Braking control method, device and equipment for intelligent driving at high speed and storage medium

Abstract

The invention provides a braking control method, device and equipment for intelligent driving at a high speed and a storage medium, and relates to the technical field of braking control, and the method comprises the steps of obtaining point cloud data of a current scene; preprocessing the point cloud data to obtain target point cloud data; performing ground segmentation on the target point cloud data to obtain ground point cloud data and non-ground point cloud data; performing clustering processing and information extraction on the non-ground point cloud data to obtain obstacle information; and carrying out decision analysis on the obstacle information, and determining the braking information of the vehicle body. In the mode, the detection precision of intelligent driving is improved, and then accurate braking control of intelligent driving is carried out at a high speed.

Description

Technical Field

[0001] The present invention relates to the field of brake control technology, and in particular to a brake control method, device, equipment and storage medium for intelligent driving at high speed. Background Art

[0002] Intelligent driving AEB (Automatic Emergency Braking) is a key vehicle safety technology that can automatically brake in an emergency to avoid a collision or reduce the severity of a collision. The effectiveness of AEB technology lies in its ability to quickly and accurately identify dangers, make decisions and perform rescue actions. According to research data, after being equipped with an AEB system, the accident rate of a car can be reduced by 27%, of which the rear-end collision reduction rate is 38%, and it can significantly reduce casualties in accidents.

[0003] However, in related technologies, the detection accuracy of intelligent driving is low, and automatic driving can only be started at a speed of less than 60km / h. Therefore, braking control of intelligent driving in high-speed scenarios is an issue that needs to be solved urgently. Summary of the invention

[0004] In view of this, the purpose of the present invention is to provide a braking control method, device, equipment and storage medium for intelligent driving at high speed, so as to improve the detection accuracy of intelligent driving and further perform precise braking control of intelligent driving at high speed.

[0005] In a first aspect, an embodiment of the present invention provides a braking control method for intelligent driving at high speed, including: acquiring point cloud data of a current scene; preprocessing the point cloud data to obtain target point cloud data; performing ground segmentation on the target point cloud data to obtain ground point cloud data and non-ground point cloud data; performing clustering processing and information extraction on the non-ground point cloud data to obtain obstacle information; performing decision analysis on the obstacle information to determine the braking information of the vehicle body.

[0006] In a preferred embodiment of the present invention, the above-mentioned acquisition of point cloud data of the current scene includes: capturing the point cloud data of the current scene in real time by means of a dot matrix laser radar installed on the top of the car; the horizontal angle of the installation dot matrix laser radar is 120 degrees, the vertical angle is 26.5 degrees, and the vertical resolution is 0.08 degrees.

[0007] In a preferred embodiment of the present invention, the above-mentioned preprocessing of the point cloud data to obtain the target point cloud data includes: performing a point cloud filtering operation on the point cloud data based on a predetermined vehicle body perception range to obtain initial point cloud data; and performing a downsampling operation on the initial point cloud data to obtain target point cloud data.

[0008] In a preferred embodiment of the present invention, the target point cloud data is subjected to ground segmentation to obtain ground point cloud data and non-ground point cloud data, including: comparing the height corresponding to the target point cloud data with a preset height threshold; taking the point cloud data with a height greater than the height threshold as the non-ground point cloud threshold; and taking the point cloud data with a height less than or equal to the height threshold as the ground point cloud data.

[0009] In a preferred embodiment of the present invention, the above-mentioned clustering processing and information extraction of non-ground point cloud data to obtain obstacle information includes: clustering the non-ground point cloud data to obtain obstacle data; determining the obstacle information based on the obstacle data; the obstacle information includes: the center point coordinates of the obstacle, x-axis range, y-axis range, z-axis range, radar radial distance and radial velocity.

[0010] In a preferred embodiment of the present invention, clustering operation is performed by the following formula: obstacle data = DBSCAN (non-ground point cloud data, min_samples, eps); the center point coordinates are determined by the following formula: (x_center, y_center, z_center); the x-axis range is determined by the following formula: (min_x, max_x); the y-axis range is determined by the following formula: (min_y, max_y); the z-axis range is determined by the following formula: (min_z, max_z); the radial distance is determined by the following formula: (radial_distance); the radial velocity is determined by the following formula: (radial_velocity).

