A road uniform rolling method based on automatic driving multi-path generation

Abstract

The present application relates to a kind of road uniform rolling methods based on automatic driving multi-path generation, belong to the technical field of automatic driving path planning, solve the problem of low efficiency of manual evaluation pavement blister rut and manual setting transverse offset in prior art, uneven road rolling leads to road pit and affects vehicle ride smoothness.The present application obtains the offset path that can guarantee the kinematic feasibility of vehicle travel by designing the offset function with configurable amplitude and adjustable maximum derivative, then calculates the offset of each path point, and then automatically calculates the position of each original path point after offset, to obtain the offset path;It has good adaptability to different road conditions, and the calculation efficiency is improved by automatically calculating offset;The effect of uniform rolling driving pavement is realized by using the way of periodic offset path translation, to ensure vehicle ride smoothness.

Description

Technical Field

[0001] This invention relates to the field of autonomous driving path planning technology, and more specifically to a method for uniform road compaction based on autonomous driving multi-path generation. Background Technology

[0002] After the planning program of an autonomous vehicle runs, it first outputs a set of discrete path point sequences, i.e., the driving path, based on the road boundary information and start-end point information within the road segment where the vehicle is located. However, when an autonomous vehicle performs a driving task, under conditions such as soft road surface or uneven road surface, the vehicle leaves ruts after driving. Repeatedly driving on the same path will create ruts and potholes, affecting the smoothness of the vehicle's driving. To ensure the smoothness of the vehicle's driving, an autonomous vehicle road equalization path generation algorithm with balanced vehicle driving is particularly important.

[0003] When autonomous vehicles repeatedly travel on the same road segment, they adjust their lateral displacement during the journey to overlap ruts from different offset paths on the road surface, achieving a uniform compaction effect. This effect is achieved when the entire road surface within the segment is evenly covered by ruts. Existing uniform compaction schemes add a fixed lateral offset to the original path; however, this scheme has the following problems: 1. Before each autonomous vehicle journey, the road surface ruts are manually assessed, and then the lateral offset is manually set to obtain the path for each journey. This process reduces the overall work efficiency of the autonomous vehicle's operations; 2. Using a uniform offset as the lateral offset parameter for each journey within the road segment results in multiple parallel offset paths. As the number of journeys increases, the overlapping of parallel paths deepens the ruts near the road centerline, eventually forming ruts that are "deep in the middle and shallow at the edges," as shown in the image. Figure 1 As shown.

[0004] In summary, existing technologies suffer from problems such as low efficiency in manually assessing road ruts and manually setting lateral offsets, and uneven road compaction leading to road potholes and affecting vehicle ride smoothness. Summary of the Invention

[0005] In view of the above problems, the present invention provides a road uniform compaction method based on autonomous driving multi-path generation, which solves the problems of low efficiency in manual assessment of road surface ruts and manual setting of lateral offset in the prior art, and uneven road compaction leading to road surface potholes and affecting vehicle driving smoothness.

[0006] This invention provides a method for uniform road compaction based on multi-path generation for autonomous driving, comprising the following steps:

[0007] Step S1. Select any one autonomous vehicle from the K autonomous vehicles, and establish an offset function based on the Frenet coordinate system of the vehicle body of the selected autonomous vehicle;

[0008] Step S2. Obtain the offset function period s based on the actual road conditions. c And the original path L0; where the original path is a sequence of N original path points, each original path point has the original path point information of the autonomous vehicle;

[0009] Step S3. Let L be the offset path of the kth offset. k k = 1;

[0010] Step S4. Calculate the travel distance s corresponding to the nth original path point on the original path L0. 0，n Convert to offset path L k The equivalent travel distance S at the nth offset path point k，n n = 1;

[0011] Step S5. Place S k，n Substituting this into the offset function established in step S1, we obtain the offset path L. k The offset l(S) of the nth offset path point k，n );

[0012] Step S6. Based on the offset l(S) obtained in step S5 k，n The offset path L is calculated using the conversion formula between the offset path and the offset path. k Information on the offset path point of the nth offset path point;

[0013] Step S7. Let n = n + 1, return to step S4, and continue until the offset path L is obtained. k The offset path information of the N offset path points is used to obtain the offset path L. k Using offset path L k Generate the kth offset path instruction, which is used to control the kth autonomous vehicle to drive along the offset path according to the kth offset path instruction and uniformly compact the road;

[0014] Step S8. Let k = k + 1, return to step S3, k = 1, 2, ..., K.

