CN107121980B - A kind of automatic driving vehicle paths planning method based on virtual constraint - Google Patents
A kind of automatic driving vehicle paths planning method based on virtual constraint Download PDFInfo
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- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
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Abstract
The present invention relates to a kind of automatic driving vehicle paths planning method based on virtual constraint, comprising the following steps: the lane center course under bodywork reference frame is obtained according to lane detection result；The course of vehicle is modified using the lane center course of Kalman filter and acquisition；The path point in original path is corrected using revised vehicle course；Based on three-dimensional laser radar grating map, the opposed lateral positions relationship of automatic driving vehicle and road is obtained；Generate virtual constraint；According to the decision instruction of automated driving system, expected path is generated.The present invention merges the accurate positioning that GPS, vision and laser radar information complete vehicle and road opposed lateral positions, high accuracy positioning equipment and high-precision map are not needed, help to improve automatic driving vehicle structuring, have the lane under the city of clear carriageway line or highway environment keep with lane-change ability, be of great significance for safe and stable, the smooth traveling of automatic driving vehicle.
Description
Technical field
The present invention relates to automatic Pilot technical field more particularly to a kind of automatic driving vehicle paths based on virtual constraint
Planing method.
Background technique
Path planning is one of core technology of automatic driving vehicle, and effect is to guarantee the completion of vehicle safety to the overall situation
The tracking in path.In urban road environment or highway environment, automatic driving vehicle is not only smoothly stable in vehicle
It is travelled in the lane of place, it is also necessary to which combining environmental perception information and intelligent decision instruction progress lane-change, doubling etc. are complex
Movement.Under the operating condition that lane is kept, in order to guarantee that vehicle accurately follows the center line and holding lateral stability in lane,
It needs vehicle restraint where it in lane；Under lane-change operating condition, need to guarantee that vehicle can accurately change to target carriage
In road, the case where riding line, crimping traveling is avoided the occurrence of.In order to achieve the above object, the path planning of automatic driving vehicle needs
Rely on the accurate positioning of vehicle and road opposed lateral positions.The side for thering is research to combine by differential GPS and high-precision map
Formula realizes the accurate positioning of vehicle Yu road lateral position relationship, but this mode is limited to the effect of differential GPS signal
The mapping of range and high-precision map.
At this stage, the three of the environment sensing information abundant such as road edge, lane line can be provided for automatic driving vehicle
It ties up laser radar and camera is still most important sensor on automatic driving vehicle.It is necessary to utilize existing sensor, if
Count the paths planning method of a kind of integrated environment perception information and general GPS positioning information.
Summary of the invention
In view of above-mentioned analysis, the present invention is intended to provide a kind of automatic driving vehicle path planning based on virtual constraint
Method, to solve the problems, such as that the prior art is limited to the sphere of action and high-precision map of differential GPS signal.
The purpose of the present invention is mainly achieved through the following technical solutions:
A kind of automatic driving vehicle paths planning method based on virtual constraint, comprising the following steps:
Step S1: the lane center course under bodywork reference frame is obtained；
Step S2: vehicle course is modified using the lane center course obtained in step S1；
Step S3: the path point in original path is corrected using the revised vehicle course step S2；
Step S4: being based on three-dimensional laser radar grating map, obtains the opposed lateral positions of automatic driving vehicle and road
Relationship；
Step S5: it is generated virtually about according to the opposed lateral positions relationship of the step S4 automatic driving vehicle obtained and road
Beam, the virtual constraint include the boundary constraint and adjacent lane boundary constraint in place lane；
Step S6: according to the decision instruction of the step S5 virtual constraint generated and automated driving system, expected path is generated.
The step S1 is further comprising the steps of:
Step S101: judging whether lane detection result is effective, if effectively, executing step S102；If invalid, continue to use
One period lane detection result；
Step S102: the lane center course under bodywork reference frame is obtained.
The step S102 is further comprising the steps of:
Step S1021: the position of lane center is calculated
Wherein, lane detection result (x1,y1)、(x2,y2)、(x3,y3) and (x4,y4) it is that left and right lane line is sat in car body
Coordinate points under mark system, (x1,y1) it is the nearly vehicle point in vehicle left front, (x2,y2) it is the remote vehicle point in left front, (x3,y3) it is right front
Nearly vehicle point, (x4,y4) it is the remote vehicle point in right front；
(xc1,yc1) and (xc2,yc2) respectively represent lane center nearly vehicle end and remote Che Duan；
Step S1022: the course of lane center is calculated:
Wherein θLaneRepresent course of the lane center under bodywork reference frame.