[0011] In a preferred embodiment of the present invention, a decision analysis is performed on the obstacle information to determine the braking information of the vehicle body, including: inputting the obstacle information into a decision-making program, analyzing the corresponding radial distance and radial speed; determining the collision time through a preset algorithm based on the analyzed radial distance and radial speed; outputting braking information based on the collision time; the braking information includes vehicle body deceleration information and braking information.

[0012] In the second aspect, an embodiment of the present invention also provides a braking control device for intelligent driving at high speed, including: a point cloud data acquisition module, used to acquire point cloud data of the current scene; a point cloud data preprocessing module, used to preprocess the point cloud data to obtain target point cloud data; a ground segmentation module, used to perform ground segmentation on the target point cloud data to obtain ground point cloud data and non-ground point cloud data; an obstacle information determination module, used to perform clustering processing and information extraction on non-ground point cloud data to obtain obstacle information; a braking information determination module, used to perform decision analysis on the obstacle information and determine the braking information of the vehicle body.

[0013] In a third aspect, an embodiment of the present invention further provides an electronic device, comprising a processor and a memory, wherein the memory stores computer executable instructions that can be executed by the processor, and the processor executes the computer executable instructions to implement the braking control method for intelligent driving at high speed according to the first aspect above.

[0014] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, which stores computer-executable instructions. When the computer-executable instructions are called and executed by a processor, the computer-executable instructions prompt the processor to implement the braking control method for intelligent driving at high speeds according to the first aspect above.

[0015] The embodiments of the present invention bring the following beneficial effects:

[0016] The embodiment of the present invention provides a braking control method, device, equipment and storage medium for intelligent driving at high speed, which obtains point cloud data of the current scene, pre-processes the point cloud data, obtains target point cloud data, performs ground segmentation on the target point cloud data, obtains ground point cloud data and non-ground point cloud data, clusters the non-ground point cloud data and extracts information, obtains obstacle information, performs decision analysis on the obstacle information, and determines the braking information of the vehicle body. In this way, the detection accuracy of intelligent driving is improved, and precise braking control of intelligent driving at high speed is performed.

[0017] Other features and advantages of the present disclosure will be set forth in the following description, or some features and advantages may be inferred or unambiguously determined from the description, or may be learned by implementing the above-mentioned technology of the present disclosure.

[0018] In order to make the above-mentioned objectives, features and advantages of the present disclosure more obvious and easy to understand, preferred embodiments are specifically cited below and described in detail with reference to the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0020] Figure 1 A flowchart of a braking control method for intelligent driving at high speed provided by an embodiment of the present invention;

[0021] Figure 2 A flowchart of another braking control method for intelligent driving at high speed provided by an embodiment of the present invention;

[0022] Figure 3 A schematic diagram of early warning and braking of a car and an obstacle provided in an embodiment of the present invention;

[0023] Figure 4 A schematic diagram of the structure of a braking control device for intelligent driving at high speed provided by an embodiment of the present invention;

[0024] Figure 5 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0025] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0026] Intelligent driving AEB (Automatic Emergency Braking) is a key vehicle safety technology that can automatically brake in an emergency to avoid a collision or reduce the severity of a collision. The effectiveness of AEB technology lies in its ability to quickly and accurately identify dangers, make decisions and perform rescue actions. According to research data, after being equipped with an AEB system, the accident rate of a car can be reduced by 27%, of which the rear-end collision reduction rate is 38%, and it can significantly reduce casualties in accidents.

[0027] Smart driving AEB systems usually use a variety of sensors and technical solutions to monitor the vehicle's surroundings and implement emergency braking. The following are some common sensors and technical solutions:

[0028] Millimeter-wave radar sensor: The millimeter-wave radar sensor detects the distance and speed of obstacles ahead by emitting microwave signals, thereby determining whether there is a risk of collision. Millimeter-wave radar sensors play an important role in AEB systems and can effectively monitor long-distance targets.

[0029] Camera: The camera can provide visual information of the vehicle's surroundings, including road signs, vehicles, pedestrians, etc. Through image recognition technology, the AEB system can identify potential collision risks and make corresponding decisions.

[0030] Infrared sensor: Infrared sensor can detect the heat radiation around the vehicle, including other vehicles or obstacles. At night or in bad weather conditions, infrared sensor can provide additional environmental perception capabilities to help AEB system monitor the surrounding conditions more comprehensively.

[0031] Ultrasonic sensors: Ultrasonic sensors can provide close-range obstacle detection, helping vehicles avoid collisions when driving at low speeds or parking. Ultrasonic sensors are often used in the low-speed collision prevention function of AEB systems to improve vehicle driving safety.

