CN117445923A - Network-connected vehicle lane change track planning method based on reachability analysis - Google Patents
Network-connected vehicle lane change track planning method based on reachability analysis Download PDFInfo
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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
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
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Abstract
The invention discloses a network-connected vehicle lane-changing track planning method based on reachability analysis, which belongs to the technical field of path planning and comprises the steps of establishing a vehicle lane-changing track function, adding constraint conditions to the vehicle lane-changing track function, wherein the constraint conditions comprise minimum safety distance constraint, safety constraint and comfort constraint, and the reachability analysis of obstacles; and (5) carrying out vehicle lane change track planning. The invention uses a mixed polynomial of fifth and sixth orders and a lane change critical collision model to plan a lane change track meeting the safety and comfort constraints; the safety avoidance track is planned by considering the safety problem caused by the emergency lane change of the front vehicle and sacrificing part of comfort. The reaction time of the internet-connected vehicle in the emergency is reduced; and solving an avoidance track function by combining the searched safe positions, and splicing the two sections of tracks into one track, so that reasonable connection of the track changing track and the avoidance track can be realized.
Description
Technical Field
The invention discloses a network-connected vehicle lane change track planning method based on reachability analysis, and belongs to the technical field of path planning.
Background
Lane changing is the most basic driving behavior in vehicle running, and active lane changing of the internet-connected autonomous vehicle CAV requires technical cooperation of a sensing system, track planning, track tracking control and the like. The lane change track planning firstly meets the constraint of vehicle dynamics and kinematics and lane change safety, and completes the task of transition from the current lane to the target lane. The current common lane change track planning method mainly comprises a search-based algorithm, a sampling-based algorithm, a curve fitting algorithm, a machine