Vehicle bottom collision prevention method, device and equipment based on laser radar and storage medium

Abstract

The invention discloses a vehicle bottom collision prevention method, device and equipment based on laser radars, and a storage medium, and relates to the technical field of automobile automatic driving, and the method comprises the steps: obtaining environment data scanned by a plurality of laser radars, the environment data comprises a rotation angle, a vertical angle and a distance value, and the plurality of laser radars are located in different areas of a vehicle; identifying the contour of the target obstacle based on the environmental data; performing vehicle bottom height difference comparison based on the contour of the target obstacle to obtain a comparison result; vehicle bottom collision prevention processing is carried out based on the comparison result, and recognition and analysis of the vehicle on external road conditions are achieved so that risk early warning can be carried out.

Description

Technical Field

[0001] The present invention relates to the field of automobile automatic driving technology, and in particular to a laser radar-based vehicle bottom anti-collision method, device, equipment and storage medium. Background Art

[0002] With the development of automobile autonomous driving technology, lidar is increasingly being used in vehicles.

[0003] Vehicles may encounter potholes and other road conditions during driving, and it is very easy for the bottom of the vehicle to bump into something, causing damage to the vehicle. In particular, if the battery of an electric vehicle is bumped, it is easy to create a risk of fire, seriously threatening the safety of the vehicle and its drivers and passengers. Therefore, the technology of how to prevent bumps is becoming more and more important.

[0004] The above contents are only used to assist in understanding the technical solution of the present invention and do not constitute an admission that the above contents are prior art. Summary of the invention

[0005] The main purpose of the present invention is to provide a method, device, equipment and storage medium for preventing the bottom of a vehicle from being bumped based on laser radar, aiming to solve the technical problem that the vehicle chassis is easily bumped.

[0006] To achieve the above object, the present invention provides a method for preventing a vehicle from being bumped under the vehicle based on a laser radar, and the method for preventing a vehicle from being bumped under the vehicle based on a laser radar comprises the following steps:

[0007] Acquire environmental data scanned by multiple laser radars, wherein the environmental data includes a rotation angle, a vertical angle, and a distance value, and the multiple laser radars are located in different areas of the vehicle;

[0008] identifying an outline of a target obstacle based on the environmental data;

[0009] Comparing the height difference of the bottom of the vehicle based on the contour of the target obstacle to obtain a comparison result;

[0010] The vehicle bottom anti-collision treatment is performed based on the comparison result.

[0011] In one embodiment, the step of identifying the outline of the target obstacle based on the environmental data comprises:

[0012] Performing coordinate transformation on the environmental data to obtain radar point cloud data;

[0013] Processing the radar point cloud data to obtain feature point cloud data;

[0014] The feature point cloud data is extracted and processed to determine the outline of the target obstacle.

[0015] In one embodiment, the step of processing the radar point cloud data to obtain feature point cloud data includes:

[0016] Performing motion compensation on the radar point cloud data using vehicle motion information to obtain motion compensated point cloud data;

[0017] Performing discrete point processing on the motion compensated point cloud data to obtain discretely processed point cloud data;

[0018] The discretely processed point cloud data is converted from the radar coordinate system to the vehicle coordinate system to obtain feature point cloud data.

[0019] In one embodiment, the step of extracting and processing the feature point cloud data to determine the contour of the target obstacle includes:

[0020] Processing the feature point cloud data to obtain ground point cloud data;

[0021] Performing region segmentation and object recognition processing on the ground point cloud data to determine target obstacles;

[0022] Performing contour extraction processing on the target obstacle to obtain a target obstacle contour.

[0023] In one embodiment, the step of comparing the height difference of the bottom of the vehicle based on the contour of the target obstacle to obtain a comparison result includes:

[0024] Get the preset coordinates of the bottom of the vehicle;

[0025] Determining a longitudinal coordinate set of the target obstacle based on the contour of the target obstacle;

[0026] A comparison is performed on the height difference of the bottom of the vehicle based on the longitudinal coordinate set of the target obstacle and the preset coordinates of the bottom of the vehicle to obtain a comparison result.