[0032] However, the detection accuracy of intelligent driving in the above-mentioned way is low, and automatic driving can only be started at a speed within 60km / h. Therefore, braking control of intelligent driving in high-speed scenarios is a problem that needs to be solved urgently.

[0033] Based on this, the embodiments of the present invention provide a braking control method, device, equipment and storage medium for intelligent driving at high speeds, which can obtain point cloud data of the current scene, pre-process the point cloud data, obtain target point cloud data, perform ground segmentation on the target point cloud data, obtain ground point cloud data and non-ground point cloud data, perform clustering and information extraction on the non-ground point cloud data, obtain obstacle information, perform decision analysis on the obstacle information, and determine the braking information of the vehicle body. In this way, the detection accuracy of intelligent driving is improved, and precise braking control of intelligent driving at high speeds is performed.

[0034] To facilitate understanding of this embodiment, a braking control method for intelligent driving at high speed disclosed in an embodiment of the present invention is first introduced in detail.

[0035] Example 1

[0036] The embodiment of the present invention provides a braking control method for intelligent driving at high speed. Figure 1 The following is a flow chart of a braking control method for high-speed intelligent driving provided by an embodiment of the present invention. Figure 1 As shown, the braking control method for intelligent driving at high speed may include the following steps:

[0037] Step S101, obtaining point cloud data of the current scene.

[0038] Among them, the point cloud data of the current scene can be captured in real time through the dot matrix laser radar installed on the top of the car; the horizontal angle of the installed dot matrix laser radar is 120 degrees, the vertical angle is 26.5 degrees, and the vertical resolution is 0.08 degrees.

[0039] Step S102: pre-process the point cloud data to obtain target point cloud data.

[0040] Specifically, preprocessing the point cloud data to obtain target point cloud data may include: performing a point cloud filtering operation on the point cloud data based on a predetermined vehicle body perception range to obtain initial point cloud data; and performing a downsampling operation on the initial point cloud data to obtain target point cloud data.

[0041] The formula for point cloud data preprocessing may be: target point cloud data=VoxelGrid(point cloud data, voxel_size).

[0042] Step S103 , performing ground segmentation on the target point cloud data to obtain ground point cloud data and non-ground point cloud data.

[0043] Specifically, performing ground segmentation on the target point cloud data to obtain ground point cloud data and non-ground point cloud data can include: comparing the height corresponding to the target point cloud data with a preset height threshold; taking the point cloud data with a height greater than the height threshold as the non-ground point cloud threshold; and taking the point cloud data with a height less than or equal to the height threshold as the ground point cloud data.

[0044] As an implementation method, the formula for ground segmentation may be: ground point cloud data, non-ground point cloud data = RANSAC (target point cloud data).

[0045] Step S104: clustering and information extraction are performed on the non-ground point cloud data to obtain obstacle information.

[0046] Among them, a clustering operation may be first performed on the non-ground point cloud data to cluster out obstacle data, and then the obstacle information may be determined based on the obstacle data.

[0047] The obstacle information may include: the center point coordinates, x-axis range, y-axis range, z-axis range, radar radial distance and radial speed of the obstacle.

[0048] Step S105, performing decision analysis on the obstacle information to determine the braking information of the vehicle body.

[0049] Among them, decision analysis can be performed through a pre-set decision-making program to determine the braking information of the vehicle body.

[0050] The braking control method for intelligent driving at high speed provided by the embodiment of the present invention can obtain point cloud data of the current scene, pre-process the point cloud data, obtain target point cloud data, perform ground segmentation on the target point cloud data, obtain ground point cloud data and non-ground point cloud data, perform clustering and information extraction on the non-ground point cloud data, obtain obstacle information, perform decision analysis on the obstacle information, and determine the braking information of the vehicle body. In this way, the detection accuracy of intelligent driving is improved, and precise braking control of intelligent driving at high speed is performed.

[0051] Example 2

[0052] An embodiment of the present invention also provides another braking control method for intelligent driving at high speed; this method is implemented on the basis of the method in the above embodiment; this method focuses on describing the specific implementation method of clustering and extracting information from non-ground point cloud data to obtain obstacle information.

[0053] Figure 2 A flowchart of another high-speed intelligent driving braking control method provided by an embodiment of the present invention, such as Figure 2 As shown, the non-ground point cloud data is clustered and information extracted to obtain obstacle information, which may include the following steps:

[0054] Step S201: clustering the non-ground point cloud data to obtain obstacle data.

