CN108732921B - Transverse extension preview switching control method for automatic driving automobile - Google Patents
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Abstract
The invention discloses a transverse extension preview switching control method of an automatic driving automobile, which consists of an upper layer controller and a lower layer controller, wherein in the upper layer controller, the transverse position deviation of a vehicle and a road center line and a front road curvature value are selected as characteristic values of an extension set to divide the extension set, a correlation function is solved, and the vehicle-road system state is divided into a classical domain, an extension domain and a non-domain; in a lower layer controller, a PD feedback controller based on transverse position deviation and course deviation is adopted in a classical domain, a PD feedforward-feedback controller based on front road curvature is adopted in an extension domain, a vehicle-road system in a non-domain is in an out-of-control state, emergency braking is adopted, and switching control of two control strategies in the classical domain and the extension domain is realized according to different states of the vehicle-road system. The invention successfully applies the extension control theory to the field of transverse control of the automatic driving automobile and meets the requirement of the transverse control precision of the automobile.
Description
Technical Field
The invention belongs to the technical field of automatic driving automobile control systems, and particularly relates to a transverse extension preview switching control method of an automatic driving automobile based on an extension control theory.
Background
The intelligent vehicle is taken as a measure for mainly solving the problems of road congestion and road traffic safety at present, and can improve the vehicle economy and reduce the environmental pollution. The intelligent vehicle development and production is turned to in the research and development centers of all universities and universities in the world, and mainly relates to the field of vehicle transverse control such as vehicle path tracking, lane keeping and vehicle lane changing.
The vehicle transverse control means that the transverse position deviation and the heading deviation are enabled to be as small as possible through algorithm decision and the action of a bottom layer execution device during the process that the vehicle runs along the expected path, and meanwhile, the vehicle has certain stability and running safety. The currently used transverse control algorithm mainly comprises feedback control, feedforward-feedback control, fuzzy control, a sliding mode control strategy, a single-point preview strategy, model prediction control, optimal control and the like. However, the above control method has many limitations, or the control effect is better under a specific working condition, and the overall control effect is not good under a mixed complex working condition.
The vehicle control is a multi-input multi-output system, and the problem of road environment where the vehicle is located needs to be considered in the vehicle lateral control, so that the control method has many limitations. The method mainly aims at the problem that a single control strategy is limited in control area, the generated extension control can describe the mutual transformation and quantitative change and qualitative change processes of things ' yes ' and ' no ' based on an extension theory proposed by Chua's, the extension control is applied to stability control and used for describing the relation between instability and stability, the stability control strategy is established according to the process, different control strategies are adopted in a classical domain and an extension domain respectively, different control strategies are adopted in a targeted mode according to state characteristics of a control object, and the transverse extension preview switching control method of the automatic driving automobile is designed, so that the whole control process can obtain good control effects.
Disclosure of Invention
The vehicle-road system state is divided into three regions according to the state of the vehicle in the road: classical domain, extended domain, non-domain. The curvature of the road where the vehicle is located in the classical domain is small, the transverse position deviation between the vehicle and the target track is small, and at the moment, the whole vehicle-road system is stable and only needs simple feedback control; when the curvature of the road at the front side is increased and a small-radius curve occurs, the transverse position deviation between the vehicle and the central line of the road is increased, the whole vehicle-road system is in a destabilization state, the response speed of the corner of the front wheel needs to be accelerated and the steady state value of the corner of the front wheel needs to be increased, the vehicle-road system is in an extension domain, a PD feedforward-feedback control strategy is adopted, the time lag of pure PD feedback control is corrected by utilizing feedforward control, the response speed is accelerated, and a satisfactory control effect is achieved.
