CN114987461A - Intelligent passenger car dynamic lane change trajectory planning method under multi-car complex traffic environment - Google Patents

Intelligent passenger car dynamic lane change trajectory planning method under multi-car complex traffic environment Download PDF

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CN114987461A
CN114987461A CN202210548395.3A CN202210548395A CN114987461A CN 114987461 A CN114987461 A CN 114987461A CN 202210548395 A CN202210548395 A CN 202210548395A CN 114987461 A CN114987461 A CN 114987461A
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lane
passenger car
vehicle
track
lane change
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聂枝根
周贻
贾元
王万琼
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Kunming University of Science and Technology
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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
    • 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
    • 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
    • B60W30/18—Propelling the vehicle
    • B60W30/18009—Propelling the vehicle related to particular drive situations
    • B60W30/18163—Lane change; Overtaking manoeuvres
    • 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
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/02—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to ambient conditions
    • B60W40/04—Traffic 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
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/10—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to vehicle motion
    • B60W40/105—Speed
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/10—Internal combustion engine [ICE] based vehicles
    • Y02T10/40—Engine management systems

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  • Automation & Control Theory (AREA)
  • Transportation (AREA)
  • Mechanical Engineering (AREA)
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Abstract

The invention relates to an intelligent passenger car dynamic lane change track planning method under a multi-car complex traffic environment, aiming at the dynamic traffic environment, firstly generating lane change candidate track clusters; secondly, performing stability analysis on the intelligent passenger car according to factors such as different road conditions, determining the minimum lane change longitudinal distance of a lane change track, and removing the unstable lane change track; then, considering the state change of surrounding vehicles, carrying out obstacle avoidance detection, obtaining the minimum lane changing longitudinal distance of the intelligent passenger car, which does not collide with the surrounding vehicles, and removing unsafe lane changing tracks; finally, according to the safe lane-changing track cluster of the longitudinal length of the lane-changing track of the real-time intelligent passenger car, considering the objective functions of the lane-changing efficiency of the vehicle and the like, optimizing the optimal lane-changing track to obtain the safe optimal lane-changing track; in addition, taking into account the sudden change of the surrounding vehicle state, lane change trajectory re-planning and speed planning are performed. The invention establishes a more real driving scene, and the intelligent bus dynamic lane change trajectory planning is more in line with the actual traffic environment.

Description

Intelligent passenger car dynamic lane change trajectory planning method under multi-car complex traffic environment
Technical Field
The invention belongs to the technical field of intelligent passenger car track planning, and particularly relates to a dynamic lane change track planning method of an intelligent passenger car in a multi-car complex traffic environment.
Background
The lane change research of the intelligent vehicles at home and abroad mainly aims at passenger cars, particularly four-wheel independent cars, and the research on intelligent passenger cars is less. However, compared with passenger cars, the passenger car has the characteristics of large self mass, more passenger carrying, four to five times of length, high height and the like, and is easy to have accidents of side turning and the like under the same lateral acceleration, thereby causing huge accidents of group death and group injury and causing great economic loss. In addition, the driver is easy to fatigue when driving the passenger car for a long time, so that the misjudgment and the misoperation of the surrounding environment are caused. Therefore, intelligent passenger car research is of great importance. Through literature research, the intelligent passenger car lane change in the actual traffic environment has the following problems to be solved.
(1) In the actual transportation process, the intelligent passenger car is in a dynamic traffic environment. However, for the lane change trajectory planning of the intelligent passenger car, many previous researches neglect factors of dynamic changes of the speed or acceleration of the surrounding vehicle by assuming that the surrounding vehicle is static or moves at a constant speed, and particularly, the acceleration of the surrounding vehicle changes suddenly when the lane is changed. Therefore, the intelligent bus lane change trajectory planning needs to consider the influence of surrounding dynamic traffic environment to perform trajectory planning and re-planning so as to obtain a safe and optimal lane change trajectory.
(2) At present, the lane change trajectory planning of the intelligent automobile considers more yaw stability of the automobile and less rollover of the automobile. However, the intelligent passenger car has the characteristics of being long, high, having more passengers and the like, and is easy to have accidents such as vehicle rollover and the like. Therefore, the intelligent passenger car trajectory planning needs to consider not only the yaw stability but also the roll stability so as to ensure the stability of the intelligent passenger car vehicle. Therefore, the lane-changing trajectory replanning method takes stability of rollover and the like of the intelligent passenger car into consideration.
Disclosure of Invention
Based on the defects, the invention provides the intelligent passenger car dynamic lane-changing track planning method under the multi-car complex traffic environment, and the lane-changing track of the intelligent passenger car is dynamically planned in real time by combining the information of the yaw stability and the roll stability of the car, the real-time motion state of surrounding traffic vehicles and the like.
In order to achieve the purpose, the invention provides an intelligent passenger car dynamic lane change track planning method under a multi-car complex traffic environment, and the method comprises the following steps of firstly, generating an intelligent passenger car candidate unconstrained lane change track cluster aiming at the dynamic traffic environment; secondly, analyzing the stability of the vehicle in the yaw and the roll according to different road conditions, determining the stable minimum lane changing longitudinal distance of the intelligent passenger car, and removing the unstable lane changing track from the unconstrained lane changing track cluster; then, considering the state change of surrounding vehicles, carrying out obstacle avoidance detection, obtaining the maximum longitudinal length value of the lane changing track of the intelligent passenger car, which is not collided with the surrounding vehicles, and eliminating unsafe lane changing tracks of collision; finally, a multi-target lane-changing track performance function is set, and an optimal intelligent bus lane-changing track is obtained in the stable and collision-free track of the bus; meanwhile, sudden changes of surrounding vehicle states are considered, and intelligent bus lane change track re-planning and speed planning are carried out.
