Frame-passing method, system and medium applicable to overloaded driverless frame vehicles

Abstract

The present invention provides a method for passing through a frame applicable to a heavy-duty driverless frame vehicle, comprising the following steps: collecting laser point cloud data through the TCP communication protocol and performing preprocessing; clustering the preprocessed laser point cloud data, and extracting two types of laser point cloud data according to the frame characteristics, namely left-side laser point cloud data and right-side laser point cloud data; weighting the left-side laser point cloud data and the right-side laser point cloud data respectively; fitting the weighted left-side point cloud data and right-side point cloud data respectively to obtain two straight-line equations based on the laser coordinate system, and extracting the straight-line equation of the frame center line according to the two frame lines; according to the straight-line equation of the frame center line, substituting it into the Bezier curve model to calculate and output the tire steering angle and the throttle amount. The present invention also provides a system for passing through a frame applicable to a heavy-duty driverless frame vehicle, which can complete passing through the frame and improve the passing-through accuracy of the frame in the case where the difference between the vehicle width and the frame width is extremely small.

Description

Technical Field

[0001] The present invention relates to the technical field of frame vehicles, and in particular, to a method and system for threading a frame applicable to a heavy-duty driverless frame vehicle, and a medium. Background Art

[0002] During the process of threading a steel coil by a large heavy-duty driverless frame vehicle, it is easy to rub against the frame, resulting in damage to the frame. Although there are fully automatic parking methods and fully automatic parking systems in the prior art, the width of the parking space for parking in the garage is wider than the width of the vehicle, and the required precision is not so high. Therefore, how to improve the precision of the frame vehicle threading the frame is particularly important.

[0003] After retrieval, patent document CN112486176A discloses a trajectory tracking control method for a driverless frame vehicle considering disturbances, including expected tracking trajectory preprocessing, preview point search, trajectory tracking error calculation, and tracking error correction considering disturbances; expected tracking trajectory preprocessing is used to obtain tracking points and determine tracking point information; the preview point search obtains the preview distance by solving and searches for tracking points within the preview distance to obtain the expected preview point; the trajectory tracking error calculation calculates the current vehicle speed deviation and lateral deviation by obtaining the current coordinates and the expected preview point; the tracking error correction considering disturbances obtains the vehicle speed deviation and lateral deviation, solves the current pedal opening and steering angle control amounts, and then controls the frame vehicle to achieve trajectory tracking control. The disadvantage of this prior art is that although it can achieve trajectory tracking, it cannot improve the precision of the frame vehicle threading the frame.

[0004] Patent document CN110395201A discloses a software development method for the vehicle controller of a distributed hybrid drive unmanned frame vehicle. The vehicle controller of the driverless frame vehicle unit includes a main control module and a communication module. The communication module receives operation instructions transmitted by a remote control driving system controller or an autonomous driving system controller through a CAN bus network and transmits them to the main control module to enter the remote control driving mode or the autonomous driving mode; the main control module performs fault diagnosis, high-voltage power-on and power-off control, mode switching control, torque distribution, anti-skid control, and slope parking control on the whole vehicle. At the same time, it also has frame lifting control requirements and hydraulic control for the frame vehicle. The controller has a data post-processing function, which is convenient for effectively monitoring and analyzing the vehicle driving state. The present invention effectively considers the multiple functional requirements of a multi-wheel distributed hybrid drive driverless frame vehicle and has good portability on the premise of shortening the development time of the vehicle controller. Although this prior art realizes vehicle control, it does not focus on improving the precision of the frame vehicle threading frame module.

[0005] Therefore, it is urgent to research and develop a threading frame system and method specifically for the case where the difference between the vehicle width and the frame width is extremely small. Summary of the Invention

[0006] In view of the deficiencies in the prior art, the object of the present invention is to provide a method, system and medium for threading a frame applicable to a heavy-duty driverless frame vehicle, which solves the problem of rubbing against the frame during the accurate threading process of transporting steel coils and can improve the precision control of the threading process.

