CN116331256A - Track changing planning method, track changing planning equipment and track changing planning storage medium for distributed driving electric automobile - Google Patents
Track changing planning method, track changing planning equipment and track changing planning storage medium for distributed driving electric automobile Download PDFInfo
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Abstract
The invention discloses a lane change track planning method, equipment and storage medium for a distributed driving electric automobile, wherein the lane change track planning method comprises the following steps: generating an unconstrained generalized lane change track cluster according to the vehicle track planning curve fitting function; selecting a lane change track cluster meeting the stability domain of the distributed driving electric automobile from the generalized lane change track clusters; in a lane change track cluster meeting the stable domain of the distributed driving electric automobile, calculating the feasible domain of the automobile according to the geometric constraint of the environment and the road boundary; based on an improved algorithm combining an analytic hierarchy process and a technology approaching to ideal, an optimal lane change track is selected by evaluating a stability index, a track tracking accuracy index, a comfort index and a lane change efficiency index. The path planning algorithm fully utilizes the advantage of independent and controllable four wheels of the distributed driving electric vehicle, integrates new dynamic characteristics of the vehicle into the path planning algorithm, and improves the running efficiency and safety of the electric vehicle.
Description
Technical Field
The invention relates to a lane change track planning method of a distributed driving intelligent electric vehicle, relates to a path planning technology of the distributed driving intelligent electric vehicle, and belongs to the field of new energy vehicle design and manufacturing.
Background
Compared with the traditional centralized electric vehicle capable of changing oil into electricity, the distributed driving electric vehicle is a brand new chassis driving configuration, a driving motor is directly arranged in or near a driving wheel, and the chassis of the novel power configuration has the advantage of full-side displacement capacity and gradually develops towards the direction of a digital chassis, so that the novel power configuration is evaluated by automobile field experts as a special chassis of the intelligent electric vehicle and an optimal carrier for realizing safe and efficient unmanned driving, and has become a mainstream trend of the development of the intelligent electric vehicle in the future. In recent years, the quantity of automobiles kept is continuously increased, so that problems are increasingly highlighted, and traffic accidents are frequent and traffic jams are aggravated. The electric, internet-connected and intelligent of the automobile become the trend of future development, so the high intelligent of the distributed driving electric vehicle becomes the necessary trend of future development. The automatic driving technology is classified into 0 level to 5 levels, and the dependency of a vehicle system on a driver is smaller along with the increase of the automobile driving automation level, the automatic driving level of the intelligent electric automobile produced in mass at the present stage is in the range of 2 levels to 3 levels, in other words, the more mature automatic driving technology can only realize the automatic driving function with the condition, so that the intelligent electric automobile automatic driving technology needs to improve the adaptability to different traffic scenes and keep the vehicle stability in scenes such as high-speed collision avoidance, emergency obstacle avoidance and the like. The distributed driving electric automobile has higher control freedom degree, the research and development difficulty of the automobile intelligent technology is higher, and the distributed driving electric automobile is intelligent and challenging.
The vehicle track planning technology is one of important links in the implementation of the automatic driving technology, and as the vehicle generally runs in a complex traffic environment or runs at a high speed, the requirement of a vehicle track planning algorithm is generally higher than that of an indoor robot, and more kinematic and dynamic factors need to be considered. Common trajectory planning algorithm classifications include graph search-based algorithms, curve fitting-based algorithms, numerical optimization-based algorithms, artificial potential field-based algorithms, sampling-based algorithms, intelligent method-based algorithms, and the like. The vehicle lane change track planning is one of common traffic scenes, when a vehicle runs on a road and overtakes, avoids obstacles and other scenes, a driving track conforming to the driving habit of a human is required to be planned in advance, meanwhile, the vehicle stability constraint, the environment geometric constraint and the road boundary constraint are met, the lane change efficiency of the planned path is high, the optimal path which is easy to track and control is planned, and the multi-target optimization of the track is realized. At present, the track planning algorithm of the centralized intelligent electric automobile is generally conservative, dynamic factors are ignored due to relatively more kinematic factors, the final result of the path planning is greatly influenced by the study of the stability domain rationality of the distributed driving electric automobile, and how to combine the kinematics and the dynamics tightly to carry out the vehicle planning becomes one of the key problems of the track planning of the distributed driving intelligent electric automobile, so that the track planning algorithm is one of the requisite routes for the high intellectualization of the automobile. Under the large background of vehicle intellectualization, it is important to fully utilize the stable domain of the distributed driving electric vehicle to carry out upper-layer path planning, and the relationship between the intelligent driving layer and the chassis control layer of the distributed driving electric vehicle is dense and inseparable.
Disclosure of Invention
The invention aims to solve the key problem of track switching planning of a distributed driving intelligent electric automobile, and provides a track switching planning method, a track switching planning device and a storage medium of the distributed driving intelligent electric automobile. The path planning algorithm fully utilizes the advantage that four wheels of the distributed driving electric automobile are independently controllable, the dynamic performance of the distributed driving electric automobile is improved compared with that of the distributed driving electric automobile, the kinematics and dynamic characteristics of the electric automobile are integrated into the path planning algorithm, and the running efficiency and the running safety of the electric automobile are improved.
The technical scheme adopted by the invention for solving the technical problems comprises the following steps:
the track change planning method for the distributed driving electric automobile is characterized by comprising the following steps of:
generating an unconstrained generalized lane change track cluster according to the vehicle track planning curve fitting function;
selecting a lane change track cluster meeting the stability domain of the distributed driving electric automobile from the generated unconstrained generalized lane change track clusters;
In the selected lane change track cluster meeting the stable domain of the distributed driving electric automobile, calculating the feasible domain of the automobile according to the geometric constraint of the environment and the road boundary;
in the calculated feasible domain of the vehicle, the optimal lane change track is selected by evaluating a stability index, a track tracking accuracy index, a comfort index and a lane change efficiency index based on an improved algorithm combining an analytic hierarchy process and a technology approaching to ideal.
Designing an unconstrained generalized lane-change track cluster comprises the following parts:
(I) And determining the optimal longitudinal displacement, speed, acceleration and jerk expression.
a) It is desirable to minimize longitudinal fluctuations and construct performance metrics that minimize longitudinal fluctuations:
the following conditional constraints need to be satisfied:
wherein eta x To longitudinal wave-motion cost function, min eta x A performance index minimum value for minimizing longitudinal fluctuation for solving; τ 0 For the initial moment of the track change,
for ending time of channel changing process, setting initial time tau 0 =0, so the time of the lane change process is +.>
x (t) isLongitudinal displacement function during lane changing of vehicle, < >>
And v x (t) represents the longitudinal vehicle speed during a lane change of the vehicle;
And a x (t) represents the longitudinal acceleration during lane change of the vehicle; / >
And j x (t) represents the longitudinal jerk during a lane change of the vehicle; l is the longitudinal distance of the lane change of the vehicle, v x0 For the initial value of the longitudinal speed during the track change, < >>
The final value of the longitudinal speed in the channel changing process;
b) Constructing Hamiltonian H for solving the performance index x Is that
Wherein, kappa x1 To correspond to v x Lagrangian, kappa x2 For corresponding a x Lagrangian, kappa x3 For corresponding j x Is a lagrangian of (c).
c) According to the pointrian maximum principle, the equation of the synergy is expressed as:
wherein m is 0 、m 1 And m 2 Is a pending constant.
The extreme value conditions are as follows:
Wherein,,
d) Under the geodetic coordinate system, the expression of the optimal longitudinal displacement, speed, acceleration and jerk in the course of channel changing can be obtained according to the steps:
wherein j is x (t)、a x (t)、v x (t) and x (t) represent functions of longitudinal jerk, acceleration, velocity and displacement of the vehicle, respectively, t being an argument of time as a function,
for lane change time, L is the longitudinal distance of the vehicle lane change, v x0 Is the initial value of the longitudinal speed.
