Road anomaly detection and vehicle lamp interaction method and system based on vehicle-mounted laser radar

Abstract

The invention provides a road anomaly detection and vehicle lamp interaction method and system based on a vehicle-mounted laser radar, and belongs to the technical field of vehicle lamp control. The method comprises the following steps: S1, transmitting and receiving a pulse signal through a vehicle-mounted laser radar; s2, calculating the pulse signals transmitted and received by the vehicle-mounted laser radar to obtain point cloud information of the surrounding environment of the vehicle-mounted laser radar, wherein the point cloud information comprises point cloud depth information; s3, based on the point cloud depth information, a point cloud clustering algorithm is adopted to detect road abnormal conditions, and a road abnormal detection result is obtained; s4, based on a road anomaly detection result, projecting a road anomaly region to a corresponding LED vehicle lamp pixel point by adopting a multi-point perspective imaging technology; and S5, carrying out local light intensity adjustment on the LED vehicle lamp pixel points corresponding to the road abnormal area by adopting a self-adaptive high beam technology so as to enable the road abnormal area to be normally displayed under the irradiation of the vehicle lamp. Through interaction between the vehicle-mounted laser radar and the vehicle lamp, the robustness and driving safety of the vehicle lamp are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle light control, and in particular to a method and system for vehicle-mounted laser radar-based road anomaly detection and vehicle light interaction. Background Art

[0002] At present, most cars are equipped with on-board laser radars so that they can sense the position, speed and other information of the surrounding roads in real time while the vehicle is driving. Since laser radar has obvious advantages in obtaining depth information, on-board laser radar is often regarded as the preferred solution in the field of road anomaly detection. The road status during the vehicle's driving process is detected in real time through the acquired on-board laser radar point cloud depth information, so as to determine whether the road has abnormal conditions such as potholes and collapses, thereby providing protection for the safety and reliability of the vehicle during driving.

[0003] Most of the existing on-board LiDAR road detection technologies focus on the information processing of the LiDAR itself. When driving at night or in low-light environments such as tunnels, the LiDAR relies on its strong reliability to analyze the pulse signals it transmits and receives to detect abnormal road conditions, and improve the accuracy of detection through traditional clustering recognition and neural network methods. However, most roads other than highways cannot be guaranteed to be absolutely flat, and in actual driving, most road abnormalities do not actually have a significant impact on the driving safety of the vehicle. Therefore, compared to improving the performance of road abnormality detection, it is obviously more practical to better enable drivers to observe the specific degree of abnormality of the road and make corresponding judgments in a timely manner.

[0004] The above problems need to be solved urgently. Summary of the invention

[0005] The purpose of the present invention is to overcome at least one technical problem existing in the prior art, and to provide a method and system for road anomaly detection and vehicle light interaction based on a vehicle-mounted laser radar.

[0006] On the one hand, an embodiment of the present invention provides a method for road anomaly detection and headlight interaction based on a vehicle-mounted laser radar, the method comprising: S1: transmitting and receiving pulse signals through a vehicle-mounted laser radar; S2: obtaining point cloud information of the surrounding environment of the vehicle-mounted laser radar by calculating the pulse signals transmitted and received by the vehicle-mounted laser radar, the point cloud information including point cloud depth information; S3: detecting road anomalies using a point cloud clustering algorithm based on the point cloud depth information to obtain a road anomaly detection result; S4: projecting the road anomaly area to the corresponding LED headlight pixel point using multi-point perspective imaging technology based on the road anomaly detection result; S5: using adaptive high beam technology to adjust the local light intensity of the LED headlight pixel points corresponding to the road anomaly area so that the road anomaly area can be displayed normally under the illumination of the headlight.

[0007] Furthermore, the vehicle-mounted laser radar integrates a signal transmitting module and a signal receiving module, the signal transmitting module is used to transmit a laser pulse signal, and the signal receiving module is used to receive the laser pulse signal.