[0007] A method for threading a frame applicable to a heavy-duty driverless frame vehicle provided by the present invention includes the following steps:

[0008] Step S1: Collect laser point cloud data through the TCP communication protocol and perform preprocessing;

[0009] Step S2: Cluster the preprocessed laser point cloud data, and extract two types of laser point cloud data according to the frame characteristics, namely, left-side laser point cloud data and right-side laser point cloud data;

[0010] Step S3: Weight the left-side laser point cloud data and the right-side laser point cloud data respectively;

[0011] Step S4: Fit the weighted left-side point cloud data and right-side point cloud data respectively to obtain two straight-line equations based on the laser coordinate system, and extract the straight-line equation of the frame center line according to the two frame lines;

[0012] Step S5: According to the straight-line equation of the frame center line, substitute it into the Bezier curve model to calculate and output the tire steering angle and throttle amount.

[0013] Preferably, step S2 includes the following steps:

[0014] Step S2.1: Convert the laser point cloud data into the right-handed coordinate axis system, where the right-handed coordinate axis system takes the traveling direction of the frame vehicle as the X-axis, the due left side of the frame vehicle as the positive direction of the Y-axis, and the laser origin for collecting the laser point cloud data as the coordinate origin;

[0015] Step S2.2: Select the centroid of the left-side laser points and the centroid of the right-side laser points;

[0016] Step S2.3: Classify the laser points into left-side laser points and right-side laser points according to the distances from the centroid of the left-side laser points and the centroid of the right-side laser points;

[0017] Step S2.4: Judge the quantity deviation between the left-side laser points and the right-side laser points and perform classification result verification.

[0018] Preferably, step S3 includes the following steps:

[0019] Step S3.1: Divide the left laser points and the right laser points into the first type of laser points and the second type of laser points respectively according to the magnitude of the X value. The points with an X value less than 6 meters in the near range are the first type of laser points, and the remaining points are the second type of laser points;

[0020] Step S3.2: Copy the second type of laser points and place them into the laser point cloud data set.

[0021] Preferably, step S4 includes the following steps:

[0022] Step S4.1: Randomly select two laser points from the left laser points and the right laser points respectively to obtain the left laser point data model y 左 = k1x + b1 and the right laser point data model y 右 = k2x + b2;

[0023] Step S4.2: Calculate the distances from the laser points on the left to the left laser straight line equation y 左 = k1x + b1 and the distances from the laser points on the right to the right laser straight line equation y 右 = k2x + b2 respectively, and screen out the number of all laser points that meet the current distance threshold range.

[0024] Preferably, step S4 further includes the following steps:

[0025] Step S4.3: Iteratively select the model with more laser points within the current model threshold range for the left laser point data model and the right laser point data model;

[0026] Step S4.4: If the threshold requirements are met, end the iteration and obtain two frame boundary lines.

[0027] Preferably, step S4 further includes step S4.5: According to the two frame line straight lines, obtain the frame center line through mean value calculation. Take the coefficients k1, k2, b1, b2 of the two straight line equations, calculate the mean values of k1, k2 and the mean values of b1, b2 respectively, and obtain the frame center line equation.

[0028] Preferably, step S5 includes the following steps:

[0029] Step S5.1: Construct a third-order Bezier planning curve model, and screen out the pre-view points of the front and rear wheels respectively as the first planning point of the Bezier curve. According to the first planning point of the Bezier and the vehicle body orientation, input them into the model and output the second planning point;

[0030] Step S5.2: Calculate the third planning point and the fourth planning point of the Bezier curve respectively according to the frame center line equation and the curve model;

[0031] Step S5.3: Calculate the Bezier curvature corresponding to the preview point according to the Bezier curve, and calculate the current tire steering angle based on the curvature.

