CN110598311A - Automatic driving vehicle track tracking method - Google Patents
Automatic driving vehicle track tracking method Download PDFInfo
- Publication number
- CN110598311A CN110598311A CN201910846823.9A CN201910846823A CN110598311A CN 110598311 A CN110598311 A CN 110598311A CN 201910846823 A CN201910846823 A CN 201910846823A CN 110598311 A CN110598311 A CN 110598311A
- Authority
- CN
- China
- Prior art keywords
- vehicle
- front wheel
- true
- autonomous vehicle
- obtaining
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 32
- 230000006870 function Effects 0.000 claims description 17
- 238000004364 calculation method Methods 0.000 claims description 16
- 230000001133 acceleration Effects 0.000 claims description 6
- 239000000126 substance Substances 0.000 claims description 6
- 238000001914 filtration Methods 0.000 claims description 3
- BULVZWIRKLYCBC-UHFFFAOYSA-N phorate Chemical compound CCOP(=S)(OCC)SCSCC BULVZWIRKLYCBC-UHFFFAOYSA-N 0.000 claims description 3
- 238000005070 sampling Methods 0.000 claims description 3
- 230000000694 effects Effects 0.000 abstract description 4
- 238000013459 approach Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000012937 correction Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000008447 perception Effects 0.000 description 1
- 230000002265 prevention Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Classifications
-
- 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
- B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units
- B60W30/18—Propelling the vehicle
- B60W30/18172—Preventing, or responsive to skidding of wheels
-
- 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
- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
-
- 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
- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
- B60W2050/0001—Details of the control system
- B60W2050/0019—Control system elements or transfer functions
- B60W2050/0028—Mathematical models, e.g. for simulation
- B60W2050/0031—Mathematical model of the vehicle
Landscapes
- Engineering & Computer Science (AREA)
- Automation & Control Theory (AREA)
- Transportation (AREA)
- Mechanical Engineering (AREA)
- Human Computer Interaction (AREA)
- Control Of Position, Course, Altitude, Or Attitude Of Moving Bodies (AREA)
Abstract
The invention relates to a track tracking method, in particular to a track tracking method for an automatic driving vehicle; in particular, it is disclosed to add a virtual representation of the front wheel inclination δ actually acting on an autonomous vehicle to the cost function of a predictive modeltrueThe model is used as a cost-effective amount for model prediction control, so that the wheel slip of the vehicle is prevented and corrected, and the track tracking effect is improved; since the wheel slip phenomenon mainly occurs when the model of the vehicle deviates from the ideal model, that is, the wheel inclination deviates from the driving track of the vehicle, the virtual front wheel inclination δ is determinedtrueAs a cost-effective amount, the problem of wheel slip is effectively solved.
Description
Technical Field
The invention relates to a track tracking method, in particular to a track tracking method for an automatic driving vehicle.
Background
The key technologies of automatic driving can be sequentially divided into environment perception, behavior decision, path planning and motion control, wherein the motion control mainly comprises two basic design methods, one is a method based on driver simulation, and the other is a control method based on dynamics modeling. The vehicle control method widely studied and applied in the control method based on the dynamics modeling is model predictive control, and can optimize the punctuality, comfort and energy-saving indexes so as to obtain optimized control; in addition, compared with the traditional serial ring PID control, the preview tracking control, the linear model prediction control and the linear quadratic LQR control, the method has the advantages of high calculation precision, strong robustness, small overshoot and good tracking effect.
However, most of the current model-based predictive control only depends on the robustness, and in the control process, if the autonomous vehicle deviates from the reference model (i.e. has a sideslip situation), the behavior decision of the autonomous vehicle is needed to reduce the problem of deviation of the set trajectory, but the deviation of the model cannot be well compensated, so that the phenomenon that the autonomous vehicle deviates from the reference model, such as the autonomous vehicle deviates from the reference model, cannot be avoided.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a method for tracking the track of an automatic driving vehicle, which can optimize the original model prediction control by increasing indexes reflecting deviation under the condition that the automatic driving vehicle deviates from a reference model, such as driving out or turning, and the like, and can effectively control the automatic driving vehicle to approach to the accurate model as much as possible by the model prediction control, thereby preventing skidding.