[0027] In one embodiment, the step of comparing the height difference of the bottom of the vehicle based on the longitudinal coordinate set of the target obstacle and the preset coordinates of the bottom of the vehicle to obtain a comparison result includes:

[0028] Comparing the longitudinal coordinate set with the preset coordinates of the bottom of the vehicle respectively to obtain a set of difference values ​​after comparison;

[0029] When the difference set includes a difference greater than a safety threshold, determining that the comparison result is that there is a risk of collision with the bottom of the vehicle;

[0030] When the difference set does not include a difference greater than the safety threshold, it is determined that the comparison result is that there is no risk of collision with the bottom of the vehicle.

[0031] In one embodiment, the step of performing anti-collision processing on the bottom of the vehicle based on the comparison result includes:

[0032] When the comparison result shows that there is a risk of collision with the bottom of the vehicle, the comparison result is displayed to issue a collision risk warning.

[0033] In addition, to achieve the above-mentioned purpose, the present invention also proposes a vehicle bottom anti-collision device based on laser radar, the device comprising:

[0034] A data acquisition module, used to acquire environmental data scanned by multiple laser radars, wherein the environmental data includes a rotation angle, a vertical angle, and a distance value, and the multiple laser radars are located in different areas of the vehicle;

[0035] A data processing module, configured to identify the contour of a target obstacle based on the environmental data;

[0036] A judgment module, used to compare the height difference of the bottom of the vehicle based on the contour of the target obstacle to obtain a comparison result;

[0037] The control module is used to perform anti-collision processing on the bottom of the vehicle based on the comparison result.

[0038] In addition, to achieve the above-mentioned purpose, the present invention also proposes a laser radar-based vehicle bottom anti-collision device, the device comprising: a memory, a processor, and a laser radar-based vehicle bottom anti-collision program stored on the memory and executable on the processor, the laser radar-based vehicle bottom anti-collision program being configured to implement the steps of the laser radar-based vehicle bottom anti-collision method as described above.

[0039] In addition, to achieve the above-mentioned purpose, the present invention also proposes a storage medium, on which a laser radar-based vehicle bottom anti-collision program is stored. When the laser radar-based vehicle bottom anti-collision program is executed by a processor, the steps of the laser radar-based vehicle bottom anti-collision method described above are implemented.

[0040] One or more technical solutions proposed in this application have at least the following technical effects:

[0041] Acquire environmental data from multiple laser radar scans, where the environmental data includes rotation angle, vertical angle and distance value, and multiple laser radars are located in different areas of the vehicle; identify the outline of the target obstacle based on the environmental data; compare the height difference of the bottom of the vehicle based on the outline of the target obstacle to obtain a comparison result; perform anti-collision processing on the bottom of the vehicle based on the comparison result, so as to enable the vehicle to identify and analyze the external road conditions and then issue a risk warning. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0043] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0044] Figure 1 A schematic diagram of a process flow provided for the first embodiment of the vehicle bottom anti-collision method based on laser radar of the present application;

[0045] Figure 2 A hardware system diagram provided for the first embodiment of the vehicle bottom anti-collision method based on laser radar of the present application;

[0046] Figure 3 A flowchart of the first embodiment of the vehicle bottom anti-collision method based on laser radar provided in the present application;

[0047] Figure 4 A schematic diagram of a flow chart of a second embodiment of a method for preventing vehicle bottom collision based on laser radar in this application;

[0048] Figure 5 A coordinate conversion diagram provided for the second embodiment of the vehicle bottom anti-collision method based on laser radar of the present application;

[0049] Figure 6 A schematic diagram of a process flow provided for the third embodiment of the vehicle bottom anti-collision method based on laser radar of the present application;

[0050] Figure 7 This is a schematic diagram of the module structure of a vehicle bottom anti-collision device based on laser radar in an embodiment of the present application;

[0051] Figure 8 This is a schematic diagram of the equipment structure of the hardware operating environment involved in the laser radar-based vehicle bottom anti-collision method in the embodiment of the present application.

