CN111324130A - Pigeon-group-imitated intelligent vehicle formation cooperative self-adaptive cruise control switching method - Google Patents
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Abstract
The invention discloses an intelligent vehicle formation cooperative self-adaptive cruise control switching method imitating a pigeon flockiAre all greater than the congestion threshold deltamaxThen the fleet enters into a hierarchical control modeh(ii) a Otherwise, the motorcade enters an equal interaction modee(ii) a Then, under the current state, each individual vehicle i in the fleet marks a neighbor vehicle j within a distance with lower communication delay so as to obtain a neighbor vehicle set
Or
Then, control input is carried out, wherein the control input comprises formation control gain, target point control gain and following control gain, and potential field functions are added into the threeMaintaining the function value in a reasonable range; finally, judging the safety, and judging whether the direction of the control input vector is at the obstacle angle theta or not by calculating the obstacle angle occupied by all obstacle vehicles in the sensible range by each individual vehicleavoiWithin the range, the safety of the driving behavior is predicted.
Description
Technical Field
The invention belongs to the field of intelligent vehicle formation control, and particularly relates to a pigeon group-simulated intelligent vehicle formation cooperative self-adaptive cruise control switching method.
Background
With technological progress and increasing human needs, smart cars are increasingly coming into view of people. At present, the intelligent vehicle team cooperative control technology has become a significant subject in the field. In 1986, the agency of path (partners for Advanced Transit and highway) was established at university of california at berkeley division, usa, which motivated the hot tide of fleet cooperative control research work. Two collaborative driving challenges (GCDC) have been held in europe in 2011 and 2016, aiming to promote the development of Collaborative Adaptive Cruise Control (CACC) and alleviate the current world traffic problems. The current team cooperative control research gradually has three major branches: the method comprises the following steps that firstly, the research direction of longitudinal cooperative Control is focused on Adaptive Cruise Control (ACC); the second is the lateral cooperative control research direction, the research focus of which is Lane changing and Lane Keeping (LKA); and thirdly, the comprehensive cooperative control research direction, wherein the research focus is the vehicle obstacle avoidance direction and the like. The cooperative self-adaptive cruise control belongs to a longitudinal coordination control technology, has very important significance for guaranteeing the safety of a large-flow traffic flow in the future, but is novel in field, and has a plurality of urgent researches because relevant theories and methods are not mature.
Pigeon swarm intelligence is a new concept in the field of swarm intelligence, and is proposed and developed rapidly by professor on the shore in the country. For example, the prefecture seashore et al is inspired by a pigeon nest homing mode to provide a pigeon nest optimization algorithm, and two different operator models are built in: map, compass operator and landmark operator, which creatively broadens the new direction of group intelligent research; boyi CHEN and the like propose a quantum mixing-based pigeon swarm optimization algorithm, and improve the multi-peak problem and the non-convex problem under high dimension in the original algorithm by using a small sample size.
The pigeon flock in nature has two formation interaction modes, namely an equal interaction mode and a hierarchical control mode. When the curvature of the flying curve of the pigeon group is larger (namely the flying state is not stable), the pigeon group is more inclined to adopt an equal interaction mode. In this mode, the individual pigeons are influenced by the neighboring pigeons within the sensible range, the flight plan of the body is determined according to the flight plan (flight direction, flight speed) of the neighboring pigeons, and the influence degree of each neighboring pigeons in the body decision plan is the same. Therefore, the consistency of the individual distance and the flight speed vector in the flying formation is kept, and the formation form is reasonable. When the curvature of the flight curve of the pigeon group is small (namely, the flight state is stable), the pigeon group is more inclined to adopt a hierarchical control mode. In this mode, the flight plan of an individual pigeon is affected by both a neighboring ordinary pigeon and a neighboring dominant pigeon. And in the body flight decision, the influence degree of the neighbor dominant pigeons is larger than that of the neighbor ordinary pigeons. (neighbor dominant pigeons, namely pigeons with superior inherent conditions in a pigeon group, such as pigeons with farther visual field and healthier conditions) are in a flatter interaction mode, the decision efficiency of the hierarchical control mode is higher, and the formation reaction is more sensitive; but the equal interaction mode can greatly ensure the safety of each individual pigeon, and the overall safety degree is improved.
However, the existing pigeon swarm intelligent concept is only used for unmanned aerial vehicle cluster battle and spacecraft formation, and a large research space is still left in other directions such as vehicle formation cooperative control and the like. The longitudinal cooperative control aims at improving the efficiency and the safety of vehicle formation adaptive cruise, but because the road condition in the real environment is extremely complex and the control difficulty of cluster formation is higher, a mature and complete vehicle team cooperative adaptive control strategy does not exist at present.
Disclosure of Invention
In order to solve the problems, the invention provides a pigeon group-simulated vehicle formation cooperative adaptive cruise control switching method, which is used for ensuring that the running speed of a fleet is in an ideal range and the vehicle formation form is kept reasonable under the working conditions of different road congestion degrees, so that the speed of the fleet passing through a congested road section and the safety of single vehicles are improved, and traffic jam and traffic accidents are reduced.
The invention provides a pigeon group imitating intelligent vehicle formation cooperative self-adaptive cruise control switching method, which adopts two cooperative control modes: equal interaction mode (equaliarian mode, noted as mode)e) And hierarchical control mode (hierarchical mode), denoted as modeh). The overall control flow chart is shown in fig. 1. Firstly, each individual vehicle calculates the road congestion according to the number of obstacle vehicles in the perception rangeAnd (4) extruding degree. Congestion degree delta calculated for all individual carsiAre all larger than the set road congestion degree threshold deltamaxThen the fleet enters into a hierarchical control modeh(ii) a The degree of congestion δ calculated if there is any one individual vehicleiLess than the congestion threshold deltamaxThen the fleet enters into an equal interaction modee. Then, under the current state, each individual vehicle i in the fleet marks the neighbor vehicle j within a distance with lower communication delay to obtain a neighbor vehicle set
(or
) And then, carrying out control input, wherein the control input comprises formation control gain, target point control gain and follow-up control gain. And all three maintain the function value within a reasonable range by adding a potential field function. In the two modes, the following control gain of the equal interaction mode assigns the weight of the influence of all the other neighbor pigeons on the body pigeon to 1; and the following control gain of the hierarchical control mode assigns a weight w to the influence of the neighbor dominant pigeon on the body, and assigns a value of 1 to the influence weight of the neighbor ordinary pigeon on the body. And finally, judging the safety direction, and carrying out secondary check on the rationality of control input. Each individual vehicle judges whether the direction of the control input vector is at the obstacle angle theta by calculating the obstacle angle occupied by all obstacle vehicles in the sensible rangeavoiWithin the range, the safety of the driving behavior is predicted.
The invention has the beneficial effects that:
(1) the cooperative control of the motorcade is combined with the characteristics of pigeon group interaction modes for the first time, and through potential field functions and gradient operation, the motorcade can keep a relatively ideal speed to pass through a set road area by formation with high flexibility under working conditions with different crowdedness degrees, so that traffic congestion is relieved, and the safety, high speed and sensitivity of road running of the motorcade are improved.
(2) The formation potential field function is simple in form, complex mathematical operations such as logarithm and index are avoided, and the operation efficiency is improved when the distance between an individual vehicle and a neighbor vehicle is controlled. And the dynamic characteristics of the system are improved by following the proportional-differential control formed by the potential field function and the formation potential field function.
(3) The potential field function and the obstacle angle are introduced, the obstacle angle is used as a secondary check of control input rationality, the group accident risk caused by unreasonable group decision is reduced, and the driving safety of the motorcade is improved.
(4) The pigeon team intelligent concept is introduced into the motorcade cooperative control field for the first time, so that the application of group intelligence in the motorcade cooperative field is widened, and the problems of road blockage and road safety in the driving of the motorcade in a crowded traffic environment are solved.
Drawings
FIG. 1 is a schematic control flow diagram of an intelligent vehicle formation cooperative adaptive cruise control switching method of an imitation pigeon flock
Detailed Description
The invention will be further explained with reference to the drawings.
As shown in fig. 1, the implementation of the present invention comprises the following steps:
step 1 road congestion degree judgment
When the vehicles are formed to run in a certain road area, each individual vehicle can automatically calculate the perceptible area R of the individual vehiclevisionDegree of congestion of the inner road. If the calculated congestion degree of all the individual vehicles is less than the congestion degree threshold value deltamaxThen the group enters the hierarchical control modeh(ii) a Otherwise, entering an equal interaction modee. Considering that the vehicle formation has N vehicles, the dynamic model of each individual vehicle i is
Wherein xi，vi，PiRespectively, the position vector, velocity vector and control input, m, of the individual vehicle iiIs the mass of the individual vehicle i. At RvisionDegree of congestion δ detected by individual vehicle iiIs composed of
Wherein α is a visual blur factor, NdiIs at RvisionThe number of detected obstacle vehicles. Congestion degree threshold δmaxIs composed of
And K is a switching mode safety factor set manually and needs to be selected according to actual conditions. When the vehicle formation runs on an open road and the formation is loose, K can be 3; k is 1 when the formation is very tight on a non-open road; otherwise K is taken to be 2. When deltai>δmaxWhen the vehicle formation is 1,2 and … … N, the vehicle formation adopts a hierarchical control mode and enters into a modeh(ii) a When there is any one individual vehicle deltai<δmaxWhen the vehicle formation adopts an equal interaction mode, entering a modee。
Step 2 neighbor vehicle determination
When the fleet needs to determine the neighbor vehicle set within the sensing range of the fleet in real time. The following definitions can therefore be made according to the two control modes.
(1) And judging neighbor vehicles in an equal interaction mode. For defining neighbor set of individual vehicle i in equal interaction mode
Is shown as
Wherein the content of the first and second substances,
is the communication distance of the individual vehicle with lower communication delay in the equal interaction mode; j is 1,2, … N is the individual vehicle in the formation of the obstacle-removing vehicle in the communication distance. x is the number ofijIs the position vector between vehicle individual i and "neighbor" vehicle individual j.
(2) And judging neighbor vehicles in the hierarchical interaction mode. For defining neighbour sets of individual vehicles i in hierarchical control mode
Expressed as:
wherein the content of the first and second substances,
is a communication distance of lower communication delay of the individual vehicle in the hierarchical control mode; j is 1,2, … N is the individual vehicle in the formation of the obstacle-removing vehicle in the communication distance.
Step 3 input control
The individual vehicle determines the driving elements of the body according to the driving information of the neighbor vehicle: the individual vehicle performs potential function gradient calculation on the displacement difference vector, the speed difference vector and the like of the neighbor vehicle relative to the body of the individual vehicle, and input control of the body is obtained. . Under the equal interaction state, the influence weights of the neighbor vehicles on the decision of the body vehicle are the same and are all assigned to 1. In the hierarchical control mode, the influence weight of the neighbor dominant vehicle on the body vehicle decision is w, and the influence weight of the neighbor ordinary vehicle on the body vehicle decision is 1.
(1) In the equal interaction mode, control is input
Is defined as:
wherein Pro is a cluster of dominant vehicles, i ∈ Pro.Kf>0 is the formation control gain, Kt>0 is the target point control gain, Kv>0 is the follow-up control gain; x is the number oftIs the target center point position vector, vij＝vi-vjIs a vehicle individual i and a 'neighbor' vehicleVelocity difference vector between vehicle individuals j.
Formation potential field function
Is composed of
Wherein R isexpIs the desired distance between individuals, RminIs the minimum safe distance between individuals, RmaxThe maximum safe distance between individuals.
In order to achieve the above-mentioned safety range,
in order to achieve the lower safety range,
target potential field function
Is composed of
Wherein R istIs the target area radius.
Following potential field function
Comprises the following steps:
wherein v isexpDesired speed, v, for individual vehiclesminIs the individual vehicle minimum speed, vmaxIs the individual vehicle maximum speed.
(2) In a hierarchical control mode, control is input
Is defined as:
the definition of the formation potential field function, the target point potential field function and the following potential field function is the same as the definition of the formation potential field function, the target point potential field function and the following potential field function. w is the follow-up control weight, which must be calibrated according to the actual road conditions.
Step 4 safe direction determination
After the individual vehicle obtains the control input, the reasonability of the control input needs to be checked, and the individual safety reduction caused by group decision is avoided. The following definitions are made: after the individual vehicle i is input and controlled, a real-time obstacle avoidance angle can be calculated according to surrounding obstacle vehicles. If the direction of the input control speed v is in the range of the obstacle angle space set, keeping the current vehicle speed and other states, and abandoning the previous step of input control; if the direction of the velocity v is outside the range of the obstacle angle space set, the input control may be performed. For an individual vehicle i, at its perceivable distance RvisionIn-vehicle detected obstacle vehicle NdAngle of obstacle theta thereofavoiIs composed of
Wherein R isobsiIs the distance, θ, from the individual vehicle i to the obstacle vehicleobsiThe included angle between the speed direction of the individual vehicle i and a connecting line between the vehicle i and the barrier vehicle is shown as α, and the included angle is a safety angle coefficient and needs to be calibrated according to the actual road condition.
The above-listed series of detailed descriptions are merely specific illustrations of possible embodiments of the present invention, and they are not intended to limit the scope of the present invention, and all equivalent means or modifications that do not depart from the technical spirit of the present invention are intended to be included within the scope of the present invention.
Claims (9)
1. A pigeon swarm-imitating intelligent vehicle formation cooperative self-adaptive cruise control switching method is characterized in that firstly, each individual vehicle calculates the road congestion degree according to the number of obstacle vehicles in a perception range, and if all the individual vehicles calculate the congestion degree deltaiAre all larger than the set road congestion degree threshold deltamaxThen the fleet enters into a hierarchical control modeh(ii) a The degree of congestion δ calculated if there is any one individual vehicleiLess than the congestion threshold deltamaxThen the fleet enters into an equal interaction modee(ii) a Then, under the current state, each individual vehicle i in the fleet marks a neighbor vehicle j within a distance with lower communication delay so as to obtain a neighbor vehicle set
Or
Then, control input is carried out, wherein the control input comprises formation control gain, target point control gain and following control gain, and the function values of the three are maintained in a reasonable range by adding a potential field function; in the two modes, the following control gain of the equal interaction mode assigns the weight of the influence of all the other neighbor pigeons on the body pigeon to 1; the following control gain of the hierarchical control mode assigns a weight w to the influence of the neighboring dominant pigeon on the body, and the weight of the influence of the neighboring ordinary pigeon on the body is assigned as 1; finally, judging the safe direction, carrying out secondary check on the rationality of the control input, and judging whether the direction of the control input vector is at an obstacle angle theta or not by calculating obstacle angles occupied by all obstacle vehicles in a sensible range by each individual vehicleavoiWithin the range, the safety of the driving behavior is predicted.
2. The pigeon flock-imitating intelligent vehicle formation cooperative adaptive cruise control switching method according to claim 1, characterized by comprising the following steps:
step 1, judging the road congestion degree, and selecting to enter a corresponding formation mode according to the congestion degree;
step 2, judging neighbor vehicles, and dividing different neighbor sets according to different control modes;
step 3, setting input control;
and 4, judging the safety direction.
3. The pigeon flock-imitating intelligent vehicle formation cooperative adaptive cruise control switching method according to claim 1, wherein the step 1 is specifically as follows:
when a vehicle formation is arranged to drive in a certain road area, each individual vehicle can automatically calculate the perceptible area R of the individual vehiclevisionDegree of congestion of inner roads, degree of congestion δ calculated if all individual vehiclesiAre all less than the congestion degree threshold deltamaxThen the group enters the hierarchical control modeh(ii) a Otherwise, entering an equal interaction modee。
4. The method for switching intelligent vehicle formation cooperative adaptive cruise control according to claim 3, wherein the congestion degree and the congestion degree threshold value in the step 1 are calculated as follows:
the vehicle formation is set to have N vehicles, and the dynamic model of each individual vehicle i is
Wherein xi，vi，uiRespectively, the position vector, velocity vector and control input, m, of the individual vehicle iiIs the mass of the individual vehicle i;
at RvisionDegree of congestion δ detected by individual vehicle iiIs composed of
Wherein α is a visual blur factor, NdiIs at RvisionThe number of detected obstacle vehicles;
congestion degree threshold δmaxIs composed of
Where K is the manually set switching pattern safety factor.