[0008] Furthermore, S2: obtains point cloud information of the surrounding environment of the vehicle-mounted laser radar by calculating the pulse signals emitted and received by the vehicle-mounted laser radar, and the point cloud information includes point cloud depth information including: the target distance is calculated based on the time difference between the pulse signal emitted by the vehicle-mounted laser radar and the pulse signal received; the point cloud information of the surrounding environment of the vehicle-mounted laser radar is calculated based on the target distance and the emission and reception angles of the vehicle-mounted laser radar.

[0009] Furthermore, the point cloud information of the surrounding environment of the vehicle-mounted laser radar is calculated based on the target distance and the transmitting and receiving angles of the vehicle-mounted laser radar, including: ; In the formula, X, Y, and Z represent the horizontal coordinate, vertical coordinate, and depth information of one of the spatial point clouds, respectively. represents the vertical angle of the laser radar, represents the rotation angle of the laser radar, and R represents the target distance value detected by the laser radar.

[0010] Furthermore, the method also includes: obtaining a road abnormality area based on the road abnormality detection result, and acquiring point cloud coordinate information corresponding to the road abnormality area.

[0011] Furthermore, the S4: projecting the road abnormality area to the corresponding LED headlight pixel point using the multi-point perspective imaging technology based on the road abnormality detection result includes: extracting point cloud coordinate information of the road abnormality area; converting the three-dimensional point cloud coordinate information corresponding to the road abnormality area into two-dimensional pixel information corresponding to the LED headlight through the multi-point perspective imaging technology.

[0012] Furthermore, the converting of the three-dimensional point cloud coordinate information corresponding to the abnormal road area into two-dimensional pixel information corresponding to the LED headlight by the multi-point perspective imaging technology includes: ; ; ; Where T is the LiDAR parameter matrix, r is the rotation matrix, and t is the translation vector; K is the headlight parameter matrix, f is the focal length, and c is the reference correction value.

[0013] Furthermore, the S5: using adaptive high beam technology to adjust the local light intensity of LED headlight pixels corresponding to the road abnormality area to make the road abnormality area display normally includes: enhancing the light intensity of LED headlight pixels corresponding to the road abnormality area through high beam technology so that the driver can clearly observe the road conditions in the road abnormality area.

[0014] In a second aspect, an embodiment of the present invention provides a road anomaly detection and headlight interaction system based on a vehicle-mounted laser radar, the system comprising a vehicle-mounted laser radar and a processor; the vehicle-mounted laser radar is suitable for transmitting and receiving pulse signals; the processor is integrated with: a point cloud information calculation module, suitable for obtaining point cloud information of the vehicle-mounted laser radar's surrounding environment by calculating the pulse signals transmitted and received by the vehicle-mounted laser radar, the point cloud information including point cloud depth information; a road anomaly result generation module, suitable for detecting road anomalies using a point cloud clustering algorithm based on the point cloud depth information to obtain a road anomaly detection result; a projection module, suitable for projecting a road anomaly area to a corresponding LED headlight pixel point using a multi-point perspective imaging technology based on the road anomaly detection result; and a headlight intensity control module, suitable for using an adaptive high beam technology to adjust the local light intensity of the LED headlight pixel points corresponding to the road anomaly area so that the road anomaly area can be displayed normally under the illumination of the headlights.

[0015] Furthermore, the vehicle-mounted laser radar integrates a signal transmitting module and a signal receiving module, the signal transmitting module is used to transmit a laser pulse signal, and the signal receiving module is used to receive the laser pulse signal.

[0016] On the other hand, the present invention also provides a computer-readable storage medium, in which one or more instructions are stored, and the computer instructions are used to enable the computer to execute the above-mentioned method of road anomaly detection and vehicle light interaction based on vehicle-mounted laser radar.

