rear, left, and right directions. Among them, for the determination of the front, rear, left, and right directions, the direction facing the vehicle of the target area can be used as the front, and in the clockwise direction, they are the front, right, rear, and left directions respectively. It can be understood that the front, rear, left, and right directions can also be determined by other methods.

[0094] It should be noted that the embodiments of the present disclosure are not only used for the scenario of automatically parking into the garage, but also for other scenarios that require determining the drivable area, such as how to determine the drivable area when controlling the vehicle to drive into a specific area (such as a narrow area).

[0095] Figure 4 is a schematic flowchart of a method for determining a drivable area provided by an embodiment of the present disclosure. The method for determining the drivable area may be executed by Figure 1 the vehicle and / or server shown in the figure. As Figure 4 shown, the method for determining the drivable area in this embodiment may include the following steps S410 to step S430.

[0096] Step S410, determine the target area according to the environmental perception data.

[0097] Among them, the target area may include a plurality of area boundary lines. For example, the target area may include N area boundary lines, and N may be any integer greater than 1. The N area boundary lines may be located in different directions of the target area respectively. For example, if the target area is a quadrilateral, the target area may include area boundary lines in the front, rear, left, and right directions respectively.

[0098] In some examples, the regional boundary line can be an actual boundary line, such as a regional boundary line actually depicted on the ground.

[0099] In other examples, the regional boundary line can also be a virtual boundary line. For example, based on environmental perception data, information about obstacles existing in the vehicle's surrounding environment is determined, and based on the obstacle information, an area where the vehicle can drive into is determined as the target area, and the boundary line between the target area and the obstacles is determined.

[0100] It should be noted that all of the N regional boundary lines can be actual boundary lines, or all can be virtual boundary lines, or some can be actual boundary lines and some can be virtual boundary lines.

[0101] In some examples, the target area can be a target parking space, and the application scenario of this method can be an automatic parking scenario. For example, the target parking space can include four regional boundary lines, that is, N is equal to 4.

[0102] In other examples, the target area is not limited to a parking space and can be any area where the vehicle can drive into or can accommodate the vehicle. For example, it can be an arbitrary quadrilateral area where the vehicle can drive into, and the target area can include four regional boundary lines, that is, N is equal to 4.

[0103] In some examples, the above environmental perception data can be data obtained by detecting the vehicle's surrounding environment through sensors during the vehicle's driving process. For example, it can include image data collected based on a vision sensor and / or point cloud data collected based on radar. The environmental perception data can be input into a pre-generated neural network model to obtain the target area and / or obstacle information output by the neural network model.

[0104] Step S420, determine at least two boundary extension areas corresponding to the target area according to the environmental perception data and multiple regional boundary lines of the target area.

[0105] Wherein, the environmental perception data can include data detected at multiple moments, and the at least two boundary extension areas can include a first boundary extension area locked at a first moment and a second boundary extension area locked at a second moment, and the first moment and the second moment can be different moments.

[0106] Exemplarily, the first boundary extension region may be a region determined according to the environmental perception data of the first sampling time window, and the first sampling time window may include the first moment and multiple sampling moments before the first moment. The second boundary extension region may be a region determined according to the environmental perception data of the second sampling time, and the second sampling time window may include the second moment and multiple sampling moments before the second moment. The sampling moment may be the moment when the vehicle detects the environmental perception data.

[0107] In this way, the first boundary extension region and the second boundary extension region can be locked at different moments respectively, so as to improve the flexibility and accuracy of the boundary extension region.

[0108] In some embodiments, M boundary extension regions corresponding to the target region may be determined according to the environmental perception data and N regional boundary lines of the target region.

[0109] Wherein, M may be a positive integer less than or equal to N, and M of the N regional boundary lines may respectively correspond to M boundary extension regions, that is, the M boundary extension regions are in one-to-one correspondence with the M regional boundary lines. The boundary extension region may be a region located outside the target region and through which the vehicle can travel when driving into the target region. Exemplarily, the boundary extension region may be a region where the regional boundary line extends outside the target region.

[0110] In some embodiments, one of the N regional boundary lines of the target region may include a driving-in boundary line serving as the vehicle's driving-in entrance; M of the other N - 1 regional boundary lines except the driving-in boundary line respectively correspond to M boundary extension regions.

[0111] In some embodiments, the boundary extension region may be a quadrilateral region composed of four corner points, and two corner points of the initial range are located on the corresponding regional boundary line of the boundary extension region or the extension line of the regional boundary line. For example, the quadrilateral region may be a rectangular region.

[0112] Step S430, determine the drivable region corresponding to the target region according to at least two boundary extension regions.

[0113] Wherein, the drivable region may be a region for path planning when the vehicle drives into the target region. For example, when the target region is determined as the target region to be driven into by the vehicle, path planning may be performed within the drivable region, and the vehicle may be controlled to drive into the target region based on the planned path.

[0114] In some examples, the drivable region may include the regions of the target region and the boundary extension region.

[0115] In some examples, based on the above at least two boundary extension regions, the drivable region where the vehicle enters the target region through the boundary extension region can be determined.

[0116] By using the method of the above step S410 to step S430, the area around the target region is divided into at least two boundary extension regions. During the movement of the vehicle, each boundary extension region can be determined at different times, and the target ranges of each boundary extension region can be obtained more flexibly and accurately. As a result, the drivable region determined based on the boundary extension region is more accurate and reliable, improving the safety and reliability of vehicle control.

[0117] In some embodiments of the present disclosure, the above step S420 may include the following step S421 and step S422:

[0118] Step S421, based on multiple region boundary lines of the target region, determine the initial range of the boundary extension region.

[0119] In some embodiments, the initial range of the boundary extension region may be a quadrilateral region composed of four corner points, and two corner points of the initial range are located on the corresponding region boundary line of the boundary extension region or the extension line of the region boundary line. For example, the quadrilateral region may be a rectangular region.

[0120] Step S422, for each boundary extension region, determine the target range of the boundary extension region according to the environmental perception data and the initial range.

[0121] Exemplarily, the obstacle information within the initial range can be determined according to the environmental perception data, and the range without obstacles in the initial range can be determined as the target range of the boundary extension region. The target range and the initial range may be the same or different.

[0122] In some embodiments, the step S421 may include: determining the entry boundary line among the multiple region boundary lines according to the current position of the vehicle; determining at least two region boundary lines among the other region boundary lines except the entry boundary line, and determining the corresponding boundary extension region and the initial range of each of the at least two region boundary lines.