[0052] The purpose, features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0053] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0054] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0055] It should be noted that the execution subject of this embodiment can be a computing service device with data processing, network communication and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or an electronic device capable of realizing the above functions, a vehicle bottom anti-collision device based on laser radar, etc. The following takes the vehicle bottom anti-collision device based on laser radar as an example to illustrate this embodiment and the following embodiments.

[0056] Based on this, the embodiment of the present application provides a method for preventing the bottom of a vehicle from being bumped based on a laser radar. Figure 1 , Figure 1 This is a flow chart of the first embodiment of the vehicle bottom anti-collision method based on laser radar in the present application.

[0057] In this embodiment, the vehicle bottom anti-collision method based on laser radar includes steps S10 to S30:

[0058] Step S10, obtaining environmental data scanned by multiple laser radars, wherein the environmental data includes a rotation angle, a vertical angle, and a distance value, and the multiple laser radars are located in different areas of the vehicle;

[0059] It should be noted that if Figure 2 The hardware system used in this strategy is shown below: including lidar, domain controller, and display system.

[0060] LiDAR measures the distance to an object by emitting and receiving laser beams. The horizontal rotation and vertical angle change of LiDAR enable it to cover a three-dimensional space, thereby obtaining three-dimensional data of the environment. Parameters such as the scanning frequency, field of view (FOV), angular resolution, and ranging accuracy of each LiDAR will affect the quality and details of the data.

[0061] It should be understood that the laser radar scans within the detection field of view to obtain scanning data, and the data in each data packet contains information about the rotation angle, distance value, and reflection intensity of the laser beam.

[0062] Among them, the laser radar includes vertical field of view angle and horizontal field of view angle; the vertical angle measurement range of the laser radar is generally ±12 degrees, ±15 degrees, etc.; the vertical angle measurement range of the laser radar can usually reach 120°, and the shape is fan-shaped, so using three laser radars can effectively cover the 360° environment around the vehicle.

[0063] It can be understood that environmental data refers to the information collected from the surrounding environment, which is used to describe or analyze the state of the environment; the rotation angle refers to the angle at which the laser radar rotates in the horizontal plane, which is used to determine the position of the laser beam in the horizontal plane; the vertical angle refers to the angle at which the laser radar is tilted in the vertical plane, which is used to determine the position of the laser beam in the vertical plane; the distance value refers to the straight-line distance from the device to the target object measured by the laser radar.

[0064] Step S20, identifying the outline of the target obstacle based on the environmental data;

[0065] It should be noted that in point cloud data processing, the outline of the target obstacle refers to the representation of the outer edge of the obstacle in three-dimensional space.

[0066] In the specific implementation, the software system in the domain controller performs data processing, converts the three lidar scanning data into feature point clouds, and then extracts obstacle targets such as stones based on the point cloud data, and selects the contours of the target objects.

[0067] Step S30, comparing the height difference of the bottom of the vehicle based on the contour of the target obstacle to obtain a comparison result;

[0068] It should be noted that it refers to the vertical distance difference between the bottom of the vehicle and the outline of the obstacle.

[0069] In the specific implementation, the chassis safety distance is set to h, and the data in the MAXZ coordinate set is compared with h. If MAXZ is set to <-h, the comparison result is that the target obstacle height meets the safety value. If MAXZ is set to >-h, the comparison result is that the target obstacle height does not meet the safety value.

[0070] Step S40, performing anti-collision processing on the bottom of the vehicle based on the comparison result.

[0071] It should be noted that the anti-collision treatment of the bottom of the vehicle may include raising the height of the vehicle chassis, changing the driving route or taking emergency braking.

[0072] It should be understood that during vehicle driving, it is necessary to continuously monitor the surrounding environment, update obstacle information and vehicle status in real time, and ensure the effectiveness of anti-collision measures.

[0073] Furthermore, when the comparison result shows that there is a risk of collision with the bottom of the vehicle, the comparison result will be displayed to issue a collision risk warning.

[0074] In a specific implementation, when the comparison result shows that the height of the target obstacle meets the safety value, the judgment result is fed back to the display system controller, and the display system does not issue an early warning.

[0075] When the comparison result shows that the height of the target obstacle does not meet the safety value, the judgment result is fed back to the display system controller, and the display system issues an early warning to remind the driver that there is a risk of collision with the bottom of the vehicle.