The method specifically comprises the following steps:
step1, selecting a series of lane change destination candidate positions on a target lane according to the position of the intelligent passenger car and the motion state of surrounding traffic vehicles, and connecting the initial position of the vehicle and the destination candidate positions by utilizing a cubic polynomial curve to generate an unconstrained lane change track cluster;
y(x)＝a 0 +a 1 x+a 2 x 2 +a 3 x 3
x, y (x) are respectively the longitudinal position and the transverse position of the intelligent passenger car; a is 0 ，a 1 ，a 2 ，a 3 Is a parameter, and satisfies:
y(x 0 )＝a 0 +a 1 x 0 +a 2 x 2 0 +a 3 x 3 0
y'(x 0 )＝a 1 +2a 2 x 0 +3a 3 x 2 0
x 0 ，y(x 0 ) Respectively setting the longitudinal and transverse initial positions of the lane change of the intelligent passenger car; y' (x) 0 ) The direction is the tangential direction of the initial position of the intelligent passenger car;
the intelligent passenger car lane change terminal motion state is as follows:
y(x f )＝a 0 +a 1 x f +a 2 x 2 f +a 3 x 3 f
y'(x f )＝a 1 +2a 2 x f +3a 3 x 2 f
x f ，y(x f ) Respectively corresponding to the longitudinal and transverse end positions of the lane change of the intelligent passenger car; y' (x) f ) The direction is the tangential direction of the terminal position of the intelligent passenger car; a is 0 ，a 1 ，a 2 ，a 3 Are parameters in the lane change trajectory.
Step2, obtaining the minimum lane-changing longitudinal distance x of the intelligent passenger car without the problems of yaw and roll through a simulation model according to the speed and the road adhesion coefficient of the intelligent passenger car and based on the saturation characteristics of tires fmin Obtaining a stable lane changing track;
(1) yaw stability of vehicle
Yaw angular velocity stability limit and centroid slip angle stability limit:
Figure BDA0003653218980000031
Figure BDA0003653218980000032
wherein r is s Is the yaw rate of the vehicle, beta s Is the centroid slip angle of the vehicle, F yr ，F yf Respectively representing the lateral offset force of the rear axle and the lateral offset force of the front axle of the vehicle, g is the gravity acceleration, mu is the road adhesion coefficient, u is the vehicle speed, m is the total vehicle mass, C αr The lateral deflection stiffness of the rear axle is represented, and a and b respectively represent the distance from the front axle and the rear axle of the vehicle to the center of mass of the vehicle;
(2) vehicle roll stability
Transverse load transfer rate
Figure BDA0003653218980000033
Wherein n is the number of vehicle axles, F Zl For vertical load on the left side of the wheel, F Zr Is the vertical load on the right side of the wheel;
(3) vehicle simulation
Setting lane-changing longitudinal distances with different speeds and different road adhesion coefficients in a vehicle simulation model;
outputting the yaw angular velocity and the centroid side deviation angle, and judging the yaw stability with a phase plane formed by the yaw angular velocity stability limit and the centroid side deviation angle stability limit to obtain the minimum lane change longitudinal distance x meeting the yaw stability of the vehicle f1 ；
Outputting the vertical loads of the left and right wheels, calculating the lateral load transfer rate LTR value, and determining the minimum lane-changing longitudinal distance x for preventing rollover f2 ；
Minimum lane change longitudinal distance x fmin When the same vehicle speed and the same road adhesion coefficient are taken, x f1 And x f2 The larger of these.
Step3 based on minimum lane change longitudinal distance x fmin Gradually carrying out collision detection from short to long to obtain the maximum lane-changing longitudinal distance of the track without collision so as to obtain a safe lane-changing track;
the intelligent bus lane changing method has the advantages that lane changing is carried out in a dynamic traffic environment, and most importantly, the intelligent bus is ensured not to collide with other dynamic traffic vehicles and other surrounding obstacles in the lane changing process. The invention regards the outline of the intelligent bus as a rectangle, the course angle of the bus is the direction of the rectangle at the moment, and a series of longitudinal distances x between the intelligent bus and the lane change are generated by a real-time planned reference lane change track f Corresponding rectangle, when the longitudinal speed of the intelligent passenger car is known, the lane changing longitudinal distance x is planned in real time at the moment t f The respective vertex positions of the determined rectangles are calculated as follows:
Figure BDA0003653218980000041
Figure BDA0003653218980000042
in the formula x 1...4 (t) and y 1...4 (t) represents the values of the 4 vertex coordinates x and y of the smart car outline rectangle, l and w represent the length and width of the smart car,
Figure BDA0003653218980000043
representing the heading angle of the intelligent passenger car, x (T) and y (T) representing the centroid position of the intelligent passenger car, and T being a time interval;
judging whether the intelligent passenger car and the traffic vehicle collide at the moment t, and then judging whether the outlines of the two cars are intersected; the invention constructs a function Bounding Space and Hierarchy (BSH) for judgment. Based on this function, the smart bus and the transportation vehicle do not collide at time t when the following criteria are met:
Figure BDA0003653218980000044
Figure BDA0003653218980000045
Figure BDA0003653218980000046
where i is 1,2,3,4, and x denotes 4 transportation vehicles, respectively K_n (t) and y K_n (t) denotes the nth vertex coordinate, x, of the smart car T_in (t) and y T_in (t) is the coordinates of the nth vertex of the ith traffic vehicle outline, and the following obstacle avoidance detection formula in lane change is obtained through the derivation:
Figure BDA0003653218980000047
minimum lane change longitudinal distance x determined from stability fmin Firstly, increasing a length to obtain a lane change track, and detecting whether the intelligent passenger car is in the track or notCollision occurs; if collision occurs, the track of the previous length is the maximum lane changing longitudinal distance under the working condition. Therefore, the safe lane-changing track cluster of the intelligent passenger car is from the track longitudinal length determined by stability to the safe lane-changing track longitudinal length determined by all surrounding vehicles. The lane change track cluster in the range can not collide with surrounding vehicles, and can not cause unstable vehicle yaw and roll in the lane change process of the intelligent passenger car.
