CN115140093A - Real-time trajectory planning method - Google Patents

Real-time trajectory planning method Download PDF

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CN115140093A
CN115140093A CN202210842503.8A CN202210842503A CN115140093A CN 115140093 A CN115140093 A CN 115140093A CN 202210842503 A CN202210842503 A CN 202210842503A CN 115140093 A CN115140093 A CN 115140093A
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lane
vehicle
representing
lane change
changing
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王金湘
彭林
严永俊
方振伍
姚亿丞
祝小元
庄伟超
殷国栋
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Southeast University
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Southeast 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
    • B60W60/00—Drive control systems specially adapted for autonomous road vehicles
    • B60W60/001—Planning or execution of driving tasks
    • B60W60/0011—Planning or execution of driving tasks involving control alternatives for a single driving scenario, e.g. planning several paths to avoid obstacles
    • 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
    • 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
    • B60W50/0098—Details of control systems ensuring comfort, safety or stability not otherwise provided for
    • 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
    • B60W60/00—Drive control systems specially adapted for autonomous road vehicles
    • B60W60/001—Planning or execution of driving tasks
    • B60W60/0015—Planning or execution of driving tasks specially adapted for safety
    • 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
    • B60W2050/0001—Details of the control system
    • B60W2050/0019—Control system elements or transfer functions
    • B60W2050/0028—Mathematical models, e.g. for simulation
    • 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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  • Engineering & Computer Science (AREA)
  • Automation & Control Theory (AREA)
  • Transportation (AREA)
  • Mechanical Engineering (AREA)
  • Human Computer Interaction (AREA)
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Abstract

The invention discloses a real-time track planning method, relates to the technical field of intelligent traffic, and solves the technical problem that the conventional track planning method is difficult to be applied to a complex dynamic traffic environment. The track planning method provided by the application can effectively take comfort, safety and solving efficiency into account, simultaneously takes the preferences of different passengers on lane changing efficiency into account, can effectively provide the acceptance degree of a user to an automatic driving automobile, and has strong practicability and wide commercial application prospect.

Description

Real-time trajectory planning method
Technical Field
The application relates to the technical field of intelligent transportation, in particular to a real-time trajectory planning method.
Background
The automatic driving automobile can effectively reduce traffic accidents, improve the comfort, solve traffic jams, improve the fuel economy and the like by means of advanced sensing, planning and control technologies, and is one of the important strategic directions for the development of the automobile industry in the future. With the popularization of the automatic driving technology and the acceptance of people to the automatic driving technology, the permeability of the automatic driving automobile on the road will gradually rise, so that the situation that human-driven automobiles and automatic driving automobiles coexist on the road for a long time or continuously exists in the future. The driving behavior and intent of human drivers are often difficult to predict and vary in real time, which puts higher demands on the real-time performance of trajectory planning.
Currently, for lane changing, most trajectory planning algorithms assume that the states of other environmental vehicles remain unchanged in the lane changing process, and only plan a trajectory at the initial time of lane changing. However, in an actual dynamic traffic environment, other environmental vehicles often adjust the speed in real time according to the surrounding environment, such as sudden acceleration or deceleration, so that the initially planned lane change track is no longer safe. Therefore, on the basis of ensuring safety, comfort and traffic efficiency, the real-time performance of the algorithm is improved, and a lane changing track is planned in a self-adaptive manner according to a dynamically changing traffic environment, which is a problem to be solved urgently.
Disclosure of Invention
The application provides a real-time track planning method, which aims to improve the real-time performance of the track planning method and adaptively plan a lane changing track while considering safety, comfort and efficiency aiming at a dynamically changing traffic environment.