[0055] Among them, the clustering operation can be performed through the following formula: obstacle data = DBSCAN (non-ground point cloud data, min_samples, eps).

[0056] Step S202: Determine obstacle information based on the obstacle data.

[0057] The obstacle information includes: the center point coordinates of the obstacle, the x-axis range, the y-axis range, the z-axis range, the radar radial distance and the radial speed.

[0058] Specifically, the center point coordinates are determined by the following formula: (x_center, y_center, z_center); the x-axis range is determined by the following formula: (min_x, max_x); the y-axis range is determined by the following formula: (min_y, max_y); the z-axis range is determined by the following formula: (min_z, max_z); the radial distance is determined by the following formula: (radial_distance); the radial velocity is determined by the following formula: (radial_velocity).

[0059] Based on the above steps, the obstacle information is analyzed and the braking information of the vehicle body is determined, which may include: inputting the obstacle information into the decision-making program, analyzing the corresponding radial distance and radial speed; determining the collision time through a preset algorithm based on the analyzed radial distance and radial speed; outputting braking information based on the collision time; the braking information includes vehicle body deceleration information and braking information.

[0060] The collision time can be calculated by the following formula: collision time = radial_distance / radial_velocity.

[0061] Specifically, the distance of the radar relative to the obstacle can be calculated by the time to collision ttc, which is specifically expressed as: distance = sqrt(x * x + y * y), ttc = distance / v, where v is the speed of the obstacle.

[0062] Among them, the judgment logic first needs to judge the speed of the obstacle: if (v < v_e) ttc = distance / v, where v_e is the minimum relative speed, which is set according to the specific speed perception accuracy of the lidar. For example, v_e = 0.08 m / s is set.

[0063] Specifically, the code of the decision program can be:

[0064] If (ttc > t_w1_min && ttc < t_w1_max) Level 1 warning

[0065] If (ttc > t_w2_min && ttc < t_w2_max) Level 2 warning

[0066] If (ttc > t_w3_min && ttc < t_w3_max) Level 3 warning

[0067] If (ttc > t_s_min && ttc < t_s_max) Emergency braking trigger

[0068] If (ttc > t_r) No collision risk

[0069] For easy understanding, Figure 3 This is a schematic diagram of early warning and braking for a vehicle and an obstacle provided by an embodiment of the present invention. Among them, t_w1, t_w2, and t_w3 respectively represent the first-level warning, the second-level warning, and the third-level warning, and t_s represents the braking time threshold point.

[0070] Furthermore, after judging the time to collision, the vehicle body motion information is transmitted to the chassis drive through the interface. If it is a warning message, after the chassis receives it, the buzzer will be triggered to make a sound. The warning levels are different, and the frequencies of the buzzer sounds are also different. The higher the level, the higher the frequency. For the issued emergency braking information, the by-wire chassis sends the information to the controller to control the motor braking.

[0071] Embodiment 3

[0072] Corresponding to the above method embodiment, an embodiment of the present invention provides a braking control device for intelligent driving at high speed. Figure 4 This is a schematic structural diagram of a braking control device for intelligent driving at high speed provided by an embodiment of the present invention. As Figure 4 shown, the braking control device for intelligent driving at high speed may include:

[0073] The point cloud data acquisition module 301 is used to acquire the point cloud data of the current scene.

[0074] The point cloud data preprocessing module 302 is used to preprocess the point cloud data to obtain target point cloud data.

[0075] The ground segmentation module 303 is used to perform ground segmentation on the target point cloud data to obtain ground point cloud data and non-ground point cloud data.

[0076] The obstacle information determination module 304 is used to perform clustering processing and information extraction on the non-ground point cloud data to obtain obstacle information.

[0077] The braking information determination module 305 is used to perform decision analysis on the obstacle information and determine the braking information of the vehicle body.

[0078] The braking control device for intelligent driving at high speed provided by the embodiment of the present invention can obtain point cloud data of the current scene, pre-process the point cloud data, obtain target point cloud data, perform ground segmentation on the target point cloud data, obtain ground point cloud data and non-ground point cloud data, perform clustering processing and information extraction on the non-ground point cloud data, obtain obstacle information, perform decision analysis on the obstacle information, and determine the braking information of the vehicle body. In this way, the detection accuracy of intelligent driving is improved, and precise braking control of intelligent driving at high speed is performed.

