CN108909721B - Vehicle yaw angle calculation method based on millimeter wave radar - Google Patents
Vehicle yaw angle calculation method based on millimeter wave radar Download PDFInfo
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
- B60W40/10—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to vehicle motion
- B60W40/114—Yaw movement
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W2552/00—Input parameters relating to infrastructure
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W2554/00—Input parameters relating to objects
Abstract
The invention relates to a vehicle yaw angle calculation method based on a millimeter wave radar, which is characterized in that the millimeter wave radar is adopted to detect static objects beside an expressway, such as guardrails and trees, straight line and curve fitting is carried out to obtain a road boundary, and the boundary is utilized to replace the geometric characteristics of a lane; and (3) adopting a long-distance millimeter wave radar to obtain a front road boundary to predict whether the road is a straight road or a curve, and adopting a middle-distance millimeter wave radar to fit the road boundaries at two sides so as to calculate the vehicle yaw angle. Compared with vehicle yaw angle calculation based on machine vision, the method has stronger anti-interference capability on factors such as weather, illumination, shadow, camera shake, traffic sign lines and the like, and is favorable for accurate decision of a lane departure early warning system.
Description
Technical Field
The invention relates to a vehicle yaw angle calculation method based on a millimeter wave radar, and belongs to the technical field of intelligent automobile active safety.
Background
According to official display of traffic departments, automobiles in China currently keep the first place in the world, the number of automobile accidents also occupies 50% of the world, and particularly serious and serious accidents on expressways are high. According to the reports of the U.S. department of transportation, about 50% of accidents are related to unintended lane departure of vehicles. The Lane Departure Warning system (LDWS for short) is an active safety technology developed for this purpose, and warns drivers of Lane Departure phenomena (such as sound, vibration or light) without Lane change intention so as to avoid rear-end collision or collision between automobiles.
The existing lane departure early warning system is mainly based on an image processing technology of machine vision, and is used for identifying lane lines and calculating departure early warning indexes to make decisions. The method is extremely easy to be interfered by factors such as weather, illumination, shadow, camera shake, traffic sign lines and the like, and the application range is limited; the departure warning indicators, such as a Time To Lane Crossing (TLC) based decision method, a lateral position and direction parameter based decision method, depend on the vehicle yaw angle. Therefore, there is a need to improve the accuracy and stability of the yaw angle of a vehicle.
Disclosure of Invention
The invention aims to provide a vehicle yaw angle calculation method based on a millimeter wave radar, and solves the technical problems of low accuracy and poor stability of vehicle yaw angle measurement in the conventional lane departure early warning system.
In order to solve the technical problems, the invention adopts the following technical scheme:
a vehicle yaw angle calculation method based on a millimeter wave radar comprises the following steps:
step one, building a vehicle yaw angle calculation control system: the device comprises a long-range millimeter wave radar, two middle-range millimeter wave radars and a controller, wherein the long-range millimeter wave radars, the two middle-range millimeter wave radars and the controller are arranged on a vehicle; the long-range millimeter wave radar and the middle-range millimeter wave radar are electrically connected with the controller; the long-distance millimeter wave radar is mounted in the middle of the front bumper of the vehicle and used for acquiring information of a road in front of the vehicle; the two middle-distance millimeter wave radars are respectively installed at the two sides of a front bumper of the vehicle, are symmetrically arranged relative to the long-distance millimeter wave radar and are used for acquiring the side road information of the vehicle; the controller comprises a front curve pre-estimation module, a side road boundary fitting module and a vehicle yaw angle calculation module. In general, the method only needs one middle-range millimeter wave radar, and the other millimeter wave radar is only designed for redundancy. The detection distance of the long-distance millimeter wave radar can reach 250m, and the detection distance of the medium-distance millimeter wave radar can reach 150 m.