The purpose of the invention is realized by the following technical scheme:
a method for tracking the trajectory of an autonomous vehicle, comprising the steps of:
s1: establishing a ground coordinate system and a body coordinate system, and performing dynamic modeling on the vehicle; the vehicle dynamics model is as follows:
wherein x and y are respectively the x-axis and y-axis of the center of mass of the automatic driving vehicle under the ground coordinate systemWhere ψ is a heading angle of the autonomous vehicle in the ground coordinate system, v is a forward speed of the autonomous vehicle, a is a forward acceleration of the autonomous vehicle, t represents a quantity at the present time, t +1 represents a quantity at the next time, L is a speed of the autonomous vehicle, andfis the wheelbase between the front and rear wheels; deltatrueThe real front wheel inclination angle is a virtual quantity, represents the current real front wheel inclination angle, and is the corresponding front wheel inclination angle of the vehicle under the condition that the front wheel is not in a slipping state in the current driving direction;
s2: obtaining self state vector X of bodytAnd last output vector Ut-1(ii) a Wherein the content of the first and second substances,
Xt＝[x，y，ψ，v]T(2)
Ut-1＝[at-1，δt-1](3)
delta is the front wheel inclination of the autonomous vehicle; when the self state vector X of the machine body at the next moment needs to be calculatedt+1Then, the X at that moment is calculatedtSubstituting into formula (1) to calculate X at the next timet+1Is related to parameter xt+1、yt+1、ψt+1And vt+1；
S3: obtaining a virtual quantity which is the current real front wheel inclination angle deltatrue；
S4: obtaining discrete waypoint paths P through sampling, carrying out interpolation calculation on the waypoint paths P, and obtaining x by length l respectivelyref，yref，ψref，vrefObtaining a continuous track f (P); wherein the content of the first and second substances,
substituting the following formula to obtain a discrete waypoint path P 'by setting a reference speed v, wherein P' comprises the state of N points;
P’＝[X1，X2，…，XN]T(5)
Xn＝[f1(ln)f2(ln)f3(ln)f4(ln)](6)
ln＝(n+1)×v×dt(7)
wherein n is the number of the point to be taken;
s5: according to self state XtAnd a waypoint path P', and obtaining the acceleration a at the current moment by calculating the minimum value of the model prediction cost function JtAnd the front wheel inclination angle delta at the present momenttSo as to obtain the optimal output vector as the current output vector Ut；
The calculation formula of the model prediction cost function J is as follows:
n is a model predictive control calculation step length, w represents the weight of each index, and the weight of each index can be assigned according to the actual condition;
s6: output vector UtAct on the autonomous vehicle, repeat S2 to S6.
In a preferred embodiment of the present invention, in S3, the current true anteversion angle δ is acquiredtrueThe method comprises the following steps:
first, by geometric relationships one can obtain:
therefore, the method comprises the following steps:
wherein R is the front wheel inclination angle deltatrueRadius of turning in time, LfD ψ represents a heading angle difference of two time intervals, dx represents an x-coordinate difference of two time intervals, dy represents a y-coordinate difference of two time intervals, dt represents a time interval, and ω is an angular velocity of the vehicle.
Preferably, a large amount of δ is obtained by equations (9) and (10)true、XtAnd UtData and by multivariate minimizationFitting a quadratic polynomial to obtain an approximate function g; wherein the functions g and XtX, y, ψ in (b) has no relation with the output vector U at that timetVelocity vtAcceleration atAnd front wheel inclination angle deltatAnd last time real object output vector Ut-1Velocity vt-1Acceleration at-1And true front wheel inclination(ii) related; the following can be obtained:
meanwhile, the calculation can be simplified to obtain:
and finally, obtaining a function g through multivariate least square polynomial fitting.
In a preferred embodiment of the present invention, in S2, the current body own state vector x is obtained by sensor filteringt。
In a preferred embodiment of the present invention, in S4, the waypoint path P is acquired by the path planner.
Compared with the prior art, the invention has the following beneficial effects:
1. when the model of the vehicle has a certain deviation from the ideal model, mainly the wheel slip phenomenon occurs, namely the wheel inclination angle has a deviation from the driving track of the vehicle; in this case, the virtual representation is used to indicate the actual front wheel inclination δ acting on the autonomous vehicletrueThe method is used as a cost-effective amount for model prediction control, so that the contact ratio of an actually-operated vehicle model and a preset ideal model is improved, the prevention and correction of wheel slip of the vehicle are facilitated, and the track tracking effect is improved.