[0032] Preferably, step S5 further includes the following steps:

[0033] Step S5.4: Judge the corresponding tire limit values under different conditions according to the tire steering angle, and output the corresponding tire angle values according to the limit values;

[0034] Step S5.5: Calculate the current vehicle speed deviation according to the current vehicle speed, and after substituting it into the vehicle speed control model, output the corresponding throttle amount.

[0035] According to a system for passing through a frame applicable to a heavy-duty driverless frame vehicle provided by the present invention, it includes:

[0036] Module M1: Collect laser point cloud data through the TCP communication protocol and perform preprocessing;

[0037] Module M2: Cluster the preprocessed laser point cloud data, and extract two types of laser point cloud data according to the frame characteristics, namely, left-side laser point cloud data and right-side laser point cloud data;

[0038] Module M3: Weight the left-side laser point cloud data and the right-side laser point cloud data respectively;

[0039] Module M4: Fit the weighted left-side point cloud data and right-side point cloud data respectively to obtain two straight line equations based on the laser coordinate system, and extract the straight line equation of the frame center line according to the two frame lines;

[0040] Module M5: According to the straight line equation of the frame center line, substitute it into the Bezier curve model to calculate and output the tire steering angle and the throttle amount.

[0041] According to a computer-readable storage medium storing a computer program, when the computer program is executed by a processor, the steps of the above method are implemented.

[0042] Compared with the prior art, the present invention has the following beneficial effects:

[0043] 1. The present invention is specifically aimed at passing through a frame where the vehicle width and the frame width differ very little, and can improve the accuracy control of passing through the frame.

[0044] 2. The present invention uses a lidar to identify the frame boundary line, obtains the frame center line according to the frame boundary line, and calculates the current tire angle in real time according to the center line and the vehicle pose, thereby realizing the process of passing through the frame with high precision and solving the problem of vehicle rubbing against the frame during the vehicle passing through the frame. Description of the Drawings

[0045] Other features, objects, and advantages of the present invention will become more apparent from the following detailed description of non-limiting embodiments read in conjunction with the accompanying drawings:

[0046] Figure 1 It is a flowchart of the method steps for threading through the frame applicable to the heavy-duty driverless frame vehicle in the present invention;

[0047] Figure 2 It is a speed control diagram of the threading-through-frame system applicable to the heavy-duty driverless frame vehicle in the present invention;

[0048] Figure 3 It is a tire control diagram of the threading-through-frame system applicable to the heavy-duty driverless frame vehicle in the present invention;

[0049] Figure 4 It is a structural diagram of the threading-through-frame system applicable to the heavy-duty driverless frame vehicle in the present invention. Detailed Embodiments

[0050] The present invention will be described in detail below with reference to specific embodiments. The following embodiments will help those skilled in the art to further understand the present invention, but do not limit the present invention in any form. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several changes and improvements can still be made. These all fall within the protection scope of the present invention.

[0051] As Figure 1 , Figure 2 and Figure 3 shown, the present invention provides a method for threading through the frame applicable to the heavy-duty driverless frame vehicle, including the following steps:

[0052] Step S1: Collect laser point cloud data through the TCP communication protocol and perform preprocessing. According to the installation environment of the laser, crop the effective laser point cloud data, that is, only obtain the laser points between 30 degrees and 150 degrees, filter the laser point data in other regions, and obtain the preprocessed point cloud data. Since the front face baffle of the vehicle body will block some laser points, and this part of the laser point data is invalid for the algorithm, it is necessary to effectively trim the laser data. After cropping and removing the useless data from the data, the amount of subsequent iterative operations is greatly reduced, thereby accelerating the efficiency of frame recognition and reducing the requirements for computer performance.

[0053] Step S2: Cluster the preprocessed laser point cloud data, and extract two types of laser point cloud data according to the frame characteristics, namely, the left laser point cloud data and the right laser point cloud data.