The step S2 is further comprising the steps of:
Step S201: initialized card Thalmann filter；
Step S202: it is navigated according to vehicle under the earth coordinates in vehicle current turning radius and this period of prediction of speed
To predicted value:
Wherein, θveh-predFor the predicted value in earth coordinates vehicle of lower current time course, θveh-correct-oldGreatly to sit
Mark is the correction value in lower upper period vehicle course, and v is the current speed of vehicle, and R is the current turning radius of vehicle, and T is rule
Draw the period；
Step S203: the difference in original path course and lane center course under bodywork reference frame is calculated:
θerror=θroad-θLane
Wherein, θerrorFor the difference in original path course and lane center course under bodywork reference frame, θroadFor car body
The course of original path, θ under coordinate systemLaneFor lane center course；
Step S204: the heading measure value of vehicle under earth coordinates is calculated:
θveh-measure=θveh-gps+θerror,
Wherein θveh-measureFor the heading measure value of vehicle under earth coordinates, θveh-gpsFor vehicle under earth coordinates
The course GPS value；
Step S205: by θ in step S202veh-predWith θ in step S204veh-measureAs the defeated of Kalman filter
Enter to obtain navigational calibration value θ of the vehicle under earth coordinatesveh-correct；
Step S206: by θveh-correctIt is assigned to θveh-correct-oldAnd enter next period.
The step S3 is further comprising the steps of:
Step S301: the deviation of the vehicle navigational calibration value and vehicle GPS course value under earth coordinates is calculated:
Δθ=θveh-correct-θveh-gps；
Wherein, θveh-correctThe navigational calibration value for being vehicle under earth coordinates, θveh-gpsIt gets off for earth coordinates
The course GPS value；
Step S302: the path point after correction is calculated using result in step S301:
Wherein, [xcorrect ycorrect θcorrect]TIt is coordinate of the path after correcting under bodywork reference frame, [xveh-road
yveh-road θveh-road]TIt is coordinate of the original path under bodywork reference frame before correcting.
The step S4 is further comprising the steps of:
Step S401: in statistics laser radar grating map, each number OGN for arranging the grid being occupiedi；
Step S402: according to the number OGN of each grid for arranging and being occupiedi, calculate the statistical mark amount of each column grid
OGSi:
Wherein i is the row number in grating map, OGSiIndicate the statistical mark amount of the i-th column grid in grating map, OtIt indicates
Grating map occupies index threshold；
Step S403: convolution kernel is generated according to road model width RW and road model width difference RWT:
Wherein KjFor the corresponding convolution kernel of jth column grid, RW is road model width, and RWT is road model width difference,
GsFor the row number of the selected grid as pulse, GsValue range be [0, RWT]；
Step S404: by result OGS in step S402iWith result K in step S403jCarry out convolution
Wherein, MW is the columns of grid in grating map, if re=1 in convolution process, corresponds to of grid column
Just add 1 with value hit；If re=-1, the non-matching value miss of corresponding grid column just adds 1；If re=0, correspond to
The unknown-value uknow of grid column just adds 1；
Step S405: the probability GC that a certain column grid is road-center is obtained according to result in step S404i:
Wherein GCiThe i-th column grid is represented as the probability of road-center；
Step S406: GC is found outiMaximum value, corresponding i is exactly the row number of the grid where road-center；
Cr=i
Wherein CrFor the grid row number where road-center；
Step S407: the grid row number where the curb of left and right is calculated；
Wherein, LrAnd RrThe row number of grid respectively where the curb of left and right；
Step S408: the opposed lateral positions relationship of vehicle and road is calculated
Wherein DlAnd DrRespectively distance of the vehicle to left and right curb.
In the step S401, from the middle of grating map one arrange, i.e., where vehicle that column, along with after amendment
The perpendicular direction in path start to start to count to the both sides of grating map.
