A guardrail cleaning vehicle and a method for detecting guardrail obstacles and providing early warning of lateral deviation of the cleaning vehicle.

Abstract

This invention discloses a guardrail cleaning vehicle and a method for guardrail obstacle detection and lateral deviation warning. The guardrail cleaning vehicle includes a vehicle body, a LiDAR, a high-definition camera, a chassis, a rotary drive, an adjustment mechanism, and a cleaning mechanism. The method includes LiDAR-based obstacle detection and lateral deviation warning. Utilizing the precise positional information and neighborhood geometric relationships of the LiDAR scan lines, planar distance ratios and smoothness features are designed to extract candidate guardrail points. A guardrail point clustering and segmentation algorithm based on neighborhood point search is used to filter out outliers. Curve fitting is used to calculate the lateral deviation value of reference points. A remote monitoring system based on a mobile network is used. The high-definition camera captures images of the scene in front of the vehicle and transmits them to the control terminal. The network routing connects the vehicle-side data to the Internet via the mobile network. FFmpeg is used to push the stream to a streaming server and display it in the control center or on a handheld terminal. The vehicle and the remote server maintain a bidirectional persistent connection via the WebSocket protocol.

Description

Technical Field

[0001] This invention relates to the field of guardrail cleaning technology, specifically to a guardrail cleaning vehicle and a method for detecting guardrail obstacles and providing early warning of lateral deviation of the cleaning vehicle. Background Technology

[0002] Guardrails refer to "protective barriers" used on roads. Guardrails are mainly used in residential areas, highways, commercial areas, public places and other places to protect personal safety. Guardrails can be seen everywhere in our lives. In recent years, guardrails have also been divided into plug-in and assembled guardrails made of aluminum alloy.

[0003] Existing patent literature largely focuses on improving cleaning efficiency and application range by modifying the contact method between the cleaning device and the guardrail. For example, Chinese patent application CN202210518962.0 adapts to guardrails of different widths by changing the spacing of the roller brushes; Chinese patent application CN202222163662.3 uses a method where a horizontal spring is installed on the outside of the sliding shaft of the cleaning device, and a vertical spring is provided on the side of the floating bracket to ensure close contact with the guardrail for cleaning. However, the adjustment range of the springs is small, and large deviations can easily damage the cleaning device and the guardrail. With the development of automation and intelligence, traditional passive adjustment methods are becoming increasingly limited, making it difficult to accurately judge the lateral deviation error between the cleaning device and the guardrail, and unable to achieve adaptive control of the hydraulic system of the cleaning equipment. Summary of the Invention

[0004] To address the current limitations of manually driven guardrail cleaning vehicles in accurately determining the lateral deviation error between the cleaning device and the guardrail, and the inability to achieve adaptive control of the hydraulic system, while ensuring the driving trajectory error range, this invention provides a guardrail cleaning vehicle and a method for guardrail obstacle detection and lateral deviation warning. The guardrail cleaning vehicle includes a vehicle body, a LiDAR, a high-definition camera, a chassis, a rotary drive, an adjustment mechanism, and a cleaning mechanism. The method includes LiDAR-based obstacle detection and lateral deviation warning. Utilizing the precise positional information and neighborhood geometric relationships of the LiDAR scan lines, planar distance ratios and smoothness features are designed to extract candidate guardrail points. A guardrail point clustering and segmentation algorithm based on neighborhood point search is used to filter out outliers. Curve fitting is used to calculate the lateral deviation value of the reference point. A remote monitoring system for the guardrail cleaning vehicle based on a 4G / 5G network is also included. The high-definition camera primarily captures images of the scene in front of the vehicle and transmits them back to the control terminal. The network routing mainly connects the vehicle-side data to the Internet via the 4G network. FFmpeg is used to push the stream to a streaming server, and then the data is directly displayed at the control center or on a handheld terminal. The vehicle and the remote server maintain a bidirectional persistent connection via the WebSocket protocol.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] A guardrail cleaning vehicle includes a truck body, a lidar sensor, a high-definition camera, a chassis, a rotary drive, an adjustment mechanism, and a cleaning mechanism. The lidar sensor is mounted on the top of the driver's cab, and the high-definition camera is mounted on the front of the driver's cab. The rotary drive is mounted on a sliding seat at the front of the truck body, which can slide on a rail on the frame, allowing the entire cleaning mechanism to rotate and move left and right relative to the truck body, selectively cleaning one side of the guardrail as needed. The adjustment mechanism controls the outward extension and lifting of the cleaning mechanism via the extension and retraction of a telescopic hydraulic cylinder. The cleaning mechanism controls the spacing between the inner and outer brush rollers via the extension and retraction of the telescopic hydraulic cylinder, and controls the rotation of the outer brush roller via a rotary hydraulic cylinder.