[0076] like Figure 3 As shown, Figure 3 The flowchart is as follows: first, the environment data is scanned by the lidar, and then the environment data scanned by the lidar is generated into point cloud data. The characteristic contour of the obstacle is selected according to the point cloud data, and the height difference between the characteristic contour and the lowest point of the vehicle is calculated. The height difference is compared to see whether it meets the safety value. If yes, the judgment result is sent to the display system controller, and the display system does not issue an early warning. If yes, the judgment result is sent to the display system controller to control the display system to issue an early warning.

[0077] This embodiment provides a method for preventing the bottom of a vehicle from being bumped or knocked off based on laser radar, which obtains environmental data scanned by multiple laser radars, wherein the environmental data includes a rotation angle, a vertical angle, and a distance value, and the multiple laser radars are located in different areas of the vehicle; the outline of a target obstacle is identified based on the environmental data; the height difference of the bottom of the vehicle is compared based on the outline of the target obstacle to obtain a comparison result; through early warning, the system helps to prevent or reduce collisions with obstacles, improves the safety of vehicle occupants and pedestrians, and can significantly reduce the incidence of traffic accidents.

[0078] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as those in the above-mentioned embodiment 1 can be referred to the above introduction, and will not be repeated in the following. Figure 4 , step S20 includes steps S201 to S203:

[0079] Step S201, coordinate conversion is performed on the environmental data to obtain radar point cloud data;

[0080] It should be noted that the original LiDAR data usually contains a large amount of point cloud information, which needs to be preprocessed to improve its usability. The preprocessing steps may include coordinate transformation, denoising, filtering, region of interest (ROI) division, etc. A pass-through filter can be used to retain or filter out point cloud data within a specific range according to the set conditions.

[0081] It should be understood that the purpose of coordinate transformation is to enable radar point cloud data to be accurately understood and used in a unified reference coordinate system.

[0082] In the specific implementation, the distance data is converted into three-dimensional coordinate XYZ values ​​according to the rotation angle of each laser scanning point data; it can be comprehensively calculated by the rotation angle a, vertical angle b and distance value c. The specific coordinate conversion is as follows: Figure 5As shown, the coordinate values ​​include: X=c*COSb*COSa Y=c*COSb*SINa Z=c*SINb.

[0083] Step S202, processing the radar point cloud data to obtain feature point cloud data;

[0084] It should be noted that data processing includes motion compensation, discrete point processing and coordinate system conversion processing.

[0085] It should be understood that feature point cloud data can provide key information in the point cloud, help simplify problems and improve processing efficiency. Feature point cloud data can be used to more quickly identify the contour and structure of an object, or for point cloud matching and recognition tasks.

[0086] Furthermore, the step S202 includes:

[0087] Motion compensation is performed on the radar point cloud data through the vehicle motion information to obtain the point cloud data after motion compensation;

[0088] Performing discrete point processing on the point cloud data after motion compensation to obtain discrete processed point cloud data;

[0089] The discrete point cloud data is converted from the radar coordinate system to the vehicle coordinate system to obtain feature point cloud data.

[0090] In the specific implementation, it means that within one frame time, after the vehicle moves, the point cloud position generated by the lidar is different, and the radar point cloud is adjusted for motion compensation through the vehicle motion information (vehicle sensor collection); the discrete points in the point cloud data obtained by the lidar are eliminated or smoothed; then the domain controller converts the lidar scanning point XYZ into the vehicle XYZ coordinates according to the position of the lidar on the vehicle; in the domain controller, the vehicle XYZ is used as the coordinate, and all the relevant information of the three lidar scanning points is imported to generate a point cloud file.

[0091] Step S203: extract and process the feature point cloud data to determine the outline of the target obstacle.

[0092] It should be noted that in point cloud data processing, the outline of the target obstacle refers to the representation of the outer edge of the obstacle in three-dimensional space.

[0093] In the specific implementation, obstacle targets such as stones are extracted based on the point cloud data, and the contour of the target object is selected at the same time.