Step two, obtaining the position information of the static objects on the road in front of the vehicle through the long-distance millimeter wave radar, transmitting the position information to the controller, selecting the static objects capable of representing the road trend by the front curve pre-estimation calculation module based on an effective static object method, and fitting a straight line l according to the position coordinates of the effective static objects1To predict whether the road ahead is a curve or not, and to prepare for fitting the actual road boundary. The static objects are guardrails, trees and the like on two sides of the road.
Thirdly, obtaining position information of a static object of a side road of the vehicle through the intermediate distance millimeter wave radar, transmitting the position information to the controller, obtaining front road characteristic information by the side road boundary fitting module based on the front curve estimation module, utilizing the position information of the static object of the side road of the vehicle obtained by the intermediate distance millimeter wave radar, and fitting a curve or a straight line to be used as an actual road boundary;
and step four, the vehicle yaw angle calculation module calculates the vehicle yaw angle according to the fitted actual road boundary aiming at the straight road condition and the curve road condition.
In the driving process of a vehicle, firstly, information of a road in front of the vehicle is obtained by using a long-distance millimeter wave radar, and a front curve estimation module judges whether the road in front is a curve or a straight road according to the information; because the long-distance millimeter wave radar can detect the object at 250 meters, the vehicle continues to move forward for a certain distance in the judgment process of the front curve estimation module, the vehicle is closer to the object at the two sides of the road in front of the vehicle, which is obtained by the long-distance millimeter wave radar at the moment, then the middle-distance millimeter wave radar obtains the static object of the effective road at the side of the vehicle, and the actual road boundary curve is accurately fitted according to the position information of the static object of the effective road. And then, according to the actual road boundary curve and by combining the current advancing direction of the vehicle, the yaw angle of the current vehicle is accurately calculated, the advancing direction of the vehicle is adjusted in time, and the driving safety is improved.
With the rapid development of sensor technology, millimeter wave radars have numerous applications in intelligent automobiles due to their strong anti-interference capability against weather, illumination, shadows and other factors. In addition, in general, guardrails or trees are arranged beside the expressway and are consistent with the direction of the lane, so that static objects such as the guardrails or the trees can reflect the geometric characteristics of the lane line, the millimeter wave radar is adopted to detect the static objects such as the guardrails and the trees beside the expressway, straight line and curve fitting is carried out to obtain a road boundary, and the boundary is used for replacing the geometric characteristics of the lane; and (3) adopting a long-distance millimeter wave radar to obtain a front road boundary to predict whether the road is a straight road or a curve, and adopting a middle-distance millimeter wave radar to fit the road boundaries at two sides so as to calculate the vehicle yaw angle. Compared with vehicle yaw angle calculation based on machine vision, the method has stronger anti-interference capability on factors such as weather, illumination, shadow, camera shake, traffic sign lines and the like, and is favorable for accurate decision of a lane departure early warning system.
In a further improvement, the front curve estimation module estimates whether the front road is a curve or not by the following steps:
1) acquiring the position information of a static object in front of the vehicle through a long-distance millimeter wave radar;
2) acquiring an effective static object group according to the position information of the static object in front;
3) selecting the effective stationary object group with the maximum number of stationary objects as a candidate target, and fitting a straight line l representing the boundary of the front road according to the position information of a plurality of stationary objects in the candidate target1；
4) According to the fitted straight line l1The slope of the road is used for judging the characteristic information of the road ahead, namely the road is a straight road or a curve.
Further improved, a rectangular coordinate system XOY of the self-vehicle is established by taking the installation position of the long-distance millimeter wave radar on the vehicle as a coordinate origin O, the advancing direction of the vehicle as a Y axis and the connecting line of the two middle-distance millimeter wave radars as an X axis, and the static objects in the effective static object group must simultaneously satisfy the following four conditions: (1) the maximum distance Y of the stationary objects in the stationary object group in the Y-axis direction in the own vehicle coordinate systemmaxGreater than a first set threshold; (2) the distance delta Y between adjacent static objects in the static object group in the Y-axis direction in the coordinate system of the self-vehicleiGreater than a second set threshold; (3) the distance Deltax of adjacent static objects in the static object group in the X-axis direction in the coordinate system of the self-vehicleiLess than a third set threshold; (4) the number n of the static objects in the static object group is larger than a fourth set threshold, n is a positive integer, and i is 1 … … n; the position coordinate of the ith static object in the rectangular coordinate system XOY of the self-vehicle is (x)i，yi) (ii) a The first set threshold, the second set threshold, the third set threshold and the fourth set threshold are all preset values.