[0123] Exemplarily, if the number of region boundary lines of the target region is N, the entry boundary line among the N region boundary lines can be determined according to the current position of the vehicle; determining M region boundary lines among the other N - 1 region boundary lines except the entry boundary line, and determining the corresponding boundary extension region of each of the M region boundary lines and the initial range of the boundary extension region.

[0124] Wherein, the driving-in boundary line is the entrance for the vehicle to drive into the target area. For example, the driving-in boundary line can be the area boundary line closest to the current position of the vehicle, or the driving-in boundary line can be the area boundary line that the vehicle needs to pass through during the process of driving from the current position into the target area.

[0125] In some examples, M can be equal to N - 1, that is, all area boundary lines among the N area boundary lines except the driving-in boundary line are used as one of the M area boundary lines. For example, if the target area includes four area boundary lines in the front, back, left, and right directions, and the driving-in boundary line is the front boundary line, then the three boundary lines on the left, right, and back sides of the target area can be used as the above-mentioned M area boundary lines. In this way, the boundary extension area corresponding to the area boundary line can be determined completely, and the range of the drivable area can be expanded as much as possible to increase the probability of the vehicle passing through.

[0126] In other examples, M can be less than N - 1. For example, two area boundary lines adjacent to the driving-in boundary line among the N area boundary lines can be used as the above-mentioned M area boundary lines. For example, if the target area includes four area boundary lines in the front, back, left, and right directions, and the driving-in boundary line is the front boundary line, the boundary lines on the left and right sides of the driving-in boundary line of the target area can be used as the above-mentioned M area boundary lines. In this way, the boundary extension area can be determined only for the adjacent area boundary lines, reducing the number of boundary extension areas and improving the operation efficiency.

[0127] In some embodiments of the present disclosure, the initial range of each boundary extension area can be determined according to the area type of the target area. Under different area types, the initial ranges of each boundary extension area can be the same or different.

[0128] The area type of the target area can be used to indicate the relative relationship between the target driving direction when the vehicle drives into the target area and the current driving direction of the vehicle. For example, if the target driving direction is the same as or differs from the current driving direction by 180 degrees, the area type of the target area can be defined as a parallel area; for another example, if the target driving direction is different from the current driving direction and the angle difference is not 180 degrees, the area type of the target area can be defined as a non-parallel area. Optionally, the non-parallel area can be further divided into a vertical area and an oblique area. For example, if the target driving direction differs from the current driving direction by 90 degrees or 270 degrees, the area type of the target area can be defined as a vertical area; for another example, if the angle difference between the target driving direction and the current driving direction is an angle other than 0 degrees, 90 degrees, 180 degrees, and 270 degrees (such as 30 degrees, 45 degrees, or 60 degrees), the area type of the target area can be defined as an oblique area.

[0129] Taking the target area as an example of a target parking space, the area type of the target area can be the parking space type of the target parking space. The parking space type can include parallel parking spaces and non-parallel parking spaces. The non-parallel parking spaces can further include perpendicular parking spaces and diagonal parking spaces. Among them: A perpendicular parking space can refer to a parking space where the vehicle is parked perpendicular to the driving lane; A parallel parking space can refer to a parking space where the vehicle is parked parallel to the driving lane; A diagonal parking space can refer to a parking space that forms a certain angle with the driving lane, such as a 45-degree angle or a 60-degree angle, and the vehicle can be parked diagonally.

[0130] Figure 5 is a schematic diagram of a boundary extension area where the target area is a vertical area provided by an embodiment of the present disclosure. As Figure 5 shown, the target area 51 is a vertical area (such as a perpendicular parking space). The target area has four corner points, namely corner point 0, corner point 1, corner point 2, and corner point 3. The target area has four area boundary lines, namely the driving-in boundary line (the line segment between corner point 0 and corner point 1), the left boundary line located on the left side of the driving-in boundary line (the line segment between corner point 0 and corner point 3), the right boundary line located on the right side of the driving-in boundary line (the line segment between corner point 1 and corner point 2), and the lower boundary line located below the driving-in boundary line (the line segment between corner point 2 and corner point 3), thereby respectively determining the left boundary extension area, the right boundary extension area, and the lower boundary extension area. Exemplarily, the left boundary line can be extended to the left by a first distance (such as 1.2 meters) and upward by a second distance (such as 1.6 meters) to obtain the left boundary extension area; the right boundary line can be extended to the right by a first distance (such as 1.2 meters) and upward by a second distance (such as 1.6 meters) to obtain the right boundary extension area; the lower boundary line can be extended downward by the second distance (such as 1.6 meters) to obtain the lower boundary extension area.

[0131] In some examples, if a region coordinate system is constructed with corner point 0 of the target area as the coordinate origin, the length direction from corner point 0 to corner point 3 as the x-axis, and the width direction from corner point 0 to corner point 1 as the y-axis. Assuming the length of the target area is x0, the width is y0, the above-mentioned first distance is y1, and the second distance is x1, then the four corner point coordinates of the left boundary extension area are (-x1, -y1), (-x1, 0), (x0, 0), (x0, -y1) respectively; the four corner point coordinates of the right boundary extension area are (-x1, y0), (-x1, y0 + y1), (x0, y0 + y1), (x0, y0) respectively; the four corner point coordinates of the lower boundary extension area are (x0, 0), (x0, y0), (x0 + x1, y0), (x0 + x1, y0) respectively.

[0132] In some examples, the target area is a target parking space, and the region coordinate system is a parking space coordinate system.

[0133] In some examples, the specific values of the first distance and the second distance can be any preset values.

[0134] In other examples, the above-mentioned first distance and second distance can be set according to the body size of the vehicle and / or the area size of the target area. For example, the first distance can be greater than or equal to half of a first preset distance, and the first preset distance can be the body width of the vehicle, the width of the target area, or the smaller value of the body width and the width of the target area. For another example, the second distance can be greater than or equal to the body width of the vehicle.

[0135] Figure 6 is a schematic diagram of a boundary extension area where the target area is a parallel area provided by an embodiment of the present disclosure. As Figure 6 shown, the target area 61 is a parallel area (such as a parallel parking space). The target area has four corner points, namely corner point 0, corner point 1, corner point 2, and corner point 3. The target area has four area boundary lines, namely the driving-in boundary line (the line segment between corner point 0 and corner point 1), the left boundary line on the left side of the driving-in boundary line (the line segment between corner point 0 and corner point 3), the right boundary line on the right side of the driving-in boundary line (the line segment between corner point 1 and corner point 2), and the lower boundary line below the driving-in boundary line (the line segment between corner point 2 and corner point 3), so as to determine the left boundary extension area, the right boundary extension area, and the lower boundary extension area respectively. Exemplarily, the left boundary line can be extended to the left by a third distance (such as 2.6 meters) and upward by a fourth distance (such as 1.6 meters) to obtain the left boundary extension area; the right boundary line can be extended to the right by a third distance (such as 2.6 meters) and upward by a fourth distance (such as 1.6 meters) to obtain the right boundary extension area; the lower boundary line can be extended downward by a fourth distance (such as 1.6 meters) to obtain the right boundary extension area.