Step4, considering the influence of lane changing efficiency and passenger comfort on the lane changing track on the safe lane changing track obtained in Step3, constructing an objective function, and selecting an optimal intelligent passenger car lane changing track;
Figure BDA0003653218980000051
wherein J represents a cost function, a (x) f ) Lateral acceleration, a (x), representing the final position of the lane change f ) max Represents the maximum lateral acceleration, t (x), in the safe trajectory cluster f ) Indicating the lane change time, t (x) f ) max Representing the maximum lane changing time in the safe track cluster, wherein omega is a weight value;
lateral acceleration a (x) at the final position f ) Calculated by the following equation:
a(x f )＝u(x f ) 2 K(x f )
in the formula, u (x) f ) Expressing the speed of the intelligent passenger car at the final position, K () expressing the curvature function of the lane change track, and K () being calculated by the following equation:
Figure BDA0003653218980000052
in the formula, y' (x) f ) And y ″ (x) f ) Respectively a first derivative and a second derivative of the lane change trajectory function;
y'(x f )＝a 1 +2a 2 x f +3a 3 x 2 f
y”(x f )＝2a 2 +6a 3 x f
wherein a is 1 ，a 2 ，a 3 As a parameter of the lane-change trajectory function, x f Is the longitudinal end position of the lane change track.
Intelligent bus lane change track running time t (x) f ) Comprises the following steps:
Figure BDA0003653218980000053
in the formula, t c The lane change time is already provided for the intelligent passenger car.
And in the Step5 intelligent passenger car lane changing process, adjusting lane changing track and speed in real time, giving up lane changing in emergency, returning to the original lane, and replanning until the intelligent passenger car reaches the target position.
When the intelligent passenger car is changing lanes, the lane changing track needs to be adjusted because the states of surrounding vehicles suddenly change violently, so that the intelligent passenger car is prevented from colliding with the surrounding vehicles. The method adjusts the track in real time by adjusting the longitudinal length of the lane-changing track curve at each moment, and ensures the optimal planning of the lane-changing track of the intelligent passenger car. The lane change track of the intelligent passenger car at each moment can be regarded as a section from the lane change to the lane change track, namely the lane change track of the intelligent passenger car can be composed of three lane change tracks with different longitudinal lengths. The specific algorithm is as follows: the intelligent bus lane change track planning algorithm obtains the states of surrounding vehicles through sensing or an internet of vehicles mode at intervals of time delta tau, combines the states of the position, the speed and the like of the intelligent bus, utilizes a cubic polynomial to plan the lane change track in real time, circulates in the way, and advances forwards until the intelligent bus reaches the target position, so that the lane change of the intelligent bus is completed.
In the lane changing process of the intelligent passenger car, when emergency occurs in the surrounding dynamic environment, the intelligent passenger car should give up lane changing and return to the original lane: when the front vehicle of the target lane is emergently braked, the deceleration is increased violently, and the acceleration of the rear vehicle of the target lane is increased sharply; the lane-changing longitudinal distance interval is less than the minimum lane-changing longitudinal distance x fmin Is short ofWhen the vehicle changes lanes safely, the intelligent vehicle gives up lane changing and plans a track returning to the original lane;
when the intelligent vehicle performs lane changing operation, the lane changing longitudinal distance interval is smaller than the minimum lane changing longitudinal distance x fmin When the lane change track is separated, speed planning is carried out, and the stability of the lane change track is ensured; when speed planning is carried out, the acceleration of the intelligent vehicle is sharply reduced, if the intelligent vehicle runs at the planned speed, the comfort of passengers is affected, at the moment, lane changing is abandoned, and the trajectory planning of returning to an original lane is carried out.
Longitudinal distance x for lane change of intelligent passenger car f Less than the minimum lane change longitudinal distance x fmin In time, speed planning is carried out on the intelligent passenger car to reduce the speed, and a cubic polynomial model is adopted:
u x (t)＝b 0 +b 1 t+b 2 t 2 +b 3 t 3
a x (t)＝b 1 +2b 2 t+3b 3 t 2
in the formula, t, u x (t)，a x (t) respectively representing the current time, the current time speed and the current time acceleration of the intelligent passenger car, wherein b 0 ，b 1 ，b 2 ，b 3 Are parameters.
Solving parameter b 0 、b 1 、b 2 、b 3 The expression is as follows:
Figure BDA0003653218980000061
Figure BDA0003653218980000062
Figure BDA0003653218980000063
wherein u is x0 To the starting moment vehicle speed, a x0 As starting time vehicle acceleration，x f For changing the longitudinal distance of the end point of the track, u lim The vehicle speed limit is a critical vehicle speed under the constraint of the boundary such as driving safety, comfort and the like. Meanwhile, in order to ensure the comfort of passengers, the maximum longitudinal acceleration of the host vehicle is selected as the maximum longitudinal acceleration a acceptable for the comfort of passengers max The maximum acceptable longitudinal braking deceleration is a min In order to satisfy the comfort requirement in the acceleration state, there are:
a min ≤b 1 +2b 2 t+3b 3 t 2 ≤a max
when critical end point velocity u lim After determination, the speed plan of the vehicle depends only on t f A variable, t f The value of (a) should satisfy the above acceleration requirement.
The minimum safe distance model is utilized to ensure that the intelligent passenger car does not collide with the front and rear cars in the target lane after lane changing, when the front car is emergently braked, the rear car also takes braking measures, and the minimum safe distance S is required to be met when the two cars do not collide 1 、S 2 、S 3 、S 4 The minimum safe distance expression is as follows:
Figure BDA0003653218980000071
Figure BDA0003653218980000072
Figure BDA0003653218980000073
Figure BDA0003653218980000074
wherein S is 1 Is the minimum safe distance, S, between the intelligent passenger car and the front car of the side lane 2 For minimum safe distance of intelligent passenger car and side lane rear carFrom, S 3 Is the minimum safe distance between the intelligent passenger car and the front car of the current road, S 4 Is the minimum safe distance v between the intelligent passenger car and the rear car of the lane V Speed, v, of a smart bus 1 Speed, v, of the vehicle in front of the side lane 2 Speed, v, of vehicles following the side-ways 3 The speed, v, of the vehicle in front of the lane 4 Is the speed of the vehicle behind the lane, a v For maximum braking deceleration of smart passenger cars, a 1 Maximum braking deceleration of the front vehicle of the side lane, a 2 Maximum braking deceleration of the rear vehicle in the side lane, a 3 For maximum braking deceleration of the front vehicle of the own lane, a 4 For maximum braking deceleration, τ, of vehicles following the track V Time delay, tau, for information interaction and control systems of intelligent passenger cars and surrounding traffic cars 2 And τ 4 Reaction time for human driver, a VV Acceleration in preparing the smart car for braking, a 22 And a 44 And preparing the acceleration during braking for the vehicle behind the side lane and the vehicle behind the lane.
