CN113335278B - Network-connected intelligent motorcade self-adaptive cruise control method and system - Google Patents
Network-connected intelligent motorcade self-adaptive cruise control method and system Download PDFInfo
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- CN113335278B CN113335278B CN202110819497.XA CN202110819497A CN113335278B CN 113335278 B CN113335278 B CN 113335278B CN 202110819497 A CN202110819497 A CN 202110819497A CN 113335278 B CN113335278 B CN 113335278B
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- 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
- B60W30/14—Adaptive cruise control
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- 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
- B60W30/14—Adaptive cruise control
- B60W30/16—Control of distance between vehicles, e.g. keeping a distance to preceding vehicle
- B60W30/165—Automatically following the path of a preceding lead vehicle, e.g. "electronic tow-bar"
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- 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
- B60W2556/00—Input parameters relating to data
- B60W2556/45—External transmission of data to or from the vehicle
- B60W2556/65—Data transmitted between vehicles
Abstract
The application belongs to the technical field of vehicle formation, and particularly relates to a network-connected intelligent vehicle team self-adaptive cruise control method and system, wherein the method comprises the following steps: constructing a communication topological relation among the head car, the navigator and the follower, and setting the follower to receive the driving information of the head car and the navigator; the current follower acquires pose information and the driving information under a Cartesian coordinate system, and coordinate transformation is carried out to obtain pose information of the follower under Frenet coordinates; and carrying out longitudinal and transverse decoupling control on the current follower based on pose information under Frenet coordinates, and sending running information to the follower by taking the follower as a pilot. According to the intelligent vehicle intelligent driving control system, cooperative control under a vehicle team scene is realized by building the V2V communication topological structure, and meanwhile, the problem of single-lane longitudinal speed control and the problem of transverse control under a complex scene are solved through the self-adaptive cruise control system, so that the safety and reliability of intelligent driving of the network-connected intelligent vehicle team under different environments are further improved after the two are combined.
Description
Technical Field
The application belongs to the technical field of vehicle formation, and particularly relates to a network-connected intelligent vehicle team self-adaptive cruise control method and system.
Background
Compared with the traditional ACC system, the cooperative self-adaptive cruise system enables vehicle running information to be transmitted and communicated between vehicles by means of a V2V technology, and automatic control of the vehicles in the longitudinal direction is achieved. Currently, research into cooperative adaptive cruise control is mainly focused on single-lane longitudinal speed control, and in fact, in some complex scenarios, cooperative adaptive cruise control needs to consider lateral control as compared to longitudinal motion control.
Disclosure of Invention
The application provides a network-connected intelligent fleet self-adaptive cruise control method and system.
In a first aspect, the application provides a network-connected intelligent fleet adaptive cruise control method,
the method is applied to a controller of the network-connected intelligent vehicle and used for controlling the network-connected intelligent vehicle to realize self-adaptive cruising, and the network-connected intelligent vehicle team self-adaptive cruising control method comprises the following steps:
step S1, constructing a communication topological relation among a head car, a navigator and a follower, and setting the follower to receive the running information of the head car and the navigator or the running information of the navigator;
step S2, the current follower acquires pose information and the driving information under a Cartesian coordinate system, and coordinate transformation is carried out to obtain pose information of the follower under Frenet coordinates;
step S3, based on pose information under Frenet coordinates, performing longitudinal and transverse decoupling control on a current follower, and sending running information to the follower by taking the follower as a pilot;
steps S2 and S3 are repeated so that the running information is sequentially transmitted to the final vehicle.
In one embodiment, the method for constructing the communication topological relation among the head car, the navigator and the follower in the step S1 includes: numbering the driving orders of the motorcade vehicles, wherein the head vehicle is 1, the navigator is K, and the follower is K+1, and the head vehicle, the navigator and the follower form formation control based on V2V communication.
In one embodiment, the driving information includes: speed information, pose information, and trajectory information.
In one embodiment, the method for obtaining pose information of the follower under the Frenet coordinate by performing coordinate transformation in the step S2 includes: the current follower obtains a curve function model by adopting a cubic B spline curve fitting principle according to track information of the head car and the navigator; obtaining track point curvature and track point curvature change rate information according to the curve function model; mathematically modeling the current follower under Frenet coordinates; and carrying out coordinate transformation according to the self-vehicle pose information and the track information under the Cartesian coordinate system acquired by the sensor unit to acquire the pose information of the self-vehicle under the Frenet coordinate.