The step S5 is further comprising the steps of:
Step S501: lane sum Cz is calculated；
Step S502: the lane C where positioning vehicle；
Lane serial number where vehicle:
The left number sequence number in lane where wherein C indicates vehicle, LW is lane width；
Step S503: according to place lane center line position (xc1,yc1) and (xc2,yc2), vehicle is calculated into place lane
The distance D of heart linec；
Step S504: according to the distance D of vehicle to place lane centercVehicle is calculated separately to a left side with lane width LW
The distance D of lane centerclWith the distance D of vehicle to right lane center linecr:
Dcl=LW-Dc
Dcr=LW+Dc
Step S505: virtual constraint, the virtual constraint packet are generated according to the distance of each lane center of vehicle distances
Lane boundary constraint where including and adjacent lane boundary constraint.
When place lane, the right and left has lane, the virtual constraint is that the left side of left-lane constrains Vll, place vehicle
The left side in road constrains Vcl, place lane the right constrain VcrAnd the right of right lane constrains Vrr；When only there is lane in left side,
The sham is constrained to the left side constraint V of left-lanell, place lane the left side constrain VclAnd the right in place lane constrains Vcr；
When only there is lane on right side, the virtual constraint is that the left side in place lane constrains Vcl, place lane the right constrain VcrAnd
The right of right lane constrains Vrr。
The step S6 is further comprising the steps of:
Step S601: judge decision instruction whether lane-change, if so, being transferred to step S603；If it is not, being then transferred to step
S602；
Step S602: the centerline in the path shift after correction to place lane is tracked as expected path,
It is transferred to step S604；
Step S603: the centerline in the path shift after correction to target lane is tracked as expected path,
It is transferred to step S604；
Step S604: path trace is carried out according to expected path.
The present invention has the beneficial effect that:
Automatic driving vehicle paths planning method proposed by the present invention based on virtual constraint, fusion GPS, vision and laser
Radar information completes the accurate positioning of vehicle and road opposed lateral positions, without high accuracy positioning equipment and accurately
Figure.It keeps can effectively ensure that vehicle is travelled along the center line in place lane when driving in lane, ensure that the automatic row of vehicle
Lateral stability when sailing；In lane-change, it can guarantee that vehicle accurately changes to target lane, avoid vehicle during automatic Pilot
Ride line or crimping traveling.The present invention help to improve automatic driving vehicle structuring, have the city of clear carriageway line or
Lane under highway environment keeps travelling with lane-change ability, safe and stable, smooth for automatic driving vehicle with weight
Want meaning.
Other features and advantages of the present invention will illustrate in the following description, also, partial become from specification
It obtains it is clear that understand through the implementation of the invention.The objectives and other advantages of the invention can be by written explanation
Specifically noted structure is achieved and obtained in book, claims and attached drawing.
Detailed description of the invention
Attached drawing is only used for showing the purpose of specific embodiment, and is not to be construed as limiting the invention, in entire attached drawing
In, identical reference symbol indicates identical component.
Fig. 1 is the automatic driving vehicle paths planning method overall flow based on virtual constraint in the present invention；
Fig. 2 is the flow chart that the present invention is modified the course of vehicle using Kalman filter theory；
Fig. 3 is the process for obtaining vehicle and road opposed lateral positions relationship in the present invention based on laser radar grating map
Figure；
Fig. 4 is the schematic diagram for counting each grid number for arranging and being occupied in the present invention on laser radar grating map；
Fig. 5 is that the generating mode of the convolution kernel when obtaining vehicle and road opposed lateral positions relationship in the present invention is illustrated
Figure；
Fig. 6 is virtual constraint product process figure in the present invention；
Fig. 7 is that virtual constraint generates schematic diagram in the present invention；
Fig. 8 is that coordinates measurement schematic diagram it is expected in the present invention.
Appended drawing reference:
Road after 401- bodywork reference frame, 402- vehicle, a column grid of 403- grating map middle, 404- correction
Diameter, the right boundary of 405- grating map, 406- count direction；
The side of the selected grid as pulse of 501- road model width difference RWT, 502-, 503- road model width
The convolution kernel K that boundary, 504- road boundary, 507- are generatedj；
702- virtual constraint, 703- lane line；
Expected path, 809- when expected path, the lane 808- when the left lane-change of 805- lane center, 807- are kept is right
Expected path when lane-change.
Specific embodiment
Specifically describing the preferred embodiment of the present invention with reference to the accompanying drawing, wherein attached drawing constitutes the application a part, and
Together with embodiments of the present invention for illustrating the principle of the present invention.