The technical scheme of the invention is as follows:
a transverse extension preview switching control method for an automatic driving automobile comprises two parts: the system comprises an upper layer controller and a lower layer controller, wherein the upper layer controller is an extension controller, the lower layer is divided into an extension domain control strategy and a classical domain control strategy according to an extension set, the classical domain adopts a PD-feedback control strategy, and the extension domain adopts a PD feedforward-feedback control strategy, and the system specifically comprises the following steps:
an upper layer controller: build up a lateral position deviation c of the vehicle from the road center linepDetermining a correlation function K (S) for a two-dimensional state space with an X axis and a road center line curvature rho as a Y axis according to an extension distance principle, and dividing the state of the vehicle-road system into a classical domain, an extension domain and a non-domain according to a correlation function value; in a lower layer controller, a PD feedback controller based on a preview error is designed in a classical domain, a PD feedforward-feedback controller based on a front road curvature is designed in an extension domain, and an emergency braking control is adopted when a vehicle-road system in a non-domain is in an out-of-control state; and establishing a vehicle-road state space equation according to the vehicle two-degree-of-freedom dynamic model and the path tracking preview deviation model, and verifying the effectiveness of the control method as a control object.
Further, the lateral position deviation epThe extension set characteristic quantity (e) of the vehicle-road model is formed by the curvature rho of the center line of the roadp，ρ)。
Further, the interval of the classical domain is: lateral position deviation ep[-0.2，0.2]Road center line curvature p 0.02, 0.02]The extension region interval is as follows: lateral position deviation ep[-10，-0.2)∪(0.2，10]Road center line curvature ρ [ -0.12, -0.02 [ -0.02, 0.12 ] U]。
Further, the calculation of the correlation function value requires that the extension distances in the two-dimensional extension set are converted into one-dimensional extension distances.
Further, the correlation function is:
wherein D (P)3，<P5，P2>，<P4，P1>)＝ρ(P3，<P5，P2>)-ρ(P3，<P4，P1>)，ρ(P3，<P4，P1>) And ρ (P)3，<P5，P2>) Are respectively a point P3Extension to classical and extension domains, P3Characteristic quantity (e) of vehicle-road model collected during vehicle drivingpρ) real-time state value, P3Connecting with the origin to intersect the boundary of the classical domain at point P1、P4Intersecting the extension domain boundary at point P2、P5。
Further, the vehicle-road state space equation is:
wherein
Is the course deviation, delta, of the vehicle from the road centerlinefIs the turning angle of the front wheels of the vehicle,
further, the specific process of dividing the vehicle-road system state into a classical domain, an extension domain and a non-domain is as follows: selecting characteristic quantity (e)pRho) as input of extension controller, establishing two-dimensional extension set, and according to real-time state value P3And converting the two-dimensional extension set into a one-dimensional extension set according to the position of the two-dimensional extension set, calculating a value of a correlation function K (S), and determining the area of the current vehicle-road system according to the value of the correlation function.
The invention has the beneficial effects that:
(1) the extension control processes the control problem from the information conversion angle, takes the degree of association K (S) of control input information as the basis for determining the selection of the control output control strategy, and can enable the unmanned vehicle to transversely control the object: the state quantity of the vehicle-road system is converted from the uncontrollable area to the stable controllable area, so that the restriction of the factors such as vehicle dynamics-road model complexity or limited use condition of the control method is eliminated, and the capabilities of transverse control and track tracking control of the unmanned vehicle are improved.
(2) According to the invention, by the principle that the extension controller divides the state area, the characteristics of simple control, good stability in a linear area and strong robustness of PD-feedback control in a classical area can be fully utilized, meanwhile, in the extension area, the vehicle-road system area is unstable, the road curvature is increased, and the time lag problem and the corresponding slow speed problem of PD feedback control in the unstable area are solved by feedforward correction, so that the response speed of the control system to the small-curvature path interference is improved, and the hysteresis and the volatility of vehicle-direction control are also reduced.
(3) According to the invention, by means of extension control, the unmanned vehicle can realize switching control between two control strategies under the complex mixed working condition that the unmanned vehicle passes through a road with small curvature and a curve with large curvature and large corner, so that the whole control process obtains smaller control error.
