CN115140094A - Real-time lane change decision-making method based on longitudinal safety interval model - Google Patents

Real-time lane change decision-making method based on longitudinal safety interval model Download PDF

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CN115140094A
CN115140094A CN202210842508.0A CN202210842508A CN115140094A CN 115140094 A CN115140094 A CN 115140094A CN 202210842508 A CN202210842508 A CN 202210842508A CN 115140094 A CN115140094 A CN 115140094A
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vehicle
lane
host vehicle
longitudinal
representing
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王金湘
严永俊
彭林
李�杰
方振伍
陶炜辰
张宁
陈建松
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
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units, or advanced driver assistance systems for ensuring comfort, stability and safety or drive control systems for propelling or retarding the vehicle
    • B60W30/18—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
    • 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
    • B60W60/00—Drive control systems specially adapted for autonomous road vehicles
    • B60W60/001—Planning or execution of driving tasks
    • B60W60/0027—Planning or execution of driving tasks using trajectory prediction for other traffic participants
    • 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
    • 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
    • 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
    • B60W2554/00—Input parameters relating to objects
    • B60W2554/40—Dynamic objects, e.g. animals, windblown objects
    • B60W2554/404—Characteristics
    • 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
    • B60W2554/00—Input parameters relating to objects
    • B60W2554/40—Dynamic objects, e.g. animals, windblown objects
    • B60W2554/404—Characteristics
    • B60W2554/4041—Position
    • 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
    • B60W2554/00—Input parameters relating to objects
    • B60W2554/40—Dynamic objects, e.g. animals, windblown objects
    • B60W2554/404—Characteristics
    • B60W2554/4042—Longitudinal speed

Abstract

The invention discloses a real-time lane change decision-making method based on a longitudinal safe interval model, which relates to the technical field of intelligent traffic and solves the technical problems that the existing decision-making method is poor in real-time performance and inapplicable in time-varying traffic environment; predicting the motion states of the host vehicle and the surrounding vehicles on the assumption that the accelerations of the host vehicle and the surrounding vehicles remain unchanged; calculating the longitudinal distance between a main vehicle and a front vehicle of a current lane in a prediction time domain, and providing a re-planning decision-making method based on a longitudinal safety distance model; designing a potential field method to select an optimal target lane according to the position relation and the speed relation of the main vehicle and surrounding vehicles at the current moment; a spline curve method is adopted to plan a safe track, so that safe and stable switching from an initial lane to a target lane of the main vehicle is realized, and safe running of the intelligent driving vehicle in a time-varying traffic environment is realized.

Description

Real-time lane change decision-making method based on longitudinal safety interval model
Technical Field
The application relates to the technical field of intelligent traffic, in particular to a real-time lane change decision-making method based on a longitudinal safety interval model.
Background
The automatic driving technology is an effective way for solving the problems of traffic safety and traffic jam. The road occupancy of autonomous vehicles will gradually increase, but this will be a lengthy process. Human-driven vehicles and autonomous cars will coexist on the road for a long time in the future. The behavior of human drivers is uncertain and their driving intentions are difficult to obtain, which puts high demands on the real-time performance of the environment perception, driving decision, trajectory planning and motion control modules of the autonomous vehicles. The driving decision and trajectory planning can be regarded as automatically driving the brain of the automobile, and the most appropriate driving behavior is decided and planned according to the environmental information provided by the sensing module and is sent to the motion control module. However, the existing decision method either needs a large amount of high-quality data to perform early training or has poor real-time performance, which causes many limitations in practical application. The problem to be solved by the application is how to make real-time decision in a dynamic complex environment and realize the safe driving of the automatic driving automobile.
Disclosure of Invention
The application provides a real-time lane change decision-making method based on a longitudinal safety interval model, and the technical purpose of the method is to solve the problem of safe driving of an automatic driving automobile in a dynamic complex environment.