[0136] In some examples, the specific values of the third distance and the fourth distance can be any preset values.

[0137] In other examples, the above-mentioned third distance and fourth distance can be set according to the body size of the vehicle and / or the area size of the target area. For example, the third distance can be greater than or equal to half of a second preset distance, and the second preset distance can be the body length of the vehicle, the length of the target area, or the smaller value of the body length and the length of the target area. For another example, the fourth distance can be greater than or equal to the body width of the vehicle.

[0138] Optionally, the fourth distance can be equal to the above-mentioned second distance, and the third distance can be greater than the above-mentioned first distance.

[0139] It should be noted that for other non-parallel areas, such as oblique areas, the same can be referred to Figure 5Determine the initial range of the boundary extension area in the manner of the vertical area shown.

[0140] In this way, different initial ranges of the boundary extension area can be determined for different types of target areas respectively.

[0141] In some embodiments, the manner of determining the target range of the boundary extension area based on the environment perception data and the initial range in step S422 above may include the following steps S4221 to S4223:

[0142] Step S4221: Determine the candidate reference points and the confidence levels of the candidate reference points of the boundary extension area at each of multiple moments according to the environment perception data.

[0143] Among them, the candidate reference points are located within the initial range, and the confidence levels of the candidate reference points of different boundary extension areas are different at at least one moment.

[0144] Step S4222: Based on the candidate reference points and the confidence levels of the candidate reference points at multiple moments, determine the target reference point and the confidence level of the target reference point of the boundary extension area at the current moment.

[0145] Step S4223: Determine the target range of the boundary extension area according to the target reference point and the confidence level.

[0146] In this way, the target reference point and the confidence level of the target reference point at the current moment can be determined based on the environment perception data detected at multiple moments, and the target range of the boundary extension area can be determined.

[0147] In some examples, the confidence level of the candidate reference points of the first boundary extension area at multiple moments after the first moment is less than the confidence level of the target reference point of the first boundary extension area at the first moment. In this way, the first boundary extension area can be locked at the first moment, avoiding the reduction of the confidence level of the candidate reference points after the first moment from affecting the reliability of the first boundary extension area.

[0148] In some embodiments, the confidence level of the candidate reference points of the second boundary extension area at multiple moments after the second moment is less than the confidence level of the target reference point of the second boundary extension area at the second moment. In this way, the second boundary extension area can be locked at the second moment, avoiding the reduction of the confidence level of the candidate reference points after the second moment from affecting the reliability of the second boundary extension area.

[0149] In some examples, if the target area and the drivable area corresponding to the target area can be displayed by a display device. The above-mentioned boundary extension area can also be displayed by the display device.

[0150] In some embodiments, the implementation of the above step S4221 may include: determining obstacle information within the initial range of the boundary extension area based on environmental perception data; determining candidate reference points for the boundary extension area according to the obstacle information; and determining the confidence level of the candidate reference points based on the coordinates of the candidate reference points and the sensor information of the vehicle. The obstacle information may include static obstacle information and / or dynamic obstacle information.

[0151] In some examples, the obstacle information may include obstacle position points represented based on freespace, such as obstacle freespace points.

[0152] Exemplarily, the obstacle information may be the result obtained by parsing the environmental perception data. The obstacle information may be composed of multiple position points (such as freespace points), and the attributes of each position point include coordinate X, coordinate Y, obstacle type (vehicle, pedestrian, unknown, etc.). Through the obstacle information, the position and / or other attribute information of the obstacle can be determined.

[0153] In some examples, if there is no obstacle information within the initial range of the boundary extension area determined according to the environmental perception data, the initial range of the boundary extension area may be used as the target range. At this time, the corner point farthest from the area boundary line among the multiple corner points of the initial range of the boundary extension area may be used as the candidate reference point.

[0154] In some other examples, if the obstacle information determined according to the environmental perception data includes the coordinates of multiple obstacle position points within the initial range of the boundary extension area, the method for determining the candidate reference points for the boundary extension area according to the obstacle information may include the following steps S11 to S13:

[0155] Step S11: Take the minimum distance among the perpendicular distances from the multiple obstacle position points to the target area boundary line as the first distance.

[0156] Wherein, the target area boundary line is the area boundary line corresponding to the boundary extension area where the obstacle position points are located.

[0157] Step S12: Take the minimum distance among the perpendicular distances from the multiple obstacle position points to the entry boundary line as the second distance.

[0158] Wherein, the entry boundary line is the boundary line serving as the vehicle entry entrance among the N area boundary lines, and the area boundary line corresponding to the boundary extension area is perpendicular to the entry boundary line.

[0159] Step S13: Determine the candidate reference points for the boundary extension area according to the area boundary line, the entry boundary line, the first distance, and the second distance.

[0160] Exemplarily, the first coordinate of the candidate reference point can be determined based on the regional boundary line and the first distance, and the second coordinate of the candidate reference point can be determined based on the driving-in boundary line and the second distance. In this way, a position point close to the target area and a position point close to the entrance of the target area can be selected from multiple obstacle position points.

[0161] As Figure 5 shown, if two obstacle position points (such as freespace points) fall within the left_boundary of the left boundary extension area of the target area 51, and the parking space coordinates of the two obstacle position points are (1.0, -0.5) and (-0.8, -0.7), then the coordinates of the left_bound_point of the candidate reference point in the left boundary extension area are (-0.8f, -0.5). Again, as Figure 6 shown, if two obstacle position points (such as freespace points) fall within the right_boundary of the right boundary extension area of the target area 61, and the parking space coordinates of the two obstacle position points are (1.2, slot_width + 0.5), (-0.6, slot_width + 1.0f), then the available space coordinate right_bound_point of the right_boundary is (-0.6f, slot_width + 0.5), where slot_width is the horizontal parking space width.

[0162] In this way, the candidate reference point in the boundary extension area can be accurately determined.

[0163] In some embodiments of the present disclosure, there can be multiple ways to determine the confidence level of the candidate reference point. Exemplarily, according to the coordinates of the candidate reference point and the sensor information of the vehicle, the ways to determine the confidence level of the candidate reference point can include:

[0164] Calculating at least one of the distance detection confidence factor, direction detection confidence factor, multi-sensor assisted detection confidence factor, and visibility detection confidence factor based on the coordinates of the candidate reference point and the sensor information of the vehicle; and determining the confidence level of the candidate reference point based on at least one of the distance detection confidence factor, direction detection confidence factor, multi-sensor assisted detection confidence factor, and visibility detection confidence factor.