[0015] Further, in step S1, the Frenet coordinate system of the vehicle body is defined as follows: the coordinate system with the center of the rear axle of the vehicle as the origin, the s-axis extending from the origin towards the vehicle's heading, and the l-axis rotating counterclockwise 90 degrees around the origin.

[0016] Further, in step S1, the offset function is specifically expressed as follows:

[0017]

[0018] Where S is the equivalent travel distance of the autonomous vehicle along the s-axis in the Frenet coordinate system, and for the k-th offset path L... k For the nth offset path point, S = S k，n ; l(S) is the offset of the offset path point relative to the corresponding original path point along the l-axis; s c is the period of the offset function; D is the maximum offset distance, specifically referring to the maximum offset distance along the l-axis between the offset path point and the corresponding original path point in a uniform compaction task.

[0019] Further, step S2, obtaining the offset function period based on the actual road conditions, specifically includes:

[0020] The maximum allowable offset distance D and the upper limit of the heading angle deviation value are obtained based on the actual road conditions. Among them, the upper limit of the heading angle deviation value Specifically, it refers to the maximum permissible difference in heading angle between a path point on the offset path and its corresponding path point on the original path; and is based on the maximum allowable offset distance D and the upper limit of the heading angle deviation value. The period s of the offset function is calculated. c .

[0021] Further, in step S2, the original waypoint information of each original waypoint includes: longitude, latitude, heading angle, and travel distance.

[0022] Further, in step S4, the travel distance s corresponding to the nth original path point on the original path L0 is... 0，n Convert to offset path L k The equivalent travel distance S at the nth offset path point k，n The method is to use modulo operation to make S k，n Satisfying 0≤S k，n <s c .

[0023] Furthermore, the offset path transformation formula is established based on the Cartesian coordinate system and the WGS8 coordinate system of the autonomous vehicle's motion.

[0024] Furthermore, the offset path point information specifically includes the longitude, latitude, heading angle, Frenet s coordinates, and Frenet l coordinates of the offset path point.

[0025] Furthermore, the original path is generated by the autonomous vehicle's planning program based on the actual road conditions.

[0026] Furthermore, K is a positive integer determined based on actual road conditions and the crushing area of ​​the autonomous vehicle, with a value range of [10, 20].

[0027] Compared with the prior art, the present invention has at least the following beneficial effects:

[0028] (1) The road uniform compaction method based on autonomous driving multi-path generation of the present invention obtains an offset path that can ensure the kinematic feasibility of vehicle driving by designing an offset function with configurable amplitude and adjustable maximum derivative value; wherein, the amplitude is configurable to ensure that the maximum offset distance does not exceed a preset threshold; the maximum derivative value is adjustable to ensure that there will not be excessive changes in heading angle at adjacent path points of the vehicle; finally, the offset of each path point is calculated by the offset function, and then the position of each original path point after offset is automatically calculated to obtain the offset path; the method has good adaptability to different road conditions, and the automatic calculation of offset improves the calculation efficiency.

[0029] (2) The road uniform compaction method based on autonomous driving multi-path generation of the present invention achieves the effect of uniform compaction of the road surface by using periodic offset path translation; an offset path is obtained by using an offset function on a certain road segment, and the function is made into a periodic function by taking the modulus. This offset function is translated along the horizontal axis to obtain other offset functions. The images of these functions overlap and cover each other on the two-dimensional plane. The resulting multiple offset paths can achieve the effect of uniform compaction of the road surface and ensure the smoothness of vehicle driving. Attached Figure Description

[0030] The accompanying drawings are for illustrative purposes only and are not intended to limit the scope of the invention.