The technical purpose of the application is realized by the following technical scheme:
a main vehicle detects a current lane and an adjacent lane and judges whether obstacles exist in the current lane and the adjacent lane, and the method comprises the following steps of:
s1: acquiring main vehicle state information and surrounding vehicle positions, speeds and accelerations;
s2: constructing a longitudinal safe interval model based on the collision risk of the main vehicle and the surrounding vehicles;
s3: predicting the motion states of the main vehicle and surrounding vehicles to obtain a prediction sequence of a future state, calculating the longitudinal distance between the main vehicle and a vehicle in front of a current lane according to the prediction sequence in a prediction time domain, judging whether the main vehicle and the surrounding vehicles have collision risks, and replanning a lane change track based on the longitudinal safe distance model if the collision risks exist until the main vehicle and the surrounding vehicles have no collision risks, and then turning to the step S4; if no collision risk exists, directly turning to the step S4;
wherein the obtaining of the prediction sequence comprises:
if the accelerations of the host vehicle and the surrounding vehicles remain unchanged, the motions of the host vehicle and the surrounding vehicles are expressed as:
Figure BDA0003750867550000011
wherein, the first and the second end of the pipe are connected with each other,
Figure BDA0003750867550000012
indicating a current longitudinal position of the vehicle around the host vehicle;
Figure BDA0003750867550000013
indicating the current speed of the vehicle around the host vehicle;
Figure BDA0003750867550000014
indicating the current acceleration of the vehicle around the host vehicle; HC represents the host vehicle; PC represents the front vehicle of the current lane; TPRepresenting a front vehicle of the target lane; TF represents the rear vehicle of the target lane;
defining the predicted time interval as delta t and the predicted step number as N p Then the predicted sequence of the longitudinal position of the car is represented as:
X I (t k +Δt|t k ),X I (t k +2Δt|t k ),...,X I (t k +N p Δt|t k )； (2)
wherein, t k Represents the current time;
s4: designing a lane potential field according to the position relation and the speed relation between the main vehicle and surrounding vehicles at the current moment, and selecting an optimal target lane according to the lane potential field;
s5: and switching the main vehicle from the initial lane to the optimal target lane is realized based on a spline curve method.
The beneficial effect of this application lies in: the real-time lane change decision method under the dynamic complex environment comprises the steps of establishing a longitudinal safety interval model considering collision risks with surrounding vehicles and sudden deceleration behaviors of a front vehicle, predicting motion states of the main vehicle and the surrounding vehicles, calculating the longitudinal interval between the main vehicle and the front vehicle of a current lane in a prediction time domain, and providing a re-planning decision method based on the longitudinal safety interval model; according to the position relation and the speed relation of the main vehicle and surrounding vehicles at the current moment, a potential field method is designed to select an optimal target lane, and a spline curve method is adopted to plan a safe track, so that safe and stable switching of the main vehicle from an initial lane to the optimal target lane is realized. The method can realize the safe driving of the automatic driving automobile in a time-varying complex environment, and has strong practicability and wide commercial application prospect.
Drawings
FIG. 1 is a flow chart of a method described herein;
FIG. 2 is a schematic illustration of a host vehicle designed according to the present application at risk of collision with surrounding vehicles;
FIG. 3 is a running track of a host vehicle behind a target lane under different acceleration conditions;
FIG. 4 is a schematic diagram of the longitudinal speed, longitudinal acceleration, front wheel steering angle and lateral acceleration of the host vehicle at a moderate acceleration of the vehicle behind the target lane;
FIG. 5 is a schematic diagram of the longitudinal speed, longitudinal acceleration, front wheel turning angle and lateral acceleration of the main vehicle under the condition of rapid acceleration of the vehicle behind the target lane.
Detailed Description
The technical solution of the present application will be described in detail below with reference to the accompanying drawings.
As shown in fig. 1, in the real-time lane change decision method based on the longitudinal safety interval model, a host vehicle detects a current lane and an adjacent lane, and determines whether there is an obstacle in the current lane and the adjacent lane, and if there is an obstacle, the method includes:
s1: the host vehicle state information and the surrounding vehicle position, velocity, and acceleration are acquired.
S2: and constructing a longitudinal safe interval model based on the collision risk of the main vehicle and the surrounding vehicles.
When the host vehicle (HC) encounters a vehicle (PC) traveling slowly ahead of the current lane, a lane change operation is required. At this time, the host vehicle may collide with the vehicle ahead of the current lane (PC), the vehicle ahead of the target lane (TP), and the vehicle behind the target lane (TF), as shown in fig. 2, with collision points respectively defined as C PC 、C TP And C TF . As shown in FIG. 2 (a), t.ltoreq.t at 0. Ltoreq.t c The condition that the host vehicle and the vehicle ahead of the current lane do not collide in the time range of (1) is expressed as:
Figure BDA0003750867550000021
wherein, t c Indicating the time when the host vehicle leaves the current lane; x PC Representing the longitudinal position of the vehicle in front of the current lane; l is PC Representing the length of the vehicle in front of the current lane; x H Indicating a longitudinal position of the host vehicle; l is H Indicating the length of the host vehicle; w H Representing the width of the host vehicle; ψ denotes a heading angle of the host vehicle. The central line of the right lane of the initial target track of the main vehicle is selected as a longitudinal axis X-axis, and the transverse offset of the central line of the right lane of the initial target track of the main vehicle is selected as a transverse axis Y-axis. Then, Y (t) c )＝W L V2,Y represents the lateral position of the main vehicle, W L Is the width of a lane.