Optionally, the expression of the curve function model obtained by adopting the principle of cubic B spline curve fitting is as follows:
wherein a is 0 ,a 1 ,a 2 ,a 3 ,a 4 ,a 5 ,b 0 ,b 1 ,b 2 ,b 3 ,b 4 ,b 5 For the fitting coefficients, X (u) is the component on the abscissa, Y (u)U is an argument, which is a component on the ordinate.
Optionally, the expression for obtaining the curvature of the track point and the curvature change rate information of the track point according to the curve function model is as follows:
the rate of curvature change is recorded asExpressed as:
wherein c i (u) is the curvature of the locus points,s is the arc length between adjacent track points, which is the curvature change rate.
Optionally, mathematical modeling is performed on the current follower in the formation under Frenet coordinates, where the expression is:
wherein s is i Representing the arc length of the projected point passing through on the reference track, i.e. the abscissa in Frenet coordinate system, y i The distance from the central line of the rear axle of the vehicle in the vertical direction of the tangent line of the projection point is shown, namely the ordinate under the Frenet coordinate system,for azimuth error, v i Delta is the speed in the forward direction of the vehicle i C is the front wheel corner of the vehicle i Is the curvature of the track point, and L is the length of the vehicle body.
Optionally, the pose information of the formation vehicles is obtained as @ under the Cartesian coordinate systemX i ,Y i ,θ i ) Expressed as:
wherein, (X i ,Y i ) Is the position coordinate of the vehicle in a Cartesian coordinate system, theta i Is the heading angle of the vehicle.
In one embodiment, the performing longitudinal-lateral decoupling control on the current follower in step S3 based on pose information in the Frenet coordinate includes: and according to the track point curvature and the track point curvature change rate information, longitudinal and transverse decoupling is carried out on the vehicle under Frenet coordinates, so that the transverse maximum control quantity and the longitudinal optimal control quantity are obtained, and the desired track processing is carried out.
In a second aspect, the present application provides an online intelligent fleet adaptive cruise control system, comprising:
the device comprises a workshop communication unit, a pose calculation unit, a sensor unit and a control unit; the workshop communication unit and the sensor unit are connected with the pose calculation unit, and the planning combination is connected with the control unit; the workshop communication unit is used for implementing V2V workshop communication, receiving the running information of the head car and the navigator in the state of the follower and sending the running information to the follower in the state of the navigator; the sensor unit is used for collecting pose information of the current follower under a Cartesian coordinate system; the pose calculating unit is used for planning pose information under the Frenet coordinate of the current follower according to the driving information of the head car and the navigator and the pose information under the Cartesian coordinate system of the current follower; and the control unit is used for calculating and processing the expected track according to the current follower pose information under the Frenet coordinate.
The intelligent control system has the beneficial effects that cooperative control under a motorcade scene is realized by constructing the V2V communication topological structure, and meanwhile, the problem of single-lane longitudinal speed control and the problem of transverse control under a complex scene are solved by the self-adaptive cruise control system, so that the safety and reliability of intelligent driving of the network-connected intelligent motorcade under different environments are further improved after the two are combined.
Additional features and advantages of the application will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the application. The objectives and other advantages of the application will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
In order to make the above objects, features and advantages of the present application more comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are needed in the description of the embodiments or the prior art will be briefly described, and it is obvious that the drawings in the description below are some embodiments of the present application, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram of the communication topology of the networked intelligent fleet adaptive cruise control method of the present application;
FIG. 2 is a fleet identity schematic of the networked intelligent fleet adaptive cruise control method of the present application;
FIG. 3 is a diagram of a vehicle kinematic model under the Frenet framework of the network-linked intelligent fleet adaptive cruise control method of the present application;
FIG. 4 is a schematic cruise control diagram of the network-connected intelligent fleet adaptive cruise control method of the present application;
FIG. 5 is a pilot-following block diagram of the network-linked intelligent fleet adaptive cruise control method of the present application;
FIG. 6 is a schematic diagram of the longitudinal control of the networked intelligent fleet adaptive cruise control method of the present application;
FIG. 7 is a diagram of the position change in the formation of a multi-intelligent vehicle formation in a turning scenario of the networked intelligent vehicle formation adaptive cruise control method of the present application;
FIG. 8 is a graph of change in speed of a formed vehicle during formation of a multi-intelligent vehicle formation in a turning scenario of the networked intelligent vehicle formation adaptive cruise control method of the present application;
FIG. 9 is a diagram of the position change in the formation of a multi-intelligent vehicle formation in the channel change scenario of the networked intelligent vehicle formation adaptive cruise control method of the present application;
fig. 10 is a graph of formation vehicle speed change in the formation of a multi-intelligent vehicle formation in the channel change scene of the network-connected intelligent vehicle formation adaptive cruise control method of the application.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present application more apparent, the technical solutions of the present application will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
Compared with the traditional ACC system, the cooperative self-adaptive cruise system enables vehicle running information to be transmitted and communicated between vehicles by means of a V2V technology, and automatic control of the vehicles in the longitudinal direction is achieved. Currently, research into cooperative adaptive cruise control is mainly focused on single-lane longitudinal speed control, and in fact, in some complex scenarios, cooperative adaptive cruise control needs to consider lateral control as compared to longitudinal motion control.