[0165] In some embodiments, the sensors installed on the vehicle can include multiple cameras (such as fisheye cameras).

[0166] In some examples, the distance detection confidence factor can be the detection confidence calculated based on the distance from the candidate reference point to the target camera and the maximum perception distance of the target camera; wherein, the target camera is the camera among the multiple cameras installed on the vehicle that can detect the candidate reference point and is the closest in distance.

[0167] Figure 7 is a schematic diagram of the relative position between a vehicle and an obstacle provided by an embodiment of the present disclosure. As Figure 7 shown, the vehicle is equipped with 4 cameras, which are respectively installed in front of the vehicle head, behind the vehicle tail, on the left side of the vehicle, and on the right side of the vehicle; when calculating the confidence, the area around the vehicle is divided into 6 areas, namely the left front (left_front) area, the right front (right_front) area, the left (left) area, the right (right) area, the left back (left_back) area, and the right back (right_back) area. Different camera sensors are used in different areas to calculate the confidence. For example, for a candidate reference point whose coordinates are in the left_front area, the left-view camera and the front-view camera are used to calculate its confidence, and the maximum value of the confidence is taken as the confidence of the candidate reference point; for a candidate reference point in the left area, the left-view camera is used to calculate its confidence; for a candidate reference point in the right_back area, the right-view camera and the rear-view camera are used to calculate its confidence, and the maximum value of the confidence is taken as the confidence of the candidate reference point.

[0168] The distance detection confidence factor can include a lateral distance detection confidence factor and / or a longitudinal distance detection confidence factor, and the maximum perception distance of the above-mentioned target camera includes a lateral maximum perception distance and / or a longitudinal maximum perception distance. The following is described separately as follows: Figure 7 Separate explanations are as follows:

[0169] Longitudinal distance detection confidence factor: As Figure 7 shown, taking the target camera as the front-view camera as an example, ref_dis_x is the longitudinal distance from the installation position of the camera to its detection boundary, that is, the longitudinal maximum perception distance of the target camera, and dis_x is the longitudinal distance from the candidate position point to the camera installation position. Exemplarily, the longitudinal distance detection confidence factor can be calculated based on the following formula (1):

[0170]

[0171] wherein, position_x_conf represents the longitudinal distance detection confidence factor of the candidate reference point, dis_x represents the longitudinal distance from the candidate reference point to the target camera, and ref_dis_x represents the longitudinal maximum perception distance of the target camera.

[0172] Lateral distance detection confidence factor: As Figure 7As shown in the figure, taking the target camera as the front camera as an example, ref_dis_y is the horizontal position from the installation position of the camera to its detection boundary, that is, the maximum horizontal perception distance of the target camera, and dis_y is the horizontal distance from the candidate position point to the camera installation position. Exemplarily, the horizontal distance detection confidence factor can be calculated based on the following formula (2):

[0173]

[0174] Among them, position_y_conf represents the horizontal distance detection confidence factor of the candidate reference point, dis_y represents the horizontal distance from the candidate reference point to the target camera, and ref_dis_y represents the maximum horizontal perception distance of the target camera.

[0175] It should be noted that ref_dis_x of different cameras can have the same or different values. For example, ref_dis_x of the front camera is 25.6 meters, and ref_dis_x of the three cameras on the left, right, and rear is 12.8 meters. ref_dis_y of different cameras can also have the same or different values. For example, ref_dis_y of the front, rear, left, and right cameras is 12.8 meters.

[0176] In this way, an accurate distance detection confidence factor can be calculated.

[0177] The direction detection confidence factor is the detection confidence determined according to the angle between the direction from the candidate reference point to the target camera and the detection center direction of the target camera. This direction detection confidence factor can also be called the orientation factor.

[0178] The detection center directions of different cameras are different and can be represented by their unit direction vectors ref_vec(x0,y0) of the orientation. For example, the unit direction vector of the front camera is (0, -1), the unit direction vector of the left camera is (-1, 0), the unit direction vector of the rear camera is (0, 1), and the unit direction vector of the right camera is (1, 0). The direction from the candidate reference point to the target camera can also be represented by a vector. For example, the vector connecting the candidate reference point and the camera installation position is vec(x,y), and the confidence corresponding to the orientation factor is calculated as follows:

[0179] Exemplarily, the direction detection confidence factor of the candidate reference point can be calculated by the following formula (3):

[0180]

[0181] Among them, yaw_conf represents the direction detection confidence factor of the candidate reference point, (x,y) represents the vector representation of the candidate reference point, and (x0,y0) represents the vector representation of the detection center direction of the target camera.

[0182] In this way, an accurate direction detection confidence factor can be calculated.

[0183] The multi-sensor assisted detection confidence factor can be used to indicate whether a candidate reference point is detected by multiple types of sensors installed on the vehicle.

[0184] Exemplarily, the multiple types of sensors may include a camera and ultrasonic waves. If the candidate reference point is detected by both the ultrasonic waves and the camera, the coordinates of the candidate reference point can be corrected based on the ultrasonic waves. At this time, the multi-sensor assisted detection confidence factor can be a preset maximum value (e.g., 1); otherwise, if the candidate reference point is only detected by the camera but not by the ultrasonic waves, then the multi-sensor assisted detection confidence factor can be a preset minimum value (e.g., 0).

[0185] The visibility detection confidence factor can be used to indicate the proportion of pixels within a specific area centered on the candidate reference point that can be detected by the camera installed on the vehicle.

[0186] Exemplarily, the specific area range can be a surrounding 7*7 square range centered on the candidate reference point. If the proportion of pixels within the specific area range that can be detected by the camera installed on the vehicle is greater than or equal to a preset proportion threshold (e.g., 0.7 or 0.8), then the visibility detection confidence factor can be a preset maximum value (e.g., 1); otherwise, if the proportion of pixels within the specific area range that can be detected by the camera installed on the vehicle is less than the preset proportion threshold, then the visibility detection confidence factor can be a preset minimum value (e.g., 0).

