CN111717189A - Lane keeping control method, device and system - Google Patents

Lane keeping control method, device and system Download PDF

Info

Publication number
CN111717189A
CN111717189A CN201910203638.8A CN201910203638A CN111717189A CN 111717189 A CN111717189 A CN 111717189A CN 201910203638 A CN201910203638 A CN 201910203638A CN 111717189 A CN111717189 A CN 111717189A
Authority
CN
China
Prior art keywords
steering wheel
target steering
lane
vehicle
curvature
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
Application number
CN201910203638.8A
Other languages
Chinese (zh)
Other versions
CN111717189B (en
Inventor
曹增
张凯
和林
甄龙豹
葛建勇
王天培
鲁宁
崔文锋
刘洪亮
张健
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Haomo Zhixing Technology Co Ltd
Original Assignee
Great Wall Motor Co Ltd
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Great Wall Motor Co Ltd filed Critical Great Wall Motor Co Ltd
Priority to CN201910203638.8A priority Critical patent/CN111717189B/en
Publication of CN111717189A publication Critical patent/CN111717189A/en
Application granted granted Critical
Publication of CN111717189B publication Critical patent/CN111717189B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

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
    • B60W10/00—Conjoint control of vehicle sub-units of different type or different function
    • B60W10/20—Conjoint control of vehicle sub-units of different type or different function including control of steering systems
    • 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, or advanced driver assistance systems for ensuring comfort, stability and safety or drive control systems for propelling or retarding the vehicle
    • B60W30/10—Path keeping
    • B60W30/12—Lane keeping
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/02—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to ambient conditions
    • B60W40/06—Road conditions
    • B60W40/072—Curvature of the road
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/10—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to vehicle motion
    • B60W40/114—Yaw movement
    • 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
    • B60W2520/00—Input parameters relating to overall vehicle dynamics
    • B60W2520/14—Yaw
    • 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
    • B60W2710/00—Output or target parameters relating to a particular sub-units
    • B60W2710/20—Steering systems

Abstract

The invention relates to the field of intelligent traffic and provides a lane keeping control method, a lane keeping control device and a lane keeping control system. The lane keeping control method includes: acquiring course angle deviation between a current course angle and a target course angle of the automatic driving vehicle and transverse position deviation between a current transverse position and a target transverse position; acquiring the curvature of a pre-aiming road of an automatic driving vehicle; determining a target steering wheel corner according to the pre-aiming road curvature, the transverse position deviation and the course angle deviation, wherein the target steering wheel corner requires that the transverse position deviation and the course angle deviation can be 0, and the pre-aiming road curvature is enabled to reach the optimal road curvature which enables the error between the actual running track and the expected track of the vehicle to be minimum; and controlling the vehicle to perform lane keeping according to the target steering wheel angle. The lane keeping control method can enable the vehicle to keep running according to the current lane under the working condition of safe running speed, and meet the requirements of the operation stability and the safety of the vehicle.