According to a preferred embodiment of the present invention, a kind of automatic driving vehicle path rule based on virtual constraint are provided
The method of drawing, as shown in Figure 1, comprising the following steps:
Step S1: the lane center course under bodywork reference frame is obtained according to lane detection result；
Specifically, the step S1 further includes following sub-step:
Step S101: judging whether lane detection result is effective, if effectively, executing step S102；If invalid, continue to use
One period lane detection result；
In the present embodiment, automatic driving vehicle carries out in the environment of having clear carriageway line and road edge in structuring
Lane is kept and lane-change, and the visual sensor of automatic driving vehicle can detecte out lane line information, it is contemplated that four crossway
Mouthful at there is no the case where lane line, so will the testing result to lane line whether effectively judge.
Step S102: the lane center course under bodywork reference frame is obtained；
Lane detection result (x1,y1)、(x2,y2)、(x3,y3) and (x4,y4) it is left and right lane line under bodywork reference frame
Coordinate points, wherein (x1,y1) it is the nearly vehicle point in vehicle left front, (x2,y2) it is the remote vehicle point in left front, (x3,y3) it is that right front is close
Che Dian, (x4,y4) it is that the remote vehicle point in right front according to this four points finds out the position of lane center and boat under bodywork reference frame
To:
Step S1021: the position of lane center is calculated
Wherein (xc1,yc1) and (xc2,yc2) respectively represent lane center nearly vehicle end and remote Che Duan；
Step S1022: the course of lane center is calculated
Wherein θLaneRepresent course of the lane center under bodywork reference frame.
Step S2: vehicle course is repaired using the lane center course obtained in Kalman filter and step S1
Just；
Since the course of the vehicle obtained from vehicle GPS has temporal lag and deviation spatially, so directly
It is used for will lead to biggish mistake when the global path under earth coordinates is converted to the local path under bodywork reference frame
Difference, so that vehicle can not accurately track Actual path；
The course of lane center has relative stability, therefore can use boat of the course to vehicle of lane center
To being modified, it is contemplated that measure the influence of noise, revised vehicle course needs to be safeguarded using Kalman filter；
Specifically, as shown in Fig. 2, the step S2 includes the following steps:
Step S201: initialized card Thalmann filter；
Step S202: it is navigated according to vehicle under the earth coordinates in vehicle current turning radius and this period of prediction of speed
To predicted value θveh-pred；
Wherein θveh-predFor the predicted value in earth coordinates vehicle of lower current time course, θveh-correct-oldGreatly to sit
Mark is the correction value in lower upper period vehicle course, and v is the current speed of vehicle, and R is the current turning radius of vehicle, and T is rule
Draw the period.
Step S203: the difference in original path course and lane center course under bodywork reference frame is calculated:
Path course and lane center course are all the courses under bodywork reference frame, and lane center course is parallel to
The tangential direction in path.Since lane center course is directly calculated under bodywork reference frame according to lane detection result
Arrive, and path course is to be converted to using the GPS coordinate of vehicle as basic point through coordinate, therefore, only require under bodywork reference frame
Path course and lane center course be equivalent to have found out the course GPS of vehicle and the difference θ of its true courseerror:
θerror=θroad-θLane
Wherein, θerrorFor the difference in original path course and lane center course under bodywork reference frame, θroadFor car body
The course of original path, θ under coordinate systemLaneFor lane center course；
Step S204: the heading measure value of vehicle under earth coordinates is calculated:
θveh-measure=θveh-gps+θerror
Wherein θveh-measureFor the heading measure value of vehicle under earth coordinates, θveh-gpsFor vehicle under earth coordinates
The course GPS value；
Step S205: by θ in step S202veh-predWith θ in step S204veh-measureAs the defeated of Kalman filter
Enter to obtain navigational calibration value θ of the vehicle under earth coordinatesveh-correct；
Step S206: by θveh-correctIt is assigned to θveh-correct-oldAnd enter next period.
Step S3: the path point in original path is corrected using the revised vehicle course step S2；
The original path is the path under car body coordinate, and the starting point of original path is bodywork reference frame origin；
Due to the inaccuracy in vehicle course, the original path under bodywork reference frame is also that there are errors, it is therefore desirable to root
According to revised vehicle course, original path is corrected, corrects the course of rear path and the course one in vehicle place lane
It causes.