[0054] Step S2.1: Convert the laser point cloud data into the right - hand coordinate system. In the right - hand coordinate system, the traveling direction of the frame vehicle is the X - axis, the positive direction of the Y - axis is on the immediate left side of the frame vehicle, and the laser origin for collecting the laser point cloud data is the origin of the coordinate axes. Due to certain deviations in the installation of the laser itself, the laser needs to be calibrated. After a series of rotational and translational coordinate transformations, the correct laser point cloud coordinates are obtained.

[0055] Step S2.2: Select the centroid of the left - side laser points and the centroid of the right - side laser points. After obtaining the laser point cloud in the coordinate system from the previous step, first, take the mean value of the smallest ten points with y - value greater than zero as the centroid of the left - side laser points, and then take the mean value of the largest ten points with y - value less than zero as the centroid of the right - side laser points. The reason for taking ten points is to prevent the influence of occasional interference points in the laser.

[0056] Step S2.3: Classify the laser points into left - side laser points and right - side laser points according to the distances from the centroid of the left - side laser points and the centroid of the right - side laser points. Traverse all laser points and calculate the distances of all laser points from the above two mass points. If a laser point is closer to the first mass point, then this point belongs to the left - side laser points; otherwise, it belongs to the right - side laser points.

[0057] Step S2.4: Judge the quantity deviation between the left - side laser points and the right - side laser points and conduct verification of the classification results. If the number of left - side laser points is greater than that of the right - side laser points and exceeds the threshold, shift the threshold for laser centroid selection 20 cm to the left, that is, take the mean value of the smallest ten points with y - value greater than 20 cm as the centroid of the left - side laser points, and take the mean value of the largest ten points with y - value less than 20 cm as the centroid of the right - side laser points; conversely, shift the laser coordinate system 20 cm to the right. Then jump to Step S1 and repeat the above steps until the number deviation between the left - side laser points and the right - side laser points is less than the set threshold.

[0058] Step S3: Weight the left - side laser point cloud data and the right - side laser point cloud data respectively.

[0059] Step S3.1: Divide the left - side laser points and the right - side laser points into the first - type laser points and the second - type laser points respectively according to the magnitude of the X - value. Points with X - value less than 6 meters nearby are the first - type laser points, and the remaining points are the second - type laser points.

[0060] Step S3.2: Copy the second - type laser points and put them into the laser point cloud data set.

[0061] Step S4: Fit the weighted left - side point cloud data and the right - side point cloud data respectively to obtain two straight - line equations based on the laser coordinate system, and extract the straight - line equation of the frame center line according to the two frame lines.

[0062] Step S4.1: Randomly select two laser points from the left laser point and the right laser point respectively to obtain the left laser point data model y 左 = k1x + b1 and the right laser point data model y 右 = k2x + b2.

[0063] Step S4.2: Calculate the distances from the laser points on the left to the left laser straight line equation y 左 = k1x + b1 and the distances from the laser points on the right to the right laser straight line equation y 右 = k2x + b2 respectively, and screen out the number of all laser points that meet the current distance threshold range.

[0064] Step S4.3: Iteratively select the model with more laser points within the current model threshold range for the left laser point data model and the right laser point data model. That is to say, check whether the result of the current model is good enough. If it meets the threshold requirements, end the iteration. If it does not meet the threshold requirements, compare the current model result with the previous model result and select the model with a better result, that is, the model with more data points that meet the model.

[0065] Step S4.4: If the threshold requirements are met, end the iteration and obtain two frame boundary lines.

[0066] Step S4.5: According to the two frame line straight lines, obtain the frame center line by mean calculation. Take the coefficients k1, k2, b1, b2 of the two straight line equations, calculate the means of k1, k2 and the means of b1, b2 respectively to obtain the frame center line equation.

[0067] Step S5: According to the straight line equation of the frame center line, substitute it into the Bezier curve model to calculate and output the tire steering angle and throttle amount.