The invention provides an intelligent passenger car dynamic lane change track planning under a multi-car complex traffic environment, which considers the factors of whether the lane change track meets the lateral and longitudinal dynamic stability of a vehicle, whether the lane change track can be safely and comfortably completed under an external dynamic complex traffic environment, the intelligent passenger car dynamic speed planning in lane change, the safe following distance after lane change and the like. The previous researches generally rarely relate to the dynamic characteristics of vehicles with lane changing tracks, the change of intelligent passenger car speed in the lane changing process and the research of safe following distances after lane changing. Therefore, the invention establishes a more real simulation driving scene by taking the conditions as constraints, so that the simulation process and the result are more in line with the dynamic lane changing process of the intelligent passenger car under the actual traffic environment.
Compared with the prior art, the invention has the beneficial effects that:
1. the method considers whether the lane change track of the intelligent passenger car meets the vehicle yaw stability and roll stability, thereby deducing the minimum stable lane change longitudinal distance x under the conditions of different vehicle speeds u and different road adhesion coefficients mu fmin 。
2. The method takes the vehicle contour as a rectangle, judges whether the lane change track is safe or not by calculating the coordinates of the vertex of the rectangle, thereby judging whether the vertex of the rectangle of the intelligent passenger car is intersected with the vertices of surrounding traffic vehicles or not, and can judge the safety of the lane change track more quickly and efficiently by using the BSH function.
3. The invention establishes a dynamic speed planning model of the intelligent passenger car in the lane changing process, and the model is used when the longitudinal distance x of the lane changing f Less than the minimum lane change longitudinal distance x fmin In time, carry out the speed planning of intelligent passenger train, guarantee the stability that intelligent passenger train changes the way and traveles.
4. The invention considers the safe following distance after lane change, and avoids collision between the intelligent passenger car and front and rear cars on the target lane after lane change.
5. The invention provides a more real, reliable and universal simulation environment for experimental simulation by establishing a complex dynamic traffic environment and considering the real-time motion state of surrounding traffic vehicles.
Drawings
Fig. 1 is a schematic diagram of the present invention.
Fig. 2 is a schematic view of a complex multi-vehicle traffic environment.
FIG. 3 is a schematic diagram of generating unconstrained trajectory clusters.
Fig. 4 is a schematic diagram of a vehicle yaw dynamics model.
FIG. 5 shows a minimum lane change longitudinal distance x according to an embodiment of the present invention fmin A three-dimensional map relating the vehicle speed u and the road surface adhesion coefficient μ.
Fig. 6 is a safety judgment schematic diagram of a lane-changing obstacle-avoiding detection algorithm.
Fig. 7 is a schematic view of the following safety distance.
FIG. 8 is a schematic diagram of a re-planning of a dynamic lane-change trajectory.
FIG. 9 is a schematic diagram of a smart bus lane-change retracing trajectory planning.
Fig. 10 is a diagram of a lane-change planning trajectory process according to an embodiment of the present invention.
Detailed Description
The invention is described in further detail below with reference to the figures and specific embodiments.
Examples
The invention provides an intelligent passenger car dynamic track-changing planning method in a multi-car complex traffic environment, which has the principle as shown in figure 1. According to the position of the intelligent passenger car and the motion state of surrounding traffic vehicles, a series of lane change endpoint candidate positions are selected on a target lane, and the initial position of the vehicle and the endpoint candidate positions are connected by utilizing a cubic polynomial curve to generate an unconstrained lane change track cluster. Secondly, obtaining the minimum lane-changing longitudinal distance x of the intelligent passenger car without the problems of yaw and roll through a simulation model according to the speed of the intelligent passenger car and the road adhesion coefficient and based on the saturation characteristic of tires fmin Obtaining a stable lane changing track; lane-changing track obstacle avoidance detection module based on minimum lane-changing longitudinal distance x fmin Performing collision detection from short to long to the maximum longitudinal length of the lane changing track to obtain the maximum lane changing longitudinal distance without collision so as to obtain a safe lane changing track; the track planning module sets multi-objective functions such as lane changing efficiency and the like, and optimizes and obtains the optimal longitudinal length of the lane changing track within the determined minimum and maximum longitudinal length ranges of the lane changing track; the track generation module generates an intelligent passenger car lane change reference track based on the longitudinal length of the track; the lane changing abandoning module is used for planning a lane changing track by giving up the lane changing track when the state of the surrounding vehicles changes suddenly, so that the lane changing of the intelligent passenger car breaks through the stable range of the vehicles, and the original car can allow the intelligent passenger car to abandon the lane changing and return to the original lane; the speed planning module is used for giving up lane change if the intelligent passenger car gives up, causing danger to the original car, planning the lane change speed of the intelligent passenger car and continuing lane change. Meanwhile, the track planning process is carried out in a rolling mode in delta tau time according to the surrounding vehicle state, the real-time road surface state and the vehicle state of the vehicle in real time until the intelligent passenger car changes to the target lane, and the track planning of the intelligent passenger car is finished.
The method comprises the following specific steps:
the first step is as follows: and establishing a complex dynamic traffic environment, and generating an unconstrained lane changing track cluster.