[0031] Figure 1 This is a schematic diagram illustrating the uniform compaction effect of a parallel offset path in existing technologies.

[0032] Figure 2 This is a flowchart of the road uniform compaction method based on autonomous driving multi-path generation disclosed in this invention;

[0033] Figure 3 This is an image of the offset function disclosed in this invention;

[0034] Figure 4 Images of the multiple offset paths disclosed in this invention;

[0035] Figure 5 This is a schematic diagram illustrating the compaction effect of multiple offset paths disclosed in this invention. Detailed Implementation

[0036] To better understand the above-described objectives, features, and advantages of the present invention, the invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, unless otherwise specified, the embodiments of the present invention and the features thereof can be combined with each other. Furthermore, the present invention can be implemented in other ways different from those described herein; therefore, the scope of protection of the present invention is not limited to the specific embodiments disclosed below.

[0037] Vehicles leave ruts when driving on soft surfaces, and these ruts become deeper when the vehicle is heavy or carrying cargo. Soft surfaces can also cause rutting and slurry formation. These ruts and slurry formations severely impact driving efficiency and safety. If autonomous vehicles repeatedly travel along the same fixed path on a particular road segment, severe rutting and slurry formations will inevitably occur under certain driving conditions, such as muddy roads or rainy / snowy weather.

[0038] This invention discloses a method for uniform road compaction based on multi-path generation for autonomous driving, such as... Figure 2 As shown, it includes the following steps:

[0039] Step S1. Select any one of the K autonomous vehicles and establish an offset function based on the Frenet coordinate system of the vehicle body.

[0040] K is a positive integer determined based on actual road conditions and the crushing area of ​​the autonomous vehicle, with a value range of [10, 20], preferably 15.

[0041] Specifically, the Frenet coordinate system of the vehicle body is defined as follows: the origin is the center of the rear axle of the vehicle, the s-axis is extended from the origin towards the vehicle's heading, and the l-axis is rotated 90 degrees counterclockwise around the origin.

[0042] The offset function is specifically expressed as follows:

[0043]

[0044] Where S is the equivalent travel distance of the autonomous vehicle along the s-axis in the Frenet coordinate system, and for the k-th offset path L... k For the nth offset path point, S = S k，n ; l(S) is the offset of the offset path point relative to the corresponding original path point along the l-axis; s c is the period of the offset function; D is the maximum offset distance, specifically referring to the maximum offset distance along the l-axis between the offset path point and the corresponding original path point in a uniform compaction task.

[0045] It is easy to see that D is the magnitude of the offset function, representing the maximum offset distance of the offset path. When D = 1, it is required that l(S) must be able to The value within is [-1, 1].

[0046] It should be noted that when the offset value l(S) < 0, it means the vehicle is veering to the left; when l(S) < 0, it means the vehicle is veering to the right.

[0047] Specifically, s c The range of values ​​for the independent variable S is determined. c Limited by the maximum allowable offset distance D and the upper limit of the heading angle deviation value Among them, the upper limit of the heading angle deviation value Specifically, it refers to the maximum permissible difference in heading angle between a path point on the offset path and its corresponding path point on the original path.

[0048] For example, when the original path is considered as a straight line, the offset path is... Figure 3 The curves in the original path are consistent. Therefore, in an approximate case, the maximum difference in heading angle between the offset path and the original path is within... Figure 3 The position of midpoint P1, The value is the slope at that point. The maximum difference in heading angle between the original path and the offset path at path points with the same index cannot exceed [a certain value]. Right now: have to:

[0049] Step S2. Obtain the offset function period s based on the actual road conditions. c And the original path L0; where the original path is a sequence of N original path points, each original path point having the original path point information of the autonomous vehicle.

[0050] First, determine the maximum allowable offset distance D and the upper limit of the heading angle deviation value based on the actual road conditions. Among them, the upper limit of the heading angle deviation value Specifically, this refers to the maximum permissible difference in heading angle between a path point on the offset path and its corresponding path point on the original path; then, based on the maximum allowable offset distance D and the upper limit of the heading angle deviation value... The period s of the offset function is calculated. c The range of values ​​for .