[0094] Furthermore, the step S203 includes:

[0095] Process the feature point cloud data to obtain ground point cloud data;

[0096] Perform region segmentation and object recognition processing on ground point cloud data to determine target obstacles;

[0097] Perform contour extraction on the target obstacle to obtain the target obstacle contour.

[0098] In the specific implementation, after the point cloud file is generated, the ground point cloud in the point cloud is extracted based on the processing method in the domain controller, such as the normal vector method, the grid method or the fitting method; then the area segmentation and object recognition are performed in the point cloud file, the obstacles are identified and the contours of the obstacles are extracted.

[0099] It should be noted that region segmentation can be achieved by dividing point cloud data into multiple regions through a clustering algorithm (such as DBSCAN), each region representing an object or a part of the ground, wherein the clustering algorithm can be based on the distance, density or connectivity between points.

[0100] Object recognition can identify different objects by analyzing the shape, size, and position of each area based on segmentation.

[0101] Contour extraction can be done by using a boundary estimation algorithm to extract the contour. By calculating the normal vector of each point in the point cloud and the relationship between the neighboring points, the boundary points are determined to form the contour.

[0102] This embodiment provides a vehicle bottom anti-collision method based on laser radar, which performs coordinate transformation on environmental data to obtain radar point cloud data; performs data processing on the radar point cloud data to obtain feature point cloud data; extracts and processes the feature point cloud data to determine the contour of the target obstacle; and can accurately extract the three-dimensional contour information of the obstacle from the environment.

[0103] Based on the first and second embodiments of the present application, in the third embodiment of the present application, the same or similar contents as those in the above embodiments can be referred to the above introduction, and will not be described in detail later. Figure 6 , step S30 includes steps S301 to S303:

[0104] Step S301, obtaining preset coordinates of the bottom of the vehicle;

[0105] It should be noted that the preset coordinates of the bottom of the vehicle are the coordinates of the lowest point of the vehicle chassis.

[0106] In the specific implementation, the lowest point of the vehicle is set as the origin of the Z-axis coordinate of the vehicle detection, X+ is the forward direction of the vehicle, Y+ is the right side of the vehicle, and Z+ is the top of the vehicle. At the same time, the layout positions of the three laser radars are at an X-direction distance of x1, x2, and x3 from the lowest point of the vehicle, at a Y-direction distance of y1, y2, and y3, and at a Z-direction distance of z1, z2, and z3. Therefore, the coordinates of the three laser radars are (x1, y1, z1), (x2, y2, z2), and (x3, y3, z3), respectively.

[0107] Step S302, determining a longitudinal coordinate set of the target obstacle based on the contour of the target obstacle;

[0108] It should be noted that for the target obstacles identified in the point cloud, their contours are identified, the contour point cloud is selected, the Z-axis MAX coordinates of the relevant points are calculated, and the MAXZ coordinates of all the target obstacles identified in the point cloud are combined into a collection.

[0109] In the specific implementation, it is assumed that the outline point cloud of the identified obstacle a consists of n points, and the coordinates of each point are (Xa1, Ya1, Za1), (Xa2, Ya2, Za2)...(Xan, Yan, Zan), and MAX(Za1, Za2...Zan) is taken as MAXZa. Similarly, for the identified obstacles b, c...m, MAXZb, MAXZc...MAXZm are taken to form the longitudinal coordinate set.

[0110] Step S303 , comparing the height difference of the bottom of the vehicle based on the longitudinal coordinate set of the target obstacle and the preset coordinates of the bottom of the vehicle to obtain a comparison result.

[0111] It should be noted that, when the chassis safety distance is set to h, the data in the MAXZ coordinate set is compared with h. If MAXZ is set to <-h, the comparison result is that the target obstacle height meets the safety value. If MAXZ is set to >-h, the comparison result is that the target obstacle height does not meet the safety value.

[0112] In a specific implementation, the chassis safety distance may be set to 35, and (MAXZa, MAXZb...MAXZm) may be compared with -35. If there is data greater than -35, it is determined that the safety value is not met.