Specifically, comprising the following steps:
Step S301: the deviation of the vehicle navigational calibration value and vehicle GPS course value under earth coordinates is calculated:
Δθ=θveh-correct-θveh-gps
Step S302: the path point after correction is calculated using result in step S301:
Wherein [xcorrect ycorrect θcorrec]t TIt is coordinate of the path after correcting under bodywork reference frame, [xveh-road
yveh-road θveh-road]TIt is coordinate of the original path under bodywork reference frame before correcting.
Step S4: being based on three-dimensional laser radar grating map, obtains the opposed lateral positions of automatic driving vehicle and road
Relationship；
The three-dimensional laser radar grating map is the grating map under bodywork reference frame, the corresponding expression of each grid
The value whether grid is occupied by barrier；
Due to the presence of road GPS point error of coordinate, the GPS positioning of vehicle, the GPS positioning of waypoint and road are relied solely on
The width of road model is the located lateral that can not determine vehicle on road, therefore can not also determine vehicle in which lane
On.It is proposed in the present invention based on laser radar grating map, acquires road-center in grating map using statistical method
Position, then using vehicle and grating map relativeness and road model width, available vehicle relative to
Road-center, road left margin, on the right of road edge position.
Specifically, as shown in figure 3, being based on three-dimensional laser radar grating map, the phase of automatic driving vehicle and road is obtained
Process to lateral position relationship the following steps are included:
Step S401: in statistics laser radar grating map, each number OGN for arranging the grid being occupiedi；
Preferably, as shown in figure 4, from the column of the middle of grating map one (that is, where vehicle that column) along with repair
The perpendicular direction in path after just starts to start to count to the both sides of grating map.
Step S402: according to the number OGN of each grid for arranging and being occupiedi, calculate the statistical mark amount of each column grid
OGSi:
Wherein i is the row number in grating map, OGSiIndicate the statistical mark amount of the i-th column grid in grating map, OtIt indicates
Grating map occupies index threshold；
The principle of assignment statistical result mark amount are as follows: if the grid number that the i-th column are occupied is greater than Ot, then this column
The statistical result mark amount of grid is assigned a value of 1；If the grid number that the i-th column are occupied is no more than Ot, then investigating (i-1)-th again
Whether the number for arranging the grid being occupied is greater than Ot, if it is then the statistical result mark amount of the i-th column grid is assigned a value of 0, such as
Fruit is no, then the statistical result mark amount of the i-th column grid is assigned a value of -1.
Step S403: convolution kernel is generated according to road model width RW and road model width difference RWT:
Wherein KjFor the corresponding convolution kernel of jth column grid, RW is road model width, and RWT is road model width difference,
GsFor the row number of the selected grid as pulse, GsValue range be [0, RWT]；
As shown in figure 5, the rule for generating convolution kernel is as described below: a certain column grid being selected in [0, RWT] range is (false
If row number is Gs) it is used as pulse grid, if row number j is less than Gs, then corresponding KjIt is assigned a value of 0；If row number j is equal to Gs, that
Corresponding KjIt is assigned a value of 1；If row number j is greater than GsAnd it is less than RW-Gs, then corresponding KjIt is assigned a value of -1；If row number is equal to
RW-Gs, then corresponding KjIt is assigned a value of 1；If row number j is greater than RW-GsAnd it is less than RW, then corresponding KjIt is assigned a value of 0.Wherein,
GsRWT is successively got from 0.
Step S404: by result OGS in step S402iWith result K in step S403jCarry out convolution
Wherein, MW is the columns of grid in grating map, if re=1 in convolution process, corresponds to of grid column
Just add 1 with value hit；If re=-1, the non-matching value miss of corresponding grid column just adds 1；If re=0, correspond to
The unknown-value uknow of grid column just adds 1.
Step S405: the probability GC that a certain column grid is road-center is obtained according to result in step S404i:
Wherein GCiThe i-th column grid is represented as the probability of road-center；
Due to OGSiAnd KjValue range be all { -1,0,1 }, and the convolution value range of j is [- RW/2, Gs], i.e. Kj
Do not take -1 in convolution process, if convolution results re=1 in convolution process, the matching value hit of corresponding grid column just adds
1；If re=-1, the non-matching value miss of corresponding grid column just adds 1；If re=0, the unknown of grid column is corresponded to
Value uknow just adds 1.If last hit=2, corresponding i-th column grid is that the probability assignment of road-center is 0.9；If
Hit=1, miss=1 or hit=1, uknow=1, then the probability assignment that corresponding i-th column grid is road-center is
0.5；If miss=2 or uknow=2 or miss=1, uknow=1, then corresponding i-th column grid is the general of road-center
Rate is assigned a value of 0.3.