[0051] The original path is generated by the autonomous vehicle's planning program based on the actual road conditions.

[0052] Optionally, the original waypoints are evenly distributed along the original path.

[0053] Specifically, the original waypoint information for each original waypoint includes: longitude, latitude, heading angle, and distance traveled.

[0054] Step S3. Let L be the offset path of the kth offset. k , k = 1.

[0055] Step S4. Calculate the travel distance s corresponding to the nth original path point on the original path L0. 0，n Convert to offset path L k The equivalent travel distance S at the nth offset path point k，n , n = 1.

[0056] The travel distance s corresponding to the nth original path point on the original path L0. 0，n Convert to offset path L k The equivalent travel distance S at the nth offset path point k，n The method is to use modulo operation to make S k，n Satisfying 0≤S k，n <s c .

[0057] Step S5. Place S k，n Substituting this into the offset function established in step S1, we obtain the offset path L. k The offset l(S) of the nth offset path point k，n ).

[0058] Step S6. Based on the offset l(S) obtained in step S5 k，n The offset path L is calculated using the conversion formula between the offset path and the offset path. k Information on the offset path point of the nth offset path point.

[0059] The offset path point information specifically includes the longitude, latitude, heading angle, Frenet s coordinates, and Frenet l coordinates of the offset path point.

[0060] The offset path transformation formula is established based on the Cartesian coordinate system and the WGS8 coordinate system of the autonomous vehicle motion.

[0061] The Cartesian coordinate system based on vehicle motion is defined as follows: the first path point of the original path is the origin, the x-axis is the direction of travel from the origin to the first path point, and the y-axis is the direction of rotation of the x-axis counterclockwise around the origin by 90 degrees.

[0062] The specific formula for the offset path transformation based on the Cartesian coordinate system defined by vehicle motion is as follows:

[0063] α = 90 - Θ;

[0064] x = X - lcosα;

[0065] y = Y + lsinα;

[0066]

[0067]

[0068]

[0069]

[0070] Where α is the angle between the vehicle's heading and the x-axis of the Cartesian coordinate system at a certain offset path point; Θ is the heading angle at the corresponding original path point; x and y are the Cartesian coordinates of the offset path point; X and Y are the Cartesian coordinates of the corresponding original path point; l is the offset of the offset path point; d is the distance between the offset path point and the corresponding original path point; θ is the heading angle of the offset path point; Ion and lat are the longitude and latitude of the offset path point; LON and LAT are the longitude and latitude of the corresponding original path point.

[0071] Step S7. Let n = n + 1, return to step S4, and continue until the offset path L is obtained. k The offset path information of the N offset path points is used to obtain the offset path L. k Using offset path L k Generate the kth offset path instruction, which is used to control the kth autonomous vehicle to travel along the offset path according to the kth offset path instruction and uniformly compact the road.

[0072] Step S8. Let k = k + 1, return to step S3, k = 1, 2, ..., K.

[0073] Specifically, the total number of offset paths K generated can be configured according to project needs; the images of the K offset paths are as follows: Figure 4 As shown.

[0074] Taking tire width into account, the autonomous vehicle travels along the corresponding offset path, and the compaction effect on the road surface is as follows: Figure 5 As shown.

[0075] Compared with existing technologies, the road uniform compaction method based on autonomous driving multi-path generation of the present invention obtains an offset path that ensures the kinematic feasibility of vehicle driving by designing an offset function with configurable amplitude and adjustable maximum derivative value. The configurable amplitude ensures that the maximum offset distance does not exceed a preset threshold; the adjustable maximum derivative value ensures that there are no excessive changes in heading angle at adjacent path points. Finally, the offset function is used to calculate the offset of each path point, and then the position of each original path point after offset is automatically calculated to obtain the offset path. The method has good adaptability to different road conditions, and the automatic calculation of offset improves computational efficiency. The uniform compaction of the road surface is achieved by using periodic offset path translation. An offset path is obtained by using the offset function on a certain road segment. The function is made periodic through modulo operation. This offset function is translated along the horizontal axis to obtain other offset functions. The images of these functions overlap on a two-dimensional plane, and the resulting multiple offset paths achieve the effect of uniform compaction of the road surface, ensuring smooth vehicle driving.