[0017] On the other hand, the present invention provides an electronic device, comprising: a memory and a processor; the memory stores at least one program instruction; the processor loads and executes the at least one program instruction to implement the above-mentioned method of road anomaly detection and vehicle light interaction based on vehicle-mounted laser radar.

[0018] The beneficial effects of the present invention are: (1) Only the on-board LiDAR point cloud depth information is used for road anomaly detection, which greatly improves the computing efficiency and saves system computing power; (2) Project the point cloud location information of abnormal road areas detected by the on-board LiDAR onto the LED pixel blocks of the headlights through multi-point perspective imaging technology, thus realizing the interaction between the LiDAR depth information and the intelligent headlights; (3) By introducing the on-board lidar point cloud information to interact with the adaptive high-beam headlights, the robustness of the headlights and driving safety are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0020] Figure 1 This is a flow chart of a method for road anomaly detection and vehicle light interaction based on a vehicle-mounted laser radar provided in Example 1 of the present invention.

[0021] Figure 2 This is a multi-point perspective imaging model diagram of an LED vehicle light provided in Example 1 of the present invention.

[0022] Figure 3 It is a schematic diagram of the structure of a road anomaly detection and vehicle light interaction system based on a vehicle-mounted laser radar provided in Example 2 of the present invention.

[0023] Figure 4 This is a partial block diagram of an electronic device provided in Example 4 of the present invention. DETAILED DESCRIPTION

[0024] It should be mentioned before discussing the exemplary embodiments in more detail that some exemplary embodiments are described as processes or methods depicted as flow charts. Although the flow charts describe the operations as sequential processes, many of the operations therein can be implemented in parallel, concurrently or simultaneously. In addition, the order of the operations can be rearranged. The process can be terminated when its operation is completed, but can also have additional steps not included in the accompanying drawings. The process can correspond to a method, function, procedure, subroutine, subprogram, etc.

[0025] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0026] The present invention will now be described in detail with reference to the accompanying drawings. This figure is a simplified schematic diagram, which only illustrates the basic structure of the present invention in a schematic manner, and therefore only shows the components related to the present invention.

[0027] Embodiment 1: For ease of understanding, the inventive concept is generally described below before describing the embodiments of the present invention in detail: During the road anomaly detection process, the on-board laser radar transmits and receives pulse signals to obtain point cloud information around the car to perform point cloud clustering and then realize road anomaly detection. It uses multi-point perspective imaging technology to project the abnormal road area to the corresponding headlight LED pixel point, and then uses adaptive high beam technology to adjust the local light brightness, so that the driver can fully observe the abnormal area on the road and make corresponding judgments and controls on the corresponding road conditions based on his own driving experience. By introducing the interaction between the on-board laser radar point cloud information and the adaptive high beam headlight, the robustness of the headlight and driving safety are improved.

[0028] The specific implementation is as follows: like Figure 1 As shown, it is a flow chart of a method for road anomaly detection and vehicle light interaction based on a vehicle-mounted laser radar provided by the present invention.

[0029] As an example, the method includes: S1: Transmit and receive pulse signals through the vehicle-mounted laser radar.

[0030] S2: Obtain point cloud information of the surrounding environment of the vehicle-mounted laser radar by calculating the pulse signals emitted and received by the vehicle-mounted laser radar, wherein the point cloud information includes point cloud depth information.

[0031] S3: Detect road anomalies using a point cloud clustering algorithm based on the point cloud depth information to obtain a road anomaly detection result.

[0032] S4: Based on the road anomaly detection result, a multi-point perspective imaging technology is used to project the road anomaly area to the corresponding LED headlight pixel point.

[0033] S5: Adaptive high beam technology is used to adjust the local light intensity of the LED headlight pixels corresponding to the abnormal road area so that the abnormal road area can be displayed normally under the illumination of the headlight.

[0034] Preferably, a signal transmitting module and a signal receiving module are integrated in the vehicle-mounted laser radar, the signal transmitting module is used to transmit a laser pulse signal, and the signal receiving module is used to receive the laser pulse signal.