[0187] In some examples, the confidence of the candidate reference point can be calculated by the following formula (4): final_conf = (A1 * pos_x_conf + A2 * pos_y_conf + A3 * yaw_conf + A4 * uss_conf) * vis_conf (4)

[0188] Wherein, position_x_conf represents the longitudinal distance detection confidence factor of the candidate reference point, position_y_conf represents the lateral distance detection confidence factor of the candidate reference point, yaw_conf represents the direction detection confidence factor of the candidate reference point, uss_conf represents the multi-sensor assisted detection confidence factor of the candidate reference point, vis_conf represents the visibility detection confidence factor of the candidate reference point, A1 to A4 are respectively the weighting coefficients of the confidence factors corresponding to each factor, and moreover, the sum of A1 to A4 is 1. The values of A1 to A4 can be set according to simulation or test conditions.

[0189] In some examples, the weighting coefficients of different cameras may be the same. In other examples, for different cameras, their weighting coefficients may be different. For example, the weighting coefficients of the longitudinal distance detection confidence factors of the front-view camera and the rear-view camera are less than those of the lateral distance detection confidence factors; while the weighting coefficients of the longitudinal distance detection confidence factors of the left-view camera and the right-view camera are greater than those of the lateral distance detection confidence factors.

[0190] Exemplarily, if the target camera of the candidate reference point is the front-view camera or the rear-view camera, the above-mentioned weighting coefficients may be respectively: A1 = 0.15, A2 = 0.35, A3 = 0.4, A4 = 0.1; if the target camera of the candidate reference point is the left-view camera or the right-view camera, the above-mentioned weighting coefficients may be respectively: A1 = 0.35, A2 = 0.15, A3 = 0.4, A4 = 0.1.

[0191] In some examples, if a certain candidate reference point is within the visible range of two cameras, the maximum value of the confidence levels calculated by the two cameras respectively is taken.

[0192] In this way, the confidence level of the candidate reference point can be accurately determined according to at least one of the distance detection confidence factor, the direction detection confidence factor, the multi-sensor assisted detection confidence factor, and the visibility detection confidence factor.

[0193] In some embodiments of the present disclosure, the multiple moments are multiple moments arranged in ascending order of time; the implementation manner of the above step S4222 may include:

[0194] When the current moment is the earliest moment among the multiple moments, the candidate reference point and confidence level of the boundary extension region at this earliest moment are used as the target reference point and confidence level at this earliest moment; or,

[0195] When the current moment is the i-th moment among the multiple moments except the earliest moment, the target reference point and confidence level at the i-th moment are determined according to the candidate reference point and confidence level at the i-th moment and the target reference point and confidence level at the (i - 1)-th moment.

[0196] Wherein, i is an integer greater than 1, and the earliest moment among the multiple moments may be the moment when the vehicle records the first frame of environmental perception data.

[0197] In some examples, the implementation manner of determining the target reference point and confidence level at the i-th moment according to the candidate reference point and confidence level at the i-th moment and the target reference point and confidence level at the (i - 1)-th moment may include at least one of the following:

[0198] Method 1: If the confidence of the candidate reference point at the i-th moment is greater than or equal to the confidence of the target reference point at the (i - 1)-th moment, then after adjusting the target reference point and confidence at the (i - 1)-th moment according to the candidate reference point and confidence at the i-th moment, the target reference point and confidence at the i-th moment are obtained.

[0199] Among them, the method of adjusting the target reference point and confidence at the (i - 1)-th moment can adopt the method of confidence weighted average. For example, the coordinates of the candidate reference point at the i-th moment and the coordinates of the target reference point at the (i - 1)-th moment can be weighted averaged according to the confidence to obtain the target reference point at the i-th moment; the confidence of the candidate reference point at the i-th moment and the confidence of the target reference point at the (i - 1)-th moment can also be weighted averaged to obtain the confidence of the target reference point at the i-th moment.

[0200] Method 2: If the confidence of the specific position point at the i-th moment is greater than or equal to the confidence of the specific position point at the (i - 1)-th moment, then after adjusting the target reference point and confidence at the (i - 1)-th moment according to the candidate reference point and confidence at the i-th moment, the target reference point and confidence at the i-th moment are obtained.

[0201] Among them, the specific position point is the target reference point at the (i - 1)-th moment.

[0202] Through this method, the specific position point at the historical moment (such as the (i - 1)-th moment) can still be locked after the position of the dynamic obstacle in the boundary extension area changes, and the confidence of the specific position point at the two moments before and after can be compared to determine whether to adjust the target reference point and confidence according to the size of the confidence. Thus, it can flexibly adapt to complex scenarios where both static and dynamic obstacles exist.

[0203] Method 3: If the confidence of the candidate reference point at the i-th moment is less than the confidence of the target reference point at the (i - 1)-th moment and the confidence of the specific position point at the i-th moment is less than the confidence of the specific position point at the (i - 1)-th moment, then the target reference point and confidence at the (i - 1)-th moment are used as the target reference point and confidence at the i-th moment. That is, the target reference point and confidence are not updated, which is equivalent to locking the boundary extension area.

[0204] Method 4: If the confidence of the candidate reference point at the i-th moment is less than the confidence of the target reference point at the (i - 1)-th moment, then the target reference point and confidence at the (i - 1)-th moment are used as the target reference point and confidence at the i-th moment.

[0205] In this way, since the candidate reference points and confidence levels of different boundary extension regions detected by the vehicle at the same position are different, and the candidate reference points and confidence levels detected for the same boundary extension region at different positions of the vehicle are also different. Therefore, the drivable region of the target region is divided into multiple boundary extension regions, and the target reference points of each boundary extension region are locked using the confidence level, so as to maximize the use of available space without collision risk, and the method of calculating the confidence level takes into account all influencing factors and is easy to expand. The output target reference points and confidence levels can be conveniently used downstream to determine the drivable region, improving the safety and reliability of vehicle control.

[0206] Figure 8 is a schematic flowchart of a process for determining a drivable region provided by an embodiment of the present disclosure. This method can be executed by Figure 1 the vehicle and / or server shown. As Figure 8 shown, the method for determining a drivable region in this embodiment may include:

[0207] Step S810, determining a target region according to environmental perception data.

[0208] Among them, the target region may have multiple region boundary lines, for example, N region boundary lines, where N is an integer greater than 1.

[0209] Among them, the target region may have multiple region boundary lines, for example, N region boundary lines, where N is an integer greater than 1.

[0210] In some examples, the implementation manner of this step S810 may refer to Figure 4 the specific implementation manner of step S410 in the embodiment shown, and will not be elaborated here.

[0211] Step S820, determining at least one boundary extension region corresponding to the target region according to the environmental perception data and the multiple region boundary lines of the target region.

[0212] In some examples, the environmental perception data may include data detected at multiple moments and used to determine the boundary extension region. Each boundary extension region is determined at a specific moment, and the range of the boundary extension region will not be updated after that specific moment.