In the embodiment, a complex dynamic traffic environment is established as shown in fig. 2, and a research object is a smart bus and is marked as (V). In the modeling process, the motion states of 4 surrounding vehicles which can affect the lane change track of the intelligent passenger car are considered at the same time. Namely a front vehicle (1) of the side lane, a rear vehicle (2) of the side lane, a front vehicle (3) of the main lane and a rear vehicle (4) of the main lane. When the intelligent passenger car receives the lane change instruction, according to the position of the intelligent passenger car and the motion state of surrounding traffic vehicles, a vehicle lane change unconstrained track cluster is generated by adopting a cubic curve, as shown in fig. 3.
y(x)＝a 0 +a 1 x+a 2 x 2 +a 3 x 3
x, y (x) are the longitudinal position and the transverse position of the intelligent passenger car respectively; a is 0 ，a 1 ，a 2 ，a 3 Is a parameter, and satisfies:
y(x 0 )＝a 0 +a 1 x 0 +a 2 x 2 0 +a 3 x 3 0
y'(x 0 )＝a 1 +2a 2 x 0 +3a 3 x 2 0
x 0 ，y(x 0 ) Respectively setting the longitudinal and transverse initial positions of the lane change of the intelligent passenger car; y' (x) 0 ) The direction is the tangential direction of the initial position of the intelligent passenger car;
the intelligent passenger car lane change terminal motion state is as follows:
y(x f )＝a 0 +a 1 x f +a 2 x 2 f +a 3 x 3 f
y'(x f )＝a 1 +2a 2 x f +3a 3 x 2 f
x f ，y(x f ) Respectively corresponding to the longitudinal and transverse end positions of the lane change of the intelligent passenger car; y' (x) f ) The direction is the tangential direction of the terminal position of the intelligent passenger car; a is 0 ，a 1 ，a 2 ，a 3 Are parameters in the lane change trajectory.
The second step is that: building model of yaw stability and roll stability of vehicle, and pushingDeriving lane-changing minimum longitudinal distance x under different vehicle speeds u and different road surface adhesion coefficients mu fmin And obtaining a stable lane-changing track.
(1) Yaw stability of vehicle
Yaw angular velocity stability limit and centroid slip angle stability limit:
Figure BDA0003653218980000101
Figure BDA0003653218980000102
wherein r is s Is the yaw rate of the vehicle, beta s Is the centroid slip angle, F, of the vehicle yr ，F yf Respectively representing the lateral deviation force of the rear axle and the lateral deviation force of the front axle of the vehicle, g is the gravity acceleration, mu is the road adhesion coefficient, u is the vehicle speed, m is the vehicle mass, C αr The rear axle cornering stiffness is indicated, and a, b represent the distance of the vehicle front and rear axles to the vehicle centre of mass, respectively.
As shown in fig. 4, lane-changing longitudinal distances of different vehicle speeds and different road adhesion coefficients are set in the vehicle simulation model; respectively obtaining a stable limit of the yaw angular velocity and a stable limit of the centroid slip angle, combining the two limits to obtain a phase plane for judging the lateral stability of the vehicle, and when the output yaw angular velocity and the centroid slip angle are in the phase plane, obtaining the lane-changing longitudinal distance x f1 And the lateral stability of the vehicle is met.
(2) Vehicle roll stability
Transverse load transfer rate
Figure BDA0003653218980000103
Wherein n is the number of vehicle axles, F Zl For vertical load on the left side of the wheel, F Zr Is the vertical load on the right side of the wheel; in the simulation vehicle model, lane-changing longitudinal distances x with different vehicle speeds u and different road surface adhesion coefficients mu are mainly set f In a lateral directionExperimental data of acceleration and vertical loads of each wheel are used as output to determine the minimum lane-changing longitudinal distance x for preventing rollover f2 。
Minimum lane change longitudinal distance x fmin When the same vehicle speed and the same road surface adhesion coefficient are taken, x f1 And x f2 The larger of these.
Minimum lane changing longitudinal distance x of intelligent passenger car fmin A three-dimensional map of vehicle speed u and road adhesion coefficient μ is shown in FIG. 5, when lane change is made by longitudinal distance x f Greater than x fmin I.e. the lane change trajectory is considered to satisfy vehicle yaw and roll stability. The minimum lane change longitudinal distance x is obtained by surface fitting the three-dimensional map shown in FIG. 5 fmin The functional expression between the vehicle speed u and the road adhesion coefficient μ is:
x fmin (u,μ)＝58.58-0.3892u-139.1μ+0.01839u 2 -1.669uμ+261.5μ 2 -5.158e -5 u 3 -0.003438u 2 μ+1.09uμ 2 -147.9μ 3
the speed u of the intelligent passenger car is set to be 90kmh in the embodiment -1 The road surface adhesion coefficient mu is 0.8, and the minimum lane change longitudinal distance x is obtained according to the functional expression and the figure 5 fmin Is 35 m. When changing the longitudinal distance x of the track f Above 35m, the lane change trajectory is satisfactory for vehicle yaw and roll stability.
The third step: obtaining the optimal lane-changing track
The lane-changing safe track cluster is obtained through the lane-changing obstacle avoidance detection algorithm and the lane-changing safe following model shown in fig. 6 and 7, and the optimal lane-changing track is obtained from the safe track cluster by optimizing the calculation objective function. The invention regards the outline of the vehicle as a rectangle, and the course angle of the vehicle is the orientation of the rectangle at the moment. Thus, a series of and longitudinal distances x are generated from the real-time planned reference lane change trajectory f A corresponding rectangle. When the longitudinal speed of the intelligent passenger car is known, the lane changing longitudinal distance x is planned in real time at the moment t f The positions of the vertices of the determined rectangle are calculated as follows:
Figure BDA0003653218980000111
Figure BDA0003653218980000112
in the formula x 1...4 (t) and y 1...4 (t) values of x and y, 4 vertex coordinates of the smart car outline rectangle, l and w represent the length and width of the smart car,
Figure BDA0003653218980000113
the current heading angle of the intelligent passenger car is shown, x (T) and y (T) show the centroid position of the intelligent passenger car, and T is a time interval. The surrounding vehicles also use a similar calculation method to the above formula. Therefore, judging whether the intelligent passenger car and the traffic car collide at the time t becomes judging whether the outlines of the two cars intersect. Changing the longitudinal distance x corresponding to each track in the stable track cluster f Carrying out strict inspection by bringing in a BSH function, and obtaining a reference track in the safe lane changing process when the following function is met; otherwise, it is an unsafe trajectory. Therefore, the safe lane-changing track cluster of the intelligent passenger car is from the track longitudinal length determined by stability to the safe lane-changing track longitudinal length determined by all surrounding vehicles. The lane change track cluster in the range can not collide with surrounding vehicles, and can not cause unstable vehicle yaw and roll in the lane change process of the intelligent passenger car.