2. The invention adds one more cost-substitute quantity delta only in the original model predictive controltrueThe method has small influence on the calculated amount of the model and good effect.
Drawings
Fig. 1 is a schematic view of vehicle trajectory processing of the automatic driving vehicle trajectory tracking method of the present invention.
FIG. 2 is a schematic view of a vehicle model for the trajectory tracking method of an autonomous vehicle according to the present invention.
FIG. 3 is a schematic diagram of a vehicle trajectory tracking system of the method for tracking a trajectory of an autonomous vehicle according to the present invention.
FIG. 4 shows δ in the trajectory tracking method of an autonomous vehicle according to the present inventiontAnd deltatrueSchematic representation of (a).
Detailed Description
The present invention will be further described with reference to the following examples and drawings, but the embodiments of the present invention are not limited thereto.
Referring to fig. 1 to 4, the method for tracking a trajectory of an autonomous vehicle according to the present embodiment includes the following steps:
s1: establishing a ground coordinate system (xoy)1 and a body coordinate system (x ' o ' y ') 2, and performing dynamic modeling on the vehicle; the vehicle dynamics model is as follows:
wherein x and y are respectively the x-axis and y-axis coordinates of the center of mass of the autonomous vehicle under the ground coordinate system 1, psi is the heading angle of the autonomous vehicle under the ground coordinate system, v is the advancing speed of the autonomous vehicle, a is the advancing acceleration of the autonomous vehicle, t represents the current time quantity, t +1 represents the next time quantity, LfIs the wheelbase between the front and rear wheels; deltatrueThe virtual quantity represents the current real front wheel inclination angle, and is the corresponding front wheel inclination angle of the vehicle under the condition that the front wheels do not slip in the current driving direction.
S2: obtaining self state vector X of body by sensor filtering (or filter)tObtaining last output vector U through recording information of the systemt-1(ii) a Wherein the content of the first and second substances,
Xt＝[x，y，ψ，v]T(2)
Ut-1＝[at-1，δt-1](3)
delta is the front wheel inclination of the autonomous vehicle; when the self state vector X of the machine body at the next moment needs to be calculatedt+1Then, the X at that moment is calculatedtSubstituting into formula (1) to calculate X at the next timet+1Is related to parameter xt+1、yt+1、ψt+1And vt+1。
S3: obtaining a virtual quantity through an observer, wherein the virtual quantity is the current real front wheel inclination angle deltatrue(ii) a The observer is specifically modeled as follows:
first, by geometric relationships one can obtain:
therefore, the method comprises the following steps:
wherein R is the front wheel inclination angle deltatrueRadius of turning in time, LfD ψ represents a heading angle difference of two time intervals, dx represents an x-coordinate difference of two time intervals, dy represents a y-coordinate difference of two time intervals, dt represents a time interval, and ω is an angular velocity of the vehicle.
In addition, a large amount of δ is obtained by the formulas (9) and (10)true、XtAnd UtObtaining approximate function g through multivariate least square polynomial fitting; wherein the functions g and XtX, y, ψ in (b) has no relation with the output vector U at that timetVelocity vtAcceleration atAnd front wheel inclination angle deltatAnd last time real object output vector Ut-1Velocity vt-1Acceleration at-1And true front wheel inclination(ii) related; the following can be obtained:
meanwhile, the calculation can be simplified to obtain:
and finally, obtaining a function g through multivariate least square polynomial fitting. In this way, the current true front wheel inclination δ at each instant can be obtained directly from the function gtrueThe calculation amount is simplified, and the calculation efficiency is improved.
S4: referring to fig. 2, discrete waypoint paths P3 are obtained by sampling by the path planner and interpolated for the waypoint paths P3 to obtain x with length lref，yref，ψref，vrefObtaining a continuous track f (P) 4; wherein the content of the first and second substances,
substituting the following formula to obtain a discrete waypoint path P '5 by setting a reference speed v, wherein P' 5 comprises the state of N points;
P’＝[X1，X2，…，XN]T(5)
Xn＝[f1(ln)f2(ln)f3(ln)f4(ln)](6)
ln＝(n+1)×v×dt(7)
where n is the number of points taken.