[0079] In some embodiments, the point cloud data acquisition module is also used to capture the point cloud data of the current scene in real time through a dot matrix laser radar installed on the top of the car; the horizontal angle of the installed dot matrix laser radar is 120 degrees, the vertical angle is 26.5 degrees, and the vertical resolution is 0.08 degrees.

[0080] In some embodiments, the point cloud data preprocessing module is also used to perform point cloud filtering operations on the point cloud data based on a predetermined vehicle body perception range to obtain initial point cloud data; and to perform downsampling operations on the initial point cloud data to obtain target point cloud data.

[0081] In some embodiments, the ground segmentation module is also used to compare the height corresponding to the target point cloud data with a preset height threshold; to use the point cloud data with a height greater than the height threshold as the non-ground point cloud threshold; and to use the point cloud data with a height less than or equal to the height threshold as the ground point cloud data.

[0082] In some embodiments, the obstacle information determination module is further used to cluster the non-ground point cloud data to obtain obstacle data, and determine the obstacle information based on the obstacle data. The obstacle information includes: the center point coordinates of the obstacle, the x-axis range, the y-axis range, the z-axis range, the radar radial distance and the radial speed.

[0083] In some embodiments, clustering operation is performed by the following formula: obstacle data = DBSCAN (non-ground point cloud data, min_samples, eps); the center point coordinates are determined by the following formula: (x_center, y_center, z_center); the x-axis range is determined by the following formula: (min_x, max_x); the y-axis range is determined by the following formula: (min_y, max_y); the z-axis range is determined by the following formula: (min_z, max_z); the radial distance is determined by the following formula: (radial_distance); the radial velocity is determined by the following formula: (radial_velocity).

[0084] In some embodiments, the braking information determination module is also used to input obstacle information into the decision-making program, analyze the corresponding radial distance and radial speed; determine the collision time through a preset algorithm based on the analyzed radial distance and radial speed; output braking information based on the collision time; the braking information includes vehicle deceleration information and braking information.

[0085] The device provided in the embodiment of the present invention has the same implementation principle and technical effects as those of the aforementioned method embodiment. For the sake of brief description, for matters not mentioned in the device embodiment, reference may be made to the corresponding contents in the aforementioned method embodiment.

[0086] Example 4

[0087] The embodiment of the present invention also provides an electronic device for executing the above-mentioned braking control method for intelligent driving at high speed; see Figure 5 A structural schematic diagram of an electronic device is shown, which includes a memory 400 and a processor 401, wherein the memory 400 is used to store one or more computer instructions, and the one or more computer instructions are executed by the processor 401 to implement the above-mentioned braking control method for intelligent driving at high speed.

[0088] Further, Figure 5 The electronic device shown further includes a bus 402 and a communication interface 403 , and the processor 401 , the communication interface 403 and the memory 400 are connected via the bus 402 .

[0089] The memory 400 may include a high-speed random access memory (RAM), and may also include a non-volatile memory, such as at least one disk storage. The communication connection between the system network element and at least one other network element is realized through at least one communication interface 403 (which may be wired or wireless), and the Internet, wide area network, local area network, metropolitan area network, etc. may be used. The bus 402 may be an ISA bus, a PCI bus, or an EISA bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 5 Only one bidirectional arrow is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0090] The processor 401 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit or software instructions in the processor 401. The above processor 401 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present invention can be implemented or executed. The general processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in the embodiment of the present invention can be directly embodied as a hardware decoding processor for execution, or a combination of hardware and software modules in the decoding processor for execution. The software module may be located in a storage medium mature in the art, such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory 400, and the processor 401 reads the information in the memory 400 and completes the steps of the method of the above embodiment in combination with its hardware.

[0091] An embodiment of the present invention also provides a computer-readable storage medium, which stores computer-executable instructions. When the computer-executable instructions are called and executed by a processor, the computer-executable instructions prompt the processor to implement the above-mentioned braking control method for intelligent driving at high speed. The specific implementation can be found in the method embodiment, which will not be repeated here.

[0092] The computer program product of the braking control method for intelligent driving at high speed provided in the embodiment of the present invention includes a computer-readable storage medium storing a non-volatile program code executable by a processor, and the instructions included in the program code can be used to execute the method described in the previous method embodiment. The specific implementation can be referred to the method embodiment, which will not be repeated here.