[0035] Preferably, S2: obtains point cloud information of the vehicle-mounted laser radar's surrounding environment by calculating the pulse signal emitted and received by the vehicle-mounted laser radar, and the point cloud information includes point cloud depth information including: the target distance is calculated based on the time difference between the pulse signal emitted by the vehicle-mounted laser radar and the pulse signal received; the point cloud information of the vehicle-mounted laser radar's surrounding environment is calculated based on the target distance and the emission and reception angles of the vehicle-mounted laser radar. Specifically, the emission wave of the vehicle-mounted laser radar is a high-frequency continuous wave, and its frequency changes with time according to the triangular wave law. The received echo frequency has the same change law as the emission frequency, but there is a time difference. The target distance can be calculated by the time difference, and the point cloud information of the vehicle-mounted laser radar's surrounding environment can be calculated in conjunction with the emission and reception angles.

[0036] Preferably, during the driving process, vehicles often encounter abnormal conditions such as damaged roads and potholes. The on-board laser radar has obvious advantages over other on-board sensors in road anomaly detection due to its strong reliability and convenient acquisition of depth information. During the road anomaly detection process, the on-board laser radar transmits a radar pulse signal through the signal transmitting module, and then receives the pulse signal through the signal receiving module. Specifically, the point cloud information of the surrounding environment of the on-board laser radar calculated based on the target distance and the transmitting and receiving angles of the on-board laser radar includes: ; In the formula, X, Y, and Z represent the horizontal coordinate, vertical coordinate, and depth information of one of the spatial point clouds, respectively. represents the vertical angle of the laser radar, represents the rotation angle of the laser radar, and R represents the target distance value detected by the laser radar.

[0037] Preferably, the method further includes: obtaining a road abnormality area based on the road abnormality detection result, and acquiring point cloud coordinate information corresponding to the road abnormality area.

[0038] Preferably, the S4: projecting the abnormal road area to the corresponding LED headlight pixel using the multi-point perspective imaging technology based on the road anomaly detection result includes: extracting the point cloud coordinate information of the abnormal road area; converting the three-dimensional point cloud coordinate information corresponding to the abnormal road area into the two-dimensional pixel information corresponding to the LED headlight by the multi-point perspective imaging technology. Specifically, since this embodiment mainly detects the abnormality of road flatness, and the road point cloud information obtained by the on-board laser radar during driving is usually stable and smooth (that is, the value of the point cloud data in each coordinate axis direction is unchanged or changes steadily with a small amplitude), and the information of the road point cloud data in the abnormal area detected by the on-board laser radar presents a regional mutation of the coordinates, especially the point cloud depth information (that is, the value of the point cloud information in the Z coordinate axis direction). This embodiment uses the point cloud depth information collected by the on-board laser radar and clusters the feature areas to achieve road anomaly detection and recognition. The point cloud coordinate information of the abnormal road area identified by point cloud clustering is extracted, and the conversion between the three-dimensional space points of the vehicle-mounted laser radar point cloud and the two-dimensional pixel points of the LED headlights is realized through multi-point perspective imaging technology, so as to clarify the LED headlight pixel points corresponding to the abnormal area detected by the vehicle-mounted laser radar. Figure 2 As shown, the conversion relationship is as follows: ; ; ; Where T is the LiDAR parameter matrix, which represents the transformation relationship between the LiDAR point cloud coordinate system and the spatial coordinate system, where r is the rotation matrix and t is the translation vector; K is the headlight parameter matrix, which represents the transformation relationship between the spatial coordinate system and the LED pixel plane coordinate system, where f is the focal length and c is the reference correction amount; Represents the pixel coordinate value projected onto the LED light.