[0007] This invention also discloses a method for detecting guardrail obstacles and providing lateral deviation warning for a guardrail cleaning vehicle. It uses lidar and a high-definition camera as detection methods, combining them to perform guardrail obstacle detection and lateral deviation warning for the cleaning vehicle, and includes the following steps:

[0008] The S100 collects data from LiDAR nodes, then obtains segmented point images by segmenting the point cloud nodes. These images are compared with the offset warning nodes to obtain the specific offset value of the guardrail cleaning vehicle. On the one hand, it activates the offset warning alarm, and on the other hand, it corrects the lateral offset between the vehicle and the guardrail through the hydraulic control of the cleaning mechanism.

[0009] When the angular deviation between the vehicle's driving direction and the guardrail direction is within the allowable range, the brush roller hydraulic adaptive control system adjusts the working pressure of the hydraulic oil according to the received extension amount. When the angular deviation is within the allowable range but the positional deviation exceeds the adaptive adjustment range of the brush roller extension rod, or when the angular deviation exceeds the allowable range and the positional deviation exceeds the adaptive adjustment range of the brush roller extension rod, the point set used to fit the straight line is translated to the side of the vehicle and a point at a distance from the front of the vehicle is selected as the aiming point. The steering wheel angle control amount is calculated in real time according to the vehicle motion model and vehicle speed and sent to the vehicle's automatic driving control system to realize the adaptive control of the guardrail sweeper.

[0010] The S200 uses a high-definition camera at the front of the guardrail cleaning vehicle to remotely monitor the vehicle via a 4G / 5G network, transmits video back to the camera to obtain real-time color images, and processes these images with offset data detected by LiDAR through an edge computing platform. This enables automatic early warning alarms and video display for obstacles at the front of the guardrail within the cleaning vehicle.

[0011] This invention utilizes the precise location information of LiDAR and the neighborhood geometric relationship of the scan line to design planar distance ratio and smoothness features to extract candidate points for guardrails; it employs a guardrail point clustering and segmentation algorithm based on neighborhood point search to filter out outliers; and it uses curve fitting to calculate the lateral deviation value of the reference point.

[0012] A vehicle coordinate system is established with the direction of travel as the Y-axis, the direction perpendicular to the ground as the Z-axis, and the direction 90° clockwise from the vehicle's forward direction as the X-axis. The center point of the brush roller is located on the X-axis. Based on the relationship between the lidar installation position and the vehicle coordinate system, the original point cloud coordinates of the lidar are transformed into the vehicle coordinate system. According to the planar distance ratio, smoothness characteristics, and constraints, a three-dimensional laser point set belonging to the guardrail is extracted and arranged from near to far, mapped to a two-dimensional XY coordinate system to construct a two-dimensional point set. The curvature is calculated point by point according to the curvature calculation formula, and a straight line is fitted. By calculating the slope of the straight line, the angular deviation between the vehicle's travel direction and the guardrail direction is calculated.

[0013] The guardrail obstacle detection includes: determining whether the guardrail exceeds a threshold based on the maximum curvature; if it exceeds the set threshold, it is determined that there is an abnormal placement of the guardrail in front.