[0076] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for uniform road compaction based on multi-path generation for autonomous driving, characterized in that, Includes the following steps: Step S1. From For each autonomous vehicle, select any one autonomous vehicle and establish an offset function based on the Frenet coordinate system of the vehicle body. Step S2. Obtain the offset function period based on the actual road conditions. and the original path The original path is a path containing... A sequence of original waypoints, each original waypoint containing original waypoint information for the autonomous vehicle; Step S3. Record the first The offset path is , ; Step S4. Combine with the original path Upper The driving distance corresponding to each original waypoint Convert to offset path Upper The equivalent travel distance of each offset path point , ; Step S5. Substituting the values ​​into the offset function established in step S1, we obtain the offset path. Upper Offset of each offset path point ; Step S6. Based on the offset obtained in step S5 The offset path is calculated using the conversion formula between the offset path and the offset path. Upper Offset path point information for each offset path point; Step S7. Let Return to step S4 until the offset path is obtained. superior The offset path point information of each offset path point is used to obtain the offset path. Using offset paths Generate the first The offset path instruction is used to control the first Taiwan autonomous vehicles in accordance with the first The offset path command travels along the offset path to uniformly compact the road; Step S8. Let Return to step S3. ; Step S1, the Frenet coordinate system of the autonomous vehicle body is defined as follows: with the rear axle center as the origin, extending from the origin towards the vehicle's heading... axis, Rotate the axis counterclockwise by 90 degrees around the origin. A coordinate system with axes; Step S1, the offset function is specifically expressed as follows: ； in, It is the upper edge of the Frenet coordinate system of the autonomous vehicle. The equivalent travel distance in the axial direction, for the first Offset path Upper Offset path points, ; It is the offset path point relative to the corresponding original path point. Offset in the axial direction; It is the period of the offset function; It refers to the maximum offset distance, specifically, in a uniform compaction task, the distance between the offset path point and the corresponding original path point. Maximum offset distance in the axial direction.

2. The road uniform compaction method based on autonomous driving multi-path generation according to claim 1, characterized in that, Step S2, obtaining the offset function period based on the actual road conditions, specifically includes: The maximum allowable offset distance is determined based on actual road conditions. and the upper limit of the heading angle deviation value Among them, the upper limit of the heading angle deviation value. Specifically, it refers to the maximum permissible difference in heading angle between a path point on the offset path and its corresponding path point on the original path; and based on the maximum permissible offset distance. and the upper limit of the heading angle deviation value The period of the offset function is calculated. .

3. The road uniform compaction method based on autonomous driving multi-path generation according to claim 2, characterized in that, Step S2: The original waypoint information of each original waypoint includes: longitude, latitude, heading angle, and travel distance.

4. The road uniform compaction method based on autonomous driving multi-path generation according to claim 3, characterized in that, Step S4, will be compared with the original path Upper The driving distance corresponding to each original waypoint Convert to offset path Upper The equivalent travel distance of each offset path point The method is modulo operation, so that satisfy .

5. The road uniform compaction method based on autonomous driving multi-path generation according to claim 4, characterized in that, The offset path transformation formula is established based on the Cartesian coordinate system and the WGS8 coordinate system of the autonomous vehicle motion.

6. The road uniform compaction method based on autonomous driving multi-path generation according to claim 5, characterized in that, Offset waypoint information specifically includes the longitude, latitude, heading angle, and Frenet coordinates of the offset waypoint. Coordinates and Frenet system coordinate.

7. The road uniform compaction method based on autonomous driving multi-path generation according to claim 6, characterized in that, The original path is generated by the autonomous vehicle's planning program based on the actual road conditions.

8. The road uniform compaction method based on autonomous driving multi-path generation according to claim 7, characterized in that, It is a positive integer determined based on actual road conditions and the area crushed by autonomous vehicles, with a value range of [10, 20].

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