The technical purpose of the application is realized by the following technical scheme:
a real-time trajectory planning method, comprising:
s1: constructing a longitudinal and transverse track-changing track model by a quintic polynomial of time sampling;
s2: constructing constraint conditions of the longitudinal and transverse lane changing track model to obtain a safe lane changing model;
s3: sampling the lane change time in the longitudinal and transverse lane change track model at equal intervals, converting the lane change track planning problem under different lane change times into a quadratic planning problem, and finally obtaining different lane change times t through a quadratic planning solver k A lower lane change track; the cost function of the quadratic programming problem comprises a comfort cost function and a safety cost function after Taylor second-order expansion, wherein the comfort cost function is constructed through an acceleration, and the safety cost function is constructed through a potential field function;
s4: from different lane change times t based on safety, comfort and lane change passing efficiency k Screening out an optimal track changing track from the lower track changing tracks;
s5: and predicting the future state of the surrounding vehicles, judging whether the distance between the main vehicle and the front vehicle of the current lane, the distance between the front vehicle of the target lane and the rear vehicle of the target lane meets the safety lane change model within the prediction steps, if so, changing the lanes, otherwise, repeating the steps S1-S4 to replan the lane change track until the main vehicle enters the target lane.
The beneficial effect of this application lies in: the method for planning the real-time track in the dynamic traffic environment establishes a safe lane change model based on a quintic polynomial, comprehensively considers the safety, comfort and traffic efficiency of lane change, optimizes the lane change track through a quadratic planning method, screens out the optimal lane change track from the optimized tracks based on different lane change time, and considers whether to replan the lane change track when the traffic environment dynamically changes in the lane change process. The method can realize safe lane changing of the automatic driving vehicle in a dynamic traffic scene, and the algorithm has high real-time performance, strong practicability and wide commercial application prospect.
Drawings
FIG. 1 is a schematic view of a general lane-change scenario described herein;
FIG. 2 is a flow chart of a method described herein;
FIG. 3 is a simplified linear schematic diagram of a non-linear emergency braking collision avoidance safety constraint designed according to the present application;
FIG. 4 is a schematic diagram of an optimal track change track cluster designed according to the present application at different sampling track change times;
FIG. 5 is a schematic diagram of relative positions of a main vehicle and other vehicles when the rear vehicle of a target lane has different accelerations according to the present application;
FIG. 6 shows the acceleration of the vehicle behind the target lane of 2m/s according to the present application 2 A schematic diagram of a simulation result of the temporal main vehicle;
FIG. 7 shows the acceleration of the vehicle behind the target lane of 3m/s according to the present application 2 And the simulation result of the time master vehicle is schematic.
Detailed Description
The technical solution of the present application will be described in detail below with reference to the accompanying drawings.
FIG. 1 is a schematic view of a general lane-changing scene, in which HC, PC, TP and TF respectively represent a main vehicle, a vehicle ahead of a current lane, a vehicle ahead of a target lane and a vehicle behind the target lane, and the driving behavior of the main vehicle is not influenced by the vehicle behind the current lane and is not considered. Fig. 2 is a flowchart of a real-time trajectory planning method according to the present application, where the real-time trajectory planning method includes:
s1: and constructing a longitudinal and transverse track-changing track model by a fifth-order polynomial sampled by time.
The longitudinal and transverse lane changing track model is expressed as follows:
Figure BDA0003750876590000021
wherein x (t) represents a longitudinal trajectory of the host vehicle; y (t) represents the transverse trajectory of the host vehicle, a i A fifth order polynomial coefficient representing the longitudinal trajectory;b i a fifth order polynomial coefficient representing a lateral trajectory; i =0,1,2,3,4,5.
Time t for changing lane by master f Determining a state of initial time of lane change and state of end time of lane change i And b i Expressed as:
Figure BDA0003750876590000022
Figure BDA0003750876590000023
wherein x is 0 Indicating the longitudinal position of the host vehicle at the initial time of lane change; v. of x,0 Indicating the longitudinal velocity of the host vehicle at the initial time of lane change; a is x,0 Indicating the longitudinal acceleration of the host vehicle at the initial time of lane change; y is 0 Indicating the lateral position of the host vehicle at the initial time of lane change; v. of y,0 Representing the lateral velocity of the host vehicle at the initial time of lane change; a is a y,0 The lateral acceleration of the host vehicle at the initial lane change time is represented; x is the number of f Indicating the longitudinal position of the host vehicle at the lane change end time; v. of x,f Indicating the longitudinal speed of the host vehicle at the lane change end time; a is x,f Indicating the longitudinal acceleration of the host vehicle at the lane change end time; y is f Indicating the lateral position of the host vehicle at the lane change end time; v. of y,f Indicating the lateral velocity of the host vehicle at the lane change end time; a is y,f Indicating the lateral acceleration of the host vehicle at the end of the lane change.