Step S406: GC is found outiMaximum value, corresponding i is exactly the row number of the grid where road-center；
Cr=i
Wherein CrFor the grid row number where road-center.
Step S407: the grid row number where the curb of left and right is calculated；
Wherein, LrAnd RrThe row number of grid respectively where the curb of left and right.
Step S408: the opposed lateral positions relationship of vehicle and road is calculated
Wherein DlAnd DrRespectively distance of the vehicle to left and right curb.
Step S5: it is generated virtually about according to the opposed lateral positions relationship of the step S4 automatic driving vehicle obtained and road
Beam, the virtual constraint include the boundary constraint and adjacent lane boundary constraint in place lane；
Since the behavior of vehicle has been divided into lane holding and two kinds of lane-change in the present invention, during lane is kept, only
The problems such as boundary constraint, collision avoidance in lane where need to considering vehicle；During lane-change, lane and target lane where only considering
Interior barrier situation.Therefore, empty according to relative positioning relationship, lane width and the road width of vehicle and road
Boundary constraint and adjacent lane boundary constraint that vehicle is currently located lane are drawn up, so that four virtual constraints can determine three
Lane, therefore can guarantee the stability of lateral direction of car movement when lane is kept, guarantee accurately to change to target in lane-change
The position of center line in lane avoids riding line or crimping traveling；
Specifically, as shown in fig. 6, the process of the boundary constraint in lane where fictionalizing and adjacent lane boundary constraint include with
Lower step:
Step S501: lane sum Cz is calculated；
Step S502: the lane C where positioning vehicle；
Lane serial number where vehicle:
The left number sequence number in lane where wherein C indicates vehicle, LW is lane width.
Step S503: according to place lane center line position (xc1,yc1) and (xc2,yc2), vehicle is calculated into place lane
The distance D of heart linec；
Step S504: according to the distance D of vehicle to place lane centercVehicle is calculated separately to a left side with lane width LW
The distance D of lane centerclWith the distance D of vehicle to right lane center linecr:
Dcl=LW-Dc
Dcr=LW+Dc
Step S505: virtual constraint, the virtual constraint packet are generated according to the distance of each lane center of vehicle distances
Lane boundary constraint where including and adjacent lane boundary constraint；
Specifically, when place lane, the right and left has lane, as shown in Fig. 7 (a), the virtual constraint is left-lane
The left side constrain Vll, place lane the left side constrain Vcl, place lane the right constrain VcrAnd the right of right lane constrains Vrr；
When only there is lane in left side, as shown in Fig. 7 (b), the sham is constrained to the left side constraint V of left-lanell, place lane a left side
Side constrains VclAnd the right in place lane constrains Vcr；When only there is lane on right side, as shown in Fig. 7 (b), the virtual constraint
V is constrained for the left side in place lanecl, place lane the right constrain VcrAnd the right of right lane constrains Vrr；
There is certain safe distance between the virtual constraint and road boundary of road boundary.
Step S6: according to the decision instruction of the step S5 virtual constraint generated and automated driving system, expected path is generated.
It is consistent with lane line direction that path after correction can guarantee course, but it is current not ensure that the path is located at
The center in place lane, thus it is calibrated after path firstly the need of moving to the position of center line for being currently located lane,
The expected path when carrying out lane holding is obtained, and in lane-change, then expected path is moved to the center line in target lane
Position；
Fig. 8 is desired coordinates measurement schematic diagram；
Specifically, comprising the following steps:
Step S601: judge decision instruction whether lane-change, if so, being transferred to step S603；If it is not, being then transferred to step
S602；
Step S602: the centerline in the path shift after correction to place lane is tracked as expected path,
It is transferred to step S604；
Step S603: the centerline in the path shift after correction to target lane is tracked as expected path,
It is transferred to step S604；
Step S604: path trace is carried out according to expected path.