[0113] The present embodiment provides a method for preventing the bottom of a vehicle from being bumped or knocked off based on laser radar, which obtains preset coordinates of the bottom of the vehicle; determines a set of longitudinal coordinates of a target obstacle based on the contour of the target obstacle; compares the height difference of the bottom of the vehicle based on the set of longitudinal coordinates of the target obstacle and the preset coordinates of the bottom of the vehicle to obtain a comparison result, and judges the bottom of the vehicle and the risk of collision and then gives a reminder through the vehicle display system, so that the autonomous driving system can identify potential obstacles in a timely manner and take appropriate measures to avoid bumps or knocks on the bottom of the vehicle.

[0114] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the vehicle bottom anti-collision method based on laser radar in the present application. More simple transformations based on this technical concept are all within the scope of protection of the present application.

[0115] This application also provides a vehicle bottom anti-collision device based on laser radar, please refer to Figure 7 The vehicle bottom anti-collision device based on laser radar includes:

[0116] A data acquisition module 10 is used to acquire environmental data scanned by multiple laser radars, wherein the environmental data includes a rotation angle, a vertical angle, and a distance value, and the multiple laser radars are located in different areas of the vehicle;

[0117] A data processing module 20, for identifying the contour of the target obstacle based on the environmental data;

[0118] A judgment module 30 is used to compare the height difference of the bottom of the vehicle based on the contour of the target obstacle to obtain a comparison result;

[0119] The control module 40 is used to perform anti-collision processing on the bottom of the vehicle based on the comparison result.

[0120] The laser radar-based vehicle bottom anti-collision device provided in the present application adopts the laser radar-based vehicle bottom anti-collision method in the above embodiment, which can solve the technical problem that the vehicle chassis is easy to collide. Compared with the prior art, the beneficial effects of the laser radar-based vehicle bottom anti-collision device provided in the present application are the same as the beneficial effects of the laser radar-based vehicle bottom anti-collision method provided in the above embodiment, and the other technical features of the laser radar-based vehicle bottom anti-collision device are the same as the features disclosed in the above embodiment method, which will not be repeated here.

[0121] In one embodiment, the data processing module 20 is further used to perform coordinate conversion on the environmental data to obtain radar point cloud data;

[0122] Process the radar point cloud data to obtain feature point cloud data;

[0123] The feature point cloud data is extracted and processed to determine the outline of the target obstacle.

[0124] In one embodiment, the data processing module 20 is further used to perform motion compensation on the radar point cloud data through the vehicle motion information to obtain the motion compensated point cloud data;

[0125] Performing discrete point processing on the point cloud data after motion compensation to obtain discrete processed point cloud data;

[0126] The discrete point cloud data is converted from the radar coordinate system to the vehicle coordinate system to obtain feature point cloud data.

[0127] In one embodiment, the data processing module 20 is further used to process the feature point cloud data to obtain ground point cloud data;

[0128] Perform region segmentation and object recognition processing on ground point cloud data to determine target obstacles;

[0129] Perform contour extraction on the target obstacle to obtain the target obstacle contour.

[0130] In one embodiment, the judgment module 30 is further used to obtain preset coordinates of the bottom of the vehicle;

[0131] Determine a set of longitudinal coordinates of the target obstacle based on the contour of the target obstacle;

[0132] The height difference of the bottom of the vehicle is compared based on the longitudinal coordinate set of the target obstacle and the preset coordinates of the bottom of the vehicle to obtain a comparison result.

[0133] In one embodiment, the judgment module 30 is further used to compare the longitudinal coordinate set with the preset coordinates of the bottom of the vehicle to obtain a set of difference values ​​after comparison;

[0134] When the difference set has a difference greater than the safety threshold, the comparison result is determined to be that there is a risk of collision with the bottom of the vehicle;

[0135] When the difference set does not contain a difference greater than the safety threshold, it is determined that the comparison result is that there is no risk of collision with the bottom of the vehicle.

[0136] In one embodiment, the control module 40 is further configured to display the comparison result when the comparison result indicates that there is a risk of collision with the bottom of the vehicle, so as to issue a collision risk warning.

[0137] The present application provides a laser radar-based vehicle bottom anti-collision device, and the laser radar-based vehicle bottom anti-collision device includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the laser radar-based vehicle bottom anti-collision method in the above-mentioned embodiment one.