Drawings
FIG. 1 is a flow chart of a lateral extension preview switching control method for an autonomous vehicle;
FIG. 2 is a two degree of freedom vehicle dynamics model diagram;
FIG. 3 is a diagram of a lateral preview deviation model;
FIG. 4 is a model diagram of course deviation;
FIG. 5 is an extension set diagram;
FIG. 6 is a two-dimensional extension set diagram;
FIG. 7 is a diagram of a set of one-dimensional extensions;
FIG. 8 is a flowchart of PD feedback control based on the preview deviation;
FIG. 9 is a diagram of a PD controller based on a preview deviation;
FIG. 10 is a flow chart of a feedforward-feedback PD controller control based on road curvature;
FIG. 11 is a diagram of a simulation platform for the extended preview lateral coordination control Simulink/Carsim;
FIG. 12 is a graph of a change in curvature of a center line of a roadway;
FIG. 13 is a diagram of vehicle trajectory tracking effects;
fig. 14 is a lateral deviation diagram.
Detailed Description
The invention will be further described in the following with reference to the description of the figures and the embodiments, without limiting the scope of the invention thereto.
FIG. 1 is a control flow chart of the lateral extension preview switching control method of the automatic driving automobile, wherein epIs a lateral deviation,
Is heading deviation, rho is road centerline curvature, deltafFor the vehicle front wheel turning angle, the control method of the present invention will be designed based on this control flowchart.
The upper controller is an expandable controller and mainly aims to acquire the transverse position deviation e of the vehicle and the central line of the road acquired by the vehicle sensorpAnd road centerline curvature ρ. According to the transverse deviation value e of the current vehicle and the road center linepThe curvature value ρ with the center line of the road divides the state in which the vehicle lateral control is located into three regions: classical domain, extended domain, non-domain. Different control strategies are adopted for different vehicle transverse control states respectively, and therefore the accurate control effect and tracking performance are achieved.
The lower layer controller: in the classical domain, the curvature of the road is small, and the transverse control implementation only needs to track the expected track under the condition of small turning angles, and at the moment, the main solution is to reduce the transverse position deviation and the transverse deviation to the minimum. And the vehicle running process is relatively stable, the instability phenomenon cannot occur, the control requirement is low, and the control target is easy to achieve. Therefore, the PD feedback control based on the preview deviation may be adopted. When the vehicle is in an extension area, the turning radius of the vehicle is small, the curvature of a road is large, the vehicle needs to respond quickly, and the vehicle can be ensured to turn wheels to a required turning angle in time. The problems of large tracking steady-state error and slow response time can occur by adopting the original PD feedback control based on the preview deviation. Therefore, under the working condition, feedforward control based on the curvature of the front road is added and restrained, so that the front wheel steering angle of the vehicle can quickly respond, the response speed of the control system to the small-curvature path disturbance is improved, and meanwhile, the hysteresis and the fluctuation of the vehicle steering control are also reduced. In the non-domain, the vehicle-road system is in an unstable state and cannot accurately realize control, and emergency braking measures are taken.
The present embodiment employs a two degree of freedom vehicle dynamics model that considers only lateral motion along the vehicle y-axis and yaw motion about the z-axis, and assumes the following:
(1) neglecting the air resistance suffered by the automobile;
(2) assuming that the road surface on which the automobile runs is horizontal, the road resistance of the ground to the automobile is 0;
(3) assuming that the steering angles of two front wheels and two rear wheels of the automobile are respectively equal and the camber angle of the wheels is 0;
(4) neglecting the action of the steering system in the steering process of the vehicle, directly taking the wheel rotation angle of the front wheel as the control input of the system;
(5) assuming that the suspension of the automobile is rigid, the body does not have up-and-down motion perpendicular to the ground, front-and-back pitching and body rolling, and the motion of the body is parallel to the ground;
(6) neglecting the left and right offset of the vehicle mass during the turning, the supporting forces of the ground in the vertical direction received by the left and right wheels are considered to be equal.