Figure BDA0003653218980000121
Figure BDA0003653218980000122
Figure BDA0003653218980000123
Where, i is 1,2,3,4, each of which refers to 4 traffic vehicles, and x is K_n (t) and y K_n (t) denotes the nth vertex coordinate, x, of the Smart coach T_in (t) and y T_in (t) is the coordinates of the nth vertex of the ith traffic vehicle outline, and the following obstacle avoidance detection formula in lane change is obtained through the derivation:
Figure BDA0003653218980000124
minimum lane change longitudinal distance x determined from stability fmin Firstly, increasing a length to obtain a lane change track, and detecting whether the intelligent passenger car collides under the track; if collision occurs, the track of the previous length is the maximum lane changing longitudinal distance under the working condition.
In the embodiment, the minimum safe distance model is utilized to ensure that the intelligent passenger car does not collide with the front and rear cars in the target lane after lane changing, when the front car is emergently braked, the rear car also takes braking measures, and the minimum safe distance S is required to be met when the two cars do not collide 1 、S 2 、S 3 、S 4 The minimum safe distance expression is as follows:
Figure BDA0003653218980000125
Figure BDA0003653218980000126
Figure BDA0003653218980000127
Figure BDA0003653218980000131
wherein S is 1 Minimum safety distance, S, for intelligent passenger car and front car of side lane 2 For minimum safety of intelligent passenger car and other lane rear carDistance, S 3 Is the minimum safe distance between the intelligent passenger car and the front car of the current road, S 4 Minimum safe distance v for intelligent passenger car and the rear car of the lane V Speed, v, of a smart bus 1 Speed, v, of vehicles ahead of the side-ways 2 Speed, v, of vehicles following the side-ways 3 The speed, v, of the vehicle in front of the lane 4 Is the speed of the vehicle behind the lane, a v For maximum braking deceleration of smart passenger cars, a 1 Maximum braking deceleration of the front vehicle of the side lane, a 2 Maximum braking deceleration of the rear vehicle in the side lane, a 3 For maximum braking deceleration of the front vehicle of the own lane, a 4 For maximum braking deceleration, τ, of vehicles following the track V Time delay, tau, for information interaction and control systems of intelligent passenger cars and surrounding traffic cars 2 And τ 4 Response time for human driver, a VV Acceleration in preparing the smart car for braking, a 22 And a 44 And preparing the acceleration during braking for the vehicle behind the side lane and the vehicle behind the lane.
The lane changing track which does not meet the minimum safe distance of the safe track cluster in the lane changing process is removed through the mode. And forming a lane change safe track cluster. And calculating the value of each track target function in the safe track cluster, and selecting the lane change track corresponding to the minimum value as the optimal lane change track at the current moment.
The objective function considers the impact of lane change efficiency and passenger comfort on the lane change trajectory:
Figure BDA0003653218980000132
wherein J represents a cost function, a (x) f ) Lateral acceleration, a (x), representing the final position of the lane change f ) max Represents the maximum lateral acceleration, t (x), in the safe trajectory cluster f ) Indicating the lane change time, t (x) f ) max Representing the maximum lane changing time in the safe track cluster, wherein omega is a weight value;
lateral acceleration a (x) at the final position f ) Calculated by the following equation:
a(x f )＝u(x f ) 2 K(x f )
in the formula, u (x) f ) Expressing the speed of the intelligent passenger car at the final position, K () expressing the curvature function of the lane change track, and K () being calculated by the following equation:
Figure BDA0003653218980000133
in the formula, y' (x) f ) And y ″ (x) f ) Respectively a first derivative and a second derivative of the lane change trajectory function;
intelligent bus lane change track running time t (x) f ) Comprises the following steps:
Figure BDA0003653218980000141
in the formula, t c The lane change time is already provided for the intelligent passenger car.
The fourth step: obtaining the track and then planning the lane-changing track
In the embodiment, because the speeds of the surrounding vehicles (1) (2) (3) (4) are suddenly changed in the lane changing process, if the intelligent passenger car is continuously planned according to the original track, the intelligent passenger car can collide with the surrounding vehicles. To ensure safety, the trajectory needs to be re-planned. And outputting the transverse position, the longitudinal position and the course angle of the vehicle at the current moment through a vehicle system, and repeatedly calling the first step, the second step and the third step to obtain a re-planned optimal lane-changing track, as shown in figures 8-9.
Fig. 10 depicts the whole lane change process of the intelligent passenger car of the embodiment under the environmental condition. As can be seen from fig. 10, at the same time, the smart bus and the surrounding vehicles do not overlap. Based on the method, the proposed track planning algorithm can adjust the lane-changing reference track in real time according to the dynamic state of surrounding vehicles, and dynamic planning of the lane-changing track is realized.
After the intelligent passenger car starts lane changing, a lane changing track minimum longitudinal length three-dimensional MAP determined by the stability of the intelligent passenger car is interpolated, and the minimum lane changing track longitudinal length determined by the stability under the working condition is 26 m. The lane changing track of the intelligent passenger car possibly needs to be re-planned due to the dynamic change of the acceleration of surrounding vehicles, and the optimal track length of the intelligent passenger car for starting lane changing is 91 m; however, the optimal track length for lane change of the intelligent passenger car is 89m along with the change of the state of the surrounding vehicles. Therefore, the track planning algorithm can adjust the lane changing track of the intelligent passenger car according to the dynamic traffic environment change so as to ensure the safe and optimal lane changing of the intelligent passenger car.
By analyzing the stability of the vehicle on the planned trajectory curve, the centroid slip angle and the yaw rate of the planned trajectory curve can be obtained, and the formed trajectory does not exceed the stability range in the center of the phase plane of the centroid slip angle and the yaw rate. Thus, the proposed trajectory planning algorithm guarantees vehicle yaw stability. The lateral acceleration is obtained to be-0.8 m/s 2 To 1.1m/s 2 Change smoothly; and the roll angle varies smoothly in the range of-0.2 deg. to 0.3 deg.. The load transfer rate LTR is [ -1,1 [)]Within the range. In view of this, the proposed trajectory planning algorithm guarantees vehicle roll stability.
It should be understood by those skilled in the art that the foregoing embodiments are merely illustrative of the technical concepts and features of the present invention, and the present invention is not limited thereto, but rather by the scope of the appended claims.