Description

Lane keeping control method, device and system
Technical Field
The invention relates to the field of intelligent traffic, in particular to a lane keeping control method, a lane keeping control device and a lane keeping control system.
Background
The automatic driving vehicle senses external environment information and information of the vehicle through various sensing systems arranged around the vehicle body, then carries out fusion and decision (corresponding to the fusion system and the decision system) on the input information, self-plans a drivable safe route according to different driving conditions, and monitors and controls the safe driving of the vehicle in real time through a control system, thereby realizing the highly automatic driving of the vehicle. The control system is a core part of an automatic driving vehicle, and the performance of the control system directly determines the safe driving and the automation degree standard of the vehicle, so that the control system is always a key point and a difficulty for various companies to research and develop and overcome. The control system is divided into a transverse control system and a longitudinal control system, the transverse control system mainly realizes real-time steering control of the automatic driving vehicle through a series of control algorithms to enable the vehicle to carry out lane keeping, automatic lane changing, dynamic obstacle avoidance, turning around, turning and the like according to a known planned driving route, and the longitudinal control system mainly controls acceleration and deceleration of the vehicle to enable the automatic driving vehicle to longitudinally drive at a certain safe driving speed to realize automatic starting, stopping, following, cruising and the like. Through the coupling of the transverse and longitudinal control, the whole control system can realize automatic control on the steering and the speed of the vehicle at the same time.
In the driving process of the vehicle, the lane keeping function occupies most of the driving time and also accords with safe driving specifications, so that the lane keeping control algorithm plays a role in a transverse control system. Currently, the mainstream lateral control algorithm, especially the lane keeping algorithm, is designed based on the vehicle dynamics and the concept of "track following". However, because the accuracy of the nonlinear vehicle dynamics model and the tire model greatly affects the operation stability of the vehicle, it is difficult to establish an accurate model for different vehicle types to reflect the real stress condition of the vehicle; and secondly, the complex model increases the solving dimensionality, and meanwhile, the calculation amount is increased, so that higher requirements are put forward on the computing capacity of the controller. In addition, the solution based on the idea of "trajectory tracking" has high requirements on positioning accuracy, and requires a high-accuracy positioning device to be mounted on the vehicle, thereby additionally increasing hardware cost.
Disclosure of Invention
In view of the above, the present invention is directed to a lane keeping control method to at least partially solve the above technical problems.
In order to achieve the purpose, the technical scheme of the invention is realized as follows:
a lane keeping control method comprising: acquiring course angle deviation between a current course angle and a target course angle of an automatic driving vehicle and transverse position deviation between a current transverse position and a target transverse position of the automatic driving vehicle; acquiring the curvature of a pre-aiming road of an automatic driving vehicle; determining a target steering wheel corner according to the pre-aiming road curvature, the transverse position deviation and the course angle deviation, wherein the target steering wheel corner requires that the transverse position deviation and the course angle deviation can be 0, and the pre-aiming road curvature is enabled to reach an optimal road curvature which enables the error between the actual running track and the expected track of the automatic driving vehicle to be minimum; and controlling the autonomous vehicle to perform lane keeping according to the target steering wheel angle.
Further, the obtaining the curvature of the pre-line of sight of the autonomous vehicle includes: obtaining a current lane line equation of the autonomous vehicle; acquiring a current pre-aiming distance of the automatic driving vehicle; and calculating the curvature of the pre-aimed road according to the current pre-aimed distance and the current lane line equation.
Further, the determining a target steering wheel angle according to the pre-line road curvature, the lateral position deviation, and the heading angle deviation comprises: setting a first controller, wherein the first controller inputs the curvature of the pre-aimed road and outputs a first target steering wheel corner which enables the curvature of the pre-aimed road to reach the optimal curvature of the road; setting a second controller that inputs the lateral position deviation and outputs a second target steering wheel angle at which the lateral position deviation is 0; setting a third controller, wherein the third controller inputs the course angle deviation and outputs a third target steering wheel rotating angle which enables the course angle deviation to be 0; and determining a final target steering wheel angle according to the first target steering wheel angle, the second target steering wheel angle and the third target steering wheel angle.
Further, the lane keeping control method further includes: in the first controller, a mapping relationship between the target steering wheel angle and the pre-line road curvature is established based on ackermann steering principle.
Further, the second controller is a fuzzy PID controller, and the control coefficient of the fuzzy PID controller is determined according to the real-time speed of the automatic driving vehicle.
Compared with the prior art, the lane keeping control method has the following advantages: the lane keeping control method has good control effect and strong robustness, can enable the vehicle to run stably, has good comfort, can enable the vehicle to keep passing through roads such as straight roads, curved roads and the like according to the current lane under the working condition of safe running speed, and meets the requirements of the operation stability and the safety of the vehicle.
Another object of the present invention is to propose a lane keeping control device to at least partially solve the above technical problem.
In order to achieve the purpose, the technical scheme of the invention is realized as follows:
a lane keep control apparatus comprising: the first acquisition module is used for acquiring course angle deviation between the current course angle and a target course angle of the automatic driving vehicle; a second obtaining module for obtaining a lateral position deviation between a current lateral position and a target lateral position of the autonomous vehicle; the third acquisition module is used for acquiring the curvature of the pre-aiming road of the automatic driving vehicle; a determining module, configured to determine a target steering wheel angle according to the pre-aimed road curvature, the lateral position deviation, and the heading angle deviation, where the target steering wheel angle requires that the lateral position deviation and the heading angle deviation be 0, and the pre-aimed road curvature is made to reach an optimal road curvature that minimizes an error between an actual travel trajectory and an expected trajectory of the autonomous vehicle; and the control module is used for controlling the automatic driving vehicle to keep a lane according to the target steering wheel angle.
Further, the third obtaining module includes: a lane line equation obtaining submodule for obtaining a current lane line equation of the autonomous vehicle; the pre-aiming distance acquisition submodule is used for acquiring the current pre-aiming distance of the automatic driving vehicle; and the pre-aiming curvature acquisition submodule is used for calculating the curvature of the pre-aiming road according to the current pre-aiming distance and the current lane line equation.
Further, the determining module comprises: a first controller for inputting the pre-addressed road curvature and outputting a first target steering wheel angle at which the pre-addressed road curvature reaches the optimal road curvature; a second controller for inputting the lateral position deviation and outputting a second target steering wheel angle at which the lateral position deviation is 0; a third controller for inputting the course angle deviation and outputting a third target steering wheel angle at which the course angle deviation is 0; and a steering wheel angle determination submodule for determining a final target steering wheel angle according to the first target steering wheel angle, the second target steering wheel angle, and the third target steering wheel angle.
Further, in the first controller, a mapping relation between a target steering wheel corner and the curvature of the pre-aiming road is established based on the Ackerman principle; and/or the second controller is a fuzzy PID controller, and the control coefficient of the fuzzy PID controller is determined according to the real-time speed of the automatic driving vehicle.
The lane keeping control device has the same advantages as the lane keeping control method compared with the prior art, and the detailed description is omitted here.
Another object of the present invention is to provide a machine-readable storage medium and a lane-keeping control system to at least partially solve the above technical problems.
In order to achieve the purpose, the technical scheme of the invention is realized as follows:
a machine-readable storage medium having instructions stored thereon for causing a machine to perform the lane-keeping control method described above.
A lane keeping control system comprising: the acquisition device is used for acquiring lane line information; the machine-readable storage medium described above; and a processor configured to obtain the lane line information from the acquisition device and execute the instructions stored in the machine-readable storage medium in conjunction with the lane line information.
The advantages of the machine-readable storage medium and the lane keeping control system are the same as those of the lane keeping control method described above with respect to the prior art, and are not described herein again.