(II) determining the optimal lateral displacement, speed, acceleration and jerk expression.
a) It is desirable to minimize lateral fluctuations and construct performance metrics that minimize lateral fluctuations:
The following conditional constraints need to be satisfied:
wherein eta y For sideward fluctuating cost function, min eta y A performance index minimum to minimize lateral fluctuations for the solution; τ 0 For the initial moment of the track change,
for ending time of channel changing process, setting initial time tau 0 =0, so the time of the lane change process is +.>
y (t) represents the lateral displacement function during a lane change of the vehicle, < >>
And v y (t) represents the lateral vehicle speed during a lane change of the vehicle;
And a y (t) represents lateral acceleration during lane change of the vehicle;
And j y (t) represents lateral jerk during a lane change of the vehicle; d is the lateral distance of the lane change of the vehicle; v y0 For the initial value of the lateral speed during the track change, < >>
For lateral speed during lane changeA final value;
b) Constructing Hamiltonian H for solving the performance index y Is that
Wherein, kappa y1 To correspond to v y Lagrangian, kappa y2 For corresponding a y Lagrangian, kappa y3 For corresponding j y Is a lagrangian of (c).
c) According to the pointrian maximum principle, the equation of the synergy is expressed as:
Wherein n is 0 、n 1 And n 2 Is a pending constant.
The extreme value conditions are as follows:
Wherein,,
d) Under the geodetic coordinate system, the expression of the optimal lateral jerk, acceleration, speed and displacement in the course of channel changing can be obtained according to the steps as follows:
Wherein j is y (t)、a y (t)、v y (t) and y (t) represent functions of lateral jerk, acceleration, velocity and displacement of the vehicle, respectively, t being an argument of time as a function,
for lane change time, D is the lateral distance of the vehicle lane change, v y0 Is the initial value of the lateral velocity.
And (III) determining a lane change track expression of the intelligent electric automobile based on the distributed driving of the fifth order polynomial.
In the course of changing lane, the longitudinal speed of the vehicle is kept constant, and the longitudinal acceleration does not change to achieve the purpose of minimizing longitudinal fluctuation of the vehicle, so that the method can obtain:
v x (t)＝v x0
wherein v is x (t) represents a longitudinal speed function in the lane change process, v x0 Is an initial value of the longitudinal vehicle speed.
The distributed driving intelligent electric vehicle lane change track expression based on the quintic polynomial is as follows:
wherein x (t)) For the longitudinal displacement in the lane changing process, y (t) is the transverse displacement in the lane changing process, t is the independent variable of the function, and the vehicle speed v is at a certain initial speed x0 Down, different channel changing time
Thus obtaining a series of unconstrained generalized lane-changing track clusters.
The track change cluster for screening the stable domain of the distributed driving electric automobile comprises the following parts:
(I) Stability domain mechanism analysis.
a) Analyzing the influence mechanism of the stability domain of the distributed driving electric automobile, and building a four-wheel vehicle model of the distributed driving electric automobile, wherein the dynamics equation is as follows
Wherein F is yij For the tire side force, the subscripts ij=fl, fr, rl, rr denote the tire front left wheel, front right wheel, rear left wheel, and rear right wheel, respectively, r denotes the yaw rate,
is the first derivative of yaw rate; beta represents the centroid slip angle, +.>
Is the first derivative of the centroid slip angle; v x Is the longitudinal vehicle speed; delta f Is the front wheel corner of the vehicle; a is the distance from the center of gravity of the vehicle to the front axle, and b is the distance from the center of gravity of the vehicle to the rear axle; l (L) f For the wheelbase of the front axle of the vehicle, l r The rear axle distance of the vehicle is; m is the mass of the whole vehicle; i z Is the moment of inertia about the z-axis.
b) Tyre side deflection angle alpha ij The calculation formula is as follows:
wherein alpha is ij For the tire slip angle, the subscripts ij=fl, fr, rl, rr denote the left front wheel, the right front wheel, the left rear wheel, and the right rear wheel of the tire, respectively.
c) Tyre vertical load F zij The calculation formula is as follows:
wherein F is zij For the tire vertical load, the subscripts ij=fl, fr, rl, rr denote the left front wheel, right front wheel, left rear wheel, and right rear wheel of the tire, respectively; a, a x For vehicle longitudinal acceleration, a y Is the vehicle lateral acceleration; h is the height of the vehicle centroid.
d) Calculating a tire side force equation based on the Fiala tire model:
wherein F is yij For tire lateral force, the subscripts ij=fl, fr, rl, rr denote the left front wheel, right front wheel, left rear wheel, and right rear wheel of the tire, respectively; c (C) α Is tire cornering stiffness; alpha slij For the slip angle corresponding to the tire entering the saturation region, subscripts ij=fl, fr, rl and rr respectively represent a left front wheel, a right front wheel, a left rear wheel and a right rear wheel of the tire; mu is the road adhesion coefficient.
(II) performing a vehicle stability domain mechanism analysis based on a phase plane method.
a) Listing a differential equation set according to the system state equation:
from the above formula:
wherein x is 1 、x 2 Is a state parameter of the vehicle system, f 1 (x 1 ,x 2 )、f 2 (x 1 ,x 2 ) Is a differential equation for the vehicle system.
b) In a vehicle system, an initial state x is assumed 0 ＝(x 1 (0),x 2 (0) A) the starting state trajectory x (t), which remains in a local range, meets the following conditions:
where x (t) is a function of the vehicle state parameter with respect to time variation,
is to determine constant, fullThe foot condition then progressively stabilizes the system over a local range so the system is now stable. In the phase plane analysis, the stable track finally converges to the balance point, and the unstable track cannot converge and finally diverges.
(III) based on a phase plane analysis method, carrying out stability domain analysis on different states of the vehicle.
The influence of different absolute speeds of the vehicle, road adhesion coefficients and front wheel rotation angles on the stability domain of the vehicle is respectively selected, and the specific steps comprise the following steps:
a) According to the phase plane analysis method, a function equation of a vehicle centroid slip angle beta and a yaw rate r can be obtained, beta is an independent variable of a function, r is an independent variable of the function, and a function expression of a stability domain is as follows:
B in the above functional expression 0 、b 1 、b 2 、b 3 The method comprises the following steps of:
b 0 ＝b/v x ，b 1 ＝tan(α slrl +α slrl )，b 2 ＝(r 2 -r 1 )/(β 2 -β 1 )，b 3 ＝r 1 -β 1 (r 2 -r 1 )/(β 2 -β 1 )
r 1 ＝ug/v x ，r 2 ＝v x /(a+b)(tan((α slfl +α slfr )/2+δ max )-tan((α slrl +α slrr )/2))，
β 2 ＝b/(a+b)(tan((α slfl +α slfr )/2+δ max )-tan((α slrl +α slrr )/2))+tan((α slrl +α slrr )/2)。
delta in the above max The expression is:
wherein b 0 、b 1 、b 2 、b 3 Undetermined coefficients, r, of the stable domain function expressions, respectively 1 、r 2 、β 1 、β 2 、δ max As an intermediate variable; alpha slfl 、α slfr 、α slrl 、α slrr The corresponding slip angles of the left front tire, the right front tire, the left rear tire and the right rear tire enter a saturation region are respectively; a is the distance from the center of gravity of the vehicle to the front axle, and b is the distance from the center of gravity of the vehicle to the rear axle; v x μ is the road adhesion coefficient, g is the gravitational acceleration, which is the longitudinal speed of the vehicle.
b) According to the dividing range of the stable domains, different absolute vehicle speeds, road surface adhesion coefficients and front wheel corners of the vehicle are respectively selected to analyze the different stable domains, and the specific steps are as follows:
when the road surface attachment coefficient and the front wheel steering angle are kept unchanged, respectively selecting different absolute vehicle speeds 5, 10, 15, 20, 25, 30, 35 and 40m/s for respectively carrying out stability domain analysis;
when the absolute vehicle speed and the front wheel steering angle are kept unchanged, respectively selecting different road surface attachment coefficients of 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9 and 1.0 for respectively carrying out stability domain analysis;
when the absolute vehicle speed and the road adhesion coefficient remain unchanged, different front wheel angles-30, -25, -20, -15, -10, -5, 0, 5, 10, 15, 20, 25 and 30deg are respectively selected for stability domain analysis.
c) According to the stability domain set obtained by the analysis, combining a real-time mass center slip angle and yaw rate calculation formula of the vehicle:
wherein v is x For the longitudinal speed of the vehicle, v y For the lateral speed of the vehicle, a y The lateral acceleration of the vehicle is represented by r, the yaw rate of the vehicle, and β, the centroid slip angle of the vehicle.