The state of the primary vehicle at the initial time can be directly obtained from a sensor, the transverse position of the lane change ending time is set as the position of the central line of the target lane, and the transverse speed and the acceleration are both set as 0, so that only the longitudinal position, the speed and the acceleration at the lane change ending time need to be optimized.
S2: and constructing constraint conditions of the longitudinal and transverse lane changing track model to obtain a safe lane changing model.
Considering the possible collision condition of the main vehicle with the front vehicle PC of the current lane, the front vehicle TP of the target lane and the rear vehicle TF of the target lane in the lane changing process, a safe lane changing model is established, and the safe lane changing model comprises the following steps:
in the lane changing process, if the vehicle in front of the current lane is possibly braked emergently, the main vehicle and the vehicle in front of the current lane should keep a safe distance, so the safety constraint conditions of the main vehicle and the vehicle in front of the current lane are represented as follows:
Figure BDA0003750876590000031
wherein, X H Longitudinal coordinates representing the host vehicle; l is H Indicating the length of the host vehicle; x PC Representing the longitudinal coordinate of the vehicle in front of the current lane; l is PC Representing the length of the vehicle in front of the current lane; v x,HC Indicating a vehicle speed of the host vehicle; v x,PC Representing the speed of a vehicle in front of the current lane; b max Represents the maximum deceleration permitted by the host vehicle; b is a mixture of PC,max Indicating the maximum deceleration, T, permitted by the vehicle ahead of the current lane s Indicating a minimum safe headway.
In the lane changing process, the safety constraint conditions of the main vehicle and the front vehicle of the target lane are expressed as follows:
Figure BDA0003750876590000032
in the lane changing process, the safety constraint conditions of the main vehicle and the rear vehicle of the target lane are expressed as follows:
Figure BDA0003750876590000033
wherein, X TP Representing the longitudinal position of the vehicle in front of the target lane; v x,TP Representing the speed of a vehicle in front of the target lane; l is a radical of an alcohol TP Representing the length of the vehicle in front of the target lane; b is a mixture of TP,max Representing the maximum deceleration of the vehicle ahead of the target lane; x TF Representing the longitudinal position of the vehicle behind the target lane; v x,TF Representing the speed of the vehicle behind the target lane; l is a radical of an alcohol TF Representing the length of the vehicle behind the target lane; b TF,max Representing the maximum deceleration of the vehicle behind the target lane;
equations (3) through (5) constitute the safe lane-changing model.
For the nonlinear quadratic safety constraint existing in the above equations (3) and (4), a simplified substitution method using linear constraint is proposed to improve the solution efficiency. As shown in fig. 3, wherein the dotted line is the secondary nonlinear safety constraint boundary for emergency braking collision avoidance, the nonlinear emergency braking collision safety constraint conditions in the equations (3) and (4) are respectively approximated by multiple straight lines, such as the straight line l in fig. 3 1 、l 2 、l 3 As shown, equations (3) and (4) are converted to:
k i' V x,HC +X HC ≤m i' ； (6)
wherein k is i' Represents a straight line l i' The slope of (a); m is a unit of i' Represents a straight line l i' The intercept of (d); i' =1,2,3; the safe lane-changing model is expressed as equation (6) and equation (5).
S3: sampling the lane change time in the longitudinal and transverse lane change track model at equal intervals, converting the lane change track planning problem under different lane change times into a quadratic planning problem, and finally obtaining different lane change times t through a quadratic planning solver k A lower lane change track; the cost function of the quadratic programming problem comprises a comfort cost function and a safety cost function after Taylor second-order expansion, wherein the comfort cost function is constructed through acceleration, and the safety cost function is constructed through a potential field function.
The lane change time has a great influence on safety, comfort and lane change passing efficiency. The too short time of changing lanes then can lead to lateral acceleration too big, and the travelling comfort is relatively poor, and the time of changing lanes overlength then can reduce the traffic efficiency of road, therefore the time of changing lanes should be controlled within reasonable scope: t is min ≤t f ≤T max 。
It is not obvious that the arbitrary designated lane change time is unreasonable, so that the lane change time t is not reasonable f At T min And T max Equally spaced samples are taken between, as shown:
Figure BDA0003750876590000041
wherein, T min Indicating a minimum allowed lane change time; t is max Representing the maximum allowable lane change time; Δ T represents a sampling time interval; n represents the number of samples.