[0213] Exemplarily, during the driving process of the vehicle, the boundary extension region is locked at a specific moment. After that specific moment, the range of the boundary extension region will not be updated and remains the range of the boundary extension region locked at the specific moment.

[0214] In one implementation, after the above-mentioned specific moment may refer to the period between the specific moment and the current moment. The current moment may be the latest moment during the vehicle's driving, for example, the moment when the latest environmental perception data is collected during the vehicle's driving. The specific moment may be a certain historical moment before the current moment. In this way, the range of the boundary extension area is no longer updated between the specific moment and the current moment. Optionally, the specific moment may also be the current moment, that is, the boundary extension area may also be determined at the current moment.

[0215] Optionally, the collection moment of the environmental perception data used to determine the boundary extension area at the specific moment includes the specific moment and / or a specific historical moment before the specific moment.

[0216] Optionally, different boundary extension areas in the above-mentioned at least one boundary extension area may be determined at different specific moments respectively. For example, they may be determined based on environmental perception data at different moments.

[0217] In some examples, the above-mentioned specific moment may be the moment when the confidence level of the target reference point of the boundary extension area is the highest. For example, the confidence level of the candidate reference points of the boundary extension area at multiple moments after the specific moment is less than the confidence level of the target reference point of the boundary extension area at the specific moment. In this way, the boundary extension area can be locked at the specific moment based on the confidence level, and the boundary extension area is no longer updated after the confidence level decreases at subsequent moments. That is, the boundary extension area at the specific moment can be used as the boundary extension area at subsequent moments; conversely, if the confidence level increases at a certain subsequent moment, for example, the confidence level of the candidate reference points of the boundary extension area at the third moment after the specific moment is greater than or equal to the confidence level of the target reference point of the boundary extension area at the specific moment, then the target reference point and the confidence level can be updated based on the candidate reference points and the confidence level at the third moment, and the updated target reference point and the confidence level are used as the new target reference point and the confidence level at the third moment. The determination methods of the candidate reference points, the target reference points, and the confidence level of the boundary extension area can refer to the descriptions in the foregoing embodiments of the present disclosure.

[0218] In some examples, the implementation manner of step S820 may refer to Figure 4 the specific implementation manner of step S420 in the illustrated embodiment, which will not be elaborated here.

[0219] Step S830, determine the drivable area corresponding to the target area according to at least one boundary extension area.

[0220] Wherein, the drivable area is the area for path planning when the vehicle drives into the target area.

[0221] In some examples, the implementation manner of step S830 may refer toFigure 4 The specific implementation manner of step S430 in the illustrated embodiment will not be elaborated herein.

[0222] By using the method of steps S810 to S830 above, at least one boundary extension area around the target area can be determined at a specific moment, and the target range of the boundary extension area can be obtained more flexibly and accurately, so that the drivable area determined according to the boundary extension area is more accurate and reliable, improving the safety and reliability of vehicle control.

[0223] Figure 9 is a schematic flowchart of a vehicle control method provided by an embodiment of the present disclosure. This vehicle control method can be executed by Figure 1 the illustrated vehicle and / or server. As Figure 9 shown, the method of this embodiment may include:

[0224] Step S910, determining a target area and a drivable area corresponding to the target area according to environmental perception data.

[0225] Among them, the target area may have multiple area boundary lines, for example, N area boundary lines, where N is an integer greater than 1.

[0226] Step S920, when the target area is determined as the target area to be entered by the vehicle, performing path planning within the drivable area and controlling the vehicle to enter the target area based on the planned path.

[0227] Among them, the drivable area is an area for path planning when the vehicle enters the target area determined according to at least one boundary extension area; the at least one boundary extension area is at least one boundary extension area corresponding to the target area determined according to environmental perception data and multiple area boundary lines of the target area; among them, the environmental perception data includes data detected at multiple moments, and each boundary extension area is determined at a specific moment.

[0228] The determination manner of the drivable area may refer to the description in the foregoing embodiments of the present disclosure and will not be elaborated herein.

[0229] In this way, according to the area boundary lines of the target area, the area around the target area is divided into M boundary extension areas. During the vehicle movement, the target range of each boundary extension area can be determined respectively, and the target range of each boundary extension area can be obtained more flexibly and accurately, so that the drivable area determined according to the boundary extension area is more accurate and reliable, improving the safety and reliability of vehicle control.

[0230] Figure 10 is a schematic structural diagram of an electronic device provided by an embodiment of the present disclosure. As Figure 10As shown, the electronic device 1000 may include a memory 1010 and a processor 1020. The memory 1010 may be used to store computer instructions, and the processor 1020 may be used to call the computer instructions from the memory 1010 to execute all or part of the steps of any of the methods in the foregoing embodiments of the present disclosure. Among them, the processor may be one or more, and the one or more processors may execute instructions alone or jointly. The memory may also be one or more, and the one or more memories may store the above computer instructions alone or jointly. Optionally, the electronic device may be Figure 1 a server and / or a vehicle in

[0231] Embodiments of the present disclosure also provide a vehicle, which may include a memory and a processor. The memory may be used to store computer instructions, and the processor may be used to call the computer instructions from the memory to execute all or part of the steps of any of the methods in the foregoing embodiments of the present disclosure. Among them, the processor may be one or more, and the one or more processors may execute instructions alone or jointly. The memory may also be one or more, and the one or more memories may store the above computer instructions alone or jointly.

[0232] The vehicle in the foregoing embodiments of the present disclosure may be an electric vehicle, a hybrid vehicle, a fuel cell vehicle, or other types of vehicles. The vehicle may be an autonomous vehicle or a non-autonomous vehicle. Exemplarily, the vehicle provided in this embodiment may be Figure 1 or Figure 2 the vehicle shown in

[0233] Embodiments of the present disclosure also provide a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements any of the methods in the foregoing embodiments of the present disclosure. Optionally, the computer-readable storage medium may be a non-transitory storage medium, but is not limited thereto, and it may also be a transitory storage medium.

[0234] Embodiments of the present disclosure also provide a chip, which may include a processing unit, and the processing unit may be used to execute all or part of the steps of any of the methods in the foregoing embodiments of the present disclosure. The chip may be in the form of an application-specific integrated circuit ASIC, a system-on-chip SOC, a field-programmable gate array FPGA, etc., and this embodiment does not limit this. Optionally, the chip may further include a storage unit, and the storage unit may be used to store computer instructions. The processing unit may be used to call the computer instructions from the storage unit to execute all or part of the steps of any of the methods in the foregoing embodiments of the present disclosure.