[0138] Reference below Figure 8, which shows a schematic diagram of the structure of a laser radar-based vehicle bottom anti-collision device suitable for implementing the embodiment of the present application. The laser radar-based vehicle bottom anti-collision device in the embodiment of the present application may include but is not limited to mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 8 The laser radar-based vehicle bottom anti-collision device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0139] like Figure 8 As shown, the vehicle bottom anti-collision device based on laser radar may include a processing device 1001 (such as a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM: Read Only Memory) 1002 or the program loaded from the storage device 1003 to the random access memory (RAM: Random Access Memory) 1004. In RAM1004, various programs and data required for the operation of the vehicle bottom anti-collision device based on laser radar are also stored. The processing device 1001, ROM1002 and RAM1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to the I / O interface 1006: input devices 1007 including, for example, a touch screen, a touchpad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices 1003 including, for example, a magnetic tape, a hard disk, etc.; and communication devices 1009. The communication device 1009 can allow the laser radar-based vehicle bottom anti-collision device to communicate wirelessly or wired with other devices to exchange data. Although the laser radar-based vehicle bottom anti-collision device with various systems is shown in the figure, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems may be implemented or have alternatively.

[0140] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0141] The laser radar-based vehicle bottom anti-collision device provided in this application adopts the laser radar-based vehicle bottom anti-collision method in the above embodiment, which can solve the technical problem that the vehicle chassis is easy to collide. Compared with the prior art, the beneficial effects of the laser radar-based vehicle bottom anti-collision device provided in this application are the same as the beneficial effects of the laser radar-based vehicle bottom anti-collision method provided in the above embodiment, and the other technical features of the laser radar-based vehicle bottom anti-collision device are the same as the features disclosed in the previous embodiment method, which will not be repeated here.

[0142] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0143] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0144] The present application provides a computer-readable storage medium having computer-readable program instructions (i.e., computer programs) stored thereon, and the computer-readable program instructions are used to execute the laser radar-based vehicle bottom anti-collision method in the above-mentioned embodiment.

[0145] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, 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), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination of the above.

[0146] The above-mentioned computer-readable storage medium may be included in the vehicle bottom anti-collision device based on laser radar; or it may exist independently without being assembled into the vehicle bottom anti-collision device based on laser radar.

[0147] The above-mentioned computer-readable storage medium carries one or more programs. When the above-mentioned one or more programs are executed by the vehicle bottom anti-collision device based on laser radar, the vehicle bottom anti-collision device based on laser radar: obtains environmental data scanned by multiple laser radars, wherein the environmental data includes rotation angle, vertical angle and distance value, and multiple laser radars are located in different areas of the vehicle; identifies the outline of the target obstacle based on the environmental data; compares the height difference of the vehicle bottom based on the outline of the target obstacle to obtain a comparison result; and performs vehicle bottom anti-collision processing based on the comparison result.

[0148] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate 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 may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0149] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0150] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0151] The readable storage medium provided in this application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned laser radar-based vehicle bottom anti-collision method, which can solve the technical problem that the vehicle chassis is easy to collide. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as the beneficial effects of the laser radar-based vehicle bottom anti-collision method provided in the above-mentioned embodiment, and will not be repeated here.

[0152] The present application also provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of the above-mentioned laser radar-based vehicle bottom anti-collision method.

[0153] The computer program product provided by this application can solve the technical problem that the vehicle chassis is easily bumped. Compared with the prior art, the beneficial effects of the computer program product provided by this application are the same as the beneficial effects of the vehicle bottom anti-bumping method based on laser radar provided in the above embodiment, which will not be repeated here.

[0154] The above descriptions are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect applications in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A method for preventing vehicle bottom collision based on laser radar, characterized in that: The vehicle bottom anti-collision method based on laser radar comprises the following steps: Acquire environmental data scanned by multiple laser radars, wherein the environmental data includes a rotation angle, a vertical angle, and a distance value, and the multiple laser radars are located in different areas of the vehicle; identifying an outline of a target obstacle based on the environmental data; Comparing the height difference of the bottom of the vehicle based on the contour of the target obstacle to obtain a comparison result; The vehicle bottom anti-collision treatment is performed based on the comparison result.