Fig. 2 is a schematic diagram of a two-degree-of-freedom dynamic model of a vehicle. The resultant force sigma F along the y axis can be obtained according to the Newton's second law theoremy，iEquilibrium equation and resultant moment sigma M around z-axiszThe equilibrium equation:
wherein m is the vehicle mass (kg); v. ofx、vyRespectively the longitudinal speed and the transverse speed of the vehicle(m·s-1)；
Is the vehicle heading angle (rad); deltafIs the vehicle front wheel corner (rad); i iszIs the moment of inertia (kg · m) of the vehicle around the z-axis2) (ii) a a. b is the distance (m) between the vehicle mass center and the front axle and the rear axle of the vehicle respectively; k is a radical of1、k2Cornering stiffness (N/rad) of front and rear wheel tires, respectively; omegarVehicle yaw rate (rad/s).
In the track tracking process of the vehicle, the motion process comprises the translational motion and the rotational motion of the vehicle, and the path tracking preview deviation model comprises a transverse preview deviation model and a heading deviation model, which are respectively shown in fig. 3 and fig. 4.
In fig. 3, A, B are two front and rear end points of the vehicle, point C is the center of mass of the vehicle, point D is the preview point, points a and b are the distance (m) between the center of mass of the vehicle and the front axle and the distance (m) between the center of mass of the vehicle and the rear axle of the vehicle, point L is the preview distance (m), and point e is the preview distance (m)pIs the transverse deviation (m) of the preview point, e is the transverse position deviation (m) at the mass center of the vehicle,
is the heading deviation (rad), c, of the vehicle from the road centerline1、c2Respectively, the lateral position deviations (m) between the front and rear ends of the vehicle and the desired path.
From the geometric relationship, one can obtain:
then
From fig. 3, it is also possible to obtain:
then
Simplified formula (6) to obtain
Namely, it is
Due to the fact that
Therefore, it is not only easy to use
To epThe derivation can be:
in the context of figure 4, it is shown,
the course deviation of the vehicle and the road center line;
is the vehicle heading angle;
is tangent to the center line of the roadThe angle of the abscissa of the earth. From the geometrical relationship it can be found that:
wherein the longitudinal speed v of the vehiclex(m/s) is a constant, and the center line curvature ρ (m) of the road-1) Is the reciprocal of the radius of the road centerline circle, a known quantity.
Thus is provided with
The change rate of the transverse position deviation between the centroid position and the road center line in the vehicle track tracking process is as follows:
due to the fact that
Therefore, it is not only easy to use
By substituting formula (15) for formula (10)
Simplified lateral speed v of vehicle state quantityyLateral acceleration
Yaw rate of vehicle
Yaw angular acceleration of vehicle
The relation with the parameters in the preview deviation model, namely the preview model:
combining the preview model with the vehicle two-degree-of-freedom dynamic model to form a vehicle-road preview deviation model, and selecting ep、
For the state quantities of the state space equation, the state space equation of the vehicle-road model can be obtained
Wherein:
the upper-layer extension controller is mainly used for determining the state area of the vehicle according to the relative position of the current vehicle and the road center and the curvature of the center line of the single road, and the specific process is as follows:
1) feature quantity extraction
The extensive control area division corresponds to the control precision and tracking error of the vehicle-road system, and the evaluation of the transverse control of the automatic driving vehicle is the most importantThe index of the system is the position deviation between the vehicle and the central line of the road, the design of the whole control system strategy is closely related to the road condition, the index reflecting the basic road condition is the curvature of the road, in addition, the transverse control of the automatic driving vehicle is established on the parameter of the curvature of the road by adopting the basis of feedforward-feedback control, therefore, the characteristic quantity of the extension controller can be used for selecting the transverse preview deviation epAnd the road center line curvature rho form a characteristic state S (e)p，ρ)。