Based on the above problems, the embodiment provides a network-connected intelligent fleet adaptive cruise control method, which not only can realize automatic control of the longitudinal speed of a single-lane vehicle, but also can realize transverse collaborative adaptive cruise control in some complex scenes.
The self-adaptive cruise control method of the network-connected intelligent motorcade comprises the following steps: step S1, constructing a communication topological relation among a head car, a navigator and a follower, and setting the follower to receive the running information of the head car and the navigator or the running information of the navigator; step S2, the current follower acquires pose information and the driving information under a Cartesian coordinate system, and coordinate transformation is carried out to obtain pose information of the follower under Frenet coordinates; step S3, based on pose information under Frenet coordinates, performing longitudinal and transverse decoupling control on a current follower, and sending running information to the follower by taking the follower as a pilot; steps S2 and S3 are repeated so that the running information is sequentially transmitted to the final vehicle.
As shown in fig. 1 and fig. 2, specifically, the method for constructing the communication topological relation among the head car, the navigator and the follower in the step S1 includes: numbering the driving orders of the motorcade vehicles, wherein the head vehicle is 1, the navigator is K, and the follower is K+1, and the head vehicle, the navigator and the follower form formation control based on V2V communication.
Optionally, the intelligent vehicle team of the application does not run in a single lane in the traditional intelligent cruising, but can realize the cooperative running of the intelligent vehicle team in multiple lanes, so that the intelligent vehicle team can build a new communication topological relation in order to avoid the situation that the initial vehicle sequence of the vehicle team is different from the actual running vehicle sequence in the running process.
In this embodiment, the travel information includes: speed information, pose information, and trajectory information.
In this embodiment, the track information of the head car and the navigator is used as the main reference basis of the expected track of the follower, so as to reduce the information calculation and processing amount of the follower.
As shown in fig. 3, specifically, the method for obtaining pose information of the follower under the Frenet coordinate by performing coordinate transformation in the step S2 includes: the current follower obtains a curve function model by adopting a cubic B spline curve fitting principle according to track information of the head car and the navigator; obtaining track point curvature and track point curvature change rate information according to the curve function model; mathematically modeling the current follower under Frenet coordinates; and carrying out coordinate transformation according to the self-vehicle pose information and the track information under the Cartesian coordinate system acquired by the sensor unit to acquire the pose information of the self-vehicle under the Frenet coordinate.
In this embodiment, the expression of the curve function model obtained by adopting the principle of cubic B-spline curve fitting is:
wherein a is 0 ,a 1 ,a 2 ,a 3 ,a 4 ,a 5 ,b 0 ,b 1 ,b 2 ,b 3 ,b 4 ,b 5 For the fitting coefficients, X (u) is the component on the abscissa, Y (u) is the component on the ordinate, u is the argument, representing the duration of the previous time from the current time, ranging from 0 to 1 second.
In this embodiment, the desired track is decomposed into multiple curves, each of which will change with the change of the local parameter, and the abscissa of the curve is represented by the length of each curve, denoted as s i (u) a control point can be found on each section of curve, the connecting line of the point and the central line of the rear axle of the vehicle is perpendicular to the tangential direction of the curve, and the control point is M i The coordinates of this point are denoted r i (u)＝[X i (u)Y i (u)] T From the mathematical relationship, it is possible to obtain:u is more than or equal to 0 and less than or equal to 1, and Vehicle_i and M i The positional deviation between them is denoted as y i (u) expressed as: />The azimuth deviation is recorded as->Expressed as: />
In this embodiment, the expression for obtaining the curvature of the track point and the curvature change rate information of the track point according to the curve function model is:
the rate of curvature change is recorded asExpressed as:
wherein c i (u) is the curvature of the locus points,s is the arc length between adjacent track points, which is the curvature change rate.