Additional features and advantages of the invention will be set forth in the detailed description which follows.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate an embodiment of the invention and, together with the description, serve to explain the invention and not to limit the invention. In the drawings:
FIG. 1 is a flow chart illustrating a lane keeping control method according to an embodiment of the present invention;
FIG. 2 is a schematic view of the driving trajectory for optimal lane keeping;
FIG. 3 is a schematic diagram of a heading angle deviation and a lateral position deviation as defined in an embodiment of the present invention;
FIG. 4 is a schematic flow chart of the method for obtaining the curvature of the pre-aimed road according to the embodiment of the invention;
FIG. 5 is a schematic flow chart illustrating a process of obtaining a target steering wheel angle according to an embodiment of the present invention;
FIG. 6 is a schematic diagram of the Ackerman steering principle;
FIG. 7 is a schematic diagram of a fuzzy logic control flow;
FIG. 8 is a schematic flow chart of lateral position deviation control in the embodiment of the present invention;
FIG. 9 is a schematic flow chart illustrating a course angle deviation control according to an embodiment of the present invention;
fig. 10 is a schematic structural view of a lane keep control apparatus according to an embodiment of the present invention;
FIG. 11 is a schematic structural diagram of a lane keeping control system according to an embodiment of the present invention; and
fig. 12 is a communication diagram of the lane keeping control system according to the embodiment of the present invention.
Description of reference numerals:
100. a first acquisition module; 200. a second acquisition module; 300. a third obtaining module; 400. a determination module; 500. a control module; 1110. a collection device; 1120. a processor.
Detailed Description
In addition, the embodiments of the present invention and the features of the embodiments may be combined with each other without conflict.
The present invention will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
Fig. 1 is a flowchart illustrating a lane-keeping control method according to an embodiment of the present invention, which is applied to a control system of an autonomous vehicle. The lane keeping refers to keeping the vehicle running on the current lane, and the optimal lane keeping running track is shown in fig. 2.
Referring to fig. 1, a lane keeping control method of an embodiment of the present invention may include the steps of:
step S110, obtaining the course angle deviation and the transverse position deviation of the automatic driving vehicle.
Wherein, the course angle refers to the included angle between the current course of the vehicle and the lane line where the vehicle is located, and the course angle deviation is the angle deviation between the current course angle and the target course angle; the lateral position deviation is a distance deviation between the current lateral position of the vehicle and the target running track. Referring to fig. 2, during vehicle hold, the target heading angle is aligned with the lane centerline direction, and the target trajectory may be, for example, the lane centerline. FIG. 3 is a schematic diagram of a heading angle deviation and a lateral position deviation defined in an embodiment of the present invention, wherein d represents the lateral position deviation and α represents the heading angle deviation. Referring to fig. 2 and 3, it can be seen that in an ideal lane keeping, d and α should be 0, so that the vehicle keeps driving safely on the center line of the lane.
Step S120, a pre-line road curvature of the autonomous vehicle is acquired.
Fig. 4 is a schematic flow chart of acquiring the curvature of the pre-aimed road in the embodiment of the present invention. As shown in fig. 4, the following steps may be included:
step S121, obtaining a current lane line equation of the automatic driving vehicle.
For example, the fusion system of the automatic driving system outputs lane line information including lane line type, lane line width, lane line reliability, etc. by fitting the lane line information, a lane line equation in a vehicle coordinate system can be obtained, for example, as
y＝c0+c1*x+c2x2+3x3(1)
In the formula, (x, y) represents a lane line coordinate, c0-c3 represents a pending parameter, different parameter values represent different types of roads, and when c2 and c3 are 0, straight line segments are represented. Wherein, the values of c0-c3 can be extracted in the lane line fitting.
And step S122, acquiring the current preview distance of the automatic driving vehicle.
When a driver drives a vehicle, the driver usually pays attention to a distance in front of the driving direction of the vehicle so as to grasp the next position (namely the front view point of the driver) where the vehicle drives, and the distance from the current position to the next position selected by the driver is the pre-aiming distance. Generally, in order to better simulate the driving process of a driver, the embodiment of the invention selects a distance in front of the driving path of the automatic driving vehicle as the pre-aiming distance when the automatic driving vehicle is subjected to lane keeping control.
In the embodiment of the invention, the pre-aiming distance of the vehicle can be obtained through the following formula:
Figure BDA0001998274180000071
in the formula, s represents the pre-aiming distance and has the unit of meter; m is a predetermined coefficient, and can be determined empirically by those skilled in the art in conjunction with the minimum turning radius of the vehicle, for example m can be 1.5; v represents the vehicle speed in kilometers/hour, and s is converted into meters/second because of the meter, and 3.6 in the formula is a related conversion parameter. d0For fixed pre-aiming distance, it is typically 4m, based on road testing experience.
And S123, calculating the curvature of the pre-aimed road according to the current pre-aimed distance and the current lane line equation.
With the current pre-line distance and current lane line equation known, the pre-line road curvature can be calculated by:
ρ＝a1*c3*s+a2*c2 (3)
where a1 and a2 are conventional parameters, e.g., 6 and 2, respectively, c2 and c3 are obtained according to equation (1) above, and s is obtained according to equation (2) above.
Accordingly, the curvature of the pre-line of sight of the vehicle is obtained.
And S130, determining a target steering wheel rotation angle according to the pre-aiming road curvature, the transverse position deviation and the course angle deviation.
Wherein the target steering wheel angle requirement enables the lateral position deviation and the heading angle deviation to be 0, and the pre-aimed road curvature to achieve an optimal road curvature that minimizes an error between an actual travel track and an expected track of the autonomous vehicle.
It can be seen that step S130 essentially comprises three control strategies, namely: the method comprises the following steps of controlling a target steering wheel angle based on the curvature of a pre-aiming road, controlling the target steering wheel angle based on the lateral position deviation and determining the target steering wheel angle based on the course angle deviation.
Accordingly, in a preferred embodiment, this may be achieved by three controllers, i.e., as shown in fig. 5, such that step S130 may include the steps of:
step S131, a first controller is set, the first controller inputs the pre-aiming road curvature and outputs a first target steering wheel corner which enables the pre-aiming road curvature to reach the optimal road curvature.
Here, since the input parameter is the pre-aiming road curvature, the first controller is substantially a pre-aiming tracking controller, which forms a closed-loop system based on the pre-aiming following theory, and estimates the pre-aiming distance ahead according to the current lane line information and the vehicle motion state, so that the error of the vehicle relative to the expected track in the route is minimized, and the required optimal pre-aiming curvature is achieved. That is, if the driver can grasp the mapping relationship between the trajectory curvature and the steering wheel angle in the continuous driving practice, the corresponding steering wheel angle is naturally determined when the driver observes a specific road curvature. Therefore, it is important to establish a mapping relationship between the target steering wheel angle and the curvature of the pre-line road in the control process of the first controller.
Preferably, in the embodiment of the present invention, a mapping relationship between the target steering wheel angle and the curvature of the pre-aimed road may be established based on Ackerman (Ackerman) steering principle, so that according to the mapping relationship,the optimum steering wheel angle required for lane keeping is obtained. Fig. 6 is a schematic view of the ackermann steering principle, in which L is the wheel base, R is the turning radius of the vehicle,fis the corner of the front wheel. Under the condition that the vehicle G is in low-speed steering, the turning radius R of the vehicle is only equal to the front wheel steering anglefAnd the Ackerman principle is satisfied. For convenience of description, the four-wheel vehicle model is simplified into a two-wheel model, that is, the turning angles of the inner and outer wheels when the vehicle turns are considered to be equal, so that L, R is used as a basisfThe geometrical relationship of the three can be obtained:
Figure BDA0001998274180000081
further, front wheel steering anglefSteering wheel angle sigma and steering system gear ratio G of vehicleiThere is a mapping relationship between:
σ＝Gi*f(5)
further, the pre-line road curvature ρ is 1/R, so that the mapping relationship between the target steering wheel angle σ and the pre-line road curvature ρ can be obtained by combining the formula (4) and the formula (5):
σ＝arctan(L.ρ)*Gi(6)
therefore, the preview following theory is combined with the Ackerman steering principle, so that the target control quantity (namely the first target steering wheel angle) is compensated, and the nonlinear motion working condition of closed-loop response can be met.
In step S132, a second controller is provided, which inputs the lateral position deviation and outputs a second target steering wheel angle at which the lateral position deviation is 0.