2) Extension set partitioning
As shown in fig. 5, a two-dimensional extension set is established, and the extension domain preview lateral deviation e is determinedpMaximum allowable range (-e)p2，ep2) (-10, 10), the maximum allowable range (-rho) of the curvature rho of the center line of the extension road2，ρ2) Is (-0.12, 0.12). Aiming at the classical domain, because the adopted control strategy is the traditional PD feedback control and has limited control capability, when a vehicle tracks the central line track of a road with a large corner, a satisfactory control effect cannot be achieved, so that the maximum allowable range of the classical domain can be obtained through multiple experiments, and when the curvature rho of the road reaches 0.02, the pre-aiming transverse deviation e is obtainedpUp to 0.2, the conventional PD control starts to have problems of large tracking deviation and lag in front wheel steering response. Thus, the classical domain lateral deviation epMaximum allowable Range (-e)p1，ep1) (-0.2, 0.2), and the maximum allowable range of the road curvature p is (-p)1，ρ1) Is (-0.02, 0.02); therefore, in the two-dimensional extension set shown in fig. 5, the classical domain interval is ep[-0.2，0.2]、ρ[-0.02，0.02](ii) a The extension region interval is ep[-10，-0.2)∪(0.2，10]、ρ[-0.12，-0.02)∪(0.02，0.12]。
3) Calculation of degree of association
The characteristic quantities selected by the traditional extension controller are deviation and deviation differential, and the embodiment selects the preview transverse deviation epAnd the road curvature rho are characteristic quantities, and the correlation function value needs to be determined by calculating the extension distance according to the extension theory proposed by zeitsu, and is shown as a two-dimensional extension set in fig. 6.
In the two-dimensional extension set, the origin (0, 0) is characteristic of the stateAnd (4) an optimal point. Suppose there is a point P in the extensibility domain3，P3Connecting the origin with P for the current state of the vehicle-road system3Point, obtain P3Shortest distance | OP toward optimum point (0, 0)3L. The straight line of the line segment intersects with the boundary of the classical domain at P1And P4Point, cross extension domain boundary at P2And P5And (4) point. In guarantee of P3Under the precondition that the distance approaching the origin is shortest, P can be determined according to the intersection points3Closest distance to the extendible domain, the classical domain.
In the one-dimensional extension set, the extension distance is substantially the minimum distance from a point to a boundary of an interval, and the extension distance in the two-dimensional extension set can be converted into the one-dimensional extension distance according to the principle, as shown in fig. 7. P3The extension distances from the point to the classical domain and the extension domain are respectively rho (P)3，<P4，P1>) And ρ (P)3，<P5，P2>) In rho (P)3，<P4，P1>) For example, the solution is as follows:
similarly, ρ (P) is obtained3，<P5，P2>) Comprises the following steps:
the correlation function may then be determined as:
wherein: d (P)3，<P5，P2>，〈P4，P1>)＝ρ(P3，<P5，P2>)-ρ(P3，<P4，P1>)。
Adaptation in classical domain based on preThe PD feedback control strategy of the aiming deviation, as shown in fig. 8, is a PD control flow chart based on the pre-aiming deviation; the preview bias contains two biases: a lateral position deviation and a heading deviation between a vehicle heading and a road desired heading. When the PD controller is designed, the two deviations need to be adjusted simultaneously to obtain the optimal front wheel steering angle input, so that the vehicle transverse control process has smaller transverse position deviation, and smaller course deviation between the vehicle and an expected track is ensured; the superposition of the two deviation adjustment quantities can be realized by means of weighted addition in design, the specific design structure of the PD controller is shown in FIG. 9, KP1、KP2Respectively, two proportional module coefficients in PD control, KD1、KD2The coefficients of the differential control modules in the two PD controls are respectively, and K is1、K2Front wheel steering angle delta for controlling output of two PDsfWeighted addition coefficients.