As shown in fig. 3, specifically, the vehicle motion state is decomposed into two directions of longitudinal and transverse relative to the reference track under the condition of ignoring the vertical motion of the vehicle and ignoring the influence of air resistance, so as to obtain a vehicle motion model under the Frenet frame, wherein O i θ is the position of the center point of the rear axle of the vehicle i Heading angle delta of Vehicle_i i Front wheel corner for vehicle_i, (X) i ,Y i ) For the position coordinates of Vehicle_i in the Cartesian coordinate system, M i Is the projection point of Vehicle_i at the nearest position from the reference track, s i Representing the arc length of the projected point passing through on the reference track, i.e. the abscissa in Frenet coordinate system, y i Representing the distance from the central line of the rear axle of the vehicle in the vertical direction of the tangent line of the projection point, namely, the ordinate in the Frenet coordinate system, theta ci For the azimuth of the reference track at that location,c is the azimuth error i For the curvature of the position reference track, L is the length of the vehicle body, and P i O i Is v in Cartesian coordinate system i Is analyzed from a geometric relationship, there is: />Wherein (1)>This can be achieved by: />The equation is transformed to obtain: />Meanwhile, the geometric relationship exists: s is(s) i ＝r ci ·θ ci ，/>The method can obtain the following steps: θ ci ＝s i ·c i ，/>Let->For azimuth errors, a vehicle model in Frenet coordinate system can be obtained.
In this embodiment, mathematical modeling is performed on the current follower in the formation under Frenet coordinates, where the expression is:
wherein s is i Representing the arc length of the projected point passing through on the reference track, i.e. the abscissa in Frenet coordinate system, y i The distance from the central line of the rear axle of the vehicle in the vertical direction of the tangent line of the projection point is shown, namely the ordinate under the Frenet coordinate system,for azimuth error, v i Delta is the speed in the forward direction of the vehicle i Is the front wheel of the vehicleCorner, c i Is the curvature of the track point, and L is the length of the vehicle body.
In the present embodiment, pose information of the formation vehicle in the cartesian coordinate system is acquired as (X i ,Y i ,θ i ) Expressed as:
wherein, (X i ,Y i ) Is the position coordinate of the vehicle in a Cartesian coordinate system, theta i For the course angle of the vehicle, acquiring pose information of the formation vehicle under the Frenet frame according to the expected track information and the pose information of the formation vehicle under the Cartesian coordinate system
As shown in fig. 4, specifically, in step S3, based on pose information in the Frenet coordinate, performing longitudinal-lateral decoupling control on the current follower includes: and according to the track point curvature and the track point curvature change rate information, longitudinal and transverse decoupling is carried out on the vehicle under Frenet coordinates, so that the transverse maximum control quantity and the longitudinal optimal control quantity are obtained, and the desired track processing is carried out.
As shown in fig. 5, specifically, an intelligent vehicle pilot-follow structure in a formation obtained from the idea of pilot-follow control.
In the present embodiment, regarding the lateral control, the object is to make the lateral error y of the vector_i to the reference trajectory projection point i Approaching 0, since the formation control method has decoupled the lateral control and the longitudinal control using the Frenet frame, the lateral control only needs to control the front wheel steering angle to accomplish the corresponding control target.
Let a be any variable, the third order chain system can be expressed as:
variable substitution a 1 ＝s，a 2 =y, can be obtained:
the mathematical derivation is carried out to obtain:
wherein the method comprises the steps of
The transverse control strategy of the embodiment adopts PD control, and after deduction, the transverse control variable control rate is obtained as follows:
wherein K is p And K d Is a scaling factor.
As shown in fig. 6, in the present embodiment, specifically, regarding the acquisition of the longitudinal control amount, in the local control strategy, each smart car is the navigator of the following smart car (except the last smart car), the ith car in the formation is taken as the controlled object, and the distance between Vehicle_i and Vehicle_i+1 in the longitudinal direction of the Frenet coordinate system is recorded asThe distance between Vehicle i and the head car is denoted as +.>
To realizeIs controlled by an exponential convergence method +.>Where k > 0, yields:
to realizeIs controlled by an exponential convergence method +.>Where k > 0, yields:
thereby, the speed control rate in the longitudinal direction is:
where ω is a weight coefficient.