[0093] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0094] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interfaces, and the indirect coupling or communication connection of devices or units can be electrical, mechanical or other forms.

[0095] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0096] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0097] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a non-volatile computer-readable storage medium that is executable by a processor. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0098] Finally, it should be noted that the above-described embodiments are only specific implementations of the present invention, which are used to illustrate the technical solutions of the present invention, rather than to limit them. The protection scope of the present invention is not limited thereto. Although the present invention is described in detail with reference to the above-mentioned embodiments, ordinary technicians in the field should understand that any technician familiar with the technical field can still modify the technical solutions recorded in the above-mentioned embodiments within the technical scope disclosed by the present invention, or can easily think of changes, or make equivalent replacements for some of the technical features therein; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A braking control method for intelligent driving at high speed, characterized in that: The method comprises: Get the point cloud data of the current scene; Preprocessing the point cloud data to obtain target point cloud data; Performing ground segmentation on the target point cloud data to obtain ground point cloud data and non-ground point cloud data; Performing clustering processing and information extraction on the non-ground point cloud data to obtain obstacle information; The obstacle information is analyzed for decision making to determine the braking information of the vehicle body.

2. The method according to claim 1, characterized in that: The step of obtaining the point cloud data of the current scene includes: The point cloud data of the current scene is captured in real time by a dot matrix laser radar installed on the top of the car; the horizontal angle of the dot matrix laser radar is 120 degrees, the vertical angle is 26.5 degrees, and the vertical resolution is 0.08 degrees.

3. The method according to claim 1, characterized in that: The preprocessing of the point cloud data to obtain target point cloud data includes: Performing a point cloud filtering operation on the point cloud data based on a predetermined vehicle body perception range to obtain initial point cloud data; A downsampling operation is performed on the initial point cloud data to obtain target point cloud data.

4. The method according to claim 1, characterized in that: The ground segmentation of the target point cloud data to obtain ground point cloud data and non-ground point cloud data includes: Comparing the height corresponding to the target point cloud data with a preset height threshold; Taking the point cloud data whose height is greater than the height threshold as the non-ground point cloud threshold; The point cloud data whose height is less than or equal to the height threshold is used as ground point cloud data.

5. The method according to claim 1, characterized in that The step of clustering the non-ground point cloud data and extracting information to obtain obstacle information includes: Performing a clustering operation on the non-ground point cloud data to obtain obstacle data; determining obstacle information based on the obstacle data; The obstacle information includes: the center point coordinates, x-axis range, y-axis range, z-axis range, radar radial distance and radial speed of the obstacle.

6. The method according to claim 5, characterized in that The clustering operation is performed by the following formula: obstacle data = DBSCAN (non-ground point cloud data, min_samples, eps); The center point coordinates are determined by the following formula: (x_center, y_center, z_center); The x-axis range is determined by the following formula: (min_x, max_x); The y-axis range is determined by the following formula: (min_y, max_y); The z-axis range is determined by the following formula: (min_z, max_z); The radial distance is determined by the following formula: (radial_distance); The radial velocity is determined by the following formula: (radial_velocity).

7. The method according to claim 6, characterized in that The step of performing decision analysis on the obstacle information to determine the braking information of the vehicle body includes: Inputting the obstacle information into a decision-making program and analyzing the corresponding radial distance and radial velocity; Determine the collision time through a preset algorithm based on the resolved radial distance and radial velocity; The braking information is output based on the collision time; the braking information includes vehicle body deceleration information and braking information.

8. A braking control device for intelligent driving at high speed, characterized in that: The device comprises: Point cloud data acquisition module, used to obtain point cloud data of the current scene; A point cloud data preprocessing module, used to preprocess the point cloud data to obtain target point cloud data; A ground segmentation module, used for performing ground segmentation on the target point cloud data to obtain ground point cloud data and non-ground point cloud data; An obstacle information determination module, used for performing clustering processing and information extraction on the non-ground point cloud data to obtain obstacle information; The braking information determination module is used to perform decision analysis on the obstacle information and determine the braking information of the vehicle body.

9. An electronic device, characterized in that: It includes a processor and a memory, wherein the memory stores computer executable instructions that can be executed by the processor, and the processor executes the computer executable instructions to implement the braking control method for intelligent driving at high speed as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions. When the computer-executable instructions are called and executed by the processor, the computer-executable instructions prompt the processor to implement the braking control method for intelligent driving at high speed as described in any one of claims 1 to 7.

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