[0068] Step S5.1: Construct a third-order Bezier planning curve model, screen the preview points of the front and rear wheels respectively as the first planning point of the Bezier curve, and according to the first planning point of the Bezier curve and the vehicle body orientation, substitute it into the model to output the second planning point of the Bezier curve;

[0069] Step S5.2: Calculate the third planning point and the fourth planning point of the Bezier curve respectively according to the frame center line equation and the curve model;

[0070] Step S5.3: Calculate the Bezier curvature corresponding to the preview point according to the Bezier curve, and calculate the current tire steering angle according to the curvature.

[0071] Step S5.4: According to the tire steering angle, judge the corresponding tire limit values under different conditions, and output the corresponding tire angle values according to the limit values;

[0072] Step S5.5: Calculate the current vehicle speed deviation based on the current vehicle speed. After substituting it into the vehicle speed control model, output the corresponding throttle amount.

[0073] The present invention also provides a system for passing through a frame applicable to a heavy-duty driverless frame vehicle, including:

[0074] Module M1: Collect laser point cloud data through the TCP communication protocol and perform preprocessing;

[0075] Module M2: Cluster the preprocessed laser point cloud data, and extract two types of laser point cloud data according to the frame characteristics, namely, left-side laser point cloud data and right-side laser point cloud data;

[0076] Module M3: Weight the left-side laser point cloud data and the right-side laser point cloud data respectively;

[0077] Module M4: Fit the weighted left-side point cloud data and right-side point cloud data respectively to obtain two straight-line equations based on the laser coordinate system, and extract the straight-line equation of the frame center line according to the two frame lines;

[0078] Module M5: According to the straight-line equation of the frame center line, substitute it into the Bezier curve model to calculate and output the tire steering angle and throttle amount.

[0079] Specifically, as Figure 4 shown, a system for passing through a frame applicable to a heavy-duty driverless frame vehicle provided by the present invention includes a navigation subsystem and a vehicle body subsystem. The navigation subsystem includes a sensing unit and a control unit, and the vehicle body subsystem includes a hydraulic steering unit and a power unit. After the sensing unit collects information, it is transmitted to the control unit for the operation of passing through the frame. The sensing unit includes a positioning module, a frame contour recognition module, and a relative position sensing module of the frame vehicle. The control unit includes a steering control module and a power control module. The heavy-duty driverless frame vehicle is detected and recognized through the positioning module, the frame contour recognition module, and the relative position sensing module of the frame vehicle. The power control module controls the power unit to control the throttle amount and the brake amount, and the steering control module controls the hydraulic steering unit to control the tire steering angle.

[0080] Among them, the sensing unit can collect data through means such as laser, ultrasonic, and high-precision positioning; high-precision positioning includes methods such as magnetic nail positioning, SLAM positioning, and GPS+inertial navigation positioning.

[0081] The present invention also provides a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0082] The present invention obtains the frame center line, i.e., the target trajectory, by identifying the frame contour with a single-line laser, and combines the current pose of the vehicle to plan a Bezier curve model in real time. The current curvature is calculated according to the Bezier curve model, and the current tire angle is deduced from the curvature.

[0083] The difference between the present invention and the implementation methods of the fully automatic parking method and the parking system lies in that the width and angle of the automatic parking space allow a relatively large error range, while the present invention completes the action of passing through the frame when the vehicle width is less than 10 cm tighter than the frame width, and has extremely high requirements for the accuracy of passing through the frame. Since the automatic parking does not consider the problem of the vehicle body pressing on the solid line of the parking space, while the present invention requires that the vehicle body cannot rub against the frame.

[0084] Those skilled in the art know that in addition to implementing the system and its various devices, modules, and units provided by the present invention in the form of pure computer-readable program code, the method steps can be logically programmed to enable the system and its various devices, modules, and units provided by the present invention to be implemented in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers, etc. to achieve the same functions. Therefore, the system and its various devices, modules, and units provided by the present invention can be regarded as a hardware component, and the devices, modules, and units included therein for implementing various functions can also be regarded as the structure within the hardware component; the devices, modules, and units for implementing various functions can also be regarded as either software modules for implementing the method or the structure within the hardware component.