Claims (9)

1. A method for planning a dynamic lane change track of an intelligent passenger car under a multi-car complex traffic environment is characterized by comprising the following steps:
firstly, generating an intelligent passenger car candidate unconstrained lane change track cluster aiming at a dynamic traffic environment; secondly, analyzing the stability of vehicle yaw and roll according to different road conditions, determining the stable minimum lane-changing longitudinal distance of the intelligent passenger car, and removing unstable lane-changing tracks from the unconstrained lane-changing track cluster; then, considering the state change of surrounding vehicles, carrying out obstacle avoidance detection, obtaining the maximum longitudinal length value of the lane changing track of the intelligent passenger car, which is not collided with the surrounding vehicles, and eliminating unsafe lane changing tracks of collision; finally, a multi-target lane-changing track performance function is set, and an optimal intelligent bus lane-changing track is obtained in the stable and collision-free track of the bus; meanwhile, sudden changes of surrounding vehicle states are considered, and intelligent bus lane changing track re-planning and speed planning are carried out.
2. The intelligent passenger car dynamic lane change trajectory planning method in the multi-car complex traffic environment according to claim 1, characterized in that: the method specifically comprises the following steps:
step1, selecting a series of lane change destination candidate positions on a target lane according to the position of the intelligent passenger car and the motion state of surrounding traffic vehicles, and connecting the initial position of the vehicle and the destination candidate positions by utilizing a cubic polynomial curve to generate an unconstrained lane change track cluster;
step2, obtaining the minimum lane-changing longitudinal distance x of the intelligent passenger car without the problems of yaw and roll through a simulation model according to the speed and the road adhesion coefficient of the intelligent passenger car and based on the saturation characteristics of tires fmin Obtaining a stable lane changing track;
step3 based on minimum lane change longitudinal distance x fmin Gradually carrying out collision detection from short to long to obtain the maximum lane-changing longitudinal distance of the track without collision so as to obtain a safe lane-changing track;
step4, considering the influence of lane changing efficiency and passenger comfort degree on the lane changing track, constructing an objective function and selecting an optimal intelligent passenger car lane changing track, wherein the safe lane changing track obtained in Step3 is obtained;
and in the Step5 intelligent passenger car lane changing process, adjusting lane changing track and speed in real time, giving up lane changing in emergency, returning to the original lane, and replanning until the intelligent passenger car reaches the target position.
3. The intelligent passenger car dynamic lane change trajectory planning method under the multi-car complex traffic environment according to claim 2, characterized in that: the specific content of Step1 is as follows:
y(x)＝a 0 +a 1 x+a 2 x 2 +a 3 x 3
x, y (x) are respectively the longitudinal position and the transverse position of the intelligent passenger car; a is 0 ，a 1 ，a 2 ，a 3 Is a parameter, and satisfies:
y(x 0 )＝a 0 +a 1 x 0 +a 2 x 2 0 +a 3 x 3 0
y'(x 0 )＝a 1 +2a 2 x 0 +3a 3 x 2 0
x 0 ，y(x 0 ) Respectively setting the longitudinal and transverse initial positions of the lane change of the intelligent passenger car; y' (x) 0 ) The direction is the tangential direction of the initial position of the intelligent passenger car;
the intelligent passenger car lane change terminal motion state is as follows:
y(x f )＝a 0 +a 1 x f +a 2 x 2 f +a 3 x 3 f
y'(x f )＝a 1 +2a 2 x f +3a 3 x 2 f
x f ，y(x f ) Respectively are longitudinal and transverse terminal positions of the lane change of the intelligent passenger car; y' (x) f ) The direction is the tangential direction of the terminal position of the intelligent passenger car; a is 0 ，a 1 ，a 2 ，a 3 Are parameters in the lane change trajectory.
4. The intelligent passenger car dynamic lane change trajectory planning method under the multi-car complex traffic environment according to claim 2, characterized in that: the specific content of Step2 is as follows:
(1) yaw stability of vehicle
Yaw angular velocity stability limit and centroid slip angle stability limit:
Figure FDA0003653218970000021
Figure FDA0003653218970000022
wherein r is s Is the yaw rate of the vehicle, beta s Is the centroid slip angle of the vehicle, F yr ，F yf Respectively representing the lateral offset force of the rear axle and the lateral offset force of the front axle of the vehicle, g is the gravity acceleration, mu is the road adhesion coefficient, u is the vehicle speed, m is the total vehicle mass, C αr The lateral deflection stiffness of the rear axle is represented, and a and b respectively represent the distance from the front axle and the rear axle of the vehicle to the center of mass of the vehicle;
(2) vehicle roll stability
Transverse load transfer rate
Figure FDA0003653218970000023
Wherein n is the number of vehicle axles, F Zl Vertical load on the left side of the wheel, F Zr Is the vertical load on the right side of the wheel;
(3) vehicle simulation
Setting lane-changing longitudinal distances with different speeds and different road adhesion coefficients in a vehicle simulation model;
outputting the yaw angular velocity and the centroid side deviation angle, and judging the yaw stability with a phase plane formed by the yaw angular velocity stability limit and the centroid side deviation angle stability limit to obtain the minimum lane change longitudinal distance x meeting the yaw stability of the vehicle f1 ；
Outputting the vertical loads of the left and right wheels, calculating the lateral load transfer rate LTR value, and determining the minimum lane-changing longitudinal distance x for preventing rollover f2 ；
Minimum lane change longitudinal distance x fmin When the same vehicle speed and the same road surface adhesion coefficient are taken, x f1 And x f2 The larger of these.