2. The vehicle bottom anti-collision method based on laser radar as claimed in claim 1, characterized in that: The step of identifying the outline of the target obstacle based on the environmental data comprises: Performing coordinate transformation on the environmental data to obtain radar point cloud data; Processing the radar point cloud data to obtain feature point cloud data; The feature point cloud data is extracted and processed to determine the outline of the target obstacle.

3. The vehicle bottom anti-collision method based on laser radar as claimed in claim 2, characterized in that: The step of processing the radar point cloud data to obtain feature point cloud data includes: Performing motion compensation on the radar point cloud data using vehicle motion information to obtain motion compensated point cloud data; Performing discrete point processing on the motion compensated point cloud data to obtain discretely processed point cloud data; The discretely processed point cloud data is converted from the radar coordinate system to the vehicle coordinate system to obtain feature point cloud data.

4. The vehicle bottom anti-collision method based on laser radar as claimed in claim 2, characterized in that: The step of extracting and processing the feature point cloud data to determine the contour of the target obstacle includes: Processing the feature point cloud data to obtain ground point cloud data; Performing region segmentation and object recognition processing on the ground point cloud data to determine target obstacles; Performing contour extraction processing on the target obstacle to obtain a target obstacle contour.

5. The vehicle bottom anti-collision method based on laser radar as claimed in claim 1, characterized in that: The step of comparing the height difference of the bottom of the vehicle based on the contour of the target obstacle to obtain a comparison result includes: Get the preset coordinates of the bottom of the vehicle; Determining a longitudinal coordinate set of the target obstacle based on the contour of the target obstacle; A comparison is performed on the height difference of the bottom of the vehicle based on the longitudinal coordinate set of the target obstacle and the preset coordinates of the bottom of the vehicle to obtain a comparison result.

6. The vehicle bottom anti-collision method based on laser radar as claimed in claim 5, characterized in that: The step of comparing the height difference of the bottom of the vehicle based on the longitudinal coordinate set of the target obstacle and the preset coordinates of the bottom of the vehicle to obtain a comparison result includes: Comparing the longitudinal coordinate set with the preset coordinates of the bottom of the vehicle respectively to obtain a set of difference values ​​after comparison; When the difference set includes a difference greater than a safety threshold, determining that the comparison result is that there is a risk of collision with the bottom of the vehicle; When the difference set does not include a difference greater than the safety threshold, it is determined that the comparison result is that there is no risk of collision with the bottom of the vehicle.

7. The vehicle bottom anti-collision method based on laser radar according to any one of claims 1 to 6, characterized in that: The step of performing anti-collision processing on the bottom of the vehicle based on the comparison result includes: When the comparison result indicates that there is a risk of collision with the bottom of the vehicle, the comparison result is displayed to issue a collision risk warning.

8. A vehicle bottom anti-collision device based on laser radar, characterized in that: The device comprises: A data acquisition module, used to acquire environmental data scanned by multiple laser radars, wherein the environmental data includes a rotation angle, a vertical angle, and a distance value, and the multiple laser radars are located in different areas of the vehicle; A data processing module, configured to identify the contour of a target obstacle based on the environmental data; A judgment module, used to compare the height difference of the bottom of the vehicle based on the contour of the target obstacle to obtain a comparison result; The control module is used to perform anti-collision processing on the bottom of the vehicle based on the comparison result.

9. A vehicle bottom anti-collision device based on laser radar, characterized in that: The device includes: a memory, a processor, and a laser radar-based vehicle bottom anti-collision program stored in the memory and executable on the processor, wherein the laser radar-based vehicle bottom anti-collision program is configured to implement the steps of the laser radar-based vehicle bottom anti-collision method as described in any one of claims 1 to 7.

10. A storage medium, characterized in that: The storage medium stores a laser radar-based vehicle bottom anti-collision program, and when the laser radar-based vehicle bottom anti-collision program is executed by the processor, the steps of the laser radar-based vehicle bottom anti-collision method as described in any one of claims 1 to 7 are implemented.

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