Considering the actual driving situation, the vehicle should meet the kinematic and dynamic requirements, expressed as:
Figure BDA0003750876590000042
wherein L is w Indicating the width of the lane in which the vehicle should remain and the speed of the vehicle should not exceed the maximum speed v allowed by the road x,max The longitudinal and lateral acceleration should be kept at a x,max And a y,max Within.
The quadratic programming problem is represented as:
Figure BDA0003750876590000043
wherein, J c Representing a comfort cost function, J s Representing the security cost function after taylor second order expansion.
Comfort is an important index of vehicle lane change performance, and is usually measured by acceleration, so that a comfort cost function is expressed as:
Figure BDA0003750876590000044
wherein j is x Represents the longitudinal acceleration; j is a function of y Represents a lateral acceleration; w is a lon Represents a longitudinal weight coefficient; w is a lat Representing the lateral weight coefficients.
In the conventional planning algorithm, safety is guaranteed through hard constraint, only comfort is considered in a cost function, a planned track tends to tend to a safety constraint boundary, however, the safety constraint changes in real time under a dynamic traffic environment, and the track easily breaks through the safety constraint boundary. The application improves the safety of the track by constructing a potential field function, wherein the potential field function is expressed as:
Figure BDA0003750876590000051
wherein, U TP Representing a potential field generated by a vehicle in front of a target lane; u shape TF Representing the potential field generated by the vehicle behind the target lane. The closer HC is to TP and TF, the higher the relative velocity is, the U TP And U TF The larger the value. The overall security cost function is then expressed as:
J(X f ,V x,f )＝w P U TP +w F U TF ； (11)
wherein, w P And w F Both represent weight coefficients.
The constructed potential field function is a non-quadratic form, and in order to improve the solving efficiency of the algorithm, the application adopts Taylor second-order expansion to the security cost function, and the safety cost function is expressed as follows:
Figure BDA0003750876590000052
wherein, X = [ X = f ,V x,f ]。
Finally, different lane changing time t can be obtained through a quadratic programming solver k And (k =0,1.,. N) determining the longitudinal position, speed and acceleration of the optimal track changing track at the end of the track changing track.
S4: from different lane change times t based on safety, comfort and lane change passage efficiency k And screening out the optimal track changing track from the lower track changing tracks.
As shown in FIG. 4, by step S3, the lane change time t can be obtained k However, the safety, comfort and traffic efficiency of the tracks planned at different track changing times are different, so that the final most optimal track changing cluster needs to be screened out comprehensivelyThe change track trajectory, expressed as:
Figure BDA0003750876590000053
wherein, J * Indicates the track-changing time t k Comfort cost function and safety cost function via Taylor second order expansion, w d And a weight coefficient representing the lane change passage efficiency.
S5: and predicting the future state of the surrounding vehicles, judging whether the distance between the main vehicle and the vehicle in front of the current lane, the distance between the main vehicle and the vehicle in front of the target lane and the distance between the main vehicle and the vehicle in rear of the target lane meet the safety constraint condition within the prediction step number, if so, changing the lane, otherwise, repeating the steps S1-S4 to replan the lane changing track until the main vehicle enters the target lane.
The optimized vehicle lane changing track can be obtained through the steps S1-S4, but in the lane changing process, the surrounding driving environment changes in real time, so that the planned track does not meet the safety requirement any more, and the lane changing track needs to be subjected to self-adaptive re-planning according to the surrounding environment. First, the future state of the surrounding vehicle is predicted, and if the acceleration of the surrounding vehicle is kept constant, the trajectory of the surrounding vehicle is expressed as:
Figure BDA0003750876590000054
wherein the content of the first and second substances,
Figure BDA0003750876590000061
representing the current longitudinal position of the surrounding vehicle;
Figure BDA0003750876590000062
representing the speed of the surrounding vehicle;
Figure BDA0003750876590000063
represents the acceleration of the surrounding vehicle; i = PC, TP, TF, PC representing the vehicle ahead of the current lane, TP representing the vehicle ahead of the target lane, TF representing the vehicle behind the target laneAnd (5) carrying out vehicle operation.