5. The intelligent passenger car dynamic lane change trajectory planning method in the multi-car complex traffic environment according to claim 2, characterized in that: the specific content of Step3 is as follows:
intelligent busThe contour of the vehicle is regarded as a rectangle, the heading angle of the vehicle is the orientation of the rectangle at the moment, and a series of longitudinal distances x between the vehicle and the lane change are generated by the reference lane change track planned in real time f Corresponding rectangle, when the longitudinal speed of the intelligent passenger car is known, the lane changing longitudinal distance x is planned in real time at the moment t f The respective vertex positions of the determined rectangles are calculated as follows:
Figure FDA0003653218970000031
Figure FDA0003653218970000032
in the formula x 1...4 (t) and y 1...4 (t) represents the values of the 4 vertex coordinates x and y of the smart car outline rectangle, l and w represent the length and width of the smart car,
Figure FDA0003653218970000033
representing the heading angle of the intelligent passenger car, x (T) and y (T) representing the centroid position of the intelligent passenger car, and T being a time interval;
judging whether the intelligent passenger car and the traffic vehicle collide at the moment t, and then judging whether the outlines of the two cars are intersected; specifically, based on the judgment of the BSH function, when the following criteria are met, the intelligent passenger car and the traffic vehicle do not collide at the moment t:
Figure FDA0003653218970000034
Figure FDA0003653218970000035
Figure FDA0003653218970000036
where, i is 1,2,3,4, each of which refers to 4 traffic vehicles, and x is K_n (t) and y K_n (t) denotes the nth vertex coordinate, x, of the Smart coach T_in (t) and y T_in (t) is the coordinates of the nth vertex of the ith traffic vehicle outline, and the following obstacle avoidance detection formula in lane change is obtained through the derivation:
Figure FDA0003653218970000041
minimum lane change longitudinal distance x determined from stability fmin Firstly, increasing a length to obtain a lane change track, and detecting whether the intelligent passenger car collides under the track; if collision occurs, the track of the previous length is the maximum lane changing longitudinal distance under the working condition.
6. The intelligent passenger car dynamic lane change trajectory planning method under the multi-car complex traffic environment according to claim 2, characterized in that: the target function of Step4 is:
Figure FDA0003653218970000042
wherein J represents a cost function, a (x) f ) Lateral acceleration, a (x), representing the final position of the lane change f ) max Represents the maximum lateral acceleration, t (x), in the safe trajectory cluster f ) Indicating the lane change time, t (x) f ) max Representing the maximum lane change time in the safe track cluster, wherein omega is a weight value;
lateral acceleration a (x) at the final position f ) Calculated by the following equation:
a(x f )＝u(x f ) 2 K(x f )
in the formula, u (x) f ) Representing the speed of the smart bus in the final position, K () representing the curvature function of the lane change trajectory, K () being calculated by the following equation:
Figure FDA0003653218970000043
in the formula, y' (x) f ) And y ″ (x) f ) Respectively a first derivative and a second derivative of the lane change trajectory function;
intelligent bus lane change track running time t (x) f ) Comprises the following steps:
Figure FDA0003653218970000044
in the formula, t c The lane change time is already provided for the intelligent passenger car.
7. The intelligent passenger car dynamic lane change trajectory planning method under the multi-car complex traffic environment according to claim 2, characterized in that:
updating the lane change track in real time in a circulating manner at intervals of a period of time delta tau until the intelligent passenger car reaches the lane change target position;
in the lane changing process of the intelligent passenger car, when emergency occurs in the surrounding dynamic environment, the intelligent passenger car should give up lane changing and return to the original lane: when the front vehicle of the target lane is emergently braked, the deceleration is increased violently, and the acceleration of the rear vehicle of the target lane is increased sharply; the lane-changing longitudinal distance interval is less than the minimum lane-changing longitudinal distance x fmin When the vehicle safety lane change is not enough, the intelligent vehicle gives up lane change and plans a track returning to the original lane;
when the intelligent vehicle carries out lane changing operation, the lane changing longitudinal distance interval is smaller than the minimum lane changing longitudinal distance x fmin When the lane change track is separated, speed planning is carried out, and the stability of the lane change track is ensured; when speed planning is carried out, the acceleration of the intelligent vehicle is sharply reduced, if the intelligent vehicle runs at the planned speed, the comfort of passengers is affected, at the moment, lane changing is abandoned, and the trajectory planning of returning to an original lane is carried out.
8. The intelligent passenger car dynamic lane change trajectory planning method under the multi-car complex traffic environment according to claim 7, characterized in that:
longitudinal distance x for lane change of intelligent passenger car f Less than the minimum lane change longitudinal distance x fmin In time, speed planning is carried out on the intelligent passenger car to reduce the speed, and a cubic polynomial model is adopted:
u x (t)＝b 0 +b 1 t+b 2 t 2 +b 3 t 3
a x (t)＝b 1 +2b 2 t+3b 3 t 2
in the formula, t, u x (t)，a x (t) respectively representing the current time, the current time speed and the current time acceleration of the intelligent passenger car, wherein b 0 ，b 1 ，b 2 ，b 3 Are parameters.
9. The intelligent passenger car dynamic lane change trajectory planning method in the multi-car complex traffic environment according to claim 2, characterized in that:
the minimum safe distance model is utilized to ensure that the intelligent passenger car does not collide with the front and rear cars in the target lane after lane changing, when the front car is emergently braked, the rear car also takes braking measures, and the minimum safe distance S is required to be met when the two cars do not collide 1 、S 2 、S 3 、S 4 The minimum safe distance expression is as follows:
Figure FDA0003653218970000051
Figure FDA0003653218970000052
Figure FDA0003653218970000053
Figure FDA0003653218970000061
wherein S is 1 Minimum safety distance, S, for intelligent passenger car and front car of side lane 2 Is the minimum safe distance, S, between the intelligent passenger car and the rear car on the side lane 3 Is the minimum safe distance between the intelligent passenger car and the front car of the current road, S 4 Is the minimum safe distance v between the intelligent passenger car and the rear car of the lane V Speed, v, of a smart bus 1 Speed, v, of vehicles ahead of the side-ways 2 Speed, v, of vehicles following the side-ways 3 The speed, v, of the vehicle in front of the lane 4 Is the speed of the vehicle behind the lane, a v For maximum braking deceleration of intelligent passenger car, a 1 Maximum braking deceleration of the front vehicle of the side lane, a 2 Maximum braking deceleration of the rear vehicle on the side lane, a 3 For maximum braking deceleration of the front vehicle of the own lane, a 4 For maximum braking deceleration, τ, of vehicles following the track V Delay time, tau, for information interaction and control system of intelligent passenger car and surrounding traffic vehicle 2 And τ 4 Reaction time for human driver, a VV Acceleration in preparing the smart car for braking, a 22 And a 44 And preparing the acceleration during braking for the vehicle behind the side lane and the vehicle behind the lane.
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