In conclusion the embodiment of the invention provides a kind of automatic driving vehicle path planning side based on virtual constraint
Method, fusion GPS, vision and laser radar information complete the accurate positioning of vehicle and road opposed lateral positions, without height
Precision positioning equipment and high-precision map.It keeps when driving, can effectively ensure that vehicle along the center in place lane in lane
Line traveling, ensure that lateral stability when vehicle automatic running；In lane-change, it can guarantee that vehicle accurately changes to target carriage
Road avoids vehicle during automatic Pilot from riding line or crimping traveling.The present invention helps to improve automatic driving vehicle in structuring
, the city for having clear carriageway line or lane under highway environment keep with lane-change ability, for automatic driving vehicle
Safe and stable, smooth traveling is of great significance.
It will be understood by those skilled in the art that realizing all or part of the process of above-described embodiment method, meter can be passed through
Calculation machine program is completed to instruct relevant hardware, and the program can be stored in computer readable storage medium.Wherein, institute
Stating computer readable storage medium is disk, CD, read-only memory or random access memory etc..
The foregoing is only a preferred embodiment of the present invention, but scope of protection of the present invention is not limited thereto,
In the technical scope disclosed by the present invention, any changes or substitutions that can be easily thought of by anyone skilled in the art,
It should be covered by the protection scope of the present invention.
Claims (10)
1. a kind of automatic driving vehicle paths planning method based on virtual constraint, which comprises the following steps:
Step S1: the lane center course under bodywork reference frame is obtained；
Step S2: vehicle course is modified using the lane center course obtained in step S1；
Step S3: the path point in original path is corrected using the revised vehicle course step S2；
Step S4: being based on three-dimensional laser radar grating map, obtains the opposed lateral positions relationship of automatic driving vehicle and road；
Step S5: generating virtual constraint according to the opposed lateral positions relationship of the step S4 automatic driving vehicle obtained and road,
The virtual constraint includes the boundary constraint and adjacent lane boundary constraint in place lane；
Step S6: according to the decision instruction of the step S5 virtual constraint generated and automated driving system, expected path is generated.
2. the method according to claim 1, wherein the step S1 the following steps are included:
Step S101: judging whether lane detection result is effective, if effectively, executing step S102；If invalid, continue to use one week
Phase lane detection result；
Step S102: the lane center course under bodywork reference frame is obtained.
3. according to the method described in claim 2, the step S102 the following steps are included:
Step S1021: the position of lane center is calculated
Wherein, lane detection result (x1,y1)、(x2,y2)、(x3,y3) and (x4,y4) it is left and right lane line in bodywork reference frame
Under coordinate points, (x1,y1) it is the nearly vehicle point in vehicle left front, (x2,y2) it is the remote vehicle point in left front, (x3,y3) it is the nearly vehicle in right front
Point, (x4,y4) it is the remote vehicle point in right front；
(xc1,yc1) and (xc2,yc2) respectively represent lane center nearly vehicle end and remote Che Duan；
Step S1022: the course of lane center is calculated:
Wherein θLaneRepresent course of the lane center under bodywork reference frame.
4. according to the method in claim 2 or 3, which is characterized in that step S2 is using Kalman filter to vehicle course
It is modified, comprising the following steps:
Step S201: initialized card Thalmann filter；
Step S202: pre- according to vehicle course under the earth coordinates in vehicle current turning radius and this period of prediction of speed
Measured value:
Wherein, θveh-predFor the predicted value in earth coordinates vehicle of lower current time course, θveh-correct-oldFor earth coordinates
The correction value in upper period vehicle course down, v are the current speed of vehicle, and R is the current turning radius of vehicle, and T is planning week
Phase；
Step S203: the difference in original path course and lane center course under bodywork reference frame is calculated:
θerror=θroad-θLane
Wherein, θerrorFor the difference in original path course and lane center course under bodywork reference frame, θroadFor car body coordinate
It is the course of lower original path, θLaneFor lane center course；
Step S204: the heading measure value of vehicle under earth coordinates is calculated:
θveh-measure=θveh-gps+θerror,
Wherein θveh-measureFor the heading measure value of vehicle under earth coordinates, θveh-gpsFor the GPS of vehicle under earth coordinates
Course value；
Step S205: by θ in step S202veh-predWith θ in step S204veh-measureInput as Kalman filter obtains
Navigational calibration value θ of the vehicle under earth coordinatesveh-correct；
Step S206: by θveh-correctIt is assigned to θveh-correct-oldAnd enter next period.