[0235] Embodiments of the present disclosure also provide a computer program product, which may include a computer program that, when executed by a processor, can implement any of the methods in the foregoing embodiments of the present disclosure.

[0236] The present disclosure may be a system, a method, and / or a computer program product. The computer program product may include a computer-readable storage medium having thereon computer-readable program instructions for causing a processor to implement any of the methods in the foregoing embodiments of the present disclosure.

[0237] A computer-readable storage medium may be a tangible device that can retain and store instructions for use by an instruction execution device. A computer-readable storage medium may be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disc (DVD), a memory stick, a floppy disk, a mechanically encoded device such as a punch card or raised structures in a groove having instructions stored thereon, and any suitable combination of the foregoing. The computer-readable storage medium as used herein is not construed as a transitory signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., an optical pulse through an optical fiber cable), or an electrical signal transmitted through a wire.

[0238] The computer-readable program instructions described herein may be downloaded from a computer-readable storage medium to various computing / processing devices, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network may include a copper transmission cable, an optical fiber transmission, a wireless transmission, a router, a firewall, a switch, a gateway computer, and / or an edge server. The network adapter or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing / processing device.

[0239] The computer program instructions for performing the operations of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state-setting data, or source code or object code written in any combination of one or more programming languages, which may include object-oriented programming languages—such as Smalltalk, C++, etc., and conventional procedural programming languages—such as the "C" language or similar programming languages. The computer-readable program instructions may be executed entirely on the user's computer, partially on the user's computer, executed as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network—including a local area network (LAN) or a wide area network (WAN)—or, alternatively, may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, by using the state information of the computer-readable program instructions to customize an electronic circuit, such as a programmable logic circuit, a field-programmable gate array (FPGA), or a programmable logic array (PLA), the electronic circuit may execute the computer-readable program instructions to implement various aspects of the present disclosure.

[0240] Aspects of the present disclosure are described herein with reference to the flowchart and / or block diagram of methods, apparatus (systems), and computer program products according to embodiments of the present disclosure. It should be understood that each block of the flowchart and / or block diagram, and combinations of blocks in the flowchart and / or block diagram, can be implemented by computer-readable program instructions.

[0241] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that the instructions, when executed by the processor of the computer or other programmable data processing apparatus, create a means for implementing the functions / actions specified in one or more blocks of the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium, which causes a computer, a programmable data processing apparatus, and / or other devices to operate in a particular manner, so that the computer-readable medium storing the instructions includes a manufacture, which includes instructions for implementing various aspects of the functions / actions specified in one or more blocks of the flowchart and / or block diagram.

[0242] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other devices to produce a computer-implemented process such that the instructions executed on the computer, other programmable data processing apparatus, or other devices implement the functions / acts specified in one or more boxes of the flowchart and / or block diagram.

[0243] The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagram may represent a module, a segment of a program, or a portion of an instruction, which contains one or more executable instructions for implementing the specified logical function. In some alternative implementations, the functions noted in the blocks may occur out of the order noted in the figures. For example, two consecutive blocks may in fact be executed substantially in parallel, or they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block of the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented by a dedicated hardware-based system that performs the specified functions or acts, or by a combination of dedicated hardware and computer instructions. It should be noted that implementations by hardware, by software, and by a combination of software and hardware are equivalent.

[0244] The embodiments of the present disclosure have been described above. The above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art in the technical field without departing from the scope and spirit of the described embodiments. The selection of the terms used herein is intended to best explain the principles of the embodiments, the practical application, or the technical improvement of the technology in the market, or to enable other ordinary skill in the technical field to understand the embodiments disclosed herein. The scope of the present disclosure is defined by the appended claims.

Claims

1. A method for determining a drivable area, characterized in that, The method includes: Determining a target area based on environmental perception data; wherein, the target area includes a plurality of area boundary lines; Determining at least two boundary extension areas corresponding to the target area according to the environmental perception data and the plurality of area boundary lines of the target area; wherein, the environmental perception data includes data detected at multiple moments, and the at least two boundary extension areas include a first boundary extension area locked at a first moment and a second boundary extension area locked at a second moment, the first moment and the second moment are different moments, and the first boundary extension area and the second boundary extension area respectively correspond to different area boundary lines; Determining a drivable area corresponding to the target area according to the at least two boundary extension areas; wherein, the drivable area is an area for path planning when the vehicle drives into the target area.

2. The method according to claim 1, characterized in that The determining at least two boundary extension areas corresponding to the target area according to the environmental perception data and the plurality of area boundary lines of the target area includes: Determining an initial range of the boundary extension area according to the plurality of area boundary lines of the target area; For each boundary extension area, determining a target range of the boundary extension area according to the environmental perception data and the initial range.

3. The method according to claim 2, wherein The determining the target range of the boundary extension area according to the environmental perception data and the initial range includes: Determining a candidate reference point and a confidence level of the candidate reference point for each moment in a plurality of moments of the boundary extension area according to the environmental perception data; wherein, the candidate reference point is located within the initial range, and the confidence levels of the candidate reference points of different boundary extension areas are different at least at one moment; Based on the candidate reference points and the confidence levels of the candidate reference points at the plurality of moments, determining a target reference point and a confidence level of the target reference point of the boundary extension area at the current moment; Determining the target range of the boundary extension area according to the target reference point and the confidence level.

4. The method according to claim 3, wherein The confidence level of the candidate reference points of the first boundary extension area at multiple moments after the first moment is less than the confidence level of the target reference point of the first boundary extension area at the first moment; The confidence level of the candidate reference points of the second boundary extension area at multiple moments after the second moment is less than the confidence level of the target reference point of the second boundary extension area at the second moment.

5. The method according to claim 3, characterized in that, The determining a candidate reference point and a confidence level of the candidate reference point for each moment in a plurality of moments of the boundary extension area according to the environmental perception data includes: Determining obstacle information located within the initial range of the boundary extension area according to the environmental perception data; Determining candidate reference points of the boundary extension area according to the obstacle information; Determining the confidence level of the candidate reference point according to the coordinates of the candidate reference point and the sensor information of the vehicle.

6. The method according to claim 5, wherein The obstacle information includes coordinates of a plurality of obstacle position points; the determining candidate reference points of the boundary extension area according to the obstacle information includes: Take the minimum distance among the perpendicular distances from multiple said obstacle position points to the target area boundary line as the first distance; wherein, the target area boundary line is the area boundary line corresponding to the boundary extension area where the obstacle position points are located; Take the minimum distance among the perpendicular distances from multiple said obstacle position points to the driving-in boundary line as the second distance; wherein, the driving-in boundary line is the boundary line that serves as the vehicle driving-in entrance among the multiple area boundary lines, and the area boundary line corresponding to the boundary extension area is perpendicular to the driving-in boundary line; Determine the candidate reference points of the boundary extension area according to the area boundary line, the driving-in boundary line, the first distance, and the second distance.