Defining a prediction time interval Deltat and a prediction step number N p And obtaining the predicted position sequences of the vehicle in front of the current lane, the vehicle in front of the target lane and the vehicle behind the target lane as follows:
X I (t 0 +Δt|t 0 ),X I (t 0 +Δt|t 0 ),...,X I (t 0 +Δt|t 0 )； (17)
at the predicted step number N p In the step, when the distance between the main vehicle and the vehicle in front of the current lane, the distance between the main vehicle and the vehicle in front of the target lane and the distance between the main vehicle and the vehicle in rear of the target lane do not meet the safe lane change model, the safe lane change model is expressed as follows:
Figure BDA0003750876590000064
and when the safety constraint condition is not met, re-planning the lane change track by executing the steps S1-S4 again until the host vehicle enters the target lane.
As a specific embodiment, the effectiveness of the method is verified through Matlab/Simulink-Carsim combined simulation.
In the simulation experiment, HC and PC travel at 20m/s and 16m/s respectively in the right lane, PC travels 35m ahead of HC, TP and TF both travel at 20m/s in the left lane, and 20m ahead of and behind HC. Since HC speed is greater than PC, HC needs to decide whether to change lanes or slow down to continue following PC. In an actual traffic scenario, if HC changes lanes, TF may slow down for courtesy, and may also speed up to prevent HC from merging into the left lane. In this experiment, TF was set at 0.5, 2 and 3m/s, respectively, after HC began to switch lanes 2 The HC makes different decisions to re-plan the trajectory. In fig. 5, different shapes represent positions of different vehicles at different times, circles represent HC, triangles represent PC, squares represent TP, and diamonds represent TF.
When the PC moving slowly forward is detected, HC and TP and TF have the same vehicle speed and the distance satisfies the safety requirement, as shown in fig. 5 (a), a lane change trajectory is planned. After HC begins to switch lanes, TF follows0.5m/s 2 Acceleration is accelerated, and the initially planned track changing track is still safe due to small acceleration. And after the lane change is finished, the HC speed is slightly larger than TP, and the HC slight deceleration adjusts the vehicle speed to follow the TP. HC keeps a safe distance with other vehicles, and meets the safety requirement.
When the acceleration of TF is 2m/s 2 Then, as shown in FIG. 5 (b), the first planned trajectory is the same as the acceleration of 0.5 m/s. However, since the acceleration is relatively large, if the HC continues to travel according to the original trajectory, the HC will collide with the TF, and the HC plans a more urgent lane change trajectory. The HC detects TF acceleration, and as shown in fig. 6, the HC accelerates to merge into the target lane, but the speed variation is small. After merging into the target lane, the HC plans the trajectory for the third time to adjust the speed to follow the preceding vehicle due to the speed difference with the preceding vehicle. The HC keeps a safe distance from other vehicles, and the longitudinal acceleration and the transverse acceleration are respectively less than 0.1g and 0.2g, so that the comfort requirement is met.
When the acceleration of TF is 3m/s 2 At the moment, the initial lane change track no longer meets the safety requirement, but a safe feasible lane change track does not exist due to the fact that the acceleration of the TF is too large. As shown in fig. 5 (c), the HC plans a safe track returning to the original lane, and after returning to the original lane, performs track planning deceleration for the third time to adjust the vehicle speed, and continues to follow the PC. As shown in FIG. 7, both the longitudinal and lateral acceleration of HC do not exceed 0.1g, meeting comfort requirements.
It will be understood by those skilled in the art that the present invention is not limited by the foregoing examples, and that the foregoing examples and descriptions are merely illustrative of the principles of the invention, and various changes and modifications can be made without departing from the spirit and scope of the invention as defined by the appended claims. The scope of the invention is defined by the appended claims and equivalents thereof.