[0039] Preferably, the S5: using adaptive high beam technology to adjust the local light intensity of the LED headlight pixel points corresponding to the road abnormal area so that the road abnormal area can be displayed normally includes: enhancing the light intensity of the LED headlight pixel points corresponding to the road abnormal area through the high beam technology so that the driver can clearly observe the road conditions in the road abnormal area. That is, in actual application, when there are potholes on the road surface, when the headlights are irradiated on the pothole area, it usually appears darker than when irradiated on a flat surface, and the driver cannot clearly observe the actual situation of the road at this time. Therefore, during the driving process of the vehicle, the headlights can illuminate the road defect area detected with stronger light so that the defective area can be clearly displayed, which is convenient for the driver to perform corresponding operations based on the road defect.

[0040] In the above embodiment, during the road anomaly detection process, the on-board laser radar transmits and receives pulse signals to obtain point cloud information around the car to perform point cloud clustering and then realize road anomaly detection, and uses multi-point perspective imaging technology to project the road abnormal area to the corresponding headlight LED pixel point, and then uses adaptive high beam technology to adjust the local light brightness, so that the driver can fully observe the abnormal area in the road and make corresponding judgments and controls on the corresponding road conditions based on his own driving experience. By introducing the interaction between the on-board laser radar point cloud information and the adaptive high beam headlight, the robustness of the headlight and driving safety are improved; by only using the on-board laser radar point cloud depth information for road anomaly detection, the calculation efficiency is greatly improved and the system computing power is saved; by projecting the point cloud position information of the road abnormal area detected by the on-board laser radar into the headlight LED pixel block through multi-point perspective imaging technology, the interaction between the laser radar depth information and the intelligent headlight is realized.

[0041] Embodiment 2: See also Figure 3 , this embodiment provides a schematic diagram of the structure of a road anomaly detection and headlight interaction system based on a vehicle-mounted laser radar.

[0042] As an example, the system includes a vehicle-mounted laser radar 1 and a processor 2; The vehicle-mounted laser radar 1 is suitable for transmitting and receiving pulse signals.

[0043] The processor 2 is integrated with: The point cloud information calculation module 210 is adapted to obtain point cloud information of the surrounding environment of the vehicle-mounted laser radar by calculating the pulse signals emitted and received by the vehicle-mounted laser radar, wherein the point cloud information includes point cloud depth information.

[0044] The road anomaly result generating module 220 is adapted to detect road anomalies by using a point cloud clustering algorithm based on the point cloud depth information to obtain a road anomaly detection result.

[0045] The projection module 230 is adapted to project the road abnormality area to the corresponding LED headlight pixel point by using the multi-point perspective imaging technology based on the road abnormality detection result.

[0046] The headlight intensity control module 240 is adapted to use the adaptive high beam technology to adjust the local light intensity of the LED headlight pixels corresponding to the abnormal road area so that the abnormal road area can be displayed normally under the illumination of the headlights.

[0047] Preferably, the vehicle-mounted laser radar 1 integrates a signal transmitting module 110 and a signal receiving module 120, the signal transmitting module 110 is used to transmit a laser pulse signal, and the signal receiving module 120 is used to receive the laser pulse signal.

[0048] It is not difficult to find that this embodiment is a system embodiment corresponding to the first embodiment, and this embodiment can be implemented in conjunction with the first embodiment. The relevant technical details mentioned in the first embodiment are still valid in this embodiment, and in order to reduce repetition, they are not repeated here. Accordingly, the relevant technical details mentioned in this embodiment can also be applied in the first embodiment.

[0049] It is worth mentioning that all modules involved in this embodiment are logical units. In practical applications, a logical unit can be a physical unit, a part of a physical unit, or a combination of multiple physical units. In addition, in order to highlight the innovative part of the present invention, this embodiment does not introduce units that are not closely related to solving the technical problem proposed by the present invention, but this does not mean that there are no other units in this embodiment.