S5: according to self state XtAnd a waypoint path P' 5, and obtaining the acceleration a at the current moment by calculating the minimum value of the model prediction cost function JtAnd the front wheel inclination angle delta at the present momenttSo as to obtain the optimal output vector as the current output vector Ut；
The calculation formula of the model prediction cost function J is as follows:
wherein, N is the model predictive control calculation step length, w represents the weight of each index, and the weight of each index can be assigned according to the actual situation.
S6: output vector UtAct on the autonomous vehicle, repeat S2 to S6.
Referring to fig. 4, when a slipping state occurs at the front wheel of the vehicle, the direction of the front wheel of the vehicle is not consistent with the direction in which the wheel should be, that is, the direction of the front wheel of the vehicle is not consistent with the traveling direction v of the vehicleVehicle with wheelsInconsistency; however, the direction in which the front wheels of the vehicle should be located can be determined by calculation or in some other way, so that this time by introducing a virtual quantity δtrue(current true front wheel inclination angle) as the direction in which the front wheel of the vehicle should be located, and the deviation value between the twoThe calculation is carried out in the cost function, so that the problem about wheel slip is solved in the whole model prediction control, and the vehicle track tracking is more accurate. The boxes in fig. 4 represent the front wheels of the vehicle.
The present invention is not limited to the above embodiments, and any other changes, modifications, substitutions, combinations, and simplifications which do not depart from the spirit and principle of the present invention should be construed as equivalents and are included in the scope of the present invention.
Claims (5)
1. A method for tracking the trajectory of an autonomous vehicle, comprising the steps of:
s1: establishing a ground coordinate system and a body coordinate system, and performing dynamic modeling on the vehicle; the vehicle dynamics model is as follows:
wherein x and y are respectively the x-axis and y-axis coordinates of the center of mass of the autonomous vehicle in the ground coordinate system, psi is the heading angle of the autonomous vehicle in the ground coordinate system, v is the forward speed of the autonomous vehicle, a is the forward acceleration of the autonomous vehicle, t represents the amount of the current time, t +1 represents the amount of the next time, LfIs the wheelbase between the front and rear wheels; deltatrueThe real front wheel inclination angle is a virtual quantity, represents the current real front wheel inclination angle, and is the corresponding front wheel inclination angle of the vehicle under the condition that the front wheel is not in a slipping state in the current driving direction;
s2: obtaining self state vector X of bodytAnd last output vector Ut-1(ii) a Wherein the content of the first and second substances,
Xt＝[x，y，ψ，v]T(2)
Ut-1＝[at-1，δt-1](3)
delta is the front wheel inclination of the autonomous vehicle; when the self state vector X of the machine body at the next moment needs to be calculatedt+1Then, the X at that moment is calculatedtSubstituting into formula (1) to calculate X at the next timet+1Is related to parameter xt+1、yt+1、ψt+1And vt+1；
S3: obtaining a virtual quantity which is the current real front wheel inclination angle deltatrue；
S4: obtaining discrete waypoint paths P through sampling, carrying out interpolation calculation on the waypoint paths P, and obtaining x by length l respectivelyref，yref，ψref，vrefObtaining a continuous track f (P); wherein the content of the first and second substances,
substituting the following formula to obtain a discrete waypoint path P 'by setting a reference speed v, wherein P' comprises the state of N points;
P’＝[X1，X2，…，XN]T(5)
Xn＝[f1(ln) f2(ln) f3(ln) f4(ln)](6)
ln＝(n+1)×v×dt(7)
wherein n is the number of the point to be taken;
s5: according to self state XtAnd a waypoint path P', and obtaining the acceleration a at the current moment by calculating the minimum value of the model prediction cost function JtAnd the front wheel inclination angle delta at the present momenttSo as to obtain the optimal output vector as the current output vector Ut；
The calculation formula of the model prediction cost function J is as follows:
n is a model predictive control calculation step length, w represents the weight of each index, and the weight of each index can be assigned according to the actual condition;
s6: output vector UtAct on the autonomous vehicle, repeat S2 to S6.