Claims (7)

1. A method for real-time trajectory planning, comprising:
s1: constructing a longitudinal and transverse track-changing track model by using a quintic polynomial for time sampling;
s2: constructing constraint conditions of the longitudinal and transverse lane changing track model to obtain a safe lane changing model;
s3: sampling the lane change time in the longitudinal and transverse lane change trajectory model at equal intervals, converting the lane change trajectory planning problem under different lane change times into a quadratic planning problem, and finally obtaining different lane change times t through a quadratic planning solver k A lower lane change track; the cost function of the quadratic programming problem comprises a comfort cost function and a safety cost function after Taylor second-order expansion, wherein the comfort cost function is constructed through an acceleration, and the safety cost function is constructed through a potential field function;
s4: from different lane change times t based on safety, comfort and lane change passing efficiency k Screening out an optimal track changing track from the lower track changing tracks;
s5: and predicting the future state of the surrounding vehicles, judging whether the distance between the main vehicle and the front vehicle of the current lane, the distance between the front vehicle of the target lane and the rear vehicle of the target lane meets the safety lane change model within the prediction steps, if so, changing the lanes, otherwise, repeating the steps S1-S4 to replan the lane change track until the main vehicle enters the target lane.
2. The method according to claim 1, wherein in step S1, the traversable trajectory model is expressed as:
Figure FDA0003750876580000011
wherein x (t) represents a longitudinal trajectory of the host vehicle; y (t) represents the transverse trajectory of the host vehicle, a i A fifth order polynomial coefficient representing a longitudinal trajectory; b i A fifth order polynomial coefficient representing the transverse trajectory; i =0,1,2,3,4,5;
time t for changing lane by master f Determining a state of initial time of lane change and state of end time of lane change i And b i Expressed as:
Figure FDA0003750876580000012
Figure FDA0003750876580000013
wherein x is 0 The longitudinal position of the main car at the initial lane changing moment is shown; v. of x,0 Indicating the longitudinal velocity of the host vehicle at the initial time of lane change; a is a x,0 Indicating the longitudinal acceleration of the host vehicle at the initial time of lane change; y is 0 The transverse position of the host vehicle at the initial lane change time is shown; v. of y,0 The transverse speed of the main vehicle at the initial lane changing moment is represented; a is y,0 Indicating the lateral acceleration of the host vehicle at the initial time of lane change; x is the number of f Indicating the longitudinal position of the host vehicle at the lane change ending time; v. of x,f Indicating the longitudinal speed of the host vehicle at the lane change end time; a is x,f Indicating the longitudinal acceleration of the host vehicle at the lane change end time; y is f Indicating the lateral position of the host vehicle at the lane change end time; v. of y,f The lateral speed of the host vehicle at the lane change ending time is represented; a is y,f Indicating the lateral acceleration of the host vehicle at the end of the lane change.
3. The method of claim 2, wherein in the step S2, the safe lane-changing model comprises:
in the lane changing process, the safety constraint conditions of the main vehicle and the vehicle in front of the current lane are expressed as follows:
Figure FDA0003750876580000014
wherein, X H Longitudinal coordinates representing the host vehicle; l is a radical of an alcohol H Indicating the length of the host vehicle; x PC Representing the longitudinal coordinate of the vehicle in front of the current lane; l is PC Representing the length of the vehicle in front of the current lane; v x,HC Indicating a vehicle speed of the host vehicle; v x,PC Representing the vehicle speed of the vehicle in front of the current lane; b is a mixture of max Represents the maximum deceleration permitted by the host vehicle;b PC,max indicating the maximum deceleration, T, permitted by the vehicle ahead of the current lane s Representing a minimum safe headway;
in the lane changing process, the safety constraint conditions of the main vehicle and the front vehicle of the target lane are expressed as follows:
Figure FDA0003750876580000021
in the lane changing process, the safety constraint conditions of the main vehicle and the rear vehicle of the target lane are expressed as follows:
Figure FDA0003750876580000022
wherein, X TP Representing the longitudinal position of the vehicle in front of the target lane; v x,TP Representing the speed of a vehicle in front of the target lane; l is TP Representing the length of the vehicle in front of the target lane; b TP,max Representing the maximum deceleration of the vehicle ahead of the target lane; x TF Representing the longitudinal position of the vehicle behind the target lane; v x,TF Representing the speed of the vehicle behind the target lane; l is TF Representing the length of the vehicle behind the target lane; b TF,max Representing the maximum deceleration of the vehicle behind the target lane;
equations (3) through (5) constitute the safe lane-changing model.