In the embodiment of the present invention, the second controller may adopt a PID controller. When a PID controller is used, the PID controller is often required to be capable of self-tuning P, I, D parameters according to the change of a controlled object, so that a plurality of groups of P (proportional), I (integral) and D (differential) parameters need to be adjusted according to different vehicle speeds, and then corresponding parameters are obtained according to different lookup tables of the vehicle speeds. However, the parameters obtained by the table lookup are not continuous, and the vehicle speed is continuously varied, so that parameters that cannot be found in the table are always encountered.
In this regard, in a preferred embodiment, it is contemplated that the fuzzy logic control and the PID control are combined to design a fuzzy PID controller to eliminate the lateral position deviation. Fig. 7 is a schematic diagram of a fuzzy logic control flow, and as shown in fig. 7, a corresponding fuzzy logic control module can be divided into four components: an input quantity fuzzification interface, an output quantity defuzzification (clarification) interface, fuzzy reasoning and a knowledge base. In the embodiment of the invention, the input (vehicle speed) and the output (P, D parameters) of the fuzzy logic control module are fuzzified to obtain a fuzzy domain. And selecting a triangular function as a membership function of the input and output fuzzy subsets for mathematical expression and simple operation.
The fuzzy subset of inputs (vehicle speed) is:
[0，5]；[5，10]；[10，15]；[15，20]；[20，25]；[25，30]；[30，35]；[35，40]；[40，45]；[45，50]；[50，55]；[55，60]；[60，65]；[65，70]；[70，75]；[75，80]；[80，85]；[85，90]；[90，95]；[95，100]；[105，110]；[110，115]；[115，120]
the knowledge base comprises a database and a rule base, wherein P, I, D parameters are calibrated according to different vehicle speeds, and the fuzzy rules are in one-to-one correspondence with the knowledge base. The fuzzy solution calculation adopts a weighted average method, and according to the antecedent condition of each rule and the input fuzzy set, according to the determined membership degree kiAs a weight, the value z is represented for the back partiWeighted average is made and clear value z is output0The formula is as follows:
Figure BDA0001998274180000101
in the formula, ziFor the endpoint values of the fuzzy subset of output quantities, kiFor degree of membership, z, of input quantities in the fuzzy subset concerned0Is the exact value of the output. The fuzzy logic control module realizes the self-adaptive function of the PID parameters to the vehicle speed, and finally realizes the self-adaptive control.
In practical applications, the embodiment of the present invention that uses the second controller to make the lateral position deviation be 0 may specifically include, as shown in fig. 8:
1) PID control section
In step S1321, the current lateral position deviation is set to e (t).
Step S1323, performing PD operation on the deviation e (t):
y＝kp*e(t)+kd*de(t)/dt (8)
where kp is the proportional (P) parameter and kd is the derivative (D) parameter, which is obtained by means of modulus control, as will be described in detail in the fuzzy control section.
Step S1325, determining whether the lateral position deviation is e (t), if so, ending the process, otherwise, returning to the formula (8), and adjusting the target control amount y until the lateral position deviation is 0.
2) Fuzzy control part
The part mainly obtains the control coefficients kp and kd of the PID controller of the formula (8) through the real-time changing vehicle speed, and specifically comprises the following steps:
in step S1322, the vehicle speed v is input and is blurred.
With reference to the above, the vehicle speed is fuzzified to obtain a fuzzy domain.
And step S1324, calculating the membership degree of the input vehicle speed.
The main membership calculation formula is as follows:
lsd1＝(v-v(i))/(v(i+1)-v(i))
lsd2＝(v(i+1)-v)/(v(i+1)-v(i)) (9)
in step S1326, fuzzy inference is performed.
The specific reasoning formula is as follows:
kp (i) < kp < kp (i +1) and kd (i) < kd < kd (i +1), if v (i) < v < v (i +1) (10)
In step S1328, the blur is deblurred.
The specific deblurring formula is as follows:
kp＝(lsd1*kp(i)+lsd2*kp(i+1))/(lsd1+lsd2)
kd＝(lsd1*kd(i)+lsd2*kd(i+1))/(lsd1+lsd2) (11)
accordingly, the fuzzy control portion supplies the PID control portion with the control coefficients kp and kd adaptive to the vehicle speed, so that the PID control portion performs PD control of the lateral position deviation based on the determined control coefficients kp, kd, resulting in a control amount for making the lateral position deviation 0.
Step S133, a third controller is provided, which inputs the course angle deviation and outputs a third target steering wheel angle at which the course angle deviation is 0.
In an embodiment of the present invention, the third controller may adopt a PID controller. Since the vehicle is always expected to have the same direction as the lane line direction during the traveling of the vehicle, the target heading angle is 0 degree. Therefore, through PID control, the course angle deviation is used as input, and then PID operation is carried out on the course angle deviation to obtain the control quantity. In the embodiment of the invention, in order to enable the designed PID controller to achieve the effect of quick response, only P control can be adopted for course angle deviation, and an ideal P parameter is obtained through real vehicle test calibration and correction.
In practical applications, the embodiment of the present invention that uses the third controller to make the heading angle deviation be 0 may specifically include, as shown in fig. 9:
step S1331, calculating a current heading angle deviation.
Knowing that the target heading angle is 0 degrees and the current heading angle is denoted by HeadingAngle, the heading angle deviation e (t) is-HeadingAngle.
And step S1332, quickly checking a PID parameter table according to the actual vehicle.
The PID parameter table is obtained through actual vehicle test calibration and correction, and shows the optimal kp values corresponding to different vehicle speeds.
In step S1333, a P control operation is performed on the course angle deviation.
The operation formula may be represented as y ═ kp × e (t), y represents the control quantity, kp is the proportionality coefficient for P control, and kp is obtained through the query in step S1332.
And step S1334, judging whether the course angle deviation is e (t) is 0, if so, ending the process, otherwise, returning to the step S1333, and adjusting the target control quantity y until the course angle deviation is 0.
Therefore, the course angle deviation is controlled to be 0 through P control on the course angle deviation, and the PID controller can achieve the effect of quick response.
Step S134, determining a final target steering wheel angle according to the first target steering wheel angle, the second target steering wheel angle, and the third target steering wheel angle.
For example, the pre-aiming tracking control is performed on the curvature of the pre-aiming road to obtain a first target steering wheel angle y1, the PD calculation is performed on the lateral position deviation to obtain a second target steering wheel angle y2, and the P calculation is performed on the heading angle deviation to obtain a third target steering wheel angle y3, so that the final target control steering wheel angle may be represented as y1+ y2+ y 3.
And step S140, controlling the automatic driving vehicle to keep a lane according to the target steering wheel angle.
For example, a steering wheel angle command may be generated according to a target steering wheel angle, the steering wheel angle command may be sent to the steering wheel controller, and the steering wheel controller may receive the steering wheel angle command, analyze the corresponding target steering wheel angle, and adjust the angle and direction of rotation of the steering wheel accordingly, so that the autonomous vehicle may keep running on the center line of the current driving lane.
With reference to the above steps S110 to S140, the lane keeping control method according to the embodiment of the present invention can control the vehicle to keep safely driving on the center line of the current driving lane in the moving state according to the lateral position and the change of the heading angle extracted in real time, and is designed based on a "feed forward-feedback control algorithm", wherein the feed forward part relates to the pre-aiming tracking control for the pre-aiming curvature, the feedback part relates to the fuzzy PID controller for the lateral position deviation and the PID control for the heading angle deviation, the required control input variables are the current lateral decision state, the lane center line, the road curvature, the heading angle, the lateral position, the vehicle speed, and the like, and the output variables are the target steering wheel rotation angle changed in real time. And judging whether the vehicle really reaches a lane keeping state generally according to whether the current transverse position deviation and the current course angle deviation of the vehicle are 0, but when the transverse position deviation and the current course angle deviation are not 0, all parts eliminate the deviation according to a designed control algorithm and readjust the vehicle posture so that the current transverse position and the driving direction of the vehicle are consistent with the lane central line.
In conclusion, the lane keeping control method provided by the embodiment of the invention is verified through a large number of real vehicle tests, so that the control effect is good, and the robustness is strong; the running is stable, and the comfort is good; in addition, the vehicle can keep passing through roads such as straight roads and curves according to the current lane under the working condition of safe driving speed, and the requirements of the operation stability and the safety of the vehicle are met. Specifically, the lane keeping control method of the embodiment of the invention can have the following advantages:
1) the lane keeping control method provided by the embodiment of the invention is designed according to the algorithm of vehicle kinematics, fuzzy-PID and PID control are respectively applied to the transverse position deviation and the course angle deviation through preview tracking control, the real-time control requirement is met, the lane keeping control method has a good lane keeping effect, and the portability of the algorithm is very high after encapsulation.