In the extensive area, the turning radius of the vehicle is small, the curvature of the road is large, the vehicle needs to respond quickly, and the vehicle can earn the required turning angle in time. The problems of large tracking steady-state error and slow response time can occur by adopting the original PD feedback control based on the preview deviation. Therefore, under the working condition, the feedforward control based on the front road curvature is adopted for restraining, the front wheel steering angle of the vehicle is fast in response, the response speed of the control system to the small-curvature path disturbance is improved, and meanwhile, the hysteresis and the fluctuation of the vehicle steering control are reduced. As shown in FIG. 10, a control flow chart of the feedforward-feedback PD controller based on the curvature of the road is added with a feedforward control module based on the curvature rho of the front road, wherein delta is shownffFront wheel angle, delta, output by feedforward control modulefbAdding the two module output values to obtain the final required front wheel corner delta for the front wheel corner output by the PD feedback control modulef。
Therefore, the design of an extension controller, a classical domain PD feedback controller and an extension domain PD feedforward-feedback controller is completed, and the whole transverse extension preview switching control method of the automatic driving automobile is formed. Based on the design of each part of the controllers, a simulation platform is built in Smulink/Carsim, and is shown in FIG. 11. Considering the target path trajectory as a trigonometric function curve: y is 4sin0.1x, the change in curvature of the target path is as shown in fig. 12, and the trajectory tracking map as shown in fig. 13 and the lateral deviation map as shown in fig. 14 can be obtained by inputting the curvature of the target path into the constructed control system.
As can be seen from fig. 13, the lateral extension preview switching control method for the autonomous vehicle has better road tracking performance, and particularly, at the part with the smallest turning radius of the trigonometric function, namely the peak and the trough of the trigonometric function, the lateral extension preview switching control method can be switched to the PD feedforward-feedback controller through the extension controller, so that the response of the front wheel turning angle of the vehicle is accelerated, the lateral deviation value when the vehicle passes through the peak and the trough of the wave is reduced, the tracking accuracy is improved, and the purpose of the design of the whole control system is also matched.
As can be seen from fig. 14, for the lateral position deviation, the extendable preview lateral control system can greatly reduce the lateral position deviation by switching the two control strategies, the peak value lateral position deviation is 0.2m, and the reduction of the lateral deviation greatly improves the stability of the vehicle in the process of continuously passing through a curve.
The above embodiments are only used for illustrating the design idea and features of the present invention, and the purpose of the present invention is to enable those skilled in the art to understand the content of the present invention and implement the present invention accordingly, and the protection scope of the present invention is not limited to the above embodiments. Therefore, all equivalent changes and modifications made in accordance with the principles and concepts disclosed herein are intended to be included within the scope of the present invention.
Claims (9)
1. A transverse extension preview switching control method of an automatic driving automobile is characterized by comprising the following steps: an upper layer controller: the transverse position deviation e of the vehicle from the center line of the road is formedpDetermining a correlation function K (S) for a two-dimensional state space with an X axis and a road center line curvature rho as a Y axis according to an extension distance principle, and dividing the state of the vehicle-road system into a classical domain, an extension domain and a non-domain according to a correlation function value; in a lower layer controller, a PD feedback controller based on a preview error is designed in a classical domain, and a PD feedback controller based on a preview error is designed in an extensive domainA PD feedforward-feedback controller based on the curvature of the road ahead; and establishing a vehicle-road state space equation according to the vehicle two-degree-of-freedom dynamic model and the path tracking preview deviation model, and verifying the effectiveness of the control method as a control object.