4. The method of claim 3, wherein converting non-linear constraints in the safe lane-change model to linear constraints comprises: and (3) and (4) are respectively approximated by adopting a plurality of straight lines, and then the equations (3) and (4) are converted into:
k i' V x,HC +X HC ≤m i' ； (6)
wherein k is i' Represents a straight line l i' The slope of (a); m is a unit of i' Represents a straight line l i' The intercept of (d); i' =1,2,3;
the safe lane-changing model is expressed as equation (6) and equation (5).
5. The method of claim 4, wherein in step S3, the lane change time t is compared f The equally spaced sampling is performed as:
Figure FDA0003750876580000023
wherein, T min Indicating a minimum allowed lane change time; t is max Representing the maximum allowable lane change time; Δ T represents a sampling time interval; n represents the number of samples;
the quadratic programming problem is represented as:
J * ＝minJ(X f ,V x,f ,a x,f )＝J C +J S
Figure FDA0003750876580000024
wherein, J c Representing a comfort cost function, J s Representing a safety cost function after Taylor second-order expansion;
the comfort cost function is expressed as:
Figure FDA0003750876580000031
wherein j is x Represents a longitudinal acceleration; j is a function of y Represents lateral acceleration; w is a lon Represents a longitudinal weight coefficient; w is a lat Represents a lateral weight coefficient;
the potential field function is expressed as:
Figure FDA0003750876580000032
wherein, U TP Representing a potential field generated by a vehicle in front of a target lane; u shape TF Representing a potential field generated by a vehicle behind the target lane;
the security cost function is represented as:
J(X f ,V x,f )＝w P U TP +w F U TF ； (11)
wherein w P And w F All represent weight coefficients;
performing Taylor second-order expansion on the security cost function, and expressing as:
Figure FDA0003750876580000033
wherein, X = [ X = f ,V x,f ]。
6. The method of claim 5, wherein the step S4 of screening out the optimal switching trajectory comprises:
Figure FDA0003750876580000034
wherein, J * Indicates the track-changing time t k Comfort cost function and safety cost function via Taylor second order expansion, w d And a weight coefficient representing the lane change passage efficiency.
7. The method of claim 5, wherein predicting the future state of the surrounding vehicle in step S5 comprises: if the acceleration of the surrounding vehicle is kept constant, the motion profile of the surrounding vehicle is expressed as:
Figure FDA0003750876580000035
wherein the content of the first and second substances,
Figure FDA0003750876580000036
representing the current longitudinal position of the surrounding vehicle;
Figure FDA0003750876580000037
representing the speed of the surrounding vehicle;
Figure FDA0003750876580000038
represents the acceleration of the surrounding vehicle; i = PC, TP, TF, PC representing the vehicle ahead of the current lane, TP representing the vehicle ahead of the target lane, TF representing the vehicle behind the target lane;
defining a prediction time interval delta t and a prediction step number N p And obtaining the predicted position sequences of the vehicle in front of the current lane, the vehicle in front of the target lane and the vehicle behind the target lane as follows:
X I (t 0 +Δt|t 0 ),X I (t 0 +Δt|t 0 ),...,X I (t 0 +Δt|t 0 )；(15)
at the predicted step number N p In the step, when the distance between the main vehicle and the vehicle in front of the current lane, the distance between the vehicle in front of the target lane and the distance between the vehicles behind the target lane do not satisfy the safe lane changing model, the safe lane changing model is expressed as follows:
Figure FDA0003750876580000041
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Publication number Priority date Publication date Assignee Title
CN117698730A (en) * 2024-01-08 2024-03-15 昆明理工大学 Optimal lane change track planning method for anti-collision dynamic intelligent network-connected vehicle

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117698730A (en) * 2024-01-08 2024-03-15 昆明理工大学 Optimal lane change track planning method for anti-collision dynamic intelligent network-connected vehicle
CN117698730B (en) * 2024-01-08 2024-05-31 昆明理工大学 Optimal lane change track planning method for anti-collision dynamic intelligent network-connected vehicle

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