2. The autonomous-vehicle trajectory tracking method of claim 1, wherein in S3, a current true forward-inclination angle δ is acquiredtrueThe method comprises the following steps:
first, by geometric relationships one can obtain:
therefore, the method comprises the following steps:
wherein R is the front wheel inclination angle deltatrueRadius of turning in time, LfD ψ represents the difference of heading angles of two time intervals, dx represents the difference of x coordinates of two time intervals, dy tableTwo time intervals are shown, the difference in y-coordinates, dt represents the time interval and ω is the angular velocity of the vehicle.
3. The autonomous vehicle trajectory tracking method of claim 2, wherein the plurality of δ is obtained by equations (9) and (10)true、XtAnd UtObtaining approximate function g through multivariate least square polynomial fitting; wherein the functions g and XtX, y, ψ in (b) has no relation with the output vector U at that timetVelocity vtAcceleration atAnd front wheel inclination angle deltatAnd last time real object output vector Ut-1Velocity vt-1Acceleration at-1And true front wheel inclination(ii) related; the following can be obtained:
meanwhile, the calculation can be simplified to obtain:
and finally, obtaining a function g through multivariate least square polynomial fitting.
4. The automatic driven vehicle trajectory tracking method according to claim 1, characterized in that in S2, the current body own state vector X is acquired through sensor filteringt。
5. The autonomous vehicle trajectory tracking method of claim 1, wherein in S4, a waypoint path P is obtained by the path planner.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910846823.9A CN110598311B (en) | 2019-09-06 | 2019-09-06 | Automatic driving vehicle track tracking method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910846823.9A CN110598311B (en) | 2019-09-06 | 2019-09-06 | Automatic driving vehicle track tracking method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110598311A true CN110598311A (en) | 2019-12-20 |
| CN110598311B CN110598311B (en) | 2022-08-02 |
Family
ID=68858082
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910846823.9A Active CN110598311B (en) | 2019-09-06 | 2019-09-06 | Automatic driving vehicle track tracking method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110598311B (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110955250A (en) * | 2019-12-25 | 2020-04-03 | 湖南大学 | Vehicle reversing path tracking control method |
| CN110989625A (en) * | 2019-12-25 | 2020-04-10 | 湖南大学 | Vehicle path tracking control method |
| CN113158415A (en) * | 2021-02-23 | 2021-07-23 | 电子科技大学长三角研究院(衢州) | Vehicle track similarity evaluation method based on error analysis |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20030040849A1 (en) * | 2001-08-21 | 2003-02-27 | Jean-Pierre Hathout | Method and system for vehicle trajectory estimation |
| US9645577B1 (en) * | 2016-03-23 | 2017-05-09 | nuTonomy Inc. | Facilitating vehicle driving and self-driving |
| CN107168312A (en) * | 2017-05-17 | 2017-09-15 | 哈尔滨工程大学 | A kind of space tracking tracking and controlling method of compensation UUV kinematics and dynamic disturbance |
| CN107561942A (en) * | 2017-09-12 | 2018-01-09 | 重庆邮电大学 | Intelligent vehicle track following model predictive control method based on model compensation |
| CN108248605A (en) * | 2018-01-23 | 2018-07-06 | 重庆邮电大学 | The transverse and longitudinal control method for coordinating that a kind of intelligent vehicle track follows |
| US20180196444A1 (en) * | 2017-01-06 | 2018-07-12 | Hyundai Motor Company | Autonomous driving system and autonomous driving method |
| CN109407677A (en) * | 2018-12-24 | 2019-03-01 | 清华大学 | The trace tracking method of automatic driving vehicle |
| CN109976159A (en) * | 2019-04-09 | 2019-07-05 | 台州学院 | Intelligent vehicle crosswise joint method based on safely controllable domain |
-
2019
- 2019-09-06 CN CN201910846823.9A patent/CN110598311B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20030040849A1 (en) * | 2001-08-21 | 2003-02-27 | Jean-Pierre Hathout | Method and system for vehicle trajectory estimation |
| US9645577B1 (en) * | 2016-03-23 | 2017-05-09 | nuTonomy Inc. | Facilitating vehicle driving and self-driving |
| US20180196444A1 (en) * | 2017-01-06 | 2018-07-12 | Hyundai Motor Company | Autonomous driving system and autonomous driving method |