7. The method according to claim 5, characterized in that, The determining the confidence level of the candidate reference points according to the coordinates of the candidate reference points and the sensor information of the vehicle includes: Calculate at least one of the distance detection confidence factor, the direction detection confidence factor, the multi-sensor assisted detection confidence factor, and the visibility detection confidence factor according to the coordinates of the candidate reference points and the sensor information of the vehicle; Determine the confidence level of the candidate reference points according to at least one of the distance detection confidence factor, the direction detection confidence factor, the multi-sensor assisted detection confidence factor, and the visibility detection confidence factor.

8. The method according to claim 7, wherein The sensors installed on the vehicle include multiple cameras, wherein: The distance detection confidence factor is the detection confidence calculated according to the distance from the candidate reference point to the target camera and the maximum sensing distance of the target camera; the target camera is the camera among the multiple cameras installed on the vehicle that can detect the candidate reference point and is the closest in distance; The direction detection confidence factor is the detection confidence determined according to the angle between the direction from the candidate reference point to the target camera and the detection center direction of the target camera; The multi-sensor assisted detection confidence factor is used to indicate whether the candidate reference point is detected by multiple types of sensors installed on the vehicle; The visibility detection confidence factor is used to indicate the proportion of pixels within a specific area range centered on the candidate reference point that can be detected by the cameras installed on the vehicle, and the specific area range is a square range centered on the candidate reference point.

9. The method according to claim 3, wherein The multiple moments are multiple moments arranged in ascending order of time; the determining the target reference point of the boundary extension area and the confidence level of the target reference point at the current moment based on the candidate reference points at the multiple moments and the confidence levels of the candidate reference points includes: In the case where the current moment is the earliest moment among the multiple moments, take the candidate reference point and confidence level of the boundary extension area at the earliest moment as the target reference point and confidence level at the earliest moment; or, In the case where the current moment is the i-th moment except the earliest moment among the multiple moments, determine the target reference point and confidence level at the i-th moment according to the candidate reference point and confidence level at the i-th moment and the target reference point and confidence level at the (i - 1)-th moment.

10. The method according to claim 9, wherein Determining the target reference point and confidence level at the \(i\)-th moment based on the candidate reference point and confidence level at the \(i\)-th moment and the target reference point and confidence level at the \((i - 1)\)-th moment includes: If the confidence level of the candidate reference point at the \(i\)-th moment is greater than or equal to the confidence level of the target reference point at the \((i - 1)\)-th moment, then after adjusting the target reference point and confidence level at the \((i - 1)\)-th moment according to the candidate reference point and confidence level at the \(i\)-th moment, the target reference point and confidence level at the \(i\)-th moment are obtained; or, If the confidence level of the specific position point at the \(i\)-th moment is greater than or equal to the confidence level of the specific position point at the \((i - 1)\)-th moment, then after adjusting the target reference point and confidence level at the \((i - 1)\)-th moment according to the candidate reference point and confidence level at the \(i\)-th moment, the target reference point and confidence level at the \(i\)-th moment are obtained; where the specific position point is the target reference point at the \((i - 1)\)-th moment; or, If the confidence level of the candidate reference point at the \(i\)-th moment is less than the confidence level of the target reference point at the \((i - 1)\)-th moment and the confidence level of the specific position point at the \(i\)-th moment is less than the confidence level of the specific position point at the \((i - 1)\)-th moment, then the target reference point and confidence level at the \((i - 1)\)-th moment are used as the target reference point and confidence level at the \(i\)-th moment.

11. The method according to claim 2, wherein Determining the initial range of the boundary extension area according to the multiple area boundary lines of the target area includes: Determining the entry boundary line among the multiple area boundary lines according to the current position of the vehicle; where the entry boundary line is the entrance for the vehicle to enter the target area; Determining at least two area boundary lines among the other area boundary lines except the entry boundary line, and determining the boundary extension area and initial range corresponding to each of the at least two area boundary lines.

12. The method according to any one of claims 1 to 11, characterized in that, The target area is a target parking space.

13. The method according to any one of claims 1 to 11, characterized in that, The method further includes: Displaying the target area and the drivable area corresponding to the target area through a display device.

14. A method for determining a drivable area, characterized in that, The method includes: Determining a target area according to environmental perception data; where the target area includes multiple area boundary lines; Determining at least one boundary extension area corresponding to the target area according to the environmental perception data and the multiple area boundary lines of the target area; where the environmental perception data includes data detected at multiple moments and used to determine the boundary extension area, each boundary extension area is determined at a specific moment, and the range of the boundary extension area is not updated after the specific moment, the specific moment is a historical moment before the current moment, and different boundary extension areas in the at least one boundary extension area respectively correspond to different area boundary lines and are determined at different specific moments; Determining the drivable area corresponding to the target area according to the at least one boundary extension area; where the drivable area is the area for path planning when the vehicle enters the target area.

15. A vehicle control method, characterized in that, The method includes: Determining a target area and the drivable area corresponding to the target area according to environmental perception data; where the target area includes multiple area boundary lines; When the target area is determined as the target area to be driven into by the vehicle, path planning is performed within the drivable area, and the vehicle is controlled to drive into the target area based on the planned path; wherein, the drivable area is an area determined according to at least one boundary extension area for path planning when the vehicle drives into the target area; the at least one boundary extension area is at least one boundary extension area corresponding to the target area determined according to the environmental perception data and multiple area boundary lines of the target area; wherein, the environmental perception data includes data detected at multiple moments, each boundary extension area is determined at a specific moment, the specific moment is a historical moment before the current moment, and different boundary extension areas in the at least one boundary extension area respectively correspond to different area boundary lines and are determined at different specific moments.

16. An electronic device, characterized in that, It includes a memory and a processor, the memory is used to store computer instructions, and the processor is used to call the computer instructions from the memory to execute the method according to any one of claims 1 to 15.

17. A vehicle, characterized in that, It includes a memory and a processor, the memory is used to store computer instructions, and the processor is used to call the computer instructions from the memory to execute the method according to any one of claims 1 to 15.

18. A computer-readable storage medium, characterized in that, A computer program is stored thereon, and when the computer program is executed by a processor, the method according to any one of claims 1 to 15 is implemented.

19. A chip, characterized in that, The chip includes a processing unit, and the processing unit is used to execute the method according to any one of claims 1 to 15.

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