Judging whether the vehicle state exceeds the range of the stable domain according to the centroid side deviation angle and the yaw rate, and removing the track change track exceeding the stable domain, so as to finally reserve the track change track meeting the stable domain of the vehicle.
Considering surrounding vehicles, pedestrians and the like as environmental geometric constraints, and lane lines, traffic rules and the like as road boundaries, the global feasible region of the vehicle is calculated to comprise the following parts:
(I) Taking the geometric centers of the vehicle and the front vehicle, taking the geometric center to the maximum length of the vehicle body as the geometric circle radius, and respectively recording R l And R is f The geometric circle is drawn, so that the tangent point of the geometric circle of the vehicle and the front vehicle becomes the critical point of collision, and therefore, the boundary line of a global feasible region can be obtained.
(II) considering the constraint of lane lines, traffic rules or rear vehicles, wherein the tangent point of the geometric circle or lane lines of the vehicle and the rear vehicle becomes a critical point of collision, and combining the steps, the closed global feasible region of the vehicle can be obtained, and the track clusters meeting the constraint of the geometric constraint, the lane lines and the traffic rules of the environment are further screened out from the track clusters meeting the stability of the distributed driving electric vehicle.
Based on an improved algorithm combining an Analytic Hierarchy Process (AHP) and a technique approaching ideal (TOPSIS), selecting an optimal lane change track by evaluating a stability index, a track tracking accuracy index, a comfort index and a lane change efficiency index comprises the following parts:
(I) And (5) establishing an evaluation index.
a) Respectively establishing evaluation indexes such as stability indexes, track tracking accuracy indexes, comfort indexes, track changing efficiency and the like of track changing track planning:
building a vehicle stability index:
wherein J is s As an index of the evaluation of the stability of the vehicle,
for changing track time, F yi (t) is a functional expression of the lateral force of the front axle or the rear axle with respect to time, F zi (t) is a functional expression of the vertical load of the front axle or the rear axle with respect to time,/->
Is the threshold value of the road adhesion coefficient.
b) Constructing an index of vehicle track tracking accuracy:
wherein J is t Is an evaluation index of the tracking accuracy of the vehicle track,
for lane change time, v x For the longitudinal speed of the vehicle,
for the vehicle centroid slip angle angular velocity function expression,/->
Is a centroid slip angle threshold value; h (t) is an ideal planned trajectory of the vehicle; y (t) is the actual driving track of the vehicle;
And the threshold value of the error between the ideal planning track and the actual running track is set.
c) Constructing vehicle comfort indexes:
wherein J is c As an index of the comfort evaluation of the vehicle,
for the channel change time, a y (t) is the longitudinal acceleration of the vehicle, < >>
For the vehicle lateral acceleration threshold value, θ (t) is the roll angle, ++>
Is the roll angle threshold.
d) Constructing lane change efficiency indexes:
wherein J is e Is an evaluation index of the lane changing efficiency of the vehicle,
is the channel changing time.
(II) improved algorithms based on a combination of Analytic Hierarchy Process (AHP) and technique approaching ideal (TOPSIS).
a) And constructing a judgment matrix. The optimal lane change track cluster is calculated from the track clusters meeting the constraint conditions, so that the complexity of the TOPSIS algorithm used alone in the multi-target calculation process can be overcome, the subjectivity of the AHP algorithm used alone in the calculation process can be overcome, and the target layer A comprises m evaluation indexes A 1 ,A 2 ,A 3 ,…,A m The target layer A corresponds to the definite sound index J of the matrix B respectively 1 ,J 2 ,J 3 ,J 4 ,…,J n A judgment matrix B is constructed, the order of which is n multiplied by n, and the matrix B is as follows:
wherein element b in the matrix ij Planning evaluation index J for lane change track i Evaluation index J for lane change track j Importance level of b ji ＝1/b ij . When (when)Element b ij When the number is equal to 1, the two lane change track planning evaluation indexes are of equal importance; when element b ij When=3, the lane change trajectory planning evaluation index J i Track planning evaluation index J of specific lane change j Slightly important; when element b ij When=5, the lane change trajectory planning evaluation index J i Track planning evaluation index J of specific lane change j Is obviously important; when element b ij When=7, the lane change trajectory planning evaluation index J i Track planning evaluation index J of specific lane change j Is of great importance; when element b ij When=9, the lane change trajectory planning evaluation index J i Track planning evaluation index J of specific lane change j Is of absolute importance; when the value of an element is 2, 4, 6, 8, it is indicated that it is in an intermediate state where the value of the element is 1, 3, 5, 7, 9.
b) Determining the index weight. According to the judgment matrix, calculating the sum of each column, normalizing each column of elements, further adding normalized results according to rows, and calculating to obtain square root vectors, wherein the final normalized square root vectors obtain sequencing weight vectors, and the calculation formula is as follows:
wherein,,
b for normalizing the results for each column of elements ij To determine the elements in the matrix, W i For normalizing the result of the processing by the result of the row addition, W i To rank the weight vectors.
c) Consistency test. Firstly calculating the maximum characteristic root of the judgment matrix B, then checking the consistency of the maximum characteristic root to obtain a consistency ratio, and when the consistency ratio is smaller than 0.1, judging that the consistency of the matrix meets the condition.
Wherein lambda is max For judging the maximum characteristic root of the matrix, the matrix B is the judgment matrix, W i For the ordered weight vectors, w is the element in the ordered weight vector, CI is the consistency check criterion, CR is the consistency ratio, RI is the average random consistency index, and n is the order of the rows or columns of the judgment matrix.
d) And (3) hierarchical total ordering. And calculating the optimal scheme synthesis weight of the index layer relative to the target layer on the basis of the hierarchical single-order result. Assume that target layer A includes m evaluation indexes A 1 ,A 2 ,A 3 ,…,A m Weight a corresponding to target layer evaluation index 1 ,a 2 ,a 3 ,…,a m The index layer J comprises n evaluation indexes J 1 ,J 2 ,J 3 ,J 4 ,…,J n Corresponding to a certain target layer A i The weight of (c) is c 1i ,c 2i ,c 3i ,…,c ni . Therefore, the weights corresponding to the indexes of the index layer are c respectively 1 ,c 2 ,c 3 ,…,c n 。
Wherein c j Weights, c, corresponding to the indexes of the index layer ij To correspond to a certain target layer A i Weight of a), a i And the weight corresponding to the evaluation index of the target layer.
e) Establishing initial evaluation index. Let n evaluation indexes be j= { J 1 ,J 2 ,J 3 ,...,J n Each evaluation index has m characteristic indexes R= { R } 1 ,r 2 ,r 3 ,…,r m Then the initial evaluation matrix is:
wherein r is ij The j index of the i-th evaluation target in the target layer.
f) Matrix normalization. Since each evaluation index has different dimensions, the evaluation indexes are normalized, and the calculation formula is:
The weighted normalization matrix calculation process is as follows:
H＝(v ij ) n×m ＝(ω j r ij ) n×m
wherein r is ij The j-th evaluation index, v, of the i-th evaluation target in the target layer ij Represents the i-th row, j-th column element, ω after weighting j And the j-th evaluation index weight is represented.
g) And calculating positive ideal solution and negative ideal solution and distance between the positive ideal solution and the negative ideal solution. The positive ideal solution means that each evaluation index takes the most ideal value solution, the negative ideal solution means that each evaluation index takes the most difference value solution, and the expression is:
the positive ideal solution is:
the negative ideal solution is:
wherein V is + To be positively and ideally understood, V - Is negative ideal solution, J 1 Is a benefit index set, J 2 Is a cost type fingerMark set, v ij Representing the elements of row i, column j after weighting.
The distances between each evaluation index and the positive ideal solution and the negative ideal solution are as follows:
wherein,,
for the distance of the respective evaluation index from the positive ideal value, < >>
V for the distance between each evaluation index and the negative ideal value ij Represents the elements of row i, column j after weighting,/-column i>
And->
Respectively correspond to the ideal solution V + And negative ideal solution V - Is a component of the group.
h) And calculating the closeness, namely calculating the relative closeness of the evaluation index and the ideal solution, and when the closeness is larger, indicating that the track change track is better.
Proximity degree:
wherein C is i In order for the degree of closeness to be the same, 
For the distance of the respective evaluation index from the positive ideal value, < >>
For the distance between each evaluation index and the negative ideal value, when the closeness degree C i The larger, i.e. the moreWhen the track is close to 1, the track change track is optimal, so that the optimal track change track is finally obtained.