2) The traditional lane keeping control method is generally designed based on the idea of track tracking, and high-precision positioning equipment needs to be installed on a vehicle, so that the hardware cost is additionally increased, and the lane keeping control method provided by the embodiment of the invention can realize the same function through lane information output by a high-definition camera, so that the system cost is greatly reduced.
3) Through the optimal pre-aiming road curvature, the preliminary grasping and utilization of the front road information can be realized in the feedforward control, the automatic driving vehicle can be enabled to perform corresponding actions in advance according to the first target steering wheel corner output by the first controller, the driving process is more comfortable and smooth, the driving condition of the driver on the road is better met, the requirement that the automatic driving vehicle can keep lanes under the road conditions of straight roads, curved roads and the like at the safe driving speed is met, the problems of over-steering and understeer caused by improper operation are avoided, and the driving stability and the driving safety of the curved roads are improved.
4) The fuzzy reasoning module for self-tuning P, I, D parameters is designed according to the change of the vehicle speed and is combined with PID control to realize self-adaptive PID control, and in addition, a mature control theory is applied, so that the feasibility is strong, the stability and the control robustness of the system are improved, and the operation efficiency is high.
Fig. 10 is a schematic configuration diagram of a lane keeping control device according to another embodiment of the present invention, which is based on the same inventive concept as the lane keeping control method described above. As shown in fig. 10, the lane keeping control apparatus may include: the first acquisition module 100 is used for acquiring course angle deviation between a current course angle and a target course angle of the automatic driving vehicle; a second obtaining module 200, configured to obtain a lateral position deviation between a current lateral position and a target lateral position of the autonomous vehicle; a third obtaining module 300, configured to obtain a pre-aimed road curvature of the autonomous vehicle; a determining module 400, configured to determine a target steering wheel angle according to the pre-aimed road curvature, the lateral position deviation, and the heading angle deviation, where the target steering wheel angle requires that the lateral position deviation and the heading angle deviation be 0, and the pre-aimed road curvature is made to reach an optimal road curvature that minimizes an error between an actual travel track and an expected track of the autonomous vehicle; and a control module 500 that controls the autonomous vehicle to perform lane keeping according to the target steering wheel angle.
In a preferred embodiment, the third obtaining module 300 includes: a lane line equation obtaining submodule for obtaining a current lane line equation of the autonomous vehicle; the pre-aiming distance acquisition submodule is used for acquiring the current pre-aiming distance of the automatic driving vehicle; and the pre-aiming curvature acquisition submodule is used for calculating the curvature of the pre-aiming road according to the current pre-aiming distance and the current lane line equation.
In a preferred embodiment, the determining module 400 may include: a first controller for inputting the pre-addressed road curvature and outputting a first target steering wheel angle at which the pre-addressed road curvature reaches the optimal road curvature; a second controller for inputting the lateral position deviation and outputting a second target steering wheel angle at which the lateral position deviation is 0; a third controller for inputting the course angle deviation and outputting a third target steering wheel angle at which the course angle deviation is 0; and a steering wheel angle determination submodule for determining a final target steering wheel angle according to the first target steering wheel angle, the second target steering wheel angle, and the third target steering wheel angle.
In a more preferred embodiment, in the first controller, a mapping relationship between a target steering wheel angle and a pre-line road curvature is established based on ackermann principle; and/or the second controller is a fuzzy PID controller, and the control coefficient of the fuzzy PID controller is determined according to the real-time speed of the automatic driving vehicle. It will be appreciated in connection with the above that the first controller may correspond to a predictive tracking controller as a feedforward controller, and the second and third controllers may correspond to a fuzzy PID controller and a conventional PID controller, respectively, as a feedback controller.
For other implementation details and effects of the embodiments of the present invention, reference may also be made to the foregoing embodiments of the lane keeping control method, which are not described herein again.
Another embodiment of the present invention also provides a machine-readable storage medium having stored thereon instructions for causing a machine to execute the lane-keeping control method described above. The machine-readable storage medium includes, but is not limited to, phase change Memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), Flash Memory (Flash Memory) or other Memory technologies, compact disc read only Memory (CD-ROM), Digital Versatile Disks (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, and the like, which can store program code.
Fig. 11 is a schematic structural diagram of a lane keeping control system according to another embodiment of the present invention, the lane keeping control system including: the collecting device 1110 is used for collecting lane line information; the machine-readable storage medium described above (not shown in the figures); and a processor 1120 configured to obtain the lane line information from the acquisition device and execute the instructions stored in the machine-readable storage medium in conjunction with the lane line information.
Wherein, collection system 1110 is the device that provides lane line information in real time for the vehicle under the lane keeping state, and it is for example the camera, and its mountable is in the middle of the windshield top, and the mounted position is adjustable, preferentially chooses the high definition digtal camera of Delfu, and the pixel is 1.2M, and detection distance can reach 150M, and frequency update is very fast simultaneously, can satisfy the real-time nature control problem. It should be noted that the camera may include a photosensitive system, an image sensor, an image processing system, and the like to accurately extract lane line information, and after acquiring the accurate lane line information, the lane line information may be provided to the processor 1120 to implement lane control, without installing a high-precision positioning device, so that the hardware cost is greatly reduced.
The machine-readable storage medium stores an algorithm program (instruction) for implementing the lane keeping control method, and the processor 1120 may control the vehicle to safely travel in the current lane according to the target travel track according to the lane line information provided by the collecting device 1110 in real time and the algorithm program downloaded to the machine-readable storage medium. The Processor 1120 may be an ECU (Electronic Control Unit) of the vehicle, or may be a conventional controller configured independently, such as a CPU, a single Chip, a DSP (Digital Signal Processor), an SOC (System On a Chip), and the like, and it is understood that these independent controllers may also be integrated into the ECU. The processor 1120 is preferably configured by a controller having a high operation speed and abundant I/O devices, and requires an input/output port capable of communicating with the CAN of the entire vehicle, an input/output port for switching signals, a network interface, and the like.
In a preferred embodiment, the lane keeping control system may further include a vehicle speed related system such as an ABS (Antilock brake system), an EPS (Electric Power Steering) and the like capable of providing vehicle speed information, so as to obtain the vehicle speed information from the vehicle related system for the parameter tuning of the fuzzy PID controller mentioned above. It should be noted that the vehicle speed related system may not be included in the lane keeping control system, but may communicate with the lane keeping control system through a CAN communication method to obtain the vehicle speed information.
Fig. 12 is a communication diagram of a lane keeping control system according to an embodiment of the present invention, where the lane keeping control system includes three parts, namely, a high-definition camera (corresponding to the above-mentioned acquisition device 1110), an ECU (corresponding to the above-mentioned processor 1120), and a vehicle speed correlation system. The high-definition camera is used for providing lane line information, including lane line types, lane line widths, lane line reliability and the like. The high-definition camera CAN output lane line information to the ECU for processing in a CAN communication mode. The ECU adopts a CPU configuration and is provided with readable storage media such as ROM, RAM, Flash Memory and the like, and the readable storage media store algorithm programs related to the lane keeping method; the vehicle speed related systems are ABS and EPS, wherein ABS CAN communicate with ECU through AD-CAN communication mode, EPS CAN communicate with ECU through PT-CAN communication mode. Therefore, all parts of the lane keeping control system are communicated and interacted by various CAN, and the lane line change is responded in real time according to the control signal output by the ECU and the vehicle speed information, so that the whole automatic driving system forms closed-loop control, the vehicle posture is adjusted in real time, and the current transverse position and the driving direction of the vehicle are kept consistent with the lane center line.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.