Further improved, a fitting straight line l of the boundary of the road in front of the vehicle is set1Slope k, fifth set thresholdA value of ktThen, the method for judging the front road characteristic information is as follows:
1) if k is positive, and k<ktIf the road ahead is right-curved;
2) if k is negative, and abs (k)<ktIf the road ahead is left-curved;
3) and the other conditions show that the road in front is a straight road.
Further improved, when the road ahead is a curve, the vehicle yaw angle calculation method is as follows:
1) fitting a road boundary curve by adopting a clothoid model, and fitting a boundary curve l by adopting a linear least square method by selecting an effective static object group;
2) drawing a normal line of a curve l through the origin of coordinates O, wherein the normal line intersects the curve l at a point A, and drawing a tangent line l of the curve l through the point A2Passing O as tangent line l2Is parallel to3Then l is3The included angle between the Y axis and the Y axis is the vehicle yaw angle beta.
Further improvement, when the road ahead is a straight road, the vehicle yaw angle calculation method is as follows: the method comprises the steps of selecting an effective static object group, fitting a boundary straight line l by a least square method, intersecting the straight line l with a Y axis to obtain an included angle delta, and knowing a vehicle yaw angle beta as delta by using a parallel line theorem.
In a further improvement, the intermediate distance millimeter wave radar obtains the position information of the static object of the effective road at the side of the vehicle according to the following steps:
1) firstly, detecting the information of the stationary objects on the corresponding side by the middle-distance millimeter wave radar corresponding to the curved side of the front road, acquiring effective stationary objects, and if the effective stationary objects exist, selecting a group with a large number of objects as a candidate stationary object group to fit a curve or a straight line;
2) if the detection of the effective static object fails due to the fact that the vehicle or other obstacles are shielded on the side, switching to the middle distance millimeter wave radar on the other side to detect and fit the road boundary;
3) and if the detection on the left side and the right side fails, outputting the road boundary information at the previous moment to replace the current road boundary information. The adaptability is strong, and the accurate fitting of the exit boundary curve is ensured.
The invention has the following beneficial effects:
1. the influence of factors such as weather, illumination, shadow, camera shake, traffic sign lines and the like is far less than that of machine vision, and the method can work normally in rainy and snowy days, foggy days or night driving and has strong stability.
2. The curve estimation strategy based on the long-distance millimeter wave radar can well reduce the road boundary fitting calculation amount, save calculation resources and improve calculation efficiency.
Drawings
Fig. 1 is a general structural view of the present invention.
Fig. 2 is a schematic diagram of a long-range millimeter wave radar information line fitting and static object number calculating method in the present invention.
FIG. 3 is a flow chart of the present invention for fitting stationary objects to road boundaries.
Fig. 4 is a schematic diagram illustrating the calculation of the yaw angle of the straight-lane vehicle according to the present invention.
FIG. 5 is a schematic diagram of the calculation of the yaw angle of the vehicle under a curve in the present invention.
Detailed Description
In order to make the purpose and technical solution of the present invention clearer, the following will clearly and completely describe the technical solution of the present invention with reference to the embodiments of the present invention.
As shown in fig. 1, the vehicle yaw angle calculation method based on the millimeter wave radar under the highway condition includes three modules: the device comprises a front curve pre-estimation module, a side road boundary fitting module and a vehicle yaw angle calculation module.
The front curve estimation module estimates whether a front road is a curve or not through the following steps: (1) acquiring azimuth information of a front static object by a long-distance millimeter wave radar; (2) obtaining a group of valid stationary objects; (3) selecting the maximum number of effective stationary object groups as candidate targets, and fitting a front road by adopting a straight line; (4) and judging the characteristic information of the road ahead, namely, the road is a straight road or a curve. A schematic diagram of the module execution is shown in fig. 2.