[0085] The specific embodiments of the present invention have been described above. It should be understood that the present invention is not limited to the above specific implementation manners, and those skilled in the art can make various changes or modifications within the scope of the claims, which does not affect the essence of the present invention. Without conflict, the embodiments of the present application and the features in the embodiments can be combined arbitrarily with each other.

Claims

1. A method for passing through a frame applicable to a heavy-duty driverless frame vehicle, characterized in that, It includes the following steps: Step S1: Collect laser point cloud data through the TCP communication protocol and perform preprocessing; Step S2: Cluster the preprocessed laser point cloud data, and extract two types of laser point cloud data according to the frame characteristics, namely, left-side laser point cloud data and right-side laser point cloud data; Step S3: Weight the left-side laser point cloud data and the right-side laser point cloud data respectively; Step S4: Fit the weighted left-side point cloud data and right-side point cloud data respectively to obtain two straight-line equations based on the laser coordinate system, and extract the straight-line equation of the frame center line according to the two frame lines; Step S5: According to the straight-line equation of the frame center line, substitute it into the Bezier curve model to calculate and output the tire steering angle and throttle amount; The said Step S2 includes the following steps: Step S2.1: Convert the laser point cloud data into the right-handed coordinate axis system. The right-handed coordinate axis system takes the traveling direction of the frame vehicle as the X axis, the due left side of the frame vehicle as the positive direction of the Y axis, and the laser origin for collecting the laser point cloud data as the coordinate origin; Step S2.2: Select the centroid of the left-side laser points and the centroid of the right-side laser points; Step S2.3: Classify the laser points into left-side laser points and right-side laser points according to the distances from the centroid of the left-side laser points and the centroid of the right-side laser points; Step S2.4: Judge the quantity deviation between the left-side laser points and the right-side laser points, and perform verification on the classification results; The said Step S3 includes the following steps: Step S3.1: Divide the left-side laser points and the right-side laser points into the first type of laser points and the second type of laser points respectively according to the magnitude of the X value. The points with an X value less than 6 meters nearby are the first type of laser points, and the remaining points are the second type of laser points; Step S3.2: Copy the second type of laser points and put them into the laser point cloud data set; The said Step S4 includes the following steps: Step S4.1: Randomly select two laser points from the left laser point and the right laser point respectively to obtain the left laser point data model y 左 = k1x + b1 and the right laser point data model y 右 = k2x + b2; Step S4.2: Calculate the distances from the laser points on the left to the left laser straight line equation y 左 = k1x + b1, and the distances from the laser points on the right to the right laser straight line equation y 右 = k2x + b2, and filter out the number of all laser points that meet the current distance threshold range; The said Step S4 also includes the following steps: Step S4.3: Iteratively select the model with more laser points within the current model threshold range for the left-side laser point data model and the right-side laser point data model; Step S4.4: If the threshold requirement is met, end the iteration and obtain two frame boundary lines; The said Step S4 also includes Step S4.5: According to the two frame line straight lines, obtain the frame center line through mean calculation. Take the coefficients k1, k2, b1, b2 of the two straight-line equations, and calculate the means of k1, k2 and b1, b2 respectively to obtain the frame center line equation; The said Step S5 includes the following steps: Step S5.1: Construct a third-order Bezier planning curve model, screen the preview points of the front and rear wheels respectively as the first planning point of the Bezier curve, and according to the first planning point of the Bezier curve and the vehicle body orientation, output the second planning point after substituting it into the model; Step S5.2: Calculate the third planning point and the fourth planning point of the Bezier curve respectively according to the frame center line equation and the curve model; Step S5.3: Calculate the Bezier curvature corresponding to the preview point according to the Bezier curve, and calculate the current tire steering angle according to the curvature; The said Step S5 also includes the following steps: Step S5.4: According to the tire steering angle, judge the corresponding tire limit values under different conditions, and output the corresponding tire angle values according to the limit values; Step S5.5: Calculate the current vehicle speed deviation based on the current vehicle speed. After substituting it into the vehicle speed control model, output the corresponding throttle amount.