[0014] If the angular deviation between the vehicle's driving direction and the guardrail's direction is within the allowable range, and the positional deviation is within the adaptive adjustment range of the brush roller telescopic rod, the brush roller hydraulic adaptive control system will make adaptive adjustments based on the received telescopic amount. If the angular deviation is within the allowable range but the positional deviation exceeds the adaptive adjustment range of the brush roller telescopic rod, or if the angular deviation exceeds the allowable range or both the positional deviation and the brush roller telescopic rod exceed the adaptive adjustment range, the selected set of points used for fitting the straight line will be translated to the side of the vehicle, and a point at a distance from the front of the vehicle will be selected as the aiming point. Based on the vehicle motion model and vehicle speed, the steering wheel angle control amount will be calculated in real time and sent to the vehicle's autonomous driving control system.

[0015] Compared with the prior art, the present invention has the following beneficial effects:

[0016] 1. In this invention, the lidar is installed on the top of the cab, and the high-definition camera is installed at the front of the cab; the rotary drive is installed in the middle space of the vehicle body, so that the entire cleaning mechanism can rotate relative to the vehicle body, and can selectively clean the guardrail on one side of the vehicle body as needed; the adjustment mechanism controls the extension and retraction and lifting of the cleaning mechanism through the extension and retraction of the hydraulic cylinder; the cleaning mechanism controls the outer brush roller to flip through the rotary hydraulic cylinder; compared with existing cleaning vehicles, it has a complete structure and superior functionality.

[0017] 2. This invention uses the precise location information of the lidar and the neighborhood geometry of the scan line to design the planar distance ratio and smoothness features to extract candidate points for the guardrail; it employs a guardrail point clustering and segmentation algorithm based on neighborhood point search to filter out outliers; and it calculates the lateral deviation value of the reference point through curve fitting.

[0018] 3. This invention utilizes a 4G / 5G network-based remote monitoring system for guardrail cleaning vehicles. High-definition cameras primarily capture images of the scene in front of the vehicle and transmit them back to the control terminal. The network routing mainly connects the vehicle's data to the internet via the 4G network. FFmpeg is used to push the stream to a streaming server, obtain video links, and then display the data directly at the control center or on a handheld terminal. The vehicle and the remote server maintain a bidirectional persistent connection via the WebSocket protocol, enabling the transmission of small data such as location and status, and providing strong real-time performance.

[0019] 4. This invention achieves integrated design of various terminal components and installation interfaces through the development and testing of an intelligent prototype based on edge computing; and conducts reliability verification considering the impact of different weather, lighting, road conditions, and other factors. Attached Figure Description

[0020] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a schematic diagram of the initial structural state of the vehicle being cleaned.

[0022] Figure 2 An explosion diagram illustrating the initial structural state of a vehicle being cleaned.

[0023] Figure 3 A schematic diagram of the overall framework for a method for detecting guardrail obstacles and providing early warning of lateral deviation of vehicles during cleaning.

[0024] Figure 4 This is a schematic diagram of obstacle detection and vehicle lateral deviation warning based on lidar.

[0025] Figure 5 This is a schematic diagram of the feature design for obstacle detection and vehicle lateral deviation warning based on lidar.

[0026] Figure 6 This is a schematic diagram of the data transmission process of a remote monitoring system for guardrail sweepers based on 4G / 5G networks.

[0027] Figure 7 This is a spatial location diagram of obstacle detection and vehicle lateral deviation warning based on lidar.

[0028] The reference numerals used in the above figures are explained as follows:

[0029] 100. Cargo box; 101. Box frame; 102. Sliding seat; 103. Sliding seat motor; 104. Pinion; 105. Rack; 200. LiDAR; 300. High-definition camera; 400. Chassis; 500. Rotary drive; 600. Adjustment mechanism; 601. Rotating frame; 602. Lifting column; 603. Lifting bracket; 604. Lifting hydraulic cylinder; 605. Telescopic arm; 606. Telescopic hydraulic cylinder; 700. Cleaning mechanism; 701. Inner brush roller frame; 702. Brush roller; 703. Brush roller motor; 704. Outer brush roller frame; 705. Rotary hydraulic cylinder; 706. Water-blocking tarpaulin; 707. Water spray pipe. Detailed Implementation

[0030] The technical solution of the present invention will be further described in detail below with reference to specific embodiments.

[0031] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present application.