Claims (10)

1. A lane keep control method characterized by comprising:
acquiring course angle deviation between a current course angle and a target course angle of an automatic driving vehicle and transverse position deviation between a current transverse position and a target transverse position of the automatic driving vehicle;
acquiring the curvature of a pre-aiming road of an automatic driving vehicle;
determining a target steering wheel corner according to the pre-aiming road curvature, the transverse position deviation and the course angle deviation, wherein the target steering wheel corner requires that the transverse position deviation and the course angle deviation can be 0, and the pre-aiming road curvature is enabled to reach an optimal road curvature which enables the error between the actual running track and the expected track of the automatic driving vehicle to be minimum; and
and controlling the automatic driving vehicle to keep a lane according to the target steering wheel angle.
2. The lane-keeping control method of claim 1, wherein the obtaining a projected road curvature of the autonomous vehicle comprises:
obtaining a current lane line equation of the autonomous vehicle;
acquiring a current pre-aiming distance of the automatic driving vehicle; and
and calculating the curvature of the pre-aimed road according to the current pre-aimed distance and the current lane line equation.
3. The lane-keeping control method of claim 1, wherein said determining a target steering wheel angle based on the pre-line road curvature, the lateral position deviation, and the heading angle deviation comprises:
setting a first controller, wherein the first controller inputs the curvature of the pre-aimed road and outputs a first target steering wheel corner which enables the curvature of the pre-aimed road to reach the optimal curvature of the road;
setting a second controller that inputs the lateral position deviation and outputs a second target steering wheel angle at which the lateral position deviation is 0;
setting a third controller, wherein the third controller inputs the course angle deviation and outputs a third target steering wheel rotating angle which enables the course angle deviation to be 0; and
and determining a final target steering wheel angle according to the first target steering wheel angle, the second target steering wheel angle and the third target steering wheel angle.
4. The lane keep control method according to claim 3, characterized by further comprising: in the first controller, a mapping relationship between the target steering wheel angle and the pre-line road curvature is established based on ackermann steering principle.
5. The lane-keeping control method of claim 3, wherein the second controller is a fuzzy PID controller, and the control coefficients of the fuzzy PID controller are determined according to a real-time vehicle speed of the autonomous vehicle.
6. A lane keep control apparatus characterized by comprising:
the first acquisition module is used for acquiring course angle deviation between the current course angle and a target course angle of the automatic driving vehicle;
a second obtaining module for obtaining a lateral position deviation between a current lateral position and a target lateral position of the autonomous vehicle;
the third acquisition module is used for acquiring the curvature of the pre-aiming road of the automatic driving vehicle;
a determining module, configured to determine a target steering wheel angle according to the pre-aimed road curvature, the lateral position deviation, and the heading angle deviation, where the target steering wheel angle requires that the lateral position deviation and the heading angle deviation be 0, and the pre-aimed road curvature is made to reach an optimal road curvature that minimizes an error between an actual travel trajectory and an expected trajectory of the autonomous vehicle; and
and the control module controls the automatic driving vehicle to keep a lane according to the target steering wheel angle.
7. The lane keep control apparatus of claim 6, wherein the third obtaining means comprises:
a lane line equation obtaining submodule for obtaining a current lane line equation of the autonomous vehicle;
the pre-aiming distance acquisition submodule is used for acquiring the current pre-aiming distance of the automatic driving vehicle; and
and the pre-aiming curvature acquisition submodule is used for calculating the curvature of the pre-aimed road according to the current pre-aiming distance and the current lane line equation.
8. The lane keep control apparatus of claim 6, wherein the determining module comprises:
a first controller for inputting the pre-addressed road curvature and outputting a first target steering wheel angle at which the pre-addressed road curvature reaches the optimal road curvature;
a second controller for inputting the lateral position deviation and outputting a second target steering wheel angle at which the lateral position deviation is 0;
a third controller for inputting the course angle deviation and outputting a third target steering wheel angle at which the course angle deviation is 0; and
the steering wheel corner determining submodule is used for determining a final target steering wheel corner according to the first target steering wheel corner, the second target steering wheel corner and the third target steering wheel corner;
preferably, in the first controller, a mapping relation between a target steering wheel corner and a pre-aiming road curvature is established based on the ackermann principle;
preferably, the second controller is a fuzzy PID controller, and the control coefficient of the fuzzy PID controller is determined according to the real-time speed of the automatic driving vehicle.
9. A machine-readable storage medium having stored thereon instructions for causing a machine to perform the lane keep control method of any of claims 1 to 5.
10. A lane keep control system, characterized by comprising:
the acquisition device is used for acquiring lane line information;
the machine-readable storage medium recited in claim 9; and
a processor configured to obtain the lane line information from the acquisition device and execute the instructions stored in the machine-readable storage medium in combination with the lane line information.
CN201910203638.8A 2019-03-18 2019-03-18 Lane keeping control method, device and system Active CN111717189B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201910203638.8A CN111717189B (en) 2019-03-18 2019-03-18 Lane keeping control method, device and system