The invention also provides a device characterized by comprising:
one or more processors;
a memory for storing one or more programs;
and when the one or more programs are executed by the one or more processors, the one or more processors are enabled to realize the track changing planning method of the distributed driving electric automobile.
The invention also provides a storage medium, on which a computer program is stored, characterized in that the program, when executed by a processor, implements the track-changing planning method for the distributed driving electric automobile as described above.
Compared with the prior art, the invention has the beneficial effects that:
1. revealing mechanical constraint and stability domain mechanism among all functional subsystems of the distributed driving electric automobile, integrating the kinematics and dynamics characteristics of the automobile into a track planning algorithm, and realizing efficient calculation of an optimal track in upper layer planning of the automobile;
2. based on constraints such as a vehicle stability domain, an environment geometry, a road boundary and the like, dividing a vehicle global feasible domain, and improving the efficiency of calculating the optimal lane change track;
3. The optimal lane change track is selected based on an improved algorithm combining an Analytic Hierarchy Process (AHP) and an ideal Technology (TOPSIS), wherein the stability index, the track tracking accuracy index, the comfort index and the lane change efficiency index are evaluated, so that the complexity of the TOPSIS algorithm in a multi-target calculation process can be overcome, and the subjectivity of the TOPSIS algorithm in the calculation process can be overcome.
Drawings
Fig. 1 is a block diagram of a lane change track planning system for a distributed driving intelligent electric vehicle in an example of the invention.
Fig. 2 is a dynamic model of a distributed drive intelligent electric vehicle.
Fig. 3 is a graph of a stability domain analysis result of the distributed driving intelligent electric vehicle with respect to a vehicle speed.
Fig. 4 is a graph of the stability domain analysis result of the distributed driving intelligent electric vehicle with respect to the adhesion coefficient.
Fig. 5 is a graph of stability domain analysis results of the distributed driving intelligent electric vehicle with respect to the front wheel rotation angle.
Detailed Description
The invention will now be described in further detail with reference to the accompanying drawings. The drawings are simplified schematic representations which merely illustrate the basic structure of the invention and therefore show only the structures which are relevant to the invention.
The invention provides a lane change track planning method of a distributed driving intelligent electric vehicle, which specifically comprises the following steps as shown in fig. 1-5:
The technical scheme adopted by the invention for solving the technical problems comprises the following steps:
step one, according to a curve interpolation method, a vehicle performs curve fitting of a track under certain specific conditions, a quintic polynomial function is selected as a track planning curve fitting function, and an unconstrained generalized track change track cluster is generated;
analyzing and summarizing the influence mechanism of the stability domain of the distributed driving electric automobile, and screening out channel change track clusters meeting the stability domain of the distributed driving electric automobile according to the series of channel change track clusters;
step three, considering surrounding vehicles, pedestrians and the like as environmental geometric constraints, and lane lines, traffic rules and the like as road boundaries, and calculating a feasible region of the vehicles;
and step four, selecting an optimal lane change track by evaluating a stability index, a track tracking accuracy index, a comfort index and a lane change efficiency index based on an improved algorithm combining an analytic hierarchy process (Analytic Hierarchy Process, AHP) and an ideal technology (Technique for Order Preference for Similarity to Ideal Solution, TOPSIS).
As a further preferred aspect of the foregoing solution, the designing of the unconstrained generalized lane-change track cluster in the step one includes the following parts:
(I) And determining the optimal longitudinal displacement, speed, acceleration and jerk expression.
a) It is desirable to minimize longitudinal fluctuations and construct performance metrics that minimize longitudinal fluctuations:
the following conditional constraints need to be satisfied:
wherein eta x To longitudinal wave-motion cost function, min eta x A performance index minimum value for minimizing longitudinal fluctuation for solving; τ 0 For the initial moment of the track change,
for ending time of channel changing process, setting initial time tau 0 =0, so the time of the lane change process is +.>
x (t) is a longitudinal displacement function during lane changing of the vehicle, < >>
And v x (t) represents the longitudinal vehicle speed during a lane change of the vehicle;
And a x (t) represents the longitudinal acceleration during lane change of the vehicle;
And j x (t) represents the longitudinal jerk during a lane change of the vehicle; l is the longitudinal distance of the lane change of the vehicle, v x0 For the initial value of the longitudinal speed during the track change, < >>
The final value of the longitudinal speed in the channel changing process;
b)constructing Hamiltonian H for solving the performance index x Is that
Wherein, kappa x1 To correspond to v x Lagrangian, kappa x2 For corresponding a x Lagrangian, kappa x3 For corresponding j x Is a lagrangian of (c).
c) According to the pointrian maximum principle, the equation of the synergy is expressed as:
Wherein m is 0 、m 1 And m 2 Is a pending constant.
The extreme value conditions are as follows:
Wherein,,
d) Under the geodetic coordinate system, the expression of the optimal longitudinal displacement, speed, acceleration and jerk in the course of channel changing can be obtained according to the steps:
wherein j is x (t)、a x (t)、v x (t) and x (t) represent functions of longitudinal jerk, acceleration, velocity and displacement of the vehicle, respectively, t being an argument of time as a function,
for lane change time, L is the longitudinal distance of the vehicle lane change, v x0 Is the initial value of the longitudinal speed.
(II) determining the optimal lateral displacement, speed, acceleration and jerk expression.
a) It is desirable to minimize lateral fluctuations and construct performance metrics that minimize lateral fluctuations:
the following conditional constraints need to be satisfied:
wherein eta y For sideward fluctuating cost function, min eta y A performance index minimum to minimize lateral fluctuations for the solution; τ 0 For the initial moment of the track change,
for ending time of channel changing process, setting initial time tau 0 =0, so the time of the lane change process is +.>
y (t) represents the lateral displacement function during a lane change of the vehicle, < >>
And v y (t) represents the lateral vehicle speed during a lane change of the vehicle;
And a y (t) represents lateral acceleration during lane change of the vehicle; / >
And j y (t) represents lateral jerk during a lane change of the vehicle; d is the lateral distance of the lane change of the vehicle; v y0 For the initial value of the lateral speed during the track change, < >>
The final value of the lateral speed in the channel changing process;
b) Constructing Hamiltonian H for solving the performance index y Is that
Wherein, kappa y1 To correspond to v y Lagrangian, kappa y2 For corresponding a y Lagrangian, kappa y3 For corresponding j y Is a lagrangian of (c).
c) According to the pointrian maximum principle, the equation of the synergy is expressed as:
Wherein n is 0 、n 1 And n 2 Is a pending constant.
The extreme value conditions are as follows:
Wherein,,
d) Under the geodetic coordinate system, the expression of the optimal lateral jerk, acceleration, speed and displacement in the course of channel changing can be obtained according to the steps as follows:
wherein j is y (t)、a y (t)、v y (t) and y (t) represent functions of lateral jerk, acceleration, velocity and displacement of the vehicle, respectively, t being an argument of time as a function,
for lane change time, D is the lateral distance of the vehicle lane change, v y0 Is the initial value of the lateral velocity.
And (III) determining a lane change track expression of the intelligent electric automobile based on the distributed driving of the fifth order polynomial.
In the course of changing lane, the longitudinal speed of the vehicle is kept constant, and the longitudinal acceleration does not change to achieve the purpose of minimizing longitudinal fluctuation of the vehicle, so that the method can obtain:
v x (t)＝v x0
Wherein v is x (t) represents a longitudinal speed function in the lane change process, v x0 Is an initial value of the longitudinal vehicle speed.
The distributed driving intelligent electric vehicle lane change track expression based on the quintic polynomial is as follows:
wherein x (t) is the longitudinal displacement in the lane changing process, y (t) is the transverse displacement in the lane changing process, t is the independent variable of the function, and the vehicle speed v is at a certain initial speed x0 Down, different channel changing time
Thus obtaining a series of unconstrained generalized lane-changing track clusters.
As a further preferred aspect of the above solution, the second screening of the lane change track cluster satisfying the stability domain of the distributed driving electric automobile includes the following parts:
(I) Stability domain mechanism analysis.
a) Analyzing the stability domain influence mechanism of the distributed driving electric automobile, and building a four-wheel vehicle model of the distributed driving electric automobile, wherein the dynamic equation is as shown in figure 2
Wherein F is yij For the tire side force, the subscripts ij=fl, fr, rl, rr denote the tire front left wheel, front right wheel, rear left wheel, and rear right wheel, respectively, r denotes the yaw rate,
is the first derivative of yaw rate; beta represents the centroid slip angle, +.>
Is the first derivative of the centroid slip angle; v x Is the longitudinal vehicle speed; delta f Is the front wheel corner of the vehicle; a is the distance from the center of gravity of the vehicle to the front axle, and b is the distance from the center of gravity of the vehicle to the rear axle; l (L) f For the wheelbase of the front axle of the vehicle, l r The rear axle distance of the vehicle is; m is the mass of the whole vehicle; i z Is the moment of inertia about the z-axis.
b) Tyre side deflection angle alpha ij The calculation formula is as follows:
wherein alpha is ij For the tire slip angle, the subscripts ij=fl, fr, rl, rr denote the left front wheel, the right front wheel, the left rear wheel, and the right rear wheel of the tire, respectively.
c) Tyre vertical load F zij The calculation formula is as follows:
wherein F is zij For the tire vertical load, the subscripts ij=fl, fr, rl, rr denote the left front wheel, right front wheel, left rear wheel, and right rear wheel of the tire, respectively; a, a x For vehicle longitudinal acceleration, a y Is the vehicle lateral acceleration; h is the height of the vehicle centroid.
d) Calculating a tire side force equation based on the Fiala tire model:
wherein F is yij For tire lateral force, the subscripts ij=fl, fr, rl, rr denote the left front wheel, right front wheel, left rear wheel, and right rear wheel of the tire, respectively; c (C) α Is tire cornering stiffness; alpha slij For the slip angle corresponding to the tire entering the saturation region, subscripts ij=fl, fr, rl and rr respectively represent a left front wheel, a right front wheel, a left rear wheel and a right rear wheel of the tire; mu is the road adhesion coefficient.
(II) performing a vehicle stability domain mechanism analysis based on a phase plane method.
a) Listing a differential equation set according to the system state equation:
from the above formula:
Wherein x is 1 、x 2 Is a state parameter of the vehicle system, f 1 (x 1 ,x 2 )、f 2 (x 1 ,x 2 ) Is a differential equation for the vehicle system.
b) In a vehicle system, an initial state x is assumed 0 ＝(x 1 (0),x 2 (0) A) the starting state trajectory x (t), which remains in a local range, meets the following conditions:
where x (t) is a function of the vehicle state parameter with respect to time variation,
if the constant is determined and the condition is satisfied, the system gradually stabilizes in a local range, so the system is stable at this time. In the phase plane analysis, the stable track finally converges to the balance point, and the unstable track cannot converge and finally diverges.
(III) based on a phase plane analysis method, carrying out stability domain analysis on different states of the vehicle.
The influence of different absolute speeds of the vehicle, road adhesion coefficients and front wheel rotation angles on the stability domain of the vehicle is respectively selected, and the specific steps comprise the following steps:
a) According to the phase plane analysis method, a function equation of a vehicle centroid slip angle beta and a yaw rate r can be obtained, beta is an independent variable of a function, r is an independent variable of the function, and a function expression of a stability domain is as follows:
b in the above functional expression 0 、b 1 、b 2 、b 3 The method comprises the following steps of:
b 0 ＝b/v x ，b 1 ＝tan(α slrl +α slrl )，b 2 ＝(r 2 -r 1 )/(β 2 -β 1 )，b 3 ＝r 1 -β 1 (r 2 -r 1 )/(β 2 -β 1 )
r 1 ＝ug/v x ，r 2 ＝v x /(a+b)(tan((α slfl +α slfr )/2+δ max )-tan((α slrl +α slrr )/2))，
β 2 ＝b/(a+b)(tan((α slfl +α slfr )/2+δ max )-tan((α slrl +α slrr )/2))+tan((α slrl +α slrr )/2)。
delta in the above max The expression is:
wherein b 0 、b 1 、b 2 、b 3 Undetermined coefficients, r, of the stable domain function expressions, respectively 1 、r 2 、β 1 、β 2 、δ max As an intermediate variable; alpha slfl 、α slfr 、α slrl 、α slrr The corresponding slip angles of the left front tire, the right front tire, the left rear tire and the right rear tire enter a saturation region are respectively; a is the distance from the center of gravity of the vehicle to the front axle, and b is the distance from the center of gravity of the vehicle to the rear axle; v x μ is the road adhesion coefficient, g is the gravitational acceleration, which is the longitudinal speed of the vehicle.
b) According to the dividing range of the stable domains, as shown in fig. 3-5, different absolute speeds of vehicles, road adhesion coefficients and front wheel corners are respectively selected to analyze the different stable domains, and the specific steps are as follows:
when the road surface attachment coefficient and the front wheel steering angle are kept unchanged, respectively selecting different absolute vehicle speeds 10, 15, 20 and 25m/s for respectively carrying out stability domain analysis;
when the absolute vehicle speed and the front wheel steering angle are kept unchanged, respectively selecting different road surface attachment coefficients of 0.2, 0.4, 0.6 and 0.8, and respectively carrying out stability domain analysis;
when the absolute vehicle speed and the road adhesion coefficient remain unchanged, respectively selecting different front wheel corners 0, 5, 10 and 15deg for stability domain analysis.
c) According to the stability domain set obtained by the analysis, combining a real-time mass center slip angle and yaw rate calculation formula of the vehicle:
wherein v is x For the longitudinal speed of the vehicle, v y For the lateral speed of the vehicle, a y The lateral acceleration of the vehicle is represented by r, the yaw rate of the vehicle, and β, the centroid slip angle of the vehicle.
Judging whether the vehicle state exceeds the range of the stable domain according to the centroid side deviation angle and the yaw rate, and removing the track change track exceeding the stable domain, so as to finally reserve the track change track meeting the stable domain of the vehicle.
As a further preferable aspect of the above solution, step three, taking surrounding vehicles, pedestrians, etc. as environmental geometric constraints, and lane lines, traffic rules, etc. as road boundaries, the calculation of the global feasible region of the vehicle includes the following parts:
(I) Taking the geometric centers of the vehicle and the front vehicle, taking the geometric center to the maximum length of the vehicle body as the geometric circle radius, and respectively recording R l And R is f The geometric circle is drawn, so that the tangent point of the geometric circle of the vehicle and the front vehicle becomes the critical point of collision, and therefore, the boundary line of a global feasible region can be obtained.
(II) considering the constraint of lane lines, traffic rules or rear vehicles, wherein the tangent point of the geometric circle or lane lines of the vehicle and the rear vehicle becomes a critical point of collision, and combining the steps, the closed global feasible region of the vehicle can be obtained, and the track clusters meeting the constraint of the geometric constraint, the lane lines and the traffic rules of the environment are further screened out from the track clusters meeting the stability of the distributed driving electric vehicle.
As a further preferred option of the above solution, the fourth step of selecting an optimal lane change trajectory by evaluating a stability index, a trajectory tracking accuracy index, a comfort index and a lane change efficiency index based on an improved algorithm of a combination of an Analytic Hierarchy Process (AHP) and a technique approaching ideal (TOPSIS) comprises the following parts:
(I) And (5) establishing an evaluation index.
a) Respectively establishing evaluation indexes such as stability indexes, track tracking accuracy indexes, comfort indexes, track changing efficiency and the like of track changing track planning:
building a vehicle stability index:
wherein J is s As an index of the evaluation of the stability of the vehicle,
for changing track time, F yi (t) is a functional expression of the lateral force of the front axle or the rear axle with respect to time, F zi (t) is a functional expression of the vertical load of the front axle or the rear axle with respect to time,/->
Is the threshold value of the road adhesion coefficient.
b) Constructing an index of vehicle track tracking accuracy:
wherein J is t Is an evaluation index of the tracking accuracy of the vehicle track,
for lane change time, v x For the longitudinal speed of the vehicle,
for the vehicle centroid slip angle angular velocity function expression,/->
Is a centroid slip angle threshold value; h (t) is an ideal planned trajectory of the vehicle; y (t) is the actual driving track of the vehicle;
And the threshold value of the error between the ideal planning track and the actual running track is set.
c) Constructing vehicle comfort indexes:
wherein J is c As an index of the comfort evaluation of the vehicle,
for the channel change time, a y (t) is the longitudinal acceleration of the vehicle, < >>
For the vehicle lateral acceleration threshold value, θ (t) is the roll angle, ++>
Is the roll angle threshold.
d) Constructing lane change efficiency indexes:
wherein J is e Is an evaluation index of the lane changing efficiency of the vehicle,
is the channel changing time.
(II) improved algorithms based on a combination of Analytic Hierarchy Process (AHP) and technique approaching ideal (TOPSIS).
a) And constructing a judgment matrix. The optimal lane change track cluster is calculated from the track clusters meeting the constraint conditions, so that the complexity of the TOPSIS algorithm used alone in the multi-target calculation process can be overcome, the subjectivity of the AHP algorithm used alone in the calculation process can be overcome, and the target layer A comprises m evaluation indexes A 1 ,A 2 ,A 3 ,…,A m The target layer A corresponds to the definite sound index J of the matrix B respectively 1 ,J 2 ,J 3 ,J 4 ,…,J n A judgment matrix B is constructed, the order of which is n multiplied by n, and the matrix B is as follows:
wherein element b in the matrix ij Planning evaluation index J for lane change track i Evaluation index J for lane change track j Importance level of b ji ＝1/b ij . When element b ij When the number is equal to 1, the two lane change track planning evaluation indexes are of equal importance; when element b ij When=3, the lane change trajectory planning evaluation index J i Track planning evaluation index J of specific lane change j Slightly important; when element b ij When=5, the lane change trajectory planning evaluation index J i Track planning evaluation index J of specific lane change j Is obviously important; when element b ij When=7, the lane change trajectory planning evaluation index J i Track planning evaluation index J of specific lane change j Is of great importance; when element b ij When=9, the lane change trajectory planning evaluation index J i Track planning evaluation index J of specific lane change j Is of absolute importance; when the value of an element is 2, 4, 6, 8, it is indicated that it is in an intermediate state where the value of the element is 1, 3, 5, 7, 9.
b) Determining the index weight. According to the judgment matrix, calculating the sum of each column, normalizing each column of elements, further adding normalized results according to rows, and calculating to obtain square root vectors, wherein the final normalized square root vectors obtain sequencing weight vectors, and the calculation formula is as follows:
wherein,,
b for normalizing the results for each column of elements ij For judging the elements in the matrix +.>
For normalizing the result of the processing by the result of the row addition, W i To rank the weight vectors.
c) Consistency test. Firstly calculating the maximum characteristic root of the judgment matrix B, then checking the consistency of the maximum characteristic root to obtain a consistency ratio, and when the consistency ratio is smaller than 0.1, judging that the consistency of the matrix meets the condition.
Wherein lambda is max For judging the maximum characteristic root of the matrix, the matrix B is the judgment matrix, W i For the ordered weight vector, w is the element in the ordered weight vector, CI is oneThe consistency check criterion, CR is the consistency ratio, RI is the average random consistency index, and n is the order of the rows or columns of the decision matrix.
d) And (3) hierarchical total ordering. And calculating the optimal scheme synthesis weight of the index layer relative to the target layer on the basis of the hierarchical single-order result. Assume that target layer A includes m evaluation indexes A 1 ,A 2 ,A 3 ,…,A m Weight a corresponding to target layer evaluation index 1 ,a 2 ,a 3 ,…,a m The index layer J comprises n evaluation indexes J 1 ,J 2 ,J 3 ,J 4 ,…,J n Corresponding to a certain target layer A i The weight of (c) is c 1i ,c 2i ,c 3i ,…,c ni . Therefore, the weights corresponding to the indexes of the index layer are c respectively 1 ,c 2 ,c 3 ,…,c n 。
Wherein c j Weights, c, corresponding to the indexes of the index layer ij To correspond to a certain target layer A i Weight of a), a i And the weight corresponding to the evaluation index of the target layer.
e) Establishing initial evaluation index. Let n evaluation indexes be j= { J 1 ,J 2 ,J 3 ,...,J n Each evaluation index has m characteristic indexes R= { R } 1 ,r 2 ,r 3 ,…,r m Then the initial evaluation matrix is:
wherein r is ij The j index of the i-th evaluation target in the target layer.
f) Matrix normalization. Since each evaluation index has different dimensions, the evaluation indexes are normalized, and the calculation formula is:
The weighted normalization matrix calculation process is as follows:
H＝(v ij ) n×m ＝(ω j r ij ) n×m
wherein r is ij The j-th evaluation index, v, of the i-th evaluation target in the target layer ij Represents the i-th row, j-th column element, ω after weighting j And the j-th evaluation index weight is represented.
g) And calculating positive ideal solution and negative ideal solution and distance between the positive ideal solution and the negative ideal solution. The positive ideal solution means that each evaluation index takes the most ideal value solution, the negative ideal solution means that each evaluation index takes the most difference value solution, and the expression is:
the positive ideal solution is:
the negative ideal solution is:
wherein V is + To be positively and ideally understood, V - Is negative ideal solution, J 1 Is a benefit index set, J 2 V is a cost index set ij Representing the elements of row i, column j after weighting.
The distances between each evaluation index and the positive ideal solution and the negative ideal solution are as follows:
wherein,,
for the distance of the respective evaluation index from the positive ideal value, < >>
V for the distance between each evaluation index and the negative ideal value ij Represents the elements of row i, column j after weighting,/-column i>
And->
Respectively correspond to the ideal solution V + And negative ideal solution V - Is a component of the group.
h) And calculating the closeness, namely calculating the relative closeness of the evaluation index and the ideal solution, and when the closeness is larger, indicating that the track change track is better.
Proximity degree:
wherein C is i In order for the degree of closeness to be the same, 
For the distance of the respective evaluation index from the positive ideal value, < >>
For the distance between each evaluation index and the negative ideal value, when the closeness degree C i The larger, i.e. closer to 1, the more optimal the track change is, and thus the optimal track change is finally obtained.
The embodiment of the invention also provides electronic equipment, which comprises:
one or more processors;
a memory for storing one or more programs;
and when the one or more programs are executed by the one or more processors, the one or more processors are enabled to realize the track changing planning method of the distributed driving electric automobile.
The lane change track planning method of the distributed driving electric automobile can be obtained through the equipment, and the obtained optimal track is sent to the distributed driving intelligent electric automobile.
The embodiment of the invention also provides a storage medium, on which a computer program is stored, which when being executed by a processor, realizes the track-changing planning method of the distributed driving electric automobile.
It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
The meaning of "and/or" as referred to in this application means that each exists alone or both.
As used herein, "connected" means either a direct connection between elements or an indirect connection between elements via other elements.
With the above-described preferred embodiments according to the present invention 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 invention. The technical scope of the present invention is not limited to the description, but must be determined according to the scope of claims.
Claims (10)
1. The track change planning method for the distributed driving electric automobile is characterized by comprising the following steps of:
generating an unconstrained generalized lane change track cluster according to the vehicle track planning curve fitting function;
selecting a lane change track cluster meeting the stability domain of the distributed driving electric automobile from the generated unconstrained generalized lane change track clusters;
in the selected lane change track cluster meeting the stable domain of the distributed driving electric automobile, calculating the feasible domain of the automobile according to the geometric constraint of the environment and the road boundary;
In the calculated feasible domain of the vehicle, the optimal lane change track is selected by evaluating a stability index, a track tracking accuracy index, a comfort index and a lane change efficiency index based on an improved algorithm combining an analytic hierarchy process and a technology approaching to ideal.
2. The method for planning a lane-changing track of a distributed driving electric vehicle according to claim 1, wherein the generating an unconstrained generalized lane-changing track cluster expression according to a vehicle track planning curve fitting function is as follows:
wherein x (t) is the longitudinal displacement in the channel changing process, y (t) is the transverse displacement in the channel changing process, t is the independent variable of the function, v x0 For the initial vehicle speed,
for lane change time, D is the lateral distance of the vehicle lane change.
3. The method for planning a lane-changing trajectory of a distributed driving electric vehicle according to claim 2, wherein generating an unconstrained generalized lane-changing trajectory cluster comprises:
constructing a performance index for minimizing longitudinal fluctuation:
the constraint of the condition to be satisfied is:
wherein eta x To longitudinal wave-motion cost function, min eta x To solve for minimizing longitudinal fluctuationsA performance index minimum of (2); τ 0 For the initial moment of the track change,
for ending time of channel changing process, setting initial time tau 0 =0, so the time of the lane change process is +.>
x (t) is a longitudinal displacement function during lane changing of the vehicle, < >>
And v x (t) represents the longitudinal vehicle speed during a lane change of the vehicle;
And a x (t) represents the longitudinal acceleration during lane change of the vehicle;
And j x (t) represents the longitudinal jerk during a lane change of the vehicle; l is the longitudinal distance of the lane change of the vehicle, v x0 For the initial value of the longitudinal speed during the track change, < >>
The final value of the longitudinal speed in the channel changing process;
constructed Hamiltonian H x The method comprises the following steps:
wherein, kappa x1 To correspond to v x Lagrangian, kappa x1 ＝m 0 ；κ x2 For corresponding a x Lagrangian, kappa x2 ＝m 1 -m 0 t；κ x3 For corresponding j x Is a lagrangian of (c);
wherein,,
according to Hamiltonian H x Solving performance indexes:
under the geodetic coordinate system, the expression of the optimal longitudinal displacement, speed, acceleration and jerk in the channel changing process is obtained as follows:
wherein j is x (t)、a x (t)、v x (t) and x (t) represent functions of longitudinal jerk, acceleration, velocity and displacement of the vehicle, respectively, t being an argument of time as a function,
for lane change time, L is the longitudinal distance of the vehicle lane change, v x0 Is of longitudinal speedAn initial value of the degree;
it is desirable to minimize lateral fluctuations and construct performance metrics that minimize lateral fluctuations:
the following conditional constraints need to be satisfied:
Wherein eta y For sideward fluctuating cost function, min eta y A performance index minimum to minimize lateral fluctuations for the solution; τ 0 For the initial moment of the track change,
for ending time of channel changing process, setting initial time tau 0 =0, so the time of the lane change process is +.>
y (t) represents the lateral displacement function during a lane change of the vehicle, < >>
And v y (t) represents the lateral vehicle speed during a lane change of the vehicle;
And a y (t) represents lateral acceleration during lane change of the vehicle;
And j y (t) represents lateral jerk during a lane change of the vehicle; d is the lateral distance of the lane change of the vehicle; v y0 For the initial value of the lateral speed during the track change, < >>
The final value of the lateral speed in the channel changing process;
construction of Hamiltonian H y ：
Wherein, kappa y1 To correspond to v y Lagrangian, kappa y1 ＝n 0 ；κ y2 For corresponding a y Lagrangian, kappa y2 ＝n 1 -n 0 t；κ y3 For corresponding j y Is a combination of the lagrangian of (c),
wherein,,
according to Hamiltonian H y Solving performance indexes:
under the geodetic coordinate system, the expression of the optimal lateral jerk, acceleration, speed and displacement in the course of channel change is obtained as follows:
wherein j is y (t)、a y (t)、v y (t) and y (t) represent functions of lateral jerk, acceleration, velocity and displacement of the vehicle, respectively, D is the lateral distance of the lane change of the vehicle, v y0 Is the initial value of the lateral velocity;
in the course of changing lane, the longitudinal speed of the vehicle is kept constant, and the longitudinal acceleration does not change to achieve the purpose of minimizing longitudinal fluctuation of the vehicle, so that the method can obtain:
v x (t)＝v x0
wherein v is x (t) represents a longitudinal speed function in the lane change process, v x0 Is an initial value of the longitudinal vehicle speed;
at a certain initial vehicle speed v x0 Down, different channel changing time
Thereby obtaining a series of unconstrained generalized lane-changing track clusters:
4. the lane-changing track planning method of the distributed driving intelligent electric vehicle according to claim 1, wherein the lane-changing track cluster meeting the stability domain of the distributed driving intelligent electric vehicle is selected from the generated unconstrained generalized lane-changing track clusters, and the method comprises the following steps:
establishing a four-wheel vehicle model of a distributed driving electric automobile:
wherein F is yij For the tire side force, the subscripts ij=fl, fr, rl, rr denote the tire front left wheel, front right wheel, rear left wheel, and rear right wheel, respectively, r denotes the yaw rate,
is the first derivative of yaw rate; beta represents the centroid slip angle, +.>
Is the first derivative of the centroid slip angle; v x Is the longitudinal vehicle speed; delta f Is the front wheel corner of the vehicle; a is the distance from the center of gravity of the vehicle to the front axle, and b is the distance from the center of gravity of the vehicle to the rear axle; l (L) f For the wheelbase of the front axle of the vehicle, l r The rear axle distance of the vehicle is; m is the mass of the whole vehicle; i z Is the moment of inertia about the z-axis;
calculating the slip angles of four tires:
wherein alpha is ij For the tire slip angle, the subscripts ij=fl, fr, rl, rr denote the left front wheel, the right front wheel, the left rear wheel, and the right rear wheel of the tire, respectively;
calculate the vertical load of four tires:
wherein F is zij For the tire vertical load, the subscripts ij=fl, fr, rl, rr denote the left front wheel, right front wheel, left rear wheel, and right rear wheel of the tire, respectively; a, a x For vehicle longitudinal acceleration, a y Is the vehicle lateral acceleration; h is the height of the mass center of the vehicle;
calculating a tire side force equation based on the Fiala tire model:
wherein F is yij For tire lateral force, the subscripts ij=fl, fr, rl, rr denote the left front wheel, right front wheel, left rear wheel, and right rear wheel of the tire, respectively; c (C) α Is tire cornering stiffness; alpha slij To saturate the tyreThe corresponding slip angles of the areas, the subscripts ij=fl, fr, rl, rr of which respectively represent the left front wheel, the right front wheel, the left rear wheel and the right rear wheel of the tire; mu is the road adhesion coefficient;
and (3) carrying out vehicle stability domain mechanism analysis based on a phase plane method, and listing a differential equation set according to a system state equation:
from the above formula:
wherein x is 1 、x 2 Is a state parameter of the vehicle system, f 1 (x 1 ,x 2 )、f 2 (x 1 ,x 2 ) Is a differential equation for the vehicle system;
in the vehicle system, an initial state x is assumed 0 ＝(x 1 (0),x 2 (0) A) the starting state trajectory x (t), which remains in a local range, meets the following conditions:
wherein x (t) is a function of the vehicle state parameter with respect to time, x l E R is a determined constant;
based on a phase plane analysis method, carrying out stability domain analysis on different states of a vehicle, and respectively selecting influences of different absolute speeds, road adhesion coefficients and front wheel corners of the vehicle on the stability domain of the vehicle, wherein the method comprises the following specific steps of:
according to the phase plane analysis method, a function equation of a vehicle centroid slip angle beta and a yaw rate r can be obtained, beta is an independent variable of a function, r is an independent variable of the function, and a function expression of a stability domain is as follows:
b in the above functional expression 0 、b 1 、b 2 、b 3 The method comprises the following steps of:
b 0 ＝b/v x ，b 1 ＝tan(α slrl +α slrl )，b 2 ＝(r 2 -r 1 )/(β 2 -β 1 )，b 3 ＝r 1 -β 1 (r 2 -r 1 )/(β 2 -β 1 )
r 1 ＝ug/v x ，r 2 ＝v x /(a+b)(tan((α slfl +α slfr )/2+δ max )-tan((α slrl +α slrr )/2))，
β 2 ＝b/(a+b)(tan((α slfl +α slfr )/2+δ max )-tan((α slrl +α slrr )/2))+tan((α slrl +α slrr )/2)。
delta in the above max The expression is:
wherein b 0 、b 1 、b 2 、b 3 Undetermined coefficients, r, of the stable domain function expressions, respectively 1 、r 2 、β 1 、β 2 、δ max As an intermediate variable; alpha slfl 、α slfr 、α slrl 、α slrr The corresponding slip angles of the left front tire, the right front tire, the left rear tire and the right rear tire enter a saturation region are respectively; a is the distance from the center of gravity of the vehicle to the front axle, and b is the distance from the center of gravity of the vehicle to the rear axle; v x Mu is road surface attachment for longitudinal speed of vehicleThe coefficient, g, is the gravitational acceleration;
According to the dividing range of the stable domains, different absolute vehicle speeds, road surface attachment coefficients and front wheel corners of the vehicle are respectively selected to analyze the different stable domains;
according to the stability domain set obtained by the analysis, combining a real-time mass center slip angle and yaw rate calculation formula of the vehicle:
wherein v is x For the longitudinal speed of the vehicle, v y For the lateral speed of the vehicle, a y The lateral acceleration of the vehicle is represented by r, the yaw rate of the vehicle and beta, the centroid side slip angle of the vehicle;
judging whether the vehicle state exceeds the range of the stable domain according to the centroid side deviation angle and the yaw rate, and removing the track change track exceeding the stable domain, so as to finally reserve the track change track meeting the stable domain of the vehicle.
5. The lane-changing track planning method of claim 1, wherein in the lane-changing track cluster selected to satisfy the stability domain of the distributed driving electric vehicle, the feasible region of the vehicle is calculated according to the environmental geometric constraint and the road boundary, comprising:
taking the geometric centers of the vehicle and the front vehicle, taking the geometric center to the maximum length of the vehicle body as the geometric circle radius, and respectively recording R l And R is f Drawing a geometric circle;
considering the lane lines, traffic rules or the constraint of the rear vehicles, the geometric circles of the vehicle and the rear vehicles or the tangent points of the lane lines become critical points of collision;
Combining the track changing tracks which finally remain to meet the vehicle stability domain to obtain a global feasible domain of the vehicle.
6. The lane-changing trajectory planning method of a distributed driving electric vehicle according to claim 1, wherein in the calculated vehicle feasibility domain, an optimal lane-changing trajectory is selected by evaluating a stability index, a trajectory tracking accuracy index, a comfort index, and a lane-changing efficiency index based on an improved algorithm combining a hierarchical analysis method and a technique approaching an ideal, comprising:
respectively establishing a stability index, a track tracking accuracy index, a comfort index and a track changing efficiency index of the track changing planning;
and calculating an optimal lane change track cluster from the track clusters meeting the constraint conditions based on an improved algorithm combining the analytic hierarchy process and the technology approaching to the ideal.
7. The method for lane-changing trajectory planning of a distributed driving electric vehicle of claim 6, wherein,
the established vehicle stability index:
wherein J is s As an index of the evaluation of the stability of the vehicle,
for changing track time, F yi (t) is a functional expression of the lateral force of the front axle or the rear axle with respect to time, F zi (t) is a functional expression of the vertical load of the front axle or the rear axle with respect to time,/- >
A threshold value for road adhesion coefficient;
the constructed vehicle track tracking accuracy index is as follows:
wherein J is t Is an evaluation index of the tracking accuracy of the vehicle track,
for lane change time, v x For the longitudinal speed of the vehicle>
For the vehicle centroid slip angle angular velocity function expression,/->
Is a centroid slip angle threshold value; h (t) is an ideal planned trajectory of the vehicle; y (t) is the actual driving track of the vehicle;
A threshold value for errors of an ideal planning track and an actual running track;
the constructed vehicle comfort index is:
wherein J is c As an index of the comfort evaluation of the vehicle,
for the channel change time, a y (t) is the longitudinal acceleration of the vehicle, < >>
For the vehicle lateral acceleration threshold value, θ (t) is the roll angle, ++>
Is the roll angle threshold.
The constructed lane change efficiency index is as follows:
wherein J is e Is an evaluation index of the lane changing efficiency of the vehicle,
the channel changing time is;
and calculating an optimal lane change track cluster from the track clusters meeting the constraint conditions based on an improved algorithm combining the analytic hierarchy process and the technology approaching to the ideal.
8. The lane-changing trajectory planning method of a distributed driving electric vehicle according to claim 7, wherein the improvement algorithm comprises the steps of:
the target layer A comprises m evaluation indexes A 1 ,A 2 ,A 3 ,…,A m The target layer A respectively corresponds to the definite sound index J of the judgment matrix B 1 ,J 2 ,J 3 ,J 4 ,…,J n Constructing a judgment matrix B, wherein the order of the judgment matrix B is n multiplied by n, and the judgment matrix B is as follows:
wherein element b in the matrix ij Planning evaluation index J for lane change track i Evaluation index J for lane change track j Importance level of b ji ＝1/b ij ；
Determining index weight, calculating the sum of each column according to the judgment matrix, normalizing each column of elements, further adding normalized results according to rows, calculating to obtain square root vectors, and finally normalizing the square root vectors to obtain sequencing weight vectors, wherein the calculation formula is as follows:
wherein,,
b for normalizing the results for each column of elements ij For judging the elements in the matrix +.>
For normalizing the result of the processing by the result of the row addition, W i Ordering the weight vectors;
the consistency test is carried out, the maximum characteristic root of the judgment matrix B is calculated firstly, then the consistency is tested to obtain a consistency ratio, and when the consistency ratio is smaller than 0.1, the consistency of the judgment matrix accords with the condition;
wherein lambda is max For judging the maximum characteristic root of the matrix, the matrix B is the judgment matrix, W i For the ordered weight vector, w is the ordered weight vectorCI is a consistency check standard, CR is a consistency ratio, RI is an average random consistency index, and n is the order of the rows or columns of the judgment matrix;
The total layer ordering, on the basis of the single layer ordering result, the optimal scheme synthesis weight of the index layer relative to the target layer is calculated; assume that target layer A includes m evaluation indexes A 1 ,A 2 ,A 3 ,…,A m Weight a corresponding to target layer evaluation index 1 ,a 2 ,a 3 ,…,a m The index layer J comprises n evaluation indexes J 1 ,J 2 ,J 3 ,J 4 ,…,J n Corresponding to a certain target layer A i The weight of (c) is c 1i ,c 2i ,c 3i ,…,c ni The method comprises the steps of carrying out a first treatment on the surface of the Therefore, the weights corresponding to the indexes of the index layer are c respectively 1 ,c 2 ,c 3 ,…,c n ：
Wherein c j Weights, c, corresponding to the indexes of the index layer ij To correspond to a certain target layer A i Weight of a), a i The weight corresponding to the evaluation index of the target layer is obtained;
initial evaluation index establishment, assuming that n evaluation indexes are J= { J 1 ,J 2 ,J 3 ,...,J n Each evaluation index has m characteristic indexes R= { R } 1 ,r 2 ,r 3 ,…,r m Then the initial evaluation matrix is:
wherein r is ij A j-th index which is an i-th evaluation target in the target layer;
matrix standardization, normalizing each evaluation index, wherein the calculation formula is as follows:
the weighted normalization matrix calculation process is as follows:
H＝(v ij ) n×m ＝(ω j r ij ) n×m
wherein r is ij The j-th evaluation index, v, of the i-th evaluation target in the target layer ij Represents the i-th row, j-th column element, ω after weighting j Representing the j-th evaluation index weight;
calculating positive ideal solution, negative ideal solution and distance between the positive ideal solution and the negative ideal solution, wherein the positive ideal solution means that each evaluation index takes the most ideal value solution, the negative ideal solution means that each evaluation index takes the most difference value solution, and the expression is as follows:
The positive ideal solution is:
the negative ideal solution is:
wherein V is + To be positively and ideally understood, V - Is negative ideal solution, J 1 Is a benefit index set, J 2 V is a cost index set ij Representing the i-th row, j-th column element after weighting;
the distances between each evaluation index and the positive ideal solution and the negative ideal solution are as follows:
wherein,,
for the distance of the respective evaluation index from the positive ideal value, < >>
V for the distance between each evaluation index and the negative ideal value ij Represents the elements of row i, column j after weighting,/-column i>
And->
Respectively correspond to the ideal solution V + And negative ideal solution V - Elements of (a) and (b);
calculating the closeness, and calculating the relative closeness of the evaluation index and the ideal solution:
proximity degree:
wherein C is i In order for the degree of closeness to be the same,
for the distance of the respective evaluation index from the positive ideal value, < >>
For the distance between each evaluation index and the negative ideal value, when the closeness degree C i The larger, i.e. closer to 1, the more optimal the track change is, and thus the optimal track change is finally obtained.
9. An apparatus, comprising:
one or more processors;
a memory for storing one or more programs;
when the one or more programs are executed by the one or more processors, the one or more processors implement the distributed drive electric vehicle lane change trajectory planning method of any one of claims 1-8.
10. A storage medium having stored thereon a computer program, which when executed by a processor implements the distributed drive electric vehicle lane change trajectory planning method of any one of claims 1 to 8.
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