In addition, a preceding vehicle of the host vehicle's current lane may be braked suddenly, which often results in a collision. Therefore, the main vehicle keeps a safe distance G with the vehicle ahead of the current lane in the lane changing process PC Expressed as:
Figure BDA0003750867550000031
wherein, V x,HC Representing the longitudinal velocity of the host vehicle; v x,PC Representing the longitudinal speed of the vehicle in front of the current lane; b max Representing a maximum deceleration of the host vehicle; b is a mixture of PC,max Representing the maximum deceleration of the vehicle in front of the current lane; t is s Represents a minimum safe time interval; v x,HC .T s Indicating the minimum safe distance that the host vehicle and the vehicle ahead of the current lane remain.
Since the heading angle ψ of the host vehicle is generally a small angle, when sin ψ ≈ 0, equation (4) is expressed as:
Figure BDA0003750867550000032
similarly, as shown in FIG. 2 (b), at t c ≤t≤t f The condition that the host vehicle and the vehicle ahead of the target lane do not collide in the time range of (1) is expressed as:
Figure BDA0003750867550000033
wherein, t f Indicating the termination time of the lane change process; x TP Indicating a longitudinal position; v x,TP Represents the longitudinal speed; l is a radical of an alcohol TP Represents a length; b TP,max Representing the maximum deceleration of the vehicle ahead of the target lane; g TP Representing the expected safe distance of the vehicle ahead of the target lane.
As shown in FIG. 2 (c), the rear of the target lane is located behind the host vehicle, so that the host vehicle only needs to keep safe with itThe total distance. At this time, at t c ≤t≤t f The condition that the host vehicle and the target lane rear vehicle do not collide in the time range of (1) is expressed as:
Figure BDA0003750867550000034
wherein X TF Representing the longitudinal position of the vehicle behind the target lane; v x,TF Representing the longitudinal 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; g TF ＝V x,TF .T s Indicating the minimum safe distance the host vehicle maintains from the vehicle behind the target lane, in relation to the longitudinal speed of the vehicle behind the target lane.
In summary, equations (3) to (7) constitute the longitudinal safe pitch model.
S3: predicting the motion states of the main vehicle and surrounding vehicles to obtain a prediction sequence of a future state, calculating the longitudinal distance between the main vehicle and the vehicles in front of the current lane according to the prediction sequence in a prediction time domain, judging whether the main vehicle and the surrounding vehicles have collision risks, and replanning a lane change track based on the longitudinal safety distance model if the collision risks exist until the main vehicle and the surrounding vehicles have no collision risks, and then turning to the step S4; if there is no collision risk, go directly to step S4.
Wherein the obtaining of the prediction sequence comprises:
if the accelerations of the host vehicle and the surrounding vehicles remain unchanged, the motions of the host vehicle and the surrounding vehicles are expressed as:
Figure BDA0003750867550000041
wherein the content of the first and second substances,
Figure BDA0003750867550000042
indicating a current longitudinal position of the vehicle around the host vehicle;
Figure BDA0003750867550000043
showing vehicles around the main vehicleThe current speed of the vehicle;
Figure BDA0003750867550000044
indicating the current acceleration of the vehicle around the host vehicle; HC represents the host vehicle; PC represents the front vehicle of the current lane; TP represents the front vehicle of the target lane; TF denotes a rear vehicle of the target lane.
Defining the predicted time interval as delta t and the predicted step number as N p The predicted sequence of the longitudinal position of the car is then expressed as:
X I (t k +Δt|t k ),X I (t k +2Δt|t k ),...,X I (t k +N p Δt|t k )； (2)
wherein, t k Indicating the current time of day.
When collision risks exist, the track changing track needs to be replanned, and the decision conditions of replanning comprise:
(1): before the main bus changes the lane, the decision conditions for re-planning are expressed as follows:
X PC (t|t k )-X(t|t k )＜G PC ； (8)
wherein, X (t | t) k ) Representing the predicted sequence of the host car in the longitudinal position. Accordingly, the host vehicle and the surrounding vehicle travel in a straight line before the lane change, and X (t | t) can be obtained according to equation (1) k )。
If the constraint of the longitudinal safe spacing model is not satisfied, executing a track changing track planning algorithm, and selecting the current moment as the starting time t of the track changing process 0 . In addition, the surrounding environment needs to be predicted in the lane changing process, and if the collision risk is caused by the sudden change of the surrounding environment in the lane changing process, the track is planned again.
(2): the main vehicle is in the lane changing process and before entering the target lane, namely t = t k +jΔt<t c The decision conditions for re-planning are expressed as:
Figure BDA0003750867550000045
accordingly, the master is in the lane change process, i.e. t k +jΔt<t f ，j＝1,2,...,N p When, X (t | t) k ) I.e. the lane change trajectory, is expressed as:
Figure BDA0003750867550000046
Figure BDA0003750867550000051
wherein X 0 Indicating a longitudinal position of the host vehicle at an initial time; v x，0 Representing a longitudinal velocity of the host vehicle at an initial time; a is x，0 Representing a longitudinal acceleration of the host vehicle at an initial time; y is 0 Indicating a lateral position of the host vehicle at an initial time; v y，0 Representing the lateral velocity of the host vehicle at an initial time; a is y，0 Representing a lateral acceleration of the host vehicle at an initial time; x f A longitudinal position indicating the host vehicle at the end time; v x，f Indicating a longitudinal velocity of the host vehicle at the end time; a is a x，f Indicating a longitudinal acceleration of the host vehicle at the end time; y is f A lateral position indicating a host vehicle at an end time; v y，f Indicating a lateral velocity of the host vehicle at the end time; a is y，f Indicating a lateral acceleration of the host vehicle at the end time; a is a i Coefficients representing a longitudinal trajectory quintic polynomial; b is a mixture of i Coefficients representing a transverse trajectory quintic polynomial; i =0,1,2,3,4,5.
Starting time t 0 =0, main lane change time is t f -t 0 ＝t f (ii) a When the main vehicle runs from the current position to the central line of the target lane, the terminal transverse position Y of the main vehicle f Is equal to 0 or W L ，W L Representing the width of the host vehicle; the re-planned lane change trajectory is tangent to the centerline of the target lane, i.e. velocity V y，f And acceleration a y，f Are all 0.
(3): after the main car is changed lane and enters the target lane, i.e. t = t k +jΔt＞t c The decision conditions for the re-planning are expressed as:
X TP (t|t k )-X(t|t k )＜G TP ； (12)
wherein G is TP 、G PC And G TF Each represents a constraint in the longitudinal safe distance model;
accordingly, the host vehicle is after the lane change, and X (t tk ) Expressed as:
Figure BDA0003750867550000052
wherein, t f Indicating the end time of the lane changing process; if t f ＞t k ，N f ＝(t f -t k )/Δt。
S4: and designing a lane potential field according to the position relation and the speed relation of the main vehicle and the surrounding vehicles at the current moment, and selecting an optimal target lane according to the lane potential field.
Describing the risk degree of each lane by adopting a potential field method, and simultaneously considering the longitudinal distance between the main car at the current moment and the cars on different lanes and the speed difference of the main car and the cars; the acceleration and deceleration behaviors of the front vehicle and the rear vehicle on different lanes are also considered. The further the front and rear vehicles are from the host vehicle on different lanes, the faster the front vehicle, the slower the rear vehicle, the greater the acceleration of the front vehicle and the smaller the acceleration of the rear vehicle, the safer the lane change behavior, so the potential field should be smaller, and vice versa. Meanwhile, the distance between the front vehicle and the rear vehicle must be greater than the safe lane change distance G saf . On the current lane, only the distance, the speed difference and the longitudinal acceleration of the front vehicle need to be considered, and the rear vehicle does not need to be considered. In terms of driving safety and traveling efficiency, the vehicle needs to be far away from a lane where a large vehicle is located; thus, the greater the vehicle mass, the higher the potential field. In addition, lane change losses should also be taken into account; the more lanes the host vehicle crosses, the larger the potential field.
Then, the potential fields of the different lanes are represented as:
Figure BDA0003750867550000061
wherein, the first and the second end of the pipe are connected with each other,
Figure BDA0003750867550000062
a potential field representing an ith lane;
Figure BDA0003750867550000063
a potential field representing a current lane; m is iP Representing the mass of the front vehicle on the ith lane;
Figure BDA0003750867550000064
indicating the longitudinal position of the front vehicle on the ith lane;
Figure BDA0003750867550000065
representing the speed of the front vehicle on the ith lane;
Figure BDA0003750867550000066
representing the acceleration of the preceding vehicle on the ith lane; m is iF Representing the mass of the rear vehicle on the ith lane;
Figure BDA0003750867550000067
indicating the longitudinal position of the rear vehicle on the ith lane;
Figure BDA0003750867550000068
representing the speed of the rear vehicle on the ith lane;
Figure BDA0003750867550000069
indicating the acceleration of the following vehicle in the ith lane.
Figure BDA00037508675500000610
Indicating the longitudinal position of the front vehicle on the current lane;
Figure BDA00037508675500000611
representing the speed of the preceding vehicle on the current lane, c j′ Represents a weight coefficient, j' =1,2,3,4,5,6.
Then selecting an optimal target lane according to the lane potential field, and expressing as:
Figure BDA00037508675500000612
wherein i * And the represented optimal target lane is the target lane of the decided trajectory re-planning.
S5: and switching the main vehicle from the initial lane to the optimal target lane is realized based on a spline curve method.
During the course of a lane change trajectory plan on a structured road, the host vehicle needs to make a smooth transition to the center of the target lane. The quintic spline curve has the advantages of high-order continuous derivation and simple calculation, so the quintic spline curve is adopted for trajectory planning in the application. Meanwhile, the movement states of the front vehicle and the rear vehicle on the target lane may also change during the lane change of the main vehicle. Therefore, not only the lateral movement but also the longitudinal movement of the host vehicle is taken into account.
The quintic spline of the longitudinal and lateral trajectories of the host vehicle is represented as:
Figure BDA00037508675500000613
wherein x (t) represents a longitudinal trajectory of the host vehicle; y (t) represents the lateral trajectory of the host vehicle;
a i and b i By an initial state S 0 ＝[X 0 ，V x，0 ，a x，0 ；Y 0 ，V y，0 ，a y，0 ] T And terminal state S f ＝[X f ，V x，f ，a x，f ；Y f ，V y，f ，a y， f] T Calculated, expressed as:
Figure BDA00037508675500000614
Figure BDA00037508675500000615
to simplify the solution, a start time t is set in the trajectory planning process 0 Is the origin, i.e. t 0 =0, the main lane change time is t f -t 0 ＝t f . The host vehicle travels from the current position to the center line of the target lane, and thus the terminal lateral position Y of the host vehicle f Is equal to 0 or W L . At the same time, to achieve a smooth transition from the initial lateral offset to the sampling terminal offset, the re-planned trajectory is set to be tangent to the centerline of the target lane, i.e. the velocity V y,f And acceleration a y,f Is set to 0. Then, according to equation (16), equations (17) to (18) are expressed as equations (10) to (11).
According to the equations (10) to (11), twelve unknown parameters can be obtained by solving twelve linearly independent equations, and then the initial state S of the host vehicle is obtained 0 And a termination state S f And a track change planning time t f The parameter a can be obtained i 、b i . Since the initial state is predetermined by the current state of the host vehicle, it can be acquired by GPS or a sensor. Therefore, the lane change trajectory can be determined by the terminal state and the lane change time, i.e., the convergence state and the convergence time of the trajectory.
The real-time lane change decision method of the embodiment is verified by constructing a dynamic complex traffic environment through a Matlab/Simulink-CarSim combined simulation platform. The host vehicle and the vehicle ahead of the current lane travel along the right lane at speeds of 20m/s and 16 m/s. The vehicle ahead of the current lane is initially located 35m ahead of the host vehicle. The vehicle in front of the target lane and the vehicle behind the target lane travel along the left lane at a speed of 20m/s, respectively at the front and rear of the host vehicle by 20 m. Since the host vehicle is faster than the vehicle ahead of the current lane, it must decide whether to change lanes or decelerate to follow the vehicle ahead of the current lane and replan a collision-free trajectory. Meanwhile, in a time-varying traffic environment in which a human-driven vehicle and an autonomous vehicle are mixed, if the autonomous host vehicle is to change lanes, the vehicle behind the target lane driven by the human may decelerate to give way to the host vehicle or accelerate to prevent the host vehicle from merging into its lane.In embodiments of the present application, when a target lane trailing vehicle finds the primary vehicle's lane change behavior, it will be at 0.5, 2 and 3m/s 2 Is accelerated. The host vehicle will then make decisions and re-plan trajectories based on the predicted driving environment. The results of the multiple re-planning and the actual travel path of the host vehicle, as well as the positions of the host vehicle and the surrounding vehicles at different times, are shown in fig. 3.
In the process of changing the main lane, the rear vehicle of the target lane takes 0.5m/s 2 The test results of acceleration are shown in fig. 3 (a). When the host vehicle (HC) detects a slowly moving front vehicle (PC), it makes a first decision and re-planning of the trajectory. Because the speed of the front vehicle is lower than that of the main vehicle, the speeds of the front vehicle (TP) and the rear vehicle (TF) of the adjacent lane are the same as that of the main vehicle, and the distances between the front vehicle (TP) and the rear vehicle (TF) of the adjacent lane and the main vehicle are safe enough. Therefore, the left lane is selected as the target lane. The planned trajectory is indicated by a solid line, and the tracked trajectory (traveling trajectory) of the host vehicle is indicated by a broken line. Because the acceleration of the vehicle behind the adjacent lane is small, the originally planned track is still safe, and therefore, the re-planning is not needed in the lane changing process. After the lane change action is finished, the speed of the host vehicle is slightly higher than that of the vehicle ahead of the adjacent lane, so that the second trajectory planning is carried out, and the host vehicle slightly decelerates to follow the vehicle ahead of the target lane, and is indicated by dotted lines. The same shape indicates the position of four vehicles at the same time, 1.5 seconds, 3.5 seconds, 5.5 seconds, and 7.5 seconds, respectively. As shown in fig. 3 (a), the squares of the same shape do not overlap, indicating that four vehicles do not collide, i.e., that driving safety is satisfactory.
In the process of changing the main vehicle into the lane, the rear vehicle of the target lane is at 2m/s 2 The test results of acceleration are shown in fig. 3 (b). The first decision and trajectory planning operation (indicated by the solid line) is the same as in the previous embodiment. However, since the acceleration of the vehicle behind the target lane is large, if the host vehicle continues to travel along the first planned lane change trajectory, the vehicle will collide with the vehicle behind the target lane. Thus, the host vehicle resumes the lane change decision and plans a more urgent safety trajectory, represented by the dotted line. The rear vehicle of the target lane is far away from the main vehicle, so the target lane is decidedNo change occurred. The host vehicle follows the second planned trajectory, the actual path of travel of which is indicated by a dashed line. As shown in fig. 4 (a), the velocity of the host vehicle does not change much in the first planning. The host vehicle accelerates into the target lane due to the acceleration behavior of the vehicle after the target lane is detected. After the main vehicle enters the target lane, the main vehicle can collide with the vehicle in front of the target lane due to the high speed, so that the third time of trajectory re-planning is carried out to decelerate the vehicle in front of the target lane, and the trajectory re-planning is represented by a dot-dash line. Finally, the speed of the main vehicle is 20m/s, which is the same as the speed of the vehicle in front of the target lane. As shown in fig. 3 (b), four vehicles do not collide, and the driving safety requirements are met. As shown in (b) - (c) of fig. 4, at a speed of 20m/s, the longitudinal and lateral accelerations of the host vehicle do not exceed 0.1g and 0.2g, respectively, indicating that the riding comfort of the host vehicle can be ensured.
In the process of changing the main lane, the rear vehicle of the target lane takes 3m/s 2 The test result of acceleration is shown in fig. 3 (c). The first planned track of the main vehicle is no longer safe, and the target lane decided by the main vehicle is the current lane, namely the initial right lane, because the acceleration of the vehicle behind the target lane is extremely large. The host vehicle plans to draw a safe trajectory back to the current lane and continue to follow the preceding vehicle of the current lane, as shown in fig. 5 (a). During and after returning to the original lane, the host vehicle may decelerate to avoid a collision with a vehicle ahead of the current lane. Finally, the speed of the host vehicle is kept at 16m/s, which is the same as the vehicle ahead of the current lane. As shown in (b) - (c) of FIG. 5, the longitudinal and lateral accelerations of the main vehicle do not exceed 0.1g, and the riding comfort requirement is met.
The foregoing is illustrative of the embodiments of the present application and the scope of protection is defined by the claims and their equivalents. It will be understood by those skilled in the art that the present invention is not limited by the foregoing examples, which are provided to illustrate the principles of the invention, and that various changes and modifications may be made without departing from the spirit and scope of the invention, which is also within the scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.

Claims (5)

1. A main vehicle detects a current lane and an adjacent lane, judges whether obstacles exist in the current lane and the adjacent lane, and is characterized in that the method comprises the following steps:
s1: acquiring main vehicle state information and surrounding vehicle positions, speeds and accelerations;
s2: constructing a longitudinal safe interval model based on the collision risk of the main vehicle and the surrounding vehicles;
s3: predicting the motion states of the main vehicle and surrounding vehicles to obtain a prediction sequence of a future state, calculating the longitudinal distance between the main vehicle and a vehicle in front of a current lane according to the prediction sequence in a prediction time domain, judging whether the main vehicle and the surrounding vehicles have collision risks, and replanning a lane change track based on the longitudinal safe distance model if the collision risks exist until the main vehicle and the surrounding vehicles have no collision risks, and then turning to the step S4; if no collision risk exists, directly turning to the step S4;
wherein the obtaining of the prediction sequence comprises:
if the accelerations of the host vehicle and the surrounding vehicles remain unchanged, the motions of the host vehicle and the surrounding vehicles are expressed as:
Figure FDA0003750867540000011
wherein, the first and the second end of the pipe are connected with each other,
Figure FDA0003750867540000012
indicating a current longitudinal position of the vehicle around the host vehicle;
Figure FDA0003750867540000013
indicating the current speed of the vehicle around the host vehicle;
Figure FDA0003750867540000014
indicating the current acceleration of the vehicle around the host vehicle; HC represents the host vehicle; PC represents the front of the current laneTurning; TP represents a front vehicle of the target lane; TF represents the rear vehicle of the target lane;
defining the time interval of prediction as delta t and the number of prediction steps as N p Then the predicted sequence of the longitudinal position of the car is represented as:
X I (t k +Δt|t k )，X I (t k +2Δt|t k )，...，X I (t k +N p Δt|t k )； (2)
wherein, t k Represents the current time;
s4: designing a lane potential field according to the position relation and the speed relation of the main vehicle and surrounding vehicles at the current moment, and selecting an optimal target lane according to the lane potential field;
s5: and switching the main vehicle from the initial lane to the optimal target lane is realized based on a spline curve method.
2. The method according to claim 1, wherein in step S2, the constructing of the longitudinal safety interval model comprises: the main vehicle may collide with the vehicle in front of the current lane, the vehicle in front of the target lane and the vehicle in back of the target lane, and the collision points are defined as C PC 、C TP And C TF Then, there are:
s21: t is more than or equal to 0 and less than or equal to t c The condition that the host vehicle and the vehicle ahead of the current lane do not collide in the time range of (1) is expressed as:
Figure FDA0003750867540000015
wherein, t c Indicating the time when the host vehicle leaves the current lane; x PC Representing the longitudinal position of the vehicle in front of the current lane; l is PC Representing the length of the vehicle in front of the current lane; x H Indicating a longitudinal position of the host vehicle; l is a radical of an alcohol H Indicating the length of the host vehicle; w is a group of H Representing the width of the host vehicle; ψ represents a heading angle of the host vehicle;
safe distance G between main vehicle and vehicle in front of current lane in lane changing process PC Expressed as:
Figure FDA0003750867540000016
wherein, V x，HC Representing a longitudinal velocity of the host vehicle; v x，PC Representing the longitudinal speed of the vehicle in front of the current lane; b is a mixture of max Representing a maximum deceleration of the host vehicle; b PC，max Representing the maximum deceleration of the vehicle in front of the current lane; t is s Represents a minimum safe time interval; v x，HC .T s Indicating a minimum safe distance kept by the main vehicle and the vehicle in front of the current lane;
when sin ψ ≈ 0, equation (4) is expressed as:
Figure FDA0003750867540000021
s22: at t c ≤t≤t f The condition that the host vehicle and the vehicle ahead of the target lane do not collide in the time range of (1) is expressed as:
Figure FDA0003750867540000022
wherein, t f Indicating the termination time of the lane change process; x TP Indicating a longitudinal position; v x，TP Represents the longitudinal speed; l is TP Represents a length; b TP，max Representing the maximum deceleration of the vehicle ahead of the target lane; g TP Representing an expected safe distance of the vehicle in front of the target lane;
s23: at t c ≤t≤t f The condition that the host vehicle and the target lane rear vehicle do not collide in the time range of (1) is expressed as:
Figure FDA0003750867540000023
wherein X TF Representing the longitudinal position of the vehicle behind the target lane; v x，TF Indicating the longitudinal direction of the vehicle behind the target laneSpeed; l is TF Representing the length of the vehicle behind the target lane; g TF ＝V x，TF .T s Indicating a minimum safe distance that the host vehicle maintains behind the target lane;
s24: equations (3) to (7) constitute the longitudinal safe space model.
3. The method according to claim 2, wherein in step S3, if there is a collision risk, the lane-changing trajectory is re-planned, and the decision conditions for re-planning include:
s31: before the main train changes the channel, the decision conditions for re-planning are represented as follows:
X PC (t|t k )-X(t|t k )＜G PC ； (8)
wherein, X (t | t) k ) A predicted sequence representing the host vehicle in a longitudinal position;
accordingly, the host vehicle and the surrounding vehicles travel in a straight line before lane change, and X (t | t) can be obtained according to equation (1) k )；
S32: the main vehicle is in the lane changing process and before entering the target lane, namely t = t k +jΔt＜t c The decision conditions for re-planning are expressed as:
Figure FDA0003750867540000024
accordingly, the master is in the lane change process, i.e. t k +jΔt＜t f ，j＝1，2，...，N p When, X (t | t) k ) I.e. the lane change trajectory, is expressed as:
Figure FDA0003750867540000031
Figure FDA0003750867540000032
wherein, X 0 Show the main carLongitudinal position at the initial time; v x，0 Representing the longitudinal velocity of the host vehicle at an initial time; a is x，0 Representing a longitudinal acceleration of the host vehicle at an initial time; y is 0 Indicating a lateral position of the host vehicle at an initial time; v y，0 Representing the lateral velocity of the host vehicle at an initial time; a is y，0 Representing a lateral acceleration of the host vehicle at an initial time; x f Indicating a longitudinal position of the host vehicle at the end time; v x，f Indicating a longitudinal velocity of the host vehicle at the end time; a is x，f Indicating a longitudinal acceleration of the host vehicle at the end time; y is f A lateral position indicating a host vehicle at an end time; v y，f Indicating a lateral velocity of the host vehicle at the end time; a is y，f Indicating a lateral acceleration of the host vehicle at the end time; a is i Coefficients representing a longitudinal trajectory quintic polynomial; b i Coefficients representing a lateral trajectory quintic polynomial; i =0,1,2,3,4,5;