5. The pigeon flock-imitated intelligent vehicle formation cooperative adaptive cruise control switching method according to claim 1, wherein the step 2 is specifically realized as follows:
the fleet determines in real time a set of neighbour vehicles within its sensible range, said set of neighbour vehicles being defined according to two control modes as follows:
(1) and (3) judging neighbor vehicles in an equal interaction mode: for defining neighbor set of individual vehicle i in equal interaction mode
Is shown as
Wherein the content of the first and second substances,
is the communication distance of the individual vehicle with lower communication delay in the equal interaction mode; j is 1,2, … N is the individual vehicle in the formation of the obstacle-removing vehicle in the communication distance. x is the number ofijIs the position vector between the vehicle individual i and the "neighbor" vehicle individual j;
(2) judging neighbor vehicles in a hierarchical interaction mode: for defining neighbour sets of individual vehicles i in hierarchical control mode
Expressed as:
wherein the content of the first and second substances,
is a communication distance of lower communication delay of the individual vehicle in the hierarchical control mode; j is 1,2, … N is the individual vehicle in the formation of the obstacle-removing vehicle in the communication distance.
6. The pigeon flock-imitated intelligent vehicle formation cooperative adaptive cruise control switching method according to claim 1, wherein the specific implementation of the step 3 comprises the following steps:
in the equal interaction mode, control is input
Is defined as:
wherein Pro is a cluster of dominant vehicles, i ∈ Pro.Kf>0 is the formation control gain, Kt>0 is the target point control gain, Kv>0 is the follow-up control gain; x is the number oftIs the target center point position vector, vij＝vi-vjIs the velocity difference vector between the vehicle individual i and the "neighbor" vehicle individual j;
formation potential field function
Is composed of
Wherein R isexpIs the desired distance between individuals, RminIs an individualWith a minimum safety distance, RmaxThe maximum safe distance between individuals.
In order to achieve the above-mentioned safety range,
in order to achieve the lower safety range,
target potential field function
Is composed of
Wherein R istIs the target area radius;
following potential field function
Comprises the following steps:
wherein v isexpDesired speed, v, for individual vehiclesminIs the individual vehicle minimum speed, vmaxIs the individual vehicle maximum speed.
7. The pigeon flock-imitated intelligent vehicle formation cooperative adaptive cruise control switching method according to claim 6, wherein the specific implementation of the step 3 further comprises the following steps:
in a hierarchical control mode, control inputs
Is defined as:
the definitions of the formation potential field function, the target point potential field function and the following potential field function are the same as those of claim 6, and w is a following control weight and is calibrated according to the actual road condition.
8. The pigeon swarm-imitating intelligent vehicle formation cooperative adaptive cruise control switching method according to claim 7, wherein in an equal interaction state, influence weights of neighbor vehicles on a body vehicle decision are the same and are all assigned to 1; in the hierarchical control mode, the influence weight of the neighbor dominant vehicle on the body vehicle decision is w, and the influence weight of the neighbor ordinary vehicle on the body vehicle decision is 1.
9. The pigeon flock-imitating intelligent vehicle formation cooperative adaptive cruise control switching method according to claim 1, wherein the step 4 is specifically as follows:
after the individual vehicle i is input and controlled, calculating a real-time obstacle avoidance angle according to surrounding obstacle vehicles; if the direction of the input control speed v is in the range of the obstacle angle space set, keeping the current vehicle speed and other states, and abandoning the previous step of input control; if the direction of the speed v is outside the obstacle angle space set range, performing input control;
for an individual vehicle i, at its perceivable distance RvisionIn-vehicle detected obstacle vehicle NdAngle of obstacle theta thereofavoiIs composed of
Wherein R isobsiIs the distance, θ, from the individual vehicle i to the obstacle vehicleobsiIs the speed of the individual vehicle iAn included angle between the vehicle i and a connecting line of the obstacle vehicle is α, and the included angle needs to be calibrated according to the actual road condition.
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| CN115454099A (en) * | 2022-10-11 | 2022-12-09 | 北京航空航天大学 | Unmanned ship course active disturbance rejection control method based on variable weight self-adaptive pigeon swarm parameter fitting |
| CN115454099B (en) * | 2022-10-11 | 2024-04-30 | 北京航空航天大学 | Unmanned ship course active disturbance rejection control method based on variable weight self-adaptive pigeon group parameter fitting |
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