In this embodiment, the related multi-intelligent vehicle formation method needs to control in the longitudinal direction and the transverse direction at the same time, the simulation verification portion selects two scenes of turning and lane changing to perform, and it is assumed that 4 intelligent vehicles are in formation, the length of each vehicle is l=2m, and the initial position of each vehicle is:
[(X 0 ,Y 0 ),(X 1 ,Y 1 ),(X 2 ,Y 2 ),(X 3 ,Y 3 )]＝[(10,1),(7,1),(4,1),(2,1)]。
as shown in fig. 7 and 8, in this embodiment, specifically, the position change condition of the multi-intelligent vehicle in the formation process of the formation is obtained through simulation, and the position change condition and the formation vehicle speed change condition of the multi-intelligent vehicle in the formation process of the formation are obtained in the turning scene.
As shown in fig. 9 and 10, in this embodiment, specifically, the position change condition and the formation vehicle speed change condition in the formation process of the multi-intelligent vehicle formation in the lane change scene.
Specifically, as can be seen from simulation results in the two scenes, the intelligent vehicle formation method can ensure that a vehicle team can quickly track a given expected track, and curvature change and speed change in the driving process have robust stability.
In this embodiment, V2V communication is specifically a communication technology that is not limited to a fixed base station, and provides direct end-to-end wireless communication for a moving vehicle, so as to implement transmission of running information inside an intelligent motorcade.
In this embodiment, the Frenet coordinate system specifically uses the lane line as a reference line in the automatic driving process, and the coordinate system can provide more accurate coordinate navigation for the vehicle in the automatic driving process relative to the coordinate system constructed by the vehicle currently running.
In this embodiment, the PD control specifically adopts correction of the property of differential control, so that stability of the system can be increased, maximum deviation and residual error can be reduced, control process can be quickened, control quality can be improved, and stability of intelligent running can be ensured.
The embodiment also provides a network-connected intelligent fleet adaptive cruise control system, which comprises: the device comprises a workshop communication unit, a pose calculation unit, a sensor unit and a control unit; the workshop communication unit and the sensor unit are connected with the pose calculation unit, and the planning combination is connected with the control unit; the workshop communication unit is used for implementing V2V workshop communication, receiving the running information of the head car and the navigator in the state of the follower and sending the running information to the follower in the state of the navigator; the sensor unit is used for collecting pose information of the current follower under a Cartesian coordinate system; the pose calculating unit is used for planning pose information under the Frenet coordinate of the current follower according to the driving information of the head car and the navigator and the pose information under the Cartesian coordinate system of the current follower; and the control unit is used for calculating and processing the expected track according to the current follower pose information under the Frenet coordinate.
The position calculation unit and the control unit in the present embodiment may be implemented in the vehicle ECU.
In summary, the network-connected intelligent fleet adaptive cruise control method and system realize cooperative control under a fleet scene by constructing the V2V communication topological structure, solve the problem of single-lane longitudinal speed control and the problem of transverse control under a complex scene through the adaptive cruise control system, and further improve the safety and reliability of intelligent driving of the network-connected intelligent fleet under different environments after the combination of the two.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other manners. The apparatus embodiments described above are merely illustrative, for example, of the flowcharts and block diagrams in the figures that illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, functional modules in the embodiments of the present application may be integrated together to form a single part, or each module may exist alone, or two or more modules may be integrated to form a single part.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
With the above-described preferred embodiments according to the present application as an illustration, the above-described descriptions can be used by persons skilled in the relevant art to make various changes and modifications without departing from the scope of the technical idea of the present application. The technical scope of the present application is not limited to the description, but must be determined according to the scope of claims.