2. The lateral extendability preview switching control method of an autonomous vehicle as claimed in claim 1, wherein said lateral position deviation epThe extension set characteristic quantity (e) of the vehicle-road model is formed by the curvature rho of the center line of the roadp，ρ)。
3. The lateral extendability preview switching control method of the automatic driving automobile according to claim 1, characterized in that the interval of the classical domain is: lateral position deviation ep[-0.2，0.2]Road center line curvature p 0.02, 0.02]。
4. The lateral extendability preview switching control method of the automatic driving car according to claim 1, wherein the extendability domain interval is: lateral position deviation ep[-10，-0.2)∪(0.2，10]Road center line curvature ρ [ -0.12, -0.02 [ -0.02, 0.12 ] U]。
5. The lateral extendability preview switching control method of the auto-pilot vehicle as claimed in claim 1, wherein the calculation of the correlation function value requires first converting the extendability distance in the two-dimensional extendability set into a one-dimensional extendability distance.
6. The lateral extendability preview switching control method of the automatic driving vehicle as claimed in claim 1, wherein the correlation function is:
wherein D (P)3，<P5，P2>，<P4，P1>)＝ρ(P3，<P5，P2>)-ρ(P3，<P4，P1>)，ρ(P3，<P4，P1>) And ρ (P)3，<P5，P2>) Are respectively a point P3Extension to classical and extension domains, P3For the extension set characteristic quantity (e) of the vehicle-road model collected during the driving of the vehiclepρ) real-time state value, P3Connecting with the origin to intersect the boundary of the classical domain at point P1、P4，P3Intersecting the extension domain boundary with the original point at point P2、P5。
7. The lateral extendability preview switching control method of an automatic driving car as claimed in claim 1, wherein the vehicle-road state space equation is:
wherein
Is the course deviation, delta, of the vehicle from the road centerlinefIs the turning angle of the front wheels of the vehicle,
wherein m is the vehicle mass; v. ofxIs the vehicle longitudinal speed; i iszIs the moment of inertia of the vehicle about the z-axis; a. b is the distance between the vehicle mass center and the front axle and the distance between the vehicle mass center and the rear axle respectively; k is a radical of1、k2Cornering stiffness of front and rear wheel tires, respectively; and L is the pre-aiming distance.
8. The lateral extendability preview switching control method of an autonomous vehicle as claimed in claim 1, wherein an emergency braking control is adopted when the non-territory vehicle-road system is out of control.
9. The lateral extendability preview switching control method of an automatic driving car according to claim 1 or 6, characterized in that the vehicle-road system state is classified as classicalThe specific processes of the domain, the extension domain and the non-domain are as follows: selecting characteristic quantity (e)pRho) as input of extension controller, establishing two-dimensional extension set, and according to real-time state value P3And converting the two-dimensional extension set into a one-dimensional extension set according to the position of the two-dimensional extension set, calculating a value of a correlation function K (S), and determining the area of the current vehicle-road system according to the value of the correlation function.
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| CN109606364B (en) * | 2018-11-19 | 2020-06-09 | 江苏大学 | Layered self-learning extensible neural network lane keeping control method |
| CN109606363B (en) * | 2018-11-19 | 2020-11-20 | 江苏大学 | Multi-state feedback intelligent automobile extension lane keeping control method |
| CN109606362B (en) * | 2018-11-19 | 2020-06-09 | 江苏大学 | Extension feedforward lane keeping control method based on road curvature |
| CN109664884B (en) * | 2018-11-19 | 2020-06-09 | 江苏大学 | Extension self-adaptive lane keeping control method under variable vehicle speed |
| CN109808707B (en) * | 2019-02-19 | 2020-12-08 | 武汉理工大学 | Automobile steering control method and controller based on stochastic model prediction |
| CN111717204B (en) * | 2019-03-18 | 2022-05-17 | 毫末智行科技有限公司 | Lateral control method and system for automatic driving vehicle |
| CN110989625B (en) * | 2019-12-25 | 2020-11-27 | 湖南大学 | Vehicle path tracking control method |
| CN112874504B (en) * | 2020-01-10 | 2022-03-04 | 合肥工业大学 | Control method of extensible entropy weight combined controller |
| CN112590802B (en) * | 2020-12-04 | 2022-12-20 | 英博超算(南京)科技有限公司 | Vehicle driving control method, device, vehicle and computer readable storage medium |
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