[0050] Embodiment 3: The embodiment of the present invention further proposes a storage medium, on which a method for interacting with vehicle lights and road anomaly detection based on a vehicle-mounted laser radar is stored, and when the program for interacting with vehicle lights and road anomaly detection based on a vehicle-mounted laser radar is executed by a processor, the steps of the method for interacting with vehicle lights and road anomaly detection based on a vehicle-mounted laser radar are implemented as described above. Since the storage medium adopts all the technical solutions of all the above embodiments, it has at least all the beneficial effects brought by the technical solutions of the above embodiments, which will not be repeated here one by one.

[0051] Embodiment 4: See also Figure 4 An embodiment of the present invention also provides an electronic device, including: a memory and a processor; the memory stores at least one program instruction; the processor loads and executes the at least one program instruction to implement the method of road anomaly detection and vehicle light interaction based on vehicle-mounted laser radar provided in Example 1.

[0052] The memory 702 and the processor 701 are connected in a bus manner, and the bus may include any number of interconnected buses and bridges, and the bus connects various circuits of one or more processors 701 and the memory 702 together. The bus can also connect various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and are therefore not further described herein. The bus interface provides an interface between the bus and the transceiver. The transceiver can be one element or multiple elements, such as multiple receivers and transmitters, providing a unit for communicating with various other devices on a transmission medium. The data processed by the processor 701 is transmitted on a wireless medium via an antenna, and further, the antenna also receives data and transmits the data to the processor 701.

[0053] The processor 701 is responsible for managing the bus and general processing, and can also provide various functions, including timing, peripheral interfaces, voltage regulation, power management and other control functions. The memory 702 can be used to store data used by the processor 701 when performing operations.

[0054] The above is only an embodiment of the present invention. The common sense such as the known specific structure and characteristics in the scheme is not described in detail here. The ordinary technicians in the relevant field know all the common technical knowledge in the technical field of the invention before the application date or priority date, can know all the existing technologies in the field, and have the ability to apply the conventional experimental means before that date. The ordinary technicians in the relevant field can improve and implement this scheme in combination with their own abilities under the enlightenment given by this application. Some typical known structures or known methods should not become obstacles for ordinary technicians in the relevant field to implement this application. It should be pointed out that for those skilled in the art, without departing from the structure of the present invention, several deformations and improvements can be made, which should also be regarded as the scope of protection of the present invention, which will not affect the effect of the implementation of the present invention and the practicality of the patent. The scope of protection required by this application shall be based on the content of its claims, and the specific implementation methods and other records in the specification can be used to interpret the content of the claims.

Claims

1. A method for road anomaly detection and vehicle light interaction based on vehicle-mounted laser radar, characterized in that: The method comprises: S1: Transmit and receive pulse signals through the vehicle-mounted laser radar; S2: obtaining point cloud information of the surrounding environment of the vehicle-mounted laser radar by calculating the pulse signals transmitted and received by the vehicle-mounted laser radar, wherein the point cloud information includes point cloud depth information; S3: Detecting road anomalies using a point cloud clustering algorithm based on the point cloud depth information to obtain a road anomaly detection result; S4: Projecting the road abnormality area to the corresponding LED headlight pixel point using multi-point perspective imaging technology based on the road abnormality detection result; S5: Adaptive high beam technology is used to adjust the local light intensity of the LED headlight pixels corresponding to the abnormal road area so that the abnormal road area can be displayed normally under the illumination of the headlight.

2. The method for road anomaly detection and vehicle light interaction based on vehicle-mounted laser radar according to claim 1 is characterized in that: The vehicle-mounted laser radar integrates a signal transmitting module and a signal receiving module, the signal transmitting module is used to transmit a laser pulse signal, and the signal receiving module is used to receive the laser pulse signal.