[0032] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0033] like Figures 1-2As shown, the guardrail sweeper of the present invention includes a vehicle body 100, a laser radar 200, a high-definition camera 300, a chassis 400, a rotary drive 500, an adjustment mechanism 600, and a cleaning mechanism 700. The vehicle body 100 is mounted on the rear beam of the cab of the chassis 400. The laser radar 200 is mounted on the top of the cab of the chassis 400, and the high-definition camera 300 is mounted on the front of the cab of the chassis 400. The rotary drive 500 is mounted on a sliding seat at the front of the vehicle body 100. The sliding seat can slide on the slide rail of the frame 101, so that the entire cleaning mechanism 700 can rotate and move left and right relative to the vehicle body 100, and can selectively clean the guardrail on one side of the vehicle body 100 as needed. The adjustment mechanism 600 controls the extension and retraction of the cleaning mechanism 700 and its lifting and lowering by the extension and retraction of the telescopic hydraulic cylinder 606. The cleaning mechanism 700 controls the spacing between the inner and outer brush rollers by the extension and retraction of the telescopic hydraulic cylinder 606, and controls the rotation of the outer brush roller by the rotary cylinder.

[0034] The vehicle body 100 includes a frame 101, a sliding seat 102, a sliding seat motor 103, a pinion 104, and a rack 105. The frame 101 is bolted to the rear beam of the cab of the chassis 400. The sliding seat 102 is mounted on the front slide rail of the frame 101. The sliding seat motor 103 is bolted to the sliding seat 102. The sliding seat 102 and the slide rail of the frame 101 slide in a horizontal direction, and the mating distance is adjusted by the sliding seat motor 103. The pinion 104 is mounted on the sliding seat motor 103. The rack 105 is bolted to the frame 101 and meshes with the pinion 104. The sliding seat motor 103 drives the pinion 104 to rotate, thereby causing the sliding seat 102 to move horizontally left and right.

[0035] The adjustment mechanism 600 includes a rotating frame 601, lifting columns 602, lifting brackets 603, lifting hydraulic cylinders 604, telescopic arms 605, and telescopic hydraulic cylinders 606. The rotating frame 601 is fixed to the rotary drive 500 by bolts. The two lifting columns 602 are vertically installed and fixed on the rotating frame 601, and slide in the vertical direction with the lifting brackets 603. The lifting hydraulic cylinders 604 control the mating distance, so that the brush rollers can be vertically adjusted in terms of lifting distance. The telescopic arm 605 is horizontally installed and fixed on the rotating frame 601, and slides in the horizontal direction with the rotating frame 601. The telescopic hydraulic cylinders 606 control the mating distance, so that the inner and outer brush rollers can be horizontally adjusted in terms of distance.

[0036] The cleaning mechanism 700 includes an inner brush roller frame 701, a brush roller 702, a brush roller motor 703, an outer brush roller frame 704, a rotary hydraulic cylinder 705, a water-blocking tarpaulin 706, and water spray pipes 707. The inner brush roller frame 701 and the outer brush roller frame 704 respectively fix the inner and outer brush rollers 702 and the brush roller motor 703, and the brush roller motor 703 provides power to the brush roller 702. The rotary hydraulic cylinder 705 is fixed to the outer brush roller frame 704, and one end of the rotary hydraulic cylinder 705 is fixed to the flange of the telescopic arm 605 by bolts, so that the outer brush roller 702 rotates around the flange of the telescopic arm 605. The water-blocking tarpaulin 706 is installed on the outside of the outer brush roller frame 704, and four water spray pipes 707 are respectively installed and fixed on both sides of the inner brush roller frame 701 and the water-blocking tarpaulin 706.

[0037] like Figures 3-7 As shown, the guardrail cleaning vehicle and the guardrail obstacle detection and cleaning vehicle lateral deviation warning method of the present invention are based on lidar obstacle detection and vehicle lateral deviation warning.

[0038] like Figure 3 As shown, the guardrail cleaning vehicle and the guardrail obstacle detection and cleaning vehicle lateral deviation early warning method of the present invention include the following steps:

[0039] The S100 obtains segmented point images through LiDAR acquisition nodes and subsequent point cloud segmentation nodes, compares them with offset warning nodes, and obtains the specific offset value of the guardrail cleaning vehicle. On the one hand, it activates the offset warning alarm, and on the other hand, it corrects the lateral offset between the vehicle and the guardrail through the hydraulic control of the cleaning equipment.