Referring to the upper schematic view of fig. 2, the effective stationary object group must satisfy the following four conditions to represent the road boundary: (1) the distance maximum value Y of the stationary objects in the stationary object group in the Y-axis direction in the coordinate system of the self-vehiclemaxGreater than a first set threshold of 30 m; (2) the distance delta Y between adjacent static objects in the static object group in the Y-axis direction in the coordinate system of the self-vehicleiGreater than a second set threshold of 5 m; (3) the distance Deltax of adjacent static objects in the static object group in the X-axis direction in the coordinate system of the self-vehicleiLess than a third set threshold value of 1 m; (4) the number n of stationary objects in the stationary object group is greater than the fourth set threshold value of 3. If all the conditions are met, the system is named as an effective static object group 1; if there are a plurality of stationary object groups satisfying the condition, they are named as effective stationary groups 2 to j. Typically, there are no more than 3 active stillness groups. As shown in fig. 2, the long-range millimeter wave radar recognizes stationary object groups on both sides of the road ahead, i.e., a stationary object group i and a stationary object group ii, and it can be seen that the number of stationary objects satisfying the above-mentioned first 3 conditions in the two groups is 6 and 5, respectively, and thus the stationary object groups are taken as candidate fitting point groups to perform straight line fitting.
The basis of the front road characteristic information judging method of the invention is as follows: assuming a fitted straight line l of the road boundary ahead1The slope is k, and the fifth set threshold is ktThen there is (1) if k is positive, and k is<ktIf the road ahead is right-curved; (2) if k is negative and abs (k)<ktIf the road ahead is left-curved; (3) the rest cases are straight paths.
Based on the above method for obtaining the effective stationary object group, the flow of fitting the side road boundary of the millimeter wave radar with the intermediate distance is illustrated in fig. 3, and is only described by taking the case that the front road is a left curve or a straight line as an example: (1) the pre-estimation module feeds the characteristic information of the front road back to the controller, the controller controls time delay (because the object at 250 meters can be detected by the long-distance millimeter wave radar, the vehicle continues to move forward for a certain distance in the judgment process of the front curve pre-estimation module, and at the moment, the vehicle is closer to the objects at two sides of the road in front of the vehicle, which are just acquired by the long-distance millimeter wave radar), and then the pre-estimation information is given to the side road boundary fitting module, so that the actual lane condition of the vehicle can be effectively reflected; (2) detecting the information of the right stationary object by a right intermediate distance millimeter wave radar, acquiring an effective stationary object, and if the effective stationary object exists, selecting a group with a large number of objects as a candidate stationary object group to fit a curve or a straight line; (3) if the detection of the effective static object fails due to the fact that the right side is shielded by the vehicle or other obstacles, the detection is switched to the left side intermediate distance millimeter wave radar for detecting and fitting the road boundary; (4) if the detection on the left side and the right side fails, outputting a road boundary at the previous moment to replace the current moment; (5) if the estimation module obtains that the front road is right-curved, only the right side in fig. 3 needs to be changed into the left side, and the left side needs to be changed into the right side.
The road boundary curve fitting of the invention adopts a clothoid model, and the model description is as follows:
y＝ax3+bx2+c
wherein a, b, c are waiting to ask the coefficient, will at least 3 stationary object coordinates substitute the above formula, adopt the linear least square method to carry out curve fitting, specifically as follows: assuming that the sum of squares of the distance deviations from points of the stationary object group to the road curve is
To minimize Q, the partial derivatives of a, b, and c are calculated for the above equations to obtain the following three equations:
converting the three formulas into a matrix form, then
The coefficients a, b and c can be obtained through the above formula, and then the road boundary curve equation can be obtained.
The vehicle yaw angle calculation module comprises straight track calculation and curve calculation.