Claims (6)
1. The network-connected intelligent motorcade self-adaptive cruise control method is characterized by comprising the following steps of:
step S1, constructing a communication topological relation among a head car, a navigator and a follower, and setting the follower to receive the running information of the head car and the navigator or the running information of the navigator;
step S2, the current follower acquires pose information and the driving information under a Cartesian coordinate system, and coordinate transformation is carried out to obtain pose information of the follower under Frenet coordinates;
step S3, based on pose information under Frenet coordinates, performing longitudinal and transverse decoupling control on a current follower, and sending running information to the follower by taking the follower as a pilot;
repeating the step S2 and the step S3 so that the running information is sequentially sent to the last vehicle;
the method for constructing the communication topological relation among the head car, the navigator and the follower in the step S1 comprises the following steps:
numbering the driving sequences of the motorcade vehicles, wherein the head vehicle is 1, the navigator is K, and the follower is K+1, wherein
The head car, the navigator and the follower all form formation control based on V2V communication;
the travel information includes: speed information, pose information and track information;
the method for obtaining pose information of the follower under Frenet coordinates by carrying out coordinate transformation in the step S2 comprises the following steps:
the current follower obtains a curve function model by adopting a cubic B spline curve fitting principle according to track information of the head car and the navigator;
obtaining track point curvature and track point curvature change rate information according to the curve function model;
mathematically modeling the current follower under Frenet coordinates;
according to the self-vehicle pose information and the track information under the Cartesian coordinate system acquired by the sensor unit, carrying out coordinate transformation to acquire the pose information of the self-vehicle under the Frenet coordinate;
the expression of the curve function model obtained by adopting the principle of cubic B spline curve fitting is as follows:
wherein a is 0 ,a 1 ,a 2 ,a 3 ,a 4 ,a 5 ,b 0 ,b 1 ,b 2 ,b 3 ,b 4 ,b 5 For the fitting coefficients, X (u) is a component on the abscissa, Y (u) is a component on the ordinate, and u is an argument.
2. The method of claim 1, wherein the step of determining the position of the substrate comprises,
the expression for obtaining the curvature of the track point and the curvature change rate information of the track point according to the curve function model is as follows:
the rate of curvature change is recorded asExpressed as:
wherein c i (u) is the curvature of the locus points,s is the arc length between adjacent track points, which is the curvature change rate.
3. The method of claim 2, wherein the step of determining the position of the substrate comprises,
mathematical modeling is carried out on the current follower in the formation under Frenet coordinates, and the expression is as follows:
wherein s is i Representing the arc length of the projected point passing on the reference track, i.e. the abscissa in Frenet coordinate system, y i The distance from the central line of the rear axle of the vehicle in the vertical direction of the tangent line of the projection point is shown, namely the ordinate under the Frenet coordinate system,for azimuth error, v i Delta is the speed in the forward direction of the vehicle i C is the front wheel corner of the vehicle i Is the curvature of the track point, and L is the length of the vehicle body.
4. The method of claim 3, wherein the obtaining is performed in a Cartesian coordinate system,
pose information of the formation vehicle is (X) i ,Y i ,θ i ) Expressed as:
wherein, (X i ,Y i ) Is the position coordinate of the vehicle in a Cartesian coordinate system, theta i Is the heading angle of the vehicle.
5. The method of claim 4, wherein the step of determining the position of the first electrode is performed,
in the step S3, based on pose information under Frenet coordinates, the method for performing longitudinal and lateral decoupling control on the current follower includes:
and according to the track point curvature and the track point curvature change rate information, longitudinal and transverse decoupling is carried out on the vehicle under Frenet coordinates, so that the transverse maximum control quantity and the longitudinal optimal control quantity are obtained, and the desired track processing is carried out.
6. A network-linked intelligent fleet adaptive cruise control system applying the network-linked intelligent fleet adaptive cruise control method according to claim 1, comprising:
the device comprises a workshop communication unit, a pose calculation unit, a sensor unit and a control unit;
the workshop communication unit is used for implementing workshop communication, receiving the driving information of the head car and the navigator in the state of the follower and sending the driving information to the follower in the state of the navigator;
the sensor unit is used for collecting pose information and running information of the current follower under a Cartesian coordinate system;
the pose calculating unit is used for planning pose information under the Frenet coordinate of the current follower according to the driving information of the head car and the navigator and the pose information under the Cartesian coordinate system of the current follower;
and the control unit is used for calculating and processing the expected track according to the current follower pose information under the Frenet coordinate.
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| CN114399906B (en) * | 2022-03-25 | 2022-06-14 | 四川省公路规划勘察设计研究院有限公司 | Vehicle-road cooperative driving assisting system and method |
| CN114694384B (en) * | 2022-05-18 | 2023-03-24 | 厦门金龙联合汽车工业有限公司 | Intelligent networking vehicle formation safety strategy design method |
| CN115171414B (en) * | 2022-06-10 | 2023-07-14 | 哈尔滨工业大学重庆研究院 | CACC follows car flow control system based on Frenet coordinate system |
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