[0040] When the angular deviation between the vehicle's driving direction and the guardrail direction is within the allowable range, the brush roller hydraulic adaptive control system adjusts the working pressure of the hydraulic oil according to the received extension amount. When the angular deviation is within the allowable range but the position deviation exceeds the adaptive adjustment range of the brush roller extension rod, or when the angular deviation exceeds the allowable range and the position deviation exceeds the adaptive adjustment range of the brush roller extension rod, the point set used to fit the straight line is translated to the side of the vehicle and a point at a distance from the front of the vehicle is selected as the aiming point. The steering wheel angle control amount is calculated in real time according to the vehicle motion model and vehicle speed and sent to the vehicle's automatic driving control system to realize the adaptive control of the guardrail sweeper.

[0041] The S200 uses a high-definition camera at the front of the guardrail cleaning vehicle to collect video data. Based on a 4G / 5G network, it transmits the video back to the cleaning vehicle to obtain real-time color images. These images are then processed by an edge computing platform along with offset data detected by LiDAR. This enables automatic early warning and video display of obstacles at the front of the guardrail within the cleaning vehicle.

[0042] like Figure 4 As shown, the method for obstacle detection and vehicle lateral deviation warning based on lidar includes the following steps:

[0043] S101 utilizes the precise location information from the lidar and the neighborhood geometry of the scan line to design planar distance ratio and smoothness features, and extracts candidate points for the guardrail.

[0044] S102 uses a guardrail point clustering and segmentation algorithm based on neighborhood point search to filter out outliers;

[0045] S103 uses curve fitting to calculate the lateral deviation of the reference point.

[0046] The planar distance ratio calculation method described in S101 is as follows: Figure 5 As shown, in one exemplary embodiment, where l s-1 l s l s+1 The scan lines obtained from lidar scanning. To construct the same scan line l s A continuous set of points, in which for Adjacent feature points in a continuous point set The distance from the (i+1)th feature point to the plane of the line connecting the ith feature point and the laser is given. The distance between the i-th feature point and the line connecting the (i-1)-th feature point and the laser is the planar distance. The planar distance ratio can be calculated using the aforementioned feature point data. Td is the design threshold for the plane distance ratio;

[0047] In step 103, the method for calculating the lateral deviation value includes: establishing a vehicle coordinate system with the forward direction as the Y-axis, the vertical ground as the Z-axis, and the forward direction rotated 90° clockwise as the X-axis. The center point of the brush roller is on the X-axis, and the X-axis coordinate of the brush roller center point is recorded as x0. Figure 6 As shown, P1, P2, P3, P n P is a point located on line L. g This indicates the center point of the brush roller with an X-axis coordinate of x0, and α is the angle between the vehicle's direction of travel and the guardrail's direction.

[0048] Based on the extrinsic parameter relationship between the lidar installation location and the vehicle coordinate system, the original point cloud coordinates of the lidar are transformed to the vehicle coordinate system; based on the planar distance ratio, smoothness characteristics, and the constraint condition y∈[0m, 10m], where m represents the unit meter, the 3D lidar point set P belonging to the guardrail is extracted. 3d Map these points to a two-dimensional XY coordinate system and arrange them from near to far to construct a two-dimensional point set P. 2d {p1, p2, ..., p n}; Calculate the curvature point by point according to the curvature calculation formula, and select P{p1, p2, ..., p m}, m≤n fit the straight line L; calculate the slope k of the straight line L, and then calculate the angle α between the vehicle driving direction and the guardrail direction, which is the angle deviation value; according to the formula of the straight line L, when y=0, we have x=x1, so we have the position deviation value |x1-x0|.

[0049] The guardrail obstacle detection includes: determining whether the guardrail exceeds a threshold based on the maximum curvature; if it exceeds the set threshold, it is determined that there is an abnormal placement of the guardrail in front.

[0050] The adaptive control of the guardrail sweeper includes: if the included angle α is within the allowable range and the position deviation value |x1-x0| is within the adaptive adjustable range of the brush roller telescopic rod, the extension / retraction amount is sent to the hydraulic adaptive control system of the roller brush to complete the adaptive process; if the included angle α is within the allowable range but the position deviation value |x1-x0| exceeds the adaptive adjustable range of the brush roller telescopic rod, or the included angle α exceeds the allowable range and the position deviation value |x1-x0| exceeds the adaptive adjustable range of the brush roller telescopic rod, then P{p1, p2, ..., p m The system is translated to the side of the vehicle and |x1-x0| is translated to obtain P′. A point d away from the front of the vehicle is selected as the aiming point within P′. Based on the vehicle motion model and vehicle speed, the steering wheel angle control amount is calculated in real time and sent to the vehicle's autonomous driving control system.

[0051] The adaptive control of the brush roller working pressure includes: adjusting the cleaning pressure of the brush rollers on both sides of the guardrail through the telescopic hydraulic cylinder 606, fixing the pressure of the brush roller 702 to clean the guardrail, and avoiding obstacles by adjusting the telescopic hydraulic cylinder 606 at the point where the guardrail detects an abnormality.

[0052] like Figure 7 As shown, the working process of the remote monitoring system for guardrail sweepers based on 4G / 5G networks includes:

[0053] The S201 high-definition camera mainly captures images of the scene in front of the vehicle and transmits them back to the control terminal.

[0054] The S202 network routing mainly connects vehicle-side data to the Internet via the 4G network;

[0055] S203 uses ffmpeg (Fast Forward Moving Picture Expert Group) to push the stream to the streaming server and obtain the video link;

[0056] S204 can be displayed directly in the control center or on a handheld terminal;

[0057] The S205 vehicle establishes a bidirectional persistent connection with the remote server via the WebSocket protocol, enabling the transmission of small data such as location and status, and possessing strong real-time performance.

[0058] The development and testing of intelligent prototypes were carried out based on edge computing. The installation interfaces of various terminal components were integrated into the cleaning mechanism. Reliability verification was carried out considering the impact of different weather, lighting, road conditions and other factors.

[0059] The working principle of this invention is as follows: Structurally, the lidar 200 is installed on the top of the vehicle body, and the high-definition camera 300 is installed at the front of the vehicle body; the rotary drive 500 is installed in the middle space of the vehicle body, allowing the entire cleaning mechanism 700 to rotate relative to the vehicle body, selectively cleaning one side of the guardrail as needed; the adjustment mechanism 600 controls the extension and retraction of the cleaning mechanism 700 and its lifting and lowering through the extension and retraction of the telescopic hydraulic cylinder 606; the cleaning mechanism 700 controls the spacing between the inner and outer brush rollers through the extension and retraction of the telescopic hydraulic cylinder 606, and controls the rotation of the outer brush roller through the rotary hydraulic cylinder 705. In terms of control and detection, the lidar 200 and the high-definition camera 300 are used as detection methods, combined to detect guardrail obstacles and provide early warning of lateral deviation of the cleaning vehicle.

[0060] Finally, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A guardrail cleaning vehicle, characterized in that, The system includes a vehicle body, a lidar unit, a high-definition camera, a chassis, a rotary drive, an adjustment mechanism, and a cleaning mechanism. The lidar unit is mounted on the top of the chassis cab, and the high-definition camera is mounted on the front of the chassis cab. The rotary drive is mounted on a sliding seat at the front of the vehicle body, which can slide on a rail on the frame, allowing the entire cleaning mechanism to rotate and move left and right relative to the vehicle body, selectively cleaning one side of the vehicle body's guardrail as needed. The adjustment mechanism controls the outward extension and lifting of the cleaning mechanism via the extension and retraction of a telescopic hydraulic cylinder. The cleaning mechanism controls the spacing between the inner and outer brush rollers via the extension and retraction of the telescopic hydraulic cylinder, and controls the rotation of the outer brush roller via a rotary hydraulic cylinder. The vehicle body includes a frame, a sliding seat, a sliding seat motor, a pinion, and a rack. The frame is bolted to the rear beam of the cab of the chassis. The sliding seat is mounted on the front slide rail of the frame. The sliding seat motor is bolted to the sliding seat. The sliding seat and the frame slide rail slide in a horizontal engagement, and the engagement distance is adjusted by the sliding seat motor. The pinion is mounted on the sliding seat motor. The rack is bolted to the frame and meshes with the pinion. The sliding seat motor drives the pinion to rotate, thereby causing the sliding seat to move horizontally left and right. The cleaning mechanism includes an inner brush roller frame, a brush roller, a brush roller motor, an outer brush roller frame, a rotary hydraulic cylinder, a water-blocking tarpaulin, and water spray pipes. The inner and outer brush roller frames respectively fix the inner and outer brush rollers and the brush roller motor, and the brush roller motor provides power to the brush roller. The rotary hydraulic cylinder is fixed to the outer brush roller frame, and one end of the rotary hydraulic cylinder is fixed to the flange of the telescopic arm end by bolts, so that the outer brush roller rotates around the flange of the telescopic arm end. The water-blocking tarpaulin is installed on the outside of the outer brush roller frame, and four water spray pipes are respectively installed and fixed on both sides of the inner brush roller frame and the water-blocking tarpaulin.

2. The guardrail cleaning vehicle according to claim 1, characterized in that, The adjustment mechanism includes a rotating frame, lifting columns, lifting brackets, lifting hydraulic cylinders, telescopic arms, and telescopic hydraulic cylinders. The rotating frame is fixed to the rotating drive by bolts. The two lifting columns are vertically installed and fixed on the rotating frame, and slide in the vertical direction with the lifting brackets. The lifting hydraulic cylinders control the mating distance, allowing the brush rollers to be vertically adjusted in height. The telescopic arm is horizontally installed and fixed on the rotating frame, and slides in the horizontal direction with the rotating frame. The telescopic hydraulic cylinders control the mating distance, allowing the inner and outer brush rollers to be horizontally adjusted in distance.

3. A method for detecting guardrail obstacles and providing lateral offset warning for a guardrail cleaning vehicle according to any one of claims 1-2, characterized in that, Using LiDAR and high-definition cameras as detection methods, the system combines these to detect guardrail obstacles and provide lateral deviation warnings for sweepers, including the following steps: The S100 collects data from LiDAR nodes, then obtains segmented point images by segmenting the point cloud nodes. These images are compared with the offset warning nodes to obtain the specific offset value of the guardrail cleaning vehicle. On the one hand, it activates the offset warning alarm, and on the other hand, it corrects the lateral offset between the vehicle and the guardrail through the hydraulic control of the cleaning mechanism. When the angular deviation between the vehicle's direction of travel and the guardrail's direction is within the allowable range, the brush roller hydraulic adaptive control system adjusts the working pressure of the hydraulic oil according to the received extension and retraction amount to achieve adaptive adjustment. When the angle deviation is within the allowable range but the position deviation exceeds the adaptive adjustment range of the brush roller telescopic rod, or when the angle deviation exceeds the allowable range and the position deviation exceeds the adaptive adjustment range of the brush roller telescopic rod, the point set used to fit the straight line is translated to the side of the vehicle and a point at a distance from the front of the vehicle is selected as the aiming point. The steering wheel angle control amount is calculated in real time based on the vehicle motion model and vehicle speed and sent to the vehicle autonomous driving control system to realize the adaptive control of the guardrail sweeper. The S200 uses a high-definition camera at the front of the guardrail cleaning vehicle to remotely monitor the vehicle via a 4G / 5G network, transmits video back to the camera to obtain real-time color images, and processes these images with offset data detected by LiDAR through an edge computing platform. This enables automatic early warning alarms and video display for obstacles at the front of the guardrail within the cleaning vehicle.

4. The method for detecting guardrail obstacles and providing lateral deviation warning for a guardrail cleaning vehicle according to claim 3, characterized in that, S100 includes: using the precise location information of the lidar and the neighborhood geometric relationship of the scan line, designing the planar distance ratio and smoothness features, and extracting candidate points for the guardrail; using a guardrail point clustering and segmentation algorithm based on neighborhood point search to filter out outliers; and using curve fitting to calculate the lateral deviation value of the reference point.

5. The method for detecting guardrail obstacles and providing lateral deviation warning for a guardrail cleaning vehicle according to claim 4, characterized in that, The planar distance ratio is ,in The distance from the (i+1)th feature point to the plane of the line connecting the ith feature point and the laser is given. The distance from the i-th feature point to the line connecting the (i-1)-th feature point and the laser is the planar distance, and Td is the design threshold for the planar distance ratio; for a point Construct a continuous set of points along the same scan line: ； point The smoothness characteristics are: ； Where k represents the midpoint of the same scan beam. The number of sampling points on the left or right side, where K represents the number of points in the point set. Let j represent the j-th point. Point The sum of the squares of the coordinate components, where i represents the position index of the current point in its scanning beam, and j represents the position label of the point adjacent to i.

6. The method for detecting guardrail obstacles and providing lateral deviation warning for a guardrail cleaning vehicle according to claim 5, characterized in that, The method for calculating the lateral deviation of the reference point includes: establishing a vehicle coordinate system with the forward direction as the Y-axis, the vertical ground as the Z-axis, and a 90° clockwise rotation of the forward direction as the X-axis. The center point of the brush roller is located on the X-axis, and the X-axis coordinate of the brush roller center point is recorded as follows: Based on the extrinsic parameter relationship between the lidar installation location and the vehicle coordinate system, the original point cloud coordinates of the lidar are transformed to the vehicle coordinate system; based on the planar distance ratio, smoothness characteristics, and... Constraints, where m is in meters, are used to extract the 3D laser point set belonging to the guardrail. Map these points to a two-dimensional XY coordinate system and arrange them from near to far to construct a two-dimensional point set. , This represents the nth point; the curvature is calculated point by point according to the curvature calculation formula, and a point set is selected. , This represents the m-th point. Fit a straight line L; calculate the slope k of line L, and then calculate the angle between the vehicle's driving direction and the guardrail's direction, which is the angle deviation value; according to the formula for line L, when y=0, we have Therefore, there is a positional deviation value. ; The guardrail obstacle detection includes: determining whether the guardrail exceeds a threshold based on the maximum curvature; if it exceeds the set threshold, it is determined that there is an abnormal placement of the guardrail in front. The adaptive control of the guardrail cleaning vehicle includes: if the included angle is within the allowable range, the position deviation value... Within the adaptive adjustable range of the brush roller telescopic rod, the adaptive process is completed by sending the telescopic amount to the brush roller hydraulic adaptive control system; if the included angle Within the allowable range and the positional deviation value Exceeding the adaptive adjustable range or angle of the brush roller telescopic rod Exceeding the allowable range or angle Exceeding the allowable range and positional deviation value If the brush roller telescopic rod exceeds its adaptive adjustable range, then... Translate to the vehicle side get ,exist The system selects a point d away from the front of the vehicle as the aiming point, calculates the steering wheel angle control value in real time based on the vehicle motion model and vehicle speed, and sends it to the vehicle's autonomous driving control system. The adaptive control of the brush roller working pressure includes: adjusting the cleaning pressure of the brush rollers on both sides of the guardrail through the telescopic hydraulic cylinder, fixing the pressure of the brush rollers to clean the guardrail, and avoiding obstacles by adjusting the telescopic hydraulic cylinder at the point where the guardrail detects an abnormality.

7. The method for detecting guardrail obstacles and providing lateral deviation warning for a guardrail cleaning vehicle according to claim 3, characterized in that, S200 includes: High-definition cameras capture images of the scene in front of the vehicle and transmit them back to the control terminal. The network router connects the vehicle-side data to the Internet via the 4G network. FFmpeg is used to push the stream to the streaming server, obtain the video link, and then display it directly in the control center or handheld terminal. The vehicle and the remote server establish a bidirectional persistent connection through the WebSocket protocol to realize the transmission of small data, including location and status, with strong real-time performance.

8. The method for detecting guardrail obstacles and providing lateral deviation warning for a guardrail cleaning vehicle according to claim 3, characterized in that, The development and testing of intelligent prototypes based on edge computing includes: The installation interfaces of various terminal components in the cleaning facility are integrated into the design; reliability verification is carried out considering the impact of different weather, lighting, and road conditions.

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