3. The method for road anomaly detection and vehicle light interaction based on vehicle-mounted laser radar according to claim 2 is characterized in that: S2: obtaining point cloud information of the surrounding environment of the vehicle-mounted laser radar by calculating the pulse signals transmitted and received by the vehicle-mounted laser radar, wherein the point cloud information includes point cloud depth information including: The target distance is calculated based on the time difference between the pulse signal emitted by the vehicle-mounted laser radar and the pulse signal received; The point cloud information of the vehicle-mounted laser radar's surrounding environment is calculated based on the target distance and the transmitting and receiving angles of the vehicle-mounted laser radar.

4. The method for road anomaly detection and vehicle light interaction based on vehicle-mounted laser radar according to claim 3 is characterized in that: The point cloud information of the surrounding environment of the vehicle-mounted laser radar is calculated based on the target distance and the transmitting and receiving angles of the vehicle-mounted laser radar, including: ； In the formula, X, Y, and Z represent the horizontal coordinate, vertical coordinate, and depth information of one of the spatial point clouds, respectively. represents the vertical angle of the laser radar, represents the rotation angle of the laser radar, and R represents the target distance value detected by the laser radar.

5. The method for road anomaly detection and vehicle light interaction based on vehicle-mounted laser radar according to claim 1 is characterized in that: The method further includes: obtaining a road abnormality area based on the road abnormality detection result, and acquiring point cloud coordinate information corresponding to the road abnormality area.

6. The method for road anomaly detection and vehicle light interaction based on vehicle-mounted laser radar according to claim 5 is characterized in that: The S4: projecting the road abnormality area to the corresponding LED headlight pixel point using multi-point perspective imaging technology based on the road abnormality detection result includes: Extract point cloud coordinate information of abnormal road areas; The three-dimensional point cloud coordinate information corresponding to the abnormal road area is converted into two-dimensional pixel information corresponding to the LED headlights through the multi-point perspective imaging technology.

7. The method for road anomaly detection and vehicle light interaction based on vehicle-mounted laser radar according to claim 6 is characterized in that: The converting of the three-dimensional point cloud coordinate information corresponding to the abnormal road area into two-dimensional pixel information corresponding to the LED headlight by the multi-point perspective imaging technology includes: ； ； ； Where T is the LiDAR parameter matrix, r is the rotation matrix, t is the translation vector; K is the headlight parameter matrix, f is the focal length, and c is the reference correction value.

8. The method for road anomaly detection and vehicle light interaction based on vehicle-mounted laser radar according to claim 1 is characterized in that: The S5: using the adaptive high beam technology to adjust the local light intensity of the LED headlight pixels corresponding to the abnormal road area so as to make the abnormal road area display normally includes: The high beam technology is used to enhance the light intensity of the LED headlight pixels corresponding to the abnormal road areas, so that the driver can clearly observe the road conditions in the abnormal road areas.

9. A road anomaly detection and vehicle light interaction system based on vehicle-mounted laser radar, characterized in that: The system includes a vehicle-mounted laser radar and a processor; The vehicle-mounted laser radar is suitable for transmitting and receiving pulse signals; The processor integrates: A point cloud information calculation module, adapted to obtain point cloud information of the surrounding environment of the vehicle-mounted laser radar by calculating the pulse signals transmitted and received by the vehicle-mounted laser radar, wherein the point cloud information includes point cloud depth information; A road anomaly result generation module is adapted to detect road anomalies using a point cloud clustering algorithm based on the point cloud depth information to obtain a road anomaly detection result; A projection module, adapted to project the road abnormality area to the corresponding LED headlight pixel point using a multi-point perspective imaging technology based on the road abnormality detection result; The headlight intensity control module is suitable for using the adaptive high beam technology to adjust the local light intensity of the LED headlight pixels corresponding to the abnormal road area so that the abnormal road area can be displayed normally under the illumination of the headlight.

10. The vehicle-mounted laser radar road anomaly detection and vehicle light interaction system according to claim 9, characterized in that: The vehicle-mounted laser radar integrates a signal transmitting module and a signal receiving module, the signal transmitting module is used to transmit a laser pulse signal, and the signal receiving module is used to receive the laser pulse signal.

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