FIG. 4 is a schematic diagram of a vehicle yaw angle calculation in a straight road, in which a boundary line l is fitted by selecting a group of effective stationary objects and using a least square method4Using a straight line l4The angle δ is obtained by intersecting the Y axis, and the vehicle yaw angle β becomes δ as can be known from the parallel line theorem.
As shown in FIG. 5, a schematic diagram of calculating a yaw angle of a vehicle under a curve is shown, a boundary curve l is fitted by selecting an effective stationary object group and adopting a linear least square method, a coordinate origin O is used for making a normal of the curve l intersect at a point A, and a tangent l of the curve l is made by crossing the point A2Cutting line l2The vehicle yaw angle beta can be calculated by translating the vehicle to the origin O along the normal OA.
The invention is only suitable for expressway, so the function of the invention can be automatically started by vehicle speed. The method has the advantages of simple structure and small calculation amount, and can effectively make up for the instability of the vehicle yaw angle calculation method based on machine vision to factors such as weather, illumination, shadow, camera shake, traffic sign lines and the like.
The embodiments of the present invention are not limited to the specific embodiments described herein, but rather, the embodiments are merely preferred embodiments of the present invention, and are not intended to limit the scope of the present invention. That is, all equivalent changes and modifications made according to the content of the claims of the present invention should be regarded as the technical scope of the present invention.
Claims (8)
1. A vehicle yaw angle calculation method based on a millimeter wave radar is characterized by comprising the following steps:
step one, building a vehicle yaw angle calculation control system: the device comprises a long-range millimeter wave radar, two middle-range millimeter wave radars and a controller, wherein the long-range millimeter wave radars, the two middle-range millimeter wave radars and the controller are arranged on a vehicle; the long-range millimeter wave radar and the middle-range millimeter wave radar are electrically connected with the controller; the long-distance millimeter wave radar is mounted in the middle of the front bumper of the vehicle and used for acquiring information of a road in front of the vehicle; the two middle-distance millimeter wave radars are respectively installed at the two sides of a front bumper of the vehicle, are symmetrically arranged relative to the long-distance millimeter wave radar and are used for acquiring the side road information of the vehicle; the controller comprises a front curve pre-estimation module, a side road boundary fitting module and a vehicle yaw angle calculation module;
step two, obtaining the position information of the static objects on the road in front of the vehicle through the long-distance millimeter wave radar, transmitting the position information to the controller, selecting the static objects capable of representing the road trend by the front curve pre-estimation calculation module based on an effective static object method, and fitting a straight line l according to the position coordinates of the effective static objects1Predicting whether the road ahead is a curve or not;
thirdly, obtaining position information of a static object of a side road of the vehicle through the intermediate distance millimeter wave radar, transmitting the position information to the controller, obtaining front road characteristic information by the side road boundary fitting module based on the front curve estimation module, utilizing the position information of the static object of the side road of the vehicle obtained by the intermediate distance millimeter wave radar, and fitting a curve or a straight line to be used as an actual road boundary;
and step four, the vehicle yaw angle calculation module calculates the vehicle yaw angle according to the fitted actual road boundary aiming at the straight road condition and the curve road condition.
2. The millimeter wave radar-based vehicle yaw angle calculation method according to claim 1, wherein the forward curve prediction calculation module predicts whether the road ahead is a curve by:
1) acquiring the position information of a static object in front of the vehicle through a long-distance millimeter wave radar;
2) acquiring an effective static object group according to the position information of the static object in front;
3) selecting the effective stationary object group with the maximum number of stationary objects as a candidate target, and fitting a straight line l representing the boundary of the front road according to the position information of a plurality of stationary objects in the candidate target1；
4) According to the fitted straight line l1The slope of the road is used for judging the characteristic information of the road ahead, namely the road is a straight road or a curve.