5. the method according to claim 1, wherein the step S3 the following steps are included:
Step S301: the deviation of the vehicle navigational calibration value and vehicle GPS course value under earth coordinates is calculated:
Δθ=θveh-correct-θveh-gps；
Wherein, θveh-correctThe navigational calibration value for being vehicle under earth coordinates, θveh-gpsFor vehicle under earth coordinates
The course GPS value；
Step S302: the path point after correction is calculated using result in step S301:
Wherein, [xcorrect ycorrect θcorrect]TIt is coordinate of the path after correcting under bodywork reference frame, [xveh-road
yveh-road θveh-road]TIt is coordinate of the original path under bodywork reference frame before correcting.
6. the method according to claim 1, wherein the step S4 the following steps are included:
Step S401: in statistics laser radar grating map, each number OGN for arranging the grid being occupiedi；
Step S402: according to the number OGN of each grid for arranging and being occupiedi, calculate the statistical mark amount OGS of each column gridi:
Wherein i is the row number in grating map, OGSiIndicate the statistical mark amount of the i-th column grid in grating map, OtIndicate grid
Map occupies index threshold；
Step S403: convolution kernel is generated according to road model width RW and road model width difference RWT:
Wherein KjFor the corresponding convolution kernel of jth column grid, RW is road model width, and RWT is road model width difference, GsFor
The row number of the selected grid as pulse, GsValue range be [0, RWT]；
Step S404: by result OGS in step S402iWith result K in step S403jCarry out convolution
Wherein, MW is the columns of grid in grating map, if re=1 in convolution process, corresponds to the matching value of grid column
Hit just adds 1；If re=-1, the non-matching value miss of corresponding grid column just adds 1；If re=0, grid is corresponded to
The unknown-value uknow of column just adds 1；
Step S405: the probability GC that a certain column grid is road-center is obtained according to result in step S404i:
Wherein GCiThe i-th column grid is represented as the probability of road-center；
Step S406: GC is found outiMaximum value, corresponding i is exactly the row number of the grid where road-center；
Cr=i
Wherein CrFor the grid row number where road-center；
Step S407: the grid row number where the curb of left and right is calculated；
Wherein, LrAnd RrThe row number of grid respectively where the curb of left and right；
Step S408: the opposed lateral positions relationship of vehicle and road is calculated
Wherein DlAnd DrRespectively distance of the vehicle to left and right curb.
7. according to the method described in claim 6, it is characterized in that, in the step S401, from the middle of grating map one
Column, i.e., that column where vehicle, start to start to the both sides of grating map along the direction perpendicular with revised path
Statistics.
8. method according to claim 6 or 7, which is characterized in that the step S5 the following steps are included:
Step S501: lane sum Cz is calculated；
Step S502: the lane C where positioning vehicle；
Lane serial number where vehicle:
The left number sequence number in lane where wherein C indicates vehicle, LW is lane width；
Step S503: according to place lane center line position (xc1,yc1) and (xc2,yc2), vehicle is calculated to place lane center
Distance Dc；
Step S504: according to the distance D of vehicle to place lane centercVehicle is calculated separately to left-lane with lane width LW
The distance D of center lineclWith the distance D of vehicle to right lane center linecr:
Dcl=LW-Dc
Dcr=LW+Dc
Step S505: virtual constraint is generated according to the distance of each lane center of vehicle distances, the virtual constraint includes institute
In lane boundary constraint and adjacent lane boundary constraint.
9. according to the method described in claim 8, it is characterized in that, when place lane the right and left has lane, the void
The quasi- left side constraint V for being constrained to left-lanell, place lane the left side constrain Vcl, place lane the right constrain VcrAnd right lane
The right constrain Vrr；When only there is lane in left side, the virtual constraint is that the left side of left-lane constrains Vll, place lane
The left side constrains VclAnd the right in place lane constrains Vcr；When only there is lane on right side, the virtual constraint is place lane
The left side constrains Vcl, place lane the right constrain VcrAnd the right of right lane constrains Vrr。
10. the method according to claim 1, wherein the step S6 the following steps are included:
Step S601: judge decision instruction whether lane-change, if so, being transferred to step S603；If it is not, being then transferred to step S602；
Step S602: the centerline in the path shift after correction to place lane is tracked as expected path, is transferred to
Step S604；
Step S603: the centerline in the path shift after correction to target lane is tracked as expected path, is transferred to
Step S604；
Step S604: path trace is carried out according to expected path.
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