| CN107168312A (en) * | 2017-05-17 | 2017-09-15 | 哈尔滨工程大学 | A kind of space tracking tracking and controlling method of compensation UUV kinematics and dynamic disturbance |
| CN107561942A (en) * | 2017-09-12 | 2018-01-09 | 重庆邮电大学 | Intelligent vehicle track following model predictive control method based on model compensation |
| CN108248605A (en) * | 2018-01-23 | 2018-07-06 | 重庆邮电大学 | The transverse and longitudinal control method for coordinating that a kind of intelligent vehicle track follows |
| CN109407677A (en) * | 2018-12-24 | 2019-03-01 | 清华大学 | The trace tracking method of automatic driving vehicle |
| CN109976159A (en) * | 2019-04-09 | 2019-07-05 | 台州学院 | Intelligent vehicle crosswise joint method based on safely controllable domain |
Non-Patent Citations (3)
| Title |
|---|
| TOSHIHIRO HIRAOKA ET AL.: "Automatic path-tracking controller of a four-wheel steering vehicle", 《VEHICLE SYSTEM DYNAMICS》 * |
| 孟祥雨: "自动驾驶汽车横向跟踪控制方法研究", 《时代汽车》 * |
| 马硕: "无人驾驶汽车应用与发展现状分析", 《汽车与驾驶维修(维修版)》 * |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110955250A (en) * | 2019-12-25 | 2020-04-03 | 湖南大学 | Vehicle reversing path tracking control method |
| CN110989625A (en) * | 2019-12-25 | 2020-04-10 | 湖南大学 | Vehicle path tracking control method |
| CN110989625B (en) * | 2019-12-25 | 2020-11-27 | 湖南大学 | Vehicle path tracking control method |
| CN110955250B (en) * | 2019-12-25 | 2021-02-23 | 湖南大学 | Vehicle reversing path tracking control method |
| CN113158415A (en) * | 2021-02-23 | 2021-07-23 | 电子科技大学长三角研究院(衢州) | Vehicle track similarity evaluation method based on error analysis |
| CN113158415B (en) * | 2021-02-23 | 2023-09-08 | 电子科技大学长三角研究院(衢州) | Vehicle track similarity evaluation method based on error analysis |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110598311B (en) | 2022-08-02 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112733270B (en) | System and method for predicting vehicle running track and evaluating risk degree of track deviation | |
| CN110598311B (en) | Automatic driving vehicle track tracking method | |
| Li et al. | Development of a new integrated local trajectory planning and tracking control framework for autonomous ground vehicles | |
| JP4586795B2 (en) | Vehicle control device | |
| CN110568758B (en) | Parameter self-adaptive transverse motion LQR control method for automatically driving automobile | |
| Altché et al. | High-speed trajectory planning for autonomous vehicles using a simple dynamic model | |
| CN110262509B (en) | Automatic vehicle driving method and device | |
| CN109799828B (en) | Transverse and longitudinal coordination control method for automatic driving vehicle | |
| CN110861642A (en) | Vehicle lateral motion control | |
| CN111098335B (en) | Method and device for calibrating odometer of double-wheel differential drive robot | |
| CN111121777A (en) | Unmanned equipment trajectory planning method and device, electronic equipment and storage medium | |
| CN111731307B (en) | Vehicle turning control method and device, vehicle and storage medium | |
| CN112793585B (en) | Automatic driving trajectory tracking control method | |
| CN111123701B (en) | Automatic driving path tracking anti-interference control method based on pipeline prediction model | |
| CN112180714A (en) | Vehicle full-speed domain transverse control method based on double feedback controllers | |
| CN114228690A (en) | Automatic driving vehicle roll control method based on DDPG and iterative control | |
| CN115402348A (en) | Transverse control system, method, electronic equipment and storage medium | |
| CN114906173A (en) | Automatic driving decision-making method based on two-point preview driver model | |
| CN114148320A (en) | Path tracking control method and device | |
| CN112238856B (en) | Intelligent vehicle overtaking track optimization method based on hybrid particle swarm optimization | |
| Kone | Lateral and longitudinal control of an autonomous racing vehicle. | |
| Vörös et al. | Lane keeping control using finite spectrum assignment with modeling errors | |
| CN115447615A (en) | Trajectory optimization method based on vehicle kinematics model predictive control | |
| CN113306573B (en) | Learning type path tracking prediction control method for automatic driving vehicle | |
| CN112406890B (en) | Dynamics-based vehicle prediction control method and processor |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |