CN110877612B - Vehicle emergency lane change danger assessment method based on vehicle kinematics and genetic algorithm - Google Patents

Vehicle emergency lane change danger assessment method based on vehicle kinematics and genetic algorithm Download PDF

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CN110877612B
CN110877612B CN201911171802.8A CN201911171802A CN110877612B CN 110877612 B CN110877612 B CN 110877612B CN 201911171802 A CN201911171802 A CN 201911171802A CN 110877612 B CN110877612 B CN 110877612B
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lane change
emergency lane
yaw rate
collision avoidance
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CN110877612A (en
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刘志强
王磊
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Jiangsu University
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units, or advanced driver assistance systems for ensuring comfort, stability and safety or drive control systems for propelling or retarding the vehicle
    • B60W30/08—Active safety systems predicting or avoiding probable or impending collision or attempting to minimise its consequences
    • B60W30/095—Predicting travel path or likelihood of collision
    • B60W30/0956—Predicting travel path or likelihood of collision the prediction being responsive to traffic or environmental parameters
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B60W50/0097—Predicting future conditions
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B60W2050/0062—Adapting control system settings
    • B60W2050/0075—Automatic parameter input, automatic initialising or calibrating means

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Abstract

The invention provides a vehicle emergency lane change danger assessment method based on vehicle kinematics and a genetic algorithm, firstly considering the constraints of the vehicle kinematics and the vehicle dynamics, planning an emergency lane change path based on a quintic polynomial, secondly representing the yaw rate required by a vehicle body in the emergency lane change process, then adopting the genetic algorithm to search the global optimum of the yaw rate required by the vehicle body, obtaining the equivalent maximum value of the yaw rate required by the vehicle body, constructing a danger assessment function F according to the constraint of the equivalent maximum yaw rate and the yaw rate, then deducing an optimized danger assessment function E based on the front and rear vehicle boundary distance, finally deducing to obtain an optimum danger assessment function Kc by setting the E to be 0, and considering that the Kc on an interval (0.8,1) is along with xflIs reduced and vxIs increased sharply, and the final danger threshold Th is 0.85 as the trigger condition of the active emergency lane change collision avoidance system.

Description

Vehicle emergency lane change danger assessment method based on vehicle kinematics and genetic algorithm
Technical Field
The invention relates to the field of automobile engineering and automobile active safety, in particular to a vehicle emergency lane change danger assessment method based on vehicle kinematics and a genetic algorithm.
Background
Under the working condition of emergency driving, a driver mostly adopts three collision avoidance behaviors of braking, lane changing, braking and lane changing, and compared with braking collision avoidance, the longitudinal distance required by the emergency lane changing collision avoidance under the working conditions of high relative speed, low adhesion coefficient, low overlapping rate and the like is smaller, and the collision avoidance efficiency is higher; the design of the emergency lane changing system comprises the following steps: a decision layer and a motion control layer. Planning the emergency road changing path in the decision layer; the motion control layer realizes the transverse control and the longitudinal control of the vehicle. When the emergency lane change is adopted at a higher speed, the deformation of tires and the lateral inclination of a vehicle body are easily caused, so that the lane change process is unstable; therefore, the evaluation method for the danger of the emergency lane change of the vehicle is necessary to evaluate whether the emergency lane change is performed actively. Therefore, the method for evaluating the emergency lane change danger of the vehicle has great theoretical significance and practical value.
At present, research scholars at home and abroad try to solve the problem of emergency collision avoidance of an automatic driving vehicle; first, most studies do not discuss hazard assessment methods related to autonomous vehicle collisions and instability; secondly, the center of gravity is often placed on the obstacle avoidance technology of a tire in a linear area or a nearly linear area; finally, many active lane change collision avoidance control schemes do not ensure path tracking capability and stability of active vehicle emergency collision avoidance because the operating conditions are subject to change due to turn response and unknown external disturbances. Therefore, a dynamic risk assessment method for vehicle emergency lane change is needed at present to assess risks of vehicle collision and unstable driving in the emergency lane change process, and finally generate triggering conditions of an active emergency lane change collision avoidance system.
Disclosure of Invention
The invention aims to predict the possibility of vehicle collision and unstable driving in emergency lane change. Therefore, the emergency lane change collision avoidance decision layer needs to include: the method for evaluating the danger of the urgent lane change and collision avoidance is beneficial to improving the safety of the urgent lane change and collision avoidance process. In order to achieve the above object, a method for evaluating an emergency lane change danger of a vehicle based on vehicle kinematics and a genetic algorithm is characterized by comprising the following steps:
step 1: planning an emergency lane change path based on a fifth-order polynomial;
step 2: representing the required yaw rate of the vehicle body;
and step 3: constructing a representation of the equivalent maximum vehicle body yaw rate and a risk evaluation function F;
and 4, step 4: according to the method, the emergency lane changing risk is more effectively evaluated according to the boundary distance between the front vehicle and the rear vehicle in the emergency lane changing process, and an optimized danger evaluation function E is deduced;
and 5: and determining the construction of the optimal risk assessment function Kc and a risk threshold Th.
Further, step 1 is based on the fifth-order polynomial emergency track change path planning method, and a collision-free emergency track change trajectory equation is derived by considering the vehicle kinematics and the condition constraint calculation of the vehicle dynamics.
Further, the emergency lane change path planning based on the fifth-order polynomial comprises the following specific steps:
pro 1: when the emergency lane change mode is adopted to avoid vehicle collision, a fifth-order polynomial equation is used for describing the emergency lane change track as follows:
y＝ATX (1)
wherein:
A＝[a0 a1 a2 a3 a4 a5]T
X＝[1 x x2 x3 x4 x5]T
x, y being collision avoidance pathsOrdinate and abscissa, and an(n ═ 1,2, 3.) is a polynomial coefficient;
pro2: the boundary constraint of the fifth order polynomial is defined as:
Figure BDA0002289346900000021
wherein:
Figure BDA0002289346900000022
x0and y (x)0) Is the longitudinal and transverse coordinates, x, of the vehicle's center of mass at the time of initial collision avoidanceTAnd yTLongitudinal and transverse coordinates of the collision avoidance track at the end point time respectively, and K is the curvature of the collision avoidance track of the vehicle;
pro 3: substituting equation (1) into the boundary constraint results in the following relationship:
BA＝[0 yT 0 0 0 0]T (3)
wherein:
Figure BDA0002289346900000031
deriving a coefficient matrix A:
Figure BDA0002289346900000032
pro 4: by combining equation (1) and equation (4), the collision avoidance trajectory is derived as:
Figure BDA0002289346900000033
further, in the step 2, the conditional constraint of the yaw rate is deduced according to the lateral acceleration and the road surface friction coefficient; and then solving the curvature of the collision avoidance track according to the emergency lane change collision avoidance track, and representing the yaw rate required by the vehicle.
Further, according to the emergency lane changing collision avoidance track, the curvature of the collision avoidance track is solved, the required yaw rate of the vehicle is represented, and the method specifically comprises the following steps:
and 2.1: the lateral acceleration of the vehicle's center of gravity may be defined as:
Figure BDA0002289346900000036
wherein:
vy＝vxtan(β)
vxis the longitudinal velocity, vvIs the lateral velocity, gamma is the yaw rate of the vehicle body, beta is the vehicle body sideslip angle;
thus, the lateral acceleration is further described as:
Figure BDA0002289346900000034
and 2.2: the lateral acceleration must be determined by a numerical limit according to the tyre-road friction coefficient, the specific relationship being:
vxγ+ac≤μg (8)
wherein: μ is the tire-road friction coefficient, g is the gravitational acceleration;
Figure BDA0002289346900000035
the following relationships are defined herein:
ac＝(1-k)μg (9)
wherein: k (0 < k < 1) is a dynamic factor;
and (2.3) Pro2: in connection with equations (8) and (9), the following relationships are derived:
Figure BDA0002289346900000041
according to the kinematics principle, the required yaw rate can be given as:
γd＝Kvx (11)
wherein: k is the trajectory curvature;
the required yaw rate is derived by connecting equations (2), (5) and (11):
Figure BDA0002289346900000042
wherein:
Figure BDA0002289346900000043
further, in the step 3, a genetic algorithm is adopted to search the global optimum of the yaw rate expression, the population size Np, the chromosome length Lc, the stop generation Gt, the cross probability Pc and the variation probability Pm are determined, the equivalent maximum value of the yaw rate of the vehicle body required by the emergency lane change is obtained through solving, and then the risk evaluation function F is constructed according to the constraint of the equivalent maximum yaw rate and the yaw rate.
Further, a genetic algorithm is adopted to search the global optimum of the yaw rate expression, the equivalent maximum value of the vehicle body yaw rate required by emergency lane changing is obtained, and then a danger evaluation function F is constructed according to the constraint of the equivalent maximum yaw rate and the yaw rate, and the specific steps are as follows:
pro3.1: considering that genetic algorithms are commonly used for searching global optima, the population size Np, the chromosome length Lc and the termination generation Gt obtained based on the optimization strategy are respectively designed to be 100, 20 and 500. The cross probability Pc and the variation probability Pm are respectively designed to be 0.8 and 0.1;
and (3.2) Pro3: when U is 0.20, an equivalent maximum value of the required body yaw rate can be obtained:
Figure BDA0002289346900000051
wherein: p1、P2Is a gain parameter, the approximate values are 5.76 and 0.59 respectively;
and (3) Pro3.3: the required body yaw rate needs to meet vehicle dynamics constraints, so the following relationship can be derived:
Figure BDA0002289346900000052
and (3.4) Pro3: in connection with equations (13) and (14), the following relationship is found:
F(vx，k，μ，xT，yT)≤0 (15)
wherein:
Figure BDA0002289346900000053
f is a danger evaluation function in the emergency lane changing process.
Further, the specific steps of step 4 are as follows:
pro4.1: in order to more effectively assess the risk and further explore the constraints of safe driving, both the lateral and longitudinal distances between the following vehicle and the lead vehicle are studied, considering the vehicle during an emergency collision.
The following relationships are defined herein:
Figure BDA0002289346900000054
wherein n and m are direct proportionality coefficients, wherein xflFor following the longitudinal distance of the vehicle radar or camera to the lead vehicle, where yflThe distance of the following vehicle is xflCorresponding lateral displacement;
suppose (x)fl，yfl) Is a point on the collision avoidance path, and is derived by combining equation (5) and equation (16):
Figure BDA0002289346900000055
and (2) Pro4.2: and (3) connecting the equation (16), the equation (17) and the risk evaluation function F to obtain an optimized risk evaluation function E:
Figure BDA0002289346900000061
further, in the step 5, setting the optimized risk assessment function E as a minimum value 0, deriving a representation of a critical power factor Kc, wherein the expression of Kc is used as an optimal risk assessment function, and the critical power factor Kc can predict and analyze the risk degree of the emergency lane change process; the method comprises the following specific steps:
and (2) Pro5.1: longitudinal distance x between following vehicle and guiding vehicleflAnd the direct scaling factor n are treated as dependent variable and independent variable, respectively, and the other parameters in equation (18) are treated as constants;
when n is approximately equal to 2.2, the longitudinal distance x between the front and rear vehicles can be obtainedflIn addition, equation (17) is analyzed, and when n is equal to 2, m is equal to 2, and n is selected to be 2 in this section in order to simplify the analysis model design process, so equation (18) is further simplified as:
Figure BDA0002289346900000062
and (2) Pro5.2: and (3) setting the risk evaluation function as a minimum value of 0, and deriving a critical power factor:
Figure BDA0002289346900000063
and (5.3) Pro5: the critical power factor Kc can predict and analyze the danger emergency degree in the collision avoidance process, the width of the passenger car is considered to be about 1.85 meters, the safety boundary distance of the transverse displacement is 0.4 meter, and y isflThe design is 2.25 meters;
critical dynamics factor Kc with vxIs increased with xflAnd μ decreases with increasing μ, and furthermore, Kc over the interval (0.8,1) decreases with xflIs sharply increased with decrease of vxIs increased sharply;
and (5.4) Pro5: when Kc is larger than a danger threshold Th, an emergency lane change collision avoidance strategy is triggered, otherwise, the strategy is not triggered;
consider Kc over the interval (0.8,1) as xflIs reduced and vxIs increased sharply, and considering the case of avoiding a collision in an emergency, Th is set to 0.85.
Implementation procedure of the invention
The invention relates to a vehicle emergency lane change danger assessment method based on vehicle kinematics and genetic algorithm, firstly considering the constraints of the vehicle kinematics and the vehicle dynamics, calculating and deriving a collision-free emergency lane change track equation based on a quintic polynomial method, secondly deriving the conditional constraint of yaw rate according to lateral acceleration and road surface friction coefficient, solving the curvature of a collision avoidance track according to the collision avoidance track, representing the yaw rate required by a vehicle body in the lane change process, secondly searching the global optimum of a yaw rate expression by adopting the genetic algorithm, obtaining the equivalent maximum value of the yaw rate, constructing a danger assessment function F according to the conditional constraint of the equivalent maximum yaw rate and the yaw rate, secondly deriving an optimized danger assessment function E based on the front and rear vehicle boundary distance, and finally completing the derivation of an optimal danger assessment function Kc and the determination of a danger threshold Th by setting E to 0, and finally, taking the risk threshold Th as a trigger condition of the emergency lane change collision avoidance system.
Compared with the prior art, the invention has the advantages that:
1. the invention considers the factors of vehicle collision and driving instability, and provides a more accurate and safer method for evaluating the danger of emergency lane change.
2. The optimal emergency lane change danger evaluation method and the danger threshold Th are used as trigger conditions of the active emergency lane change collision avoidance system, and the upper layer controller is simple in logic and good in practicability.
Drawings
FIG. 1: decision logic diagram for emergency lane change and collision avoidance
FIG. 2: vehicle emergency collision avoidance trajectory diagram based on fifth-order polynomial
FIG. 3: optimization strategy map based on genetic algorithm
FIG. 4: schematic diagram of vehicle emergency collision avoidance scheme
FIG. 5: relation graph of dynamic factor and vehicle distance and longitudinal vehicle speed
Detailed Description
The embodiments of the present invention will be described in conjunction with the accompanying drawings for better understanding of the invention by the researchers hereafter.
Fig. 1 is a logic diagram of an emergency lane change collision avoidance decision. The main idea of the invention is introduced on the whole, firstly an emergency lane change path is planned based on a quintic polynomial method, and related sensor data is substituted into an optimal danger evaluation function Kc for calculation, when a calculation result is greater than a danger threshold Th, an emergency lane change control actuator is triggered, otherwise, the system is not triggered. The key points of the invention are as follows: the method comprises the steps of emergency lane changing path planning based on a quintic polynomial method, derivation of an optimal danger evaluation function Kc, determination of a danger threshold value and application of the optimal danger evaluation function Kc in an emergency lane changing collision avoidance system.
Step 1: fifth-order polynomial-based emergency road changing path planning
As shown in fig. 2, an X-Y coordinate system is established, with the origin of coordinates being the center of gravity of the following vehicle, the direction of vehicle travel being the X-axis, and the direction perpendicular to the road being the Y-axis. Acquiring relevant data of a lane change starting point and a lane change ending point based on environment perception data, and the method comprises the following steps: speed, acceleration, vehicle distance, etc. The emergency road changing path planning method based on the fifth-order polynomial considers the condition constraint calculation of vehicle kinematics and vehicle dynamics to deduce a collision-free emergency road changing track equation.
Pro 1: when the emergency lane change mode is adopted to avoid vehicle collision, a fifth-order polynomial equation is used for describing the emergency lane change track as follows:
y＝ATX (1)
wherein:
A＝[a0 a1 a2 a3 a4 a5]T
X＝[1 x x2 x3 x4 x5]T
x, y are the ordinate and abscissa of the collision avoidance path, and an (n 1,2, 3.) is a polynomial coefficient.
Pro2: the boundary constraint of the fifth order polynomial is defined as:
Figure BDA0002289346900000081
wherein:
Figure BDA0002289346900000082
x0and y0Is the longitudinal and transverse coordinates, x, of the vehicle's center of mass at the time of initial collision avoidanceTAnd yTK is the curvature of the collision avoidance trajectory for the vehicle, being the longitudinal and lateral coordinates of the end point time of the collision avoidance trajectory, respectively.
Pro 3: substituting equation 1 into the boundary constraint yields the following relationship:
BA＝[0 y T 0 0 0 0]T (3)
wherein:
Figure BDA0002289346900000091
deriving a coefficient matrix A:
Figure BDA0002289346900000092
pro 4: by combining equation 1 and equation 4, the collision avoidance trajectory is derived as:
Figure BDA0002289346900000093
step 2: characterization of the required yaw rate of the vehicle body.
The vehicle body emergency collision avoidance path planning only considers the vehicle kinematic constraint condition, and in the actual emergency lane changing and collision avoidance process, the automobile tire is easy to sideslip due to high deformation, so that the automobile can not stably run. Therefore, in the collision avoidance process, vehicle dynamics constraint conditions need to be considered, and an emergency lane change danger evaluation method is established to continuously evaluate the risks of collision and unstable driving.
Deducing the conditional constraint of the yaw rate according to the lateral acceleration and the road surface friction coefficient; and then solving the curvature of the collision avoidance track according to the emergency lane change collision avoidance track, and representing the yaw rate required by the vehicle.
Pro 1: the lateral acceleration of the vehicle's center of gravity may be defined as:
Figure BDA0002289346900000095
wherein:
vy＝vxtan(β)
vxis the longitudinal velocity, vyIs the lateral velocity, gamma is the yaw rate of the vehicle body, and beta is the vehicle body sideslip angle.
Thus, the lateral acceleration is further described as:
Figure BDA0002289346900000094
pro2: the lateral acceleration must be determined by a numerical limit according to the tyre-road friction coefficient, the specific relationship being:
vxγ+ac≤μg (8)
wherein: mu is the tire-road friction coefficient, g is the acceleration of gravity
Figure BDA0002289346900000101
The following relationships are defined herein:
ac＝(1-k)μg (9)
wherein: k (0 < k < 1) is a dynamic factor
Pro 3: in connection with equations (8) and (9), the following relationships are derived:
Figure BDA0002289346900000102
according to the kinematics principle, the required yaw rate can be given as:
γd＝Kvx (11)
wherein: k is the curvature of the track
The required yaw rate is derived by concatenating equations 2, 5 and 11 as:
Figure BDA0002289346900000103
wherein:
Figure BDA0002289346900000104
and step 3: and (4) representing the equivalent maximum yaw rate and constructing a risk evaluation function F.
Considering that genetic algorithm is commonly used for searching global optimum and can solve any continuous or discrete optimization problem, firstly obtaining the equivalent maximum value of the yaw rate, and then constructing a risk evaluation function F according to the constraint of the equivalent maximum yaw rate and the yaw rate.
3.1 genetic Algorithm-based optimization strategies
The optimization strategy based on the genetic algorithm is shown in fig. 3, and the specific algorithm steps are as follows:
pro 1: generating an initial population; randomly generating 100 initial string structure data, forming an initial group by 100 individuals, and starting iteration by taking the 100 initial string structure data as initial points;
pro2: evaluating and detecting the adaptability value; the adaptability value indicates the superiority and inferiority of the solution, and the adaptability value is calculated through an adaptability function;
the fitness function is:
Figure BDA0002289346900000111
pro 3: selecting the best individual by a wheel disc method; selecting excellent individuals from the current population, so that the excellent individuals have a chance to be used as father generations to be used as next generation to breed offspring, wherein the selection principle is that the probability that the individuals with strong adaptability contribute one or more offspring to the next generation is high, and the relative fitness of each individual is the probability that each individual is inherited to the next generation population;
the relative fitness, i.e. the genetic probability, is:
Figure BDA0002289346900000112
pro 4: crossing; a new generation of individuals is obtained by crossover operations, which combines the characteristics of its parent individuals. The cross probability Pc is 0.87, and the information exchange is embodied by the cross;
pro 5: mutation; performing mutation operation by adopting a basic bit mutation method, firstly determining the gene mutation position of each individual, determining the mutation probability Pm to be 0.1, and taking the original gene value of the mutation point;
3.2 construction of the Risk assessment function F
Pro 1: based on an optimization strategy of a genetic algorithm, when U is 0.20 by iterative optimization, the optimal solution is obtained by the vehicle body yaw rate required by urgent lane change;
pro2: when U is 0.20, the equivalent maximum value of the required yaw rate can be obtained:
Figure BDA0002289346900000113
wherein: p1、P2Is a gain parameter, the approximate values are 5.76 and 0.59 respectively;
pro 3: the required yaw rate needs to satisfy the vehicle dynamics constraints, so the following relationship can be derived:
Figure BDA0002289346900000121
pro 4: in connection with equations 13 and 14, the following relationship is found:
F(vx，k，μ.xT，yT)≤0 (15)
wherein:
Figure BDA0002289346900000122
f is a danger evaluation function in the emergency lane changing process.
And 4, step 4: and constructing an optimized danger evaluation function E.
The boundary distance of front and rear vehicles in the lane changing process can be researched to more effectively evaluate the lane changing risk, and the coordinate (x) of the minimum point of the vehicle boundary distance is definedfl，yfl) And then, an optimized danger evaluation function E is deduced by combining a lane change collision avoidance trajectory formula and a danger evaluation function F.
And (2) Prol: in order to more effectively assess the risk and further explore the constraints of safe driving, both the lateral and longitudinal distances between the following vehicle and the lead vehicle are studied, considering the vehicle during an emergency collision.
The following relationships are defined herein:
Figure BDA0002289346900000123
wherein n and m are direct proportionality coefficients. As shown in fig. 4, wherein x is a schematic diagram of a vehicle emergency collision avoidance schemeflFor following the longitudinal distance of the vehicle radar or camera to the lead vehicle, where yflThe distance of the following vehicle is xflCorresponding lateral displacement.
Suppose (x)fl，yfl) Is a point on the collision avoidance path and is derived in conjunction with equation 5, equation 16:
Figure BDA0002289346900000124
pro2: and (3) connecting the equation 16, the equation 17 and the risk evaluation function F to obtain an optimized risk evaluation function E:
Figure BDA0002289346900000131
and 5: construction of optimal risk assessment function Kc and determination of risk threshold Th
Setting the optimized risk evaluation function E as a minimum value 0, deducing a characteristic expression of a critical power factor Kc, taking the expression of Kc as an optimal risk evaluation function, and predicting and analyzing the risk degree of the emergency lane change process by the critical power factor Kc. And determining a danger threshold Th, and using the danger threshold Th as a condition for triggering the emergency lane-changing collision avoidance system.
Pro 1: longitudinal distance x between following vehicle and guiding vehicleflAnd the direct scaling factor n are treated as dependent variable and independent variable, respectively, and the other parameters in equation 18 are treated as constants.
When n is approximately equal to 2.2, the longitudinal distance x between the front and rear vehicles can be obtainedflIs measured. Further, analyzing equation 17, when n is equal to 2, m is equal to 2. Therefore, to simplify the analytical model design process, n is chosen to be 2 in this section, so equation 18 is further simplified as:
Figure BDA0002289346900000132
pro2: and (3) setting the risk evaluation function as a minimum value of 0, and deriving a critical power factor:
Figure BDA0002289346900000133
pro 3: the critical power factor Kc can predict and analyze the danger emergency degree in the collision avoidance process, the width of the passenger car is considered to be about 1.85 meters, the safety boundary distance of the transverse displacement is 0.4 meter, and y isflThe design is 2.25 meters.
As shown in FIG. 5, the critical dynamics factor Kc varies with vxIs increased with xflAnd increases and decreases in μ. Furthermore, Kc in the interval (0.8,1) follows xflIs sharply increased with decrease of vxIs increased sharply.
Pro 3: when v isxAnd when the threshold value is larger than the danger threshold value Th, an emergency lane change collision avoidance strategy is triggered, otherwise, the strategy is not triggered.
Consider Kc over the interval (0.8,1) as xflIs reduced and vxThe risk threshold Th is set to 0.85 because of a sharp increase in the number of cells and the emphasis of the present study on avoiding a collision in an emergency.
As described above, the key points include: the method comprises the steps of emergency lane changing path planning based on a quintic polynomial method, derivation of an optimal danger evaluation function Kc, determination of a danger threshold value and application of the optimal danger evaluation function Kc in an emergency lane changing collision avoidance system. For convenience of derivation, the invention is further susceptible to further modification and improvement by persons skilled in the art without departing from the spirit and scope of the invention, wherein the relevant data of body width, safety margin distance, etc. are given the values of commonly fixed parameters, and therefore the invention is to be limited only by the contents and scope of the appended claims, which are intended to cover all alternatives and equivalents as may be included within the spirit and scope of the invention as defined by the appended claims.
In summary, according to the method for evaluating the danger of the vehicle emergency lane change based on the vehicle kinematics and the genetic algorithm, firstly, the constraints of the vehicle kinematics and the vehicle dynamics are considered, a collision-free emergency lane change track equation is drawn based on a quintic polynomial equation, secondly, the conditional constraint of the yaw rate and the collision avoidance track are deduced according to the lateral acceleration and the road surface friction coefficient, the curvature of the collision avoidance track is solved, the yaw rate required by the vehicle body in the emergency lane change process is represented, then, the genetic algorithm is adopted to search the global optimum of the yaw rate required by the vehicle body, the equivalent maximum value of the yaw rate required by the vehicle body is obtained, the danger evaluation function F is constructed according to the equivalent maximum yaw rate and the yaw rate conditional constraint, and then, the danger evaluation function F is constructed based on the previous and next vehicleDeducing an optimized risk evaluation function E from the vehicle boundary distance, finally deducing to obtain an optimal risk evaluation function Kc by setting E to be 0, and considering that Kc along with x on the interval (0.8,1)flIs reduced and vxIs increased sharply, and the final danger threshold Th is 0.85 as the trigger condition of the active emergency lane change collision avoidance system.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an illustrative embodiment," "an example," "a specific example," or "some examples" or the like mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
While embodiments of the invention have been shown and described, it will be understood by those of ordinary skill in the art that: various changes, modifications, substitutions and alterations can be made to the embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

Claims (9)

1. A vehicle emergency lane change danger assessment method based on vehicle kinematics and genetic algorithm is characterized by comprising the following steps:
step 1: planning an emergency lane change path based on a fifth-order polynomial;
step 2: representing the required yaw rate of the vehicle body;
and step 3: constructing a representation of the equivalent maximum vehicle body yaw rate and a risk evaluation function F;
and 4, step 4: according to the method, the emergency lane changing risk is more effectively evaluated according to the boundary distance between the front vehicle and the rear vehicle in the emergency lane changing process, and an optimized danger evaluation function E is deduced;
and 5: and determining the construction of an optimal risk evaluation function and a risk threshold Th.
2. The vehicle kinematic and genetic algorithm-based vehicle emergency lane change danger assessment method according to claim 1, wherein the step 1 is an emergency lane change path planning method based on a fifth-order polynomial, and a collision-free emergency lane change trajectory equation is derived by considering conditional constraint calculation of vehicle kinematics and vehicle dynamics.
3. The vehicle kinematic and genetic algorithm-based vehicle emergency lane change risk assessment method according to claim 2, wherein: the emergency lane change path planning method based on the fifth-order polynomial comprises the following specific steps:
pro 1: when the emergency lane change mode is adopted to avoid vehicle collision, a fifth-order polynomial equation is used for describing the emergency lane change track as follows:
y＝ATX (1)
wherein:
A＝[a0 a1 a2 a3 a4 a5]T
X＝[1 x x2 x3 x4 x5]T
x, y are the ordinate and abscissa of the collision avoidance path, and anIs a polynomial coefficient, where n is 1,2,3 …;
pro2 boundary constraints for the fifth order polynomial are defined as:
Figure FDA0003243930690000011
wherein:
Figure FDA0003243930690000012
x0and y (x)0) Is the longitudinal and transverse coordinates, x, of the vehicle's center of mass at the time of initial collision avoidanceTAnd yTRespectively as the end point time of the collision avoidance trajectoryLongitudinal and transverse coordinates of the vehicle center of mass, and K is the curvature of the vehicle collision avoidance track;
pro 3: substituting equation (1) into the boundary constraint results in the following relationship:
BA＝[0 yT 0 0 0 0]T (3)
wherein:
Figure FDA0003243930690000021
deriving a coefficient matrix A:
Figure FDA0003243930690000022
pro 4: by combining equation (1) and equation (4), the collision avoidance trajectory is derived as:
Figure FDA0003243930690000023
4. the vehicle kinematic and genetic algorithm-based vehicle emergency lane change risk assessment method according to claim 1, wherein: in the step 2, the conditional constraint of the yaw rate is deduced according to the lateral acceleration and the road surface friction coefficient; and then solving the curvature of the collision avoidance track according to the emergency lane change collision avoidance track, and representing the yaw rate required by the vehicle.
5. The vehicle kinematic and genetic algorithm-based vehicle emergency lane change risk assessment method according to claim 4, wherein: solving the curvature of the collision avoidance track according to the emergency lane change collision avoidance track, representing the required yaw rate of the vehicle, and specifically comprising the following steps:
and 2.1: the lateral acceleration of the vehicle's center of gravity may be defined as:
Figure FDA0003243930690000024
wherein:
vy＝vxtan(β)
vxis the longitudinal velocity, vyIs the lateral velocity, gamma is the yaw rate of the vehicle body, beta is the vehicle body sideslip angle;
thus, the lateral acceleration is further described as:
Figure FDA0003243930690000031
and 2.2: the lateral acceleration must be determined by a numerical limit according to the tyre-road friction coefficient, the specific relationship being:
vxγ+ay≤μg (8)
wherein: μ is the tire-road friction coefficient, g is the gravitational acceleration;
Figure FDA0003243930690000032
the following relationships are defined herein:
ay＝(1-k)μg (9)
wherein: k is a dynamic factor, and 0 < k < 1;
and (2.3) Pro2: in connection with equations (8) and (9), the following relationships are derived:
Figure FDA0003243930690000033
according to the kinematics principle, the required yaw rate can be given as:
γd＝Kvx (11)
wherein K is the curvature of the track;
the required yaw rate is derived by connecting equations (2), (5) and (11):
Figure FDA0003243930690000034
wherein:
Figure FDA0003243930690000035
6. the vehicle kinematic and genetic algorithm-based vehicle emergency lane change risk assessment method according to claim 1, wherein: in the step 3, a genetic algorithm is adopted to search the global optimum of the yaw rate expression, the population size Np, the chromosome length Lc, the stop generation Gt, the cross probability Pc and the variation probability Pm are determined, the equivalent maximum value of the yaw rate of the vehicle body required by the emergency lane change is obtained through solving, and then the risk evaluation function F is constructed according to the constraint of the equivalent maximum yaw rate and the yaw rate.
7. The vehicle kinematic and genetic algorithm-based vehicle emergency lane change risk assessment method according to claim 6, wherein: the method comprises the following steps of searching global optimization of a yaw rate expression by adopting a genetic algorithm, obtaining an equivalent maximum value of a vehicle body yaw rate required by emergency lane changing, and constructing a risk evaluation function F according to the constraint of the equivalent maximum yaw rate and the yaw rate, wherein the specific steps are as follows:
pro3.1: considering that a genetic algorithm is commonly used for searching global optimality, the population size Np, the chromosome length Lc and the termination generation Gt which are obtained based on an optimization strategy are respectively designed to be 100, 20 and 500, and the cross probability Pc and the variation probability Pm are respectively designed to be 0.8 and 0.1;
and (3.2) Pro3: when U is 0.20, an equivalent maximum value of the required body yaw rate can be obtained:
Figure FDA0003243930690000041
wherein: p1、P2Is a gain parameter, approximatelyValues of 5.76, 0.59, respectively;
and (3) Pro3.3: the required body yaw rate needs to meet vehicle dynamics constraints, so the following relationship can be derived:
Figure FDA0003243930690000042
and (3.4) Pro3: in connection with equations (13) and (14), the following relationship is found:
F(vx,k,μ,xT,yT)≤0 (15)
wherein:
Figure FDA0003243930690000043
f is a danger evaluation function in the emergency lane changing process.
8. The vehicle kinematic and genetic algorithm-based vehicle emergency lane change risk assessment method according to claim 1, wherein: the specific steps of the step 4 are as follows:
pro4.1: considering the vehicle emergency collision avoidance period, in order to more effectively evaluate risks and further explore the constraint conditions of safe driving, the transverse and longitudinal distances between the following vehicle and the guide vehicle are researched;
the following relationships are defined herein:
Figure FDA0003243930690000051
wherein n and m are direct proportionality coefficients, wherein xflFor following the longitudinal distance of the vehicle radar or camera to the lead vehicle, where yflThe distance of the following vehicle is xflCorresponding lateral displacement;
suppose (x)fl,yfl) Is a point on the collision avoidance path, and is derived by combining equation (5) and equation (16):
Figure FDA0003243930690000052
and (2) Pro4.2: and (3) connecting the equation (16), the equation (17) and the risk evaluation function F to obtain an optimized risk evaluation function E:
Figure FDA0003243930690000053
9. the vehicle kinematic and genetic algorithm-based vehicle emergency lane change risk assessment method according to claim 8, wherein: in the step 5, the optimized risk assessment function E is set as a minimum value 0, a representation formula of a critical power factor Kc is deduced, an expression of the Kc is used as an optimal risk assessment function, and the critical power factor Kc can predict and analyze the risk degree of the emergency lane changing process; the method comprises the following specific steps:
and (2) Pro5.1: longitudinal distance x between following vehicle and guiding vehicleflAnd the direct scaling factor n are treated as dependent variable and independent variable, respectively, and the other parameters in equation (18) are treated as constants;
when n is approximately equal to 2.2, the longitudinal distance x between the front and rear vehicles can be obtainedflIn addition, equation (17) is analyzed, and when n is equal to 2, m is equal to 2, and n is selected to be 2 in this section in order to simplify the analysis model design process, so equation (18) is further simplified as:
Figure FDA0003243930690000054
and (2) Pro5.2: the risk assessment function is set to a minimum value of 0, and a critical power factor is derived:
Figure FDA0003243930690000061
and (5.3) Pro5: the critical power factor Kc can predict and analyze the danger emergency degree in the collision avoidance process, the width of the passenger car is considered to be about 1.85 meters, the safety boundary distance of the transverse displacement is 0.4 meter, and y isflThe design is 2.25 meters;
critical dynamics factor Kc with vxIs increased with xflAnd μ decreases with increasing μ, and furthermore, Kc over the interval (0.8,1) decreases with xflIs sharply increased with decrease of vxIs increased sharply;
and (5.4) Pro5: when Kc is larger than a danger threshold Th, an emergency lane change collision avoidance strategy is triggered, otherwise, the strategy is not triggered;
consider Kc over the interval (0.8,1) as xflIs reduced and vxIs increased sharply, and considering the case of avoiding a collision in an emergency, Th is set to 0.85.
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