3. The millimeter wave radar-based vehicle yaw angle calculation method according to claim 2, wherein a rectangular coordinate system XOY of the vehicle is established with an installation position of the long-range millimeter wave radar on the vehicle as a coordinate origin O, a vehicle advancing direction as a Y-axis, and a connection line of two middle-range millimeter wave radars as an X-axis, and the stationary objects in the effective stationary object group must satisfy the following four conditions at the same time: (1) the maximum distance Y of the stationary objects in the stationary object group in the Y-axis direction in the own vehicle coordinate systemmaxGreater than a first set threshold; (2) the distance delta Y between adjacent static objects in the static object group in the Y-axis direction in the coordinate system of the self-vehicleiGreater than a second set threshold; (3) the distance Deltax of adjacent static objects in the static object group in the X-axis direction in the coordinate system of the self-vehicleiLess than a third set threshold; (4) the number n of the static objects in the static object group is larger than a fourth set threshold, n is a positive integer, and i is 1 … … n; the position coordinate of the ith static object in the rectangular coordinate system XOY of the self-vehicle is (x)i，yi) (ii) a The first set threshold, the second set threshold, the third set threshold and the fourth set threshold are all preset values.
4. The millimeter wave radar-based vehicle yaw angle calculation method according to claim 3, wherein a fitting straight line l of a road boundary ahead of the vehicle is set1The slope is k, and the fifth set threshold is ktThen, the method for judging the front road characteristic information is as follows: 1) if k is positive, and k<ktIf the road ahead is right-curved;
2) if k is negative, and abs (k)<ktIf the road ahead is left-curved;
3) and the other conditions show that the road in front is a straight road.
5. The millimeter wave radar-based vehicle yaw angle calculation method according to claim 4, wherein in the case where the road ahead is a curve, the vehicle yaw angle calculation method is as follows:
1) fitting a road boundary curve by adopting a clothoid model, and fitting a boundary curve l by adopting a linear least square method by selecting an effective static object group;
2) drawing a normal line of a curve l through the origin of coordinates O, wherein the normal line intersects the curve l at a point A, and drawing a tangent line l of the curve l through the point A2Passing O as tangent line l2Is parallel to3Then l is3The included angle between the Y axis and the Y axis is the vehicle yaw angle beta.
6. The millimeter wave radar-based vehicle yaw angle calculation method according to claim 4, wherein in a case where the road ahead is a straight road, the vehicle yaw angle calculation method is as follows: by selecting effective static object group, fitting out boundary straight line l by least square method4Using a straight line l4The angle δ is obtained by intersecting the Y axis, and the vehicle yaw angle β becomes δ as can be known from the parallel line theorem.
7. The millimeter-wave radar-based vehicle yaw angle calculation method according to claim 5, wherein the intermediate millimeter-wave radar obtains position information of a stationary object on an effective road on the side of the vehicle according to the following steps:
1) firstly, detecting the information of the stationary objects on the corresponding side by the middle-distance millimeter wave radar corresponding to the curved side of the front road, acquiring effective stationary objects, and if the effective stationary objects exist, selecting a group with a large number of objects as a candidate stationary object group to fit a curve or a straight line;
2) if the detection of the effective static object fails due to the fact that the vehicle or other obstacles are shielded on the side, switching to the middle distance millimeter wave radar on the other side to detect and fit the road boundary;
3) and if the detection on the left side and the right side fails, outputting the road boundary information at the previous moment to replace the current road boundary information.
8. The millimeter wave radar-based vehicle yaw angle calculation method of claim 7, wherein the side road boundary fitting module fits the road boundary curve using a clothoid model, which is described as:
y＝ax3+bx2+c；
wherein a, b and c are coefficients to be solved, and the position coordinate of the ith static object in the rectangular coordinate system XOY of the self-vehicle is (x)i，yi)；
Substituting at least 3 stationary object coordinates into the formula, and performing curve fitting by adopting a linear least square method, wherein the method specifically comprises the following steps: assuming that the sum of squared deviations of distances from points of the stationary object group to the road curve is:
to minimize Q, the partial derivatives of a, b, and c are calculated for the above equations to obtain the following three equations:
converting the three formulas into a matrix form, the following steps are carried out:
the coefficients a, b and c can be obtained through the above formula, and then the road boundary curve equation can be obtained.
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