starting time t 0 =0, main lane change time is t f -t 0 ＝t f (ii) a When the main vehicle runs from the current position to the central line of the target lane, the terminal transverse position Y of the main vehicle f Is equal to 0 or W L ，W L Representing the width of the host vehicle; the re-planned lane change trajectory is tangent to the centerline of the target lane, i.e. velocity V y，f And acceleration a y，f Are all 0;
s33: after the main car is changed lane and enters the target lane, i.e. t = t k +jΔt＞t c The decision conditions for the re-planning are expressed as:
X TP (t|t k )-X(t|t k )＜G TP ； (12)
wherein, G TP 、G PC And G TF Each represents a constraint in the longitudinal safe distance model;
accordingly, the host vehicle is after the lane change, and X (t | t) assuming that the longitudinal velocity of the host vehicle after the lane change remains unchanged k ) Expressed as:
Figure FDA0003750867540000033
wherein, t f Indicating the end time of the lane changing process; if t is f ＞t k ，N f ＝(t f -t k )/Δt。
4. The method according to claim 3, characterized in that in step S4, the potential fields of different lanes are represented as:
Figure FDA0003750867540000041
wherein the content of the first and second substances,
Figure FDA0003750867540000042
a potential field representing an ith lane;
Figure FDA0003750867540000043
a potential field representing a current lane; m is iP Representing the mass of the front vehicle on the ith lane;
Figure FDA0003750867540000044
indicating the longitudinal position of the front vehicle on the ith lane;
Figure FDA0003750867540000045
representing the speed of the front vehicle on the ith lane;
Figure FDA0003750867540000046
representing the acceleration of the preceding vehicle on the ith lane; m is iF Representing the mass of the rear vehicle on the ith lane;
Figure FDA0003750867540000047
the longitudinal position of a rear vehicle on the ith lane is shown;
Figure FDA0003750867540000048
representing the speed of the rear vehicle on the ith lane;
Figure FDA0003750867540000049
representing the acceleration of the rear vehicle on the ith lane;
Figure FDA00037508675400000410
indicating the longitudinal position of the front vehicle on the current lane;
Figure FDA00037508675400000411
representing the speed of the preceding vehicle on the current lane, c j′ Represents a weight coefficient, j' =1,2,3,4,5,6;
then an optimal target lane is selected according to the lane potential field, which is expressed as:
Figure FDA00037508675400000412
wherein i * The indicated optimal target lane.
5. The method according to claim 4, wherein in step S5, the spline curve method is a quintic spline curve, and the quintic spline curve of the longitudinal trajectory and the lateral trajectory of the host vehicle is expressed as:
Figure FDA00037508675400000413
wherein x (t) represents a longitudinal trajectory of the host vehicle; y (t) represents the lateral trajectory of the host vehicle;
a i and b i By an initial state S 0 ＝[X 0 ，V x，0 ，a x，0 ；Y 0 ，V y，0 ，a y，0 ] T And terminal state S f ＝[X f ，V x，f ，a x，f ；Y f ，V y，f ，a y，f ] T Calculated, expressed as:
Figure FDA00037508675400000414
Figure FDA00037508675400000415
wherein, according to formula (16), formulae (17) to (18) are represented as (10) to (11).
CN202210842508.0A 2022-07-18 2022-07-18 Real-time lane change decision-making method based on longitudinal safety interval model Pending CN115140094A (en)

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116259185A (en) * 2023-01-30 2023-06-13 湖南大学无锡智能控制研究院 Vehicle behavior decision method and device fusing prediction algorithm in parking lot scene
CN117746639A (en) * 2024-02-18 2024-03-22 江苏大学 Background traffic flow model construction method and system based on automatic driving

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116259185A (en) * 2023-01-30 2023-06-13 湖南大学无锡智能控制研究院 Vehicle behavior decision method and device fusing prediction algorithm in parking lot scene
CN116259185B (en) * 2023-01-30 2023-10-13 湖南大学无锡智能控制研究院 Vehicle behavior decision method and device fusing prediction algorithm in parking lot scene
CN117746639A (en) * 2024-02-18 2024-03-22 江苏大学 Background traffic flow model construction method and system based on automatic driving

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