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201910203638.8A CN111717189B (en) 2019-03-18 2019-03-18 Lane keeping control method, device and system

Publications (2)

Publication Number Publication Date
CN111717189A true CN111717189A (en) 2020-09-29
CN111717189B CN111717189B (en) 2022-03-29

Family

ID=72563101

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201910203638.8A Active CN111717189B (en) 2019-03-18 2019-03-18 Lane keeping control method, device and system

Country Status (1)

Country Link
CN (1) CN111717189B (en)

Cited By (34)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112356828A (en) * 2020-11-06 2021-02-12 广州小鹏自动驾驶科技有限公司 Vehicle transverse control method and device, vehicle and readable storage medium
CN112356844A (en) * 2020-11-19 2021-02-12 苏州智加科技有限公司 Method, device and equipment for controlling vehicle driving direction
CN112373316A (en) * 2020-11-27 2021-02-19 深兰人工智能(深圳)有限公司 Vehicle and vehicle speed control method and device thereof
CN112440997A (en) * 2020-10-29 2021-03-05 武汉光庭科技有限公司 Curve lane keeping method and system based on preview algorithm, server and medium
CN112477847A (en) * 2020-12-11 2021-03-12 清华大学苏州汽车研究院（吴江） Traffic jam auxiliary control method and system
CN112537303A (en) * 2020-12-14 2021-03-23 英博超算(南京)科技有限公司 Intelligent vehicle lane centering keeping method
CN112622899A (en) * 2021-01-18 2021-04-09 中国重汽集团济南动力有限公司 Vehicle lane keeping method and system based on preview area control
CN112677991A (en) * 2020-12-11 2021-04-20 武汉格罗夫氢能汽车有限公司 Hydrogen energy automobile lane departure prevention device
CN112799055A (en) * 2020-12-28 2021-05-14 深圳承泰科技有限公司 Method and device for detecting detected vehicle and electronic equipment
CN112837554A (en) * 2021-03-09 2021-05-25 济南大学 AGV positioning navigation method and system based on binocular camera
CN112874536A (en) * 2021-01-19 2021-06-01 英博超算(南京)科技有限公司 Intelligent vehicle deflector rod lane changing method
CN112959994A (en) * 2021-05-18 2021-06-15 天津所托瑞安汽车科技有限公司 Path following algorithm, device, equipment and medium
CN112977444A (en) * 2021-02-24 2021-06-18 武汉光庭信息技术股份有限公司 Lane keeping advanced auxiliary driving control method and system and electronic equipment
CN113002620A (en) * 2021-03-12 2021-06-22 重庆长安汽车股份有限公司 Method and system for correcting angle deviation of automatic driving steering wheel and vehicle
CN113264050A (en) * 2021-06-24 2021-08-17 三一专用汽车有限责任公司 Vehicle control method, lane switching system and vehicle
CN113276836A (en) * 2021-05-31 2021-08-20 爱驰汽车有限公司 Vehicle transverse control method and device, computer equipment and storage medium
CN113311698A (en) * 2021-05-26 2021-08-27 三一专用汽车有限责任公司 Lane keeping control method, control device and vehicle
CN113415276A (en) * 2021-07-30 2021-09-21 东风商用车有限公司 Intelligent driving pre-aiming control method and device and storage medium
CN113525384A (en) * 2021-09-13 2021-10-22 国汽智控(北京)科技有限公司 Lateral control method and controller for vehicle
CN113665587A (en) * 2021-08-24 2021-11-19 东风柳州汽车有限公司 Lateral control method, device, storage medium, and apparatus for autonomous vehicle
CN113696890A (en) * 2021-09-23 2021-11-26 中国第一汽车股份有限公司 Lane keeping method, apparatus, device, medium, and system
CN113778072A (en) * 2020-10-26 2021-12-10 北京京东乾石科技有限公司 Mobile robot control method, mobile robot control device, storage medium, and mobile robot
CN113815646A (en) * 2021-09-14 2021-12-21 上汽通用五菱汽车股份有限公司 Intelligent driving method of vehicle, vehicle and readable storage medium
CN113954831A (en) * 2021-11-26 2022-01-21 阿波罗智能技术(北京)有限公司 Vehicle transverse control method and device and automatic driving vehicle
CN114019962A (en) * 2021-10-26 2022-02-08 三一专用汽车有限责任公司 Vehicle lane change control method and device and vehicle
CN114148320A (en) * 2021-12-08 2022-03-08 华人运通(上海)自动驾驶科技有限公司 Path tracking control method and device
CN114523978A (en) * 2020-11-03 2022-05-24 上海汽车集团股份有限公司 Method and device for generating rear road model
CN114852171A (en) * 2022-04-25 2022-08-05 上海仙途智能科技有限公司 Vehicle and steering control method and device thereof, storage medium and terminal
CN115166743A (en) * 2022-08-30 2022-10-11 长沙隼眼软件科技有限公司 Lane automatic calibration method and device, electronic equipment and storage medium
CN115223131A (en) * 2021-11-09 2022-10-21 广州汽车集团股份有限公司 Adaptive cruise following target vehicle detection method and device and automobile
CN116118751A (en) * 2023-04-19 2023-05-16 深圳佑驾创新科技有限公司 Control method and device for vehicle, vehicle and storage medium
CN116513175A (en) * 2023-07-03 2023-08-01 北京斯年智驾科技有限公司 Correction method, device, equipment and medium for driving deviation in automatic driving
CN116572972A (en) * 2023-07-03 2023-08-11 中国第一汽车股份有限公司 Transverse control method and device of vehicle, electronic equipment and storage medium
WO2023241050A1 (en) * 2022-06-17 2023-12-21 上海华兴数字科技有限公司 Vehicle lateral control method and system, and vehicle

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20150061781A (en) * 2013-11-28 2015-06-05 현대모비스 주식회사 Method for controlling cornering of vehicle and apparatus thereof
CN107097785A (en) * 2017-05-25 2017-08-29 江苏大学 A kind of adaptive intelligent vehicle crosswise joint method of preview distance
CN107323450A (en) * 2017-06-08 2017-11-07 广州汽车集团股份有限公司 The control method and device of vehicle lane change, storage medium
CN108791289A (en) * 2018-04-28 2018-11-13 华为技术有限公司 A kind of control method for vehicle and device
CN109131325A (en) * 2018-08-15 2019-01-04 江苏大学 The three-dimensional of intelligent driving automobile can open up the pre- lane for taking aim at switching and keep control method

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20150061781A (en) * 2013-11-28 2015-06-05 현대모비스 주식회사 Method for controlling cornering of vehicle and apparatus thereof
CN107097785A (en) * 2017-05-25 2017-08-29 江苏大学 A kind of adaptive intelligent vehicle crosswise joint method of preview distance
CN107323450A (en) * 2017-06-08 2017-11-07 广州汽车集团股份有限公司 The control method and device of vehicle lane change, storage medium
CN108791289A (en) * 2018-04-28 2018-11-13 华为技术有限公司 A kind of control method for vehicle and device
CN109131325A (en) * 2018-08-15 2019-01-04 江苏大学 The three-dimensional of intelligent driving automobile can open up the pre- lane for taking aim at switching and keep control method

Cited By (45)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113778072A (en) * 2020-10-26 2021-12-10 北京京东乾石科技有限公司 Mobile robot control method, mobile robot control device, storage medium, and mobile robot
CN112440997A (en) * 2020-10-29 2021-03-05 武汉光庭科技有限公司 Curve lane keeping method and system based on preview algorithm, server and medium
CN114523978B (en) * 2020-11-03 2024-01-16 上海汽车集团股份有限公司 Rear road model generation method and device
CN114523978A (en) * 2020-11-03 2022-05-24 上海汽车集团股份有限公司 Method and device for generating rear road model
CN112356828A (en) * 2020-11-06 2021-02-12 广州小鹏自动驾驶科技有限公司 Vehicle transverse control method and device, vehicle and readable storage medium
CN112356844A (en) * 2020-11-19 2021-02-12 苏州智加科技有限公司 Method, device and equipment for controlling vehicle driving direction
CN112373316A (en) * 2020-11-27 2021-02-19 深兰人工智能(深圳)有限公司 Vehicle and vehicle speed control method and device thereof
CN112677991A (en) * 2020-12-11 2021-04-20 武汉格罗夫氢能汽车有限公司 Hydrogen energy automobile lane departure prevention device
CN112477847A (en) * 2020-12-11 2021-03-12 清华大学苏州汽车研究院（吴江） Traffic jam auxiliary control method and system
CN112677991B (en) * 2020-12-11 2022-06-07 武汉格罗夫氢能汽车有限公司 Hydrogen energy automobile lane departure prevention device
CN112537303A (en) * 2020-12-14 2021-03-23 英博超算(南京)科技有限公司 Intelligent vehicle lane centering keeping method
CN112799055A (en) * 2020-12-28 2021-05-14 深圳承泰科技有限公司 Method and device for detecting detected vehicle and electronic equipment
CN112622899A (en) * 2021-01-18 2021-04-09 中国重汽集团济南动力有限公司 Vehicle lane keeping method and system based on preview area control
CN112622899B (en) * 2021-01-18 2022-04-01 中国重汽集团济南动力有限公司 Vehicle lane keeping method and system based on preview area control
CN112874536A (en) * 2021-01-19 2021-06-01 英博超算(南京)科技有限公司 Intelligent vehicle deflector rod lane changing method
CN112874536B (en) * 2021-01-19 2023-09-12 英博超算(南京)科技有限公司 Intelligent vehicle deflector rod track changing method
CN112977444A (en) * 2021-02-24 2021-06-18 武汉光庭信息技术股份有限公司 Lane keeping advanced auxiliary driving control method and system and electronic equipment
CN112977444B (en) * 2021-02-24 2022-03-08 武汉光庭信息技术股份有限公司 Lane keeping advanced auxiliary driving control method and system and electronic equipment
CN112837554A (en) * 2021-03-09 2021-05-25 济南大学 AGV positioning navigation method and system based on binocular camera
CN113002620A (en) * 2021-03-12 2021-06-22 重庆长安汽车股份有限公司 Method and system for correcting angle deviation of automatic driving steering wheel and vehicle
CN112959994A (en) * 2021-05-18 2021-06-15 天津所托瑞安汽车科技有限公司 Path following algorithm, device, equipment and medium
CN113311698A (en) * 2021-05-26 2021-08-27 三一专用汽车有限责任公司 Lane keeping control method, control device and vehicle
CN113276836A (en) * 2021-05-31 2021-08-20 爱驰汽车有限公司 Vehicle transverse control method and device, computer equipment and storage medium
CN113264050A (en) * 2021-06-24 2021-08-17 三一专用汽车有限责任公司 Vehicle control method, lane switching system and vehicle
CN113415276A (en) * 2021-07-30 2021-09-21 东风商用车有限公司 Intelligent driving pre-aiming control method and device and storage medium
CN113665587A (en) * 2021-08-24 2021-11-19 东风柳州汽车有限公司 Lateral control method, device, storage medium, and apparatus for autonomous vehicle
CN113525384B (en) * 2021-09-13 2022-04-19 国汽智控(北京)科技有限公司 Lateral control method and controller for vehicle
CN113525384A (en) * 2021-09-13 2021-10-22 国汽智控(北京)科技有限公司 Lateral control method and controller for vehicle
CN113815646A (en) * 2021-09-14 2021-12-21 上汽通用五菱汽车股份有限公司 Intelligent driving method of vehicle, vehicle and readable storage medium
CN113696890A (en) * 2021-09-23 2021-11-26 中国第一汽车股份有限公司 Lane keeping method, apparatus, device, medium, and system
CN114019962A (en) * 2021-10-26 2022-02-08 三一专用汽车有限责任公司 Vehicle lane change control method and device and vehicle
CN115223131A (en) * 2021-11-09 2022-10-21 广州汽车集团股份有限公司 Adaptive cruise following target vehicle detection method and device and automobile
CN113954831B (en) * 2021-11-26 2023-09-19 阿波罗智能技术(北京)有限公司 Vehicle transverse control method and device and automatic driving vehicle
CN113954831A (en) * 2021-11-26 2022-01-21 阿波罗智能技术(北京)有限公司 Vehicle transverse control method and device and automatic driving vehicle
CN114148320A (en) * 2021-12-08 2022-03-08 华人运通(上海)自动驾驶科技有限公司 Path tracking control method and device
CN114148320B (en) * 2021-12-08 2023-10-20 华人运通(上海)自动驾驶科技有限公司 Path tracking control method and equipment
CN114852171B (en) * 2022-04-25 2023-08-15 上海仙途智能科技有限公司 Vehicle, steering control method and device thereof, storage medium and terminal
CN114852171A (en) * 2022-04-25 2022-08-05 上海仙途智能科技有限公司 Vehicle and steering control method and device thereof, storage medium and terminal
WO2023241050A1 (en) * 2022-06-17 2023-12-21 上海华兴数字科技有限公司 Vehicle lateral control method and system, and vehicle
CN115166743A (en) * 2022-08-30 2022-10-11 长沙隼眼软件科技有限公司 Lane automatic calibration method and device, electronic equipment and storage medium
CN116118751B (en) * 2023-04-19 2023-08-29 深圳佑驾创新科技有限公司 Control method and device for vehicle, vehicle and storage medium
CN116118751A (en) * 2023-04-19 2023-05-16 深圳佑驾创新科技有限公司 Control method and device for vehicle, vehicle and storage medium
CN116572972A (en) * 2023-07-03 2023-08-11 中国第一汽车股份有限公司 Transverse control method and device of vehicle, electronic equipment and storage medium
CN116513175A (en) * 2023-07-03 2023-08-01 北京斯年智驾科技有限公司 Correction method, device, equipment and medium for driving deviation in automatic driving
CN116513175B (en) * 2023-07-03 2023-09-22 北京斯年智驾科技有限公司 Correction method, device, equipment and medium for driving deviation in automatic driving

Also Published As

Publication number Publication date
CN111717189B (en) 2022-03-29

Similar Documents

Publication Publication Date Title
CN111717189B (en) Lane keeping control method, device and system
CN111717204B (en) Lateral control method and system for automatic driving vehicle
EP3932761A1 (en) Vehicle abnormal lane change control method, device and system
CN111717192B (en) Control method and system for automatically driving vehicle
CN110481541B (en) Safe and stable control method for automobile tire burst
CN111016893B (en) Intelligent vehicle extensible game lane keeping self-adaptive cruise control system and control method under congestion environment
CN110262509A (en) Vehicular automatic driving method and apparatus
JP2000302055A (en) Traffic lane followup control device
CN105774905A (en) Collision avoidance control integrated with electric power steering controller and rear steer
CN113619574A (en) Vehicle avoidance method and device, computer equipment and storage medium
CN112238856B (en) Intelligent vehicle overtaking track optimization method based on hybrid particle swarm optimization
JP2001134320A (en) Lane follow-up controller
CN111959506A (en) Vehicle and control method and device for vehicle formation driving
CN115447615A (en) Trajectory optimization method based on vehicle kinematics model predictive control
Zhang et al. Adaptive backstepping fuzzy lateral motion control approach for autonomous vehicles
Jeong Path Tracking Control for Four-Wheel-Steering Autonomous Vehicles based on Adaptive Sliding Mode Control with Control Allocation
CN116080754B (en) Transverse control method for autonomous driving of vehicle
CN110723200A (en) Steering centering and intermediate position control system and control method thereof
JP7430214B2 (en) control calculation device
Jian et al. An Optimal Controller for Trajectory Tracking of Automated Guided Vehicle
CN115837912B (en) Track tracking-based instruction lane changing method and system
Li et al. Study on lateral assisted control for commercial vehicles
CN116560371A (en) Self-adaptive model predictive control-based automatic driving vehicle path tracking method
Liu et al. Optimization Design of Vehicle Path Tracking Controller Based on High Accuracy Positioning
Tang et al. Tracking Control for Autonomous Four-Wheel Independently Driven Vehicle Based on Deep Reinforcement Learning

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
TA01 Transfer of patent application right

Effective date of registration: 20210521

Address after: 100055 1802, 18 / F, building 3, yard 9, Guang'an Road, Fengtai District, Beijing

Applicant after: Momo Zhixing Technology Co.,Ltd.

Address before: 071000 No. 2266 Chaoyang South Street, Hebei, Baoding

Applicant before: Great Wall Motor Co.,Ltd.

TA01 Transfer of patent application right
GR01 Patent grant
GR01 Patent grant