2. A system for passing through a frame applicable to a heavy-duty driverless frame vehicle, characterized in that, Including: Module M1: Collect and preprocess the laser point cloud data through the TCP communication protocol; Module M2: Cluster the preprocessed laser point cloud data, and extract two types of laser point cloud data, namely the left laser point cloud data and the right laser point cloud data, according to the frame characteristics; Module M3: Weight the left laser point cloud data and the right laser point cloud data respectively; Module M4: Fit the weighted left point cloud data and right point cloud data respectively to obtain two straight line equations based on the laser coordinate system, and extract the straight line equation of the frame center line according to the two frame lines; Module M5: According to the straight line equation of the frame center line, substitute it into the Bezier curve model to calculate and output the tire steering angle and throttle amount; The said Module M2 includes: Module M2.1: Convert the laser point cloud data into the right-handed coordinate axis system. The right-handed coordinate axis system takes the traveling direction of the frame vehicle as the X axis, the due left of the frame vehicle as the positive direction of the Y axis, and the laser origin for collecting the laser point cloud data as the coordinate origin; Module M2.2: Select the centroid of the left laser points and the centroid of the right laser points; Module M2.3: Classify the laser points into left laser points and right laser points according to the distance from the centroid of the left laser points and the centroid of the right laser points; Module M2.4: Judge the quantity deviation between the left laser points and the right laser points, and perform classification result verification; The said Module M3 includes: Module M3.1: Divide the left laser points and the right laser points into the first type of laser points and the second type of laser points respectively according to the magnitude of the X value. The points with an X value less than 6 meters nearby are the first type of laser points, and the remaining points are the second type of laser points; Module M3.2: Copy the second type of laser points and put them into the laser point cloud data set; The said Module M4 includes: Module M4.1: Randomly select two laser points from the left laser point and the right laser point respectively to obtain the left laser point data model y 左 = k1x + b1 and the right laser point data model y 右 = k2x + b2; Module M4.2: Calculate the distances from the laser points on the left to the left laser straight line equation y 左 = k1x + b1, and the distances from the laser points on the right to the right laser straight line equation y 右 = k2x + b2, and filter out the number of all laser points that meet the current distance threshold range; The said Module M4 also includes: Module M4.3: Iteratively select the model with more laser points within the current model threshold range for the left laser point data model and the right laser point data model; Module M4.4: If the threshold requirement is met, end the iteration and obtain two frame boundary lines; The said Module M4 also includes Module M4.5: According to the two frame lines, obtain the frame center line through mean calculation. Take the coefficients k1, k2, b1, b2 of the two straight line equations, and calculate the mean of k1, k2 and the mean of b1, b2 respectively to obtain the frame center line equation; The said Module M5 includes: Module M5.1: Construct a third-order Bezier planning curve model, screen the preview points of the front and rear wheels respectively as the first planning point of the Bezier curve, and output the second planning point after substituting it into the model according to the first Bezier planning point and the vehicle body orientation; Module M5.2: Calculate the third planning point and the fourth planning point of the Bezier curve respectively according to the frame center line equation and the curve model; Module M5.3: Calculate the Bezier curvature corresponding to the preview point according to the Bezier curve, and calculate the current tire steering angle according to the curvature; The said Module M5 also includes: Module M5.4: Determine the corresponding tire limit values under different conditions based on the tire steering angle, and output the corresponding tire angle values according to the limit values; Module M5.5: Calculate the current vehicle speed deviation based on the current vehicle speed, and output the corresponding throttle amount after bringing it into the vehicle speed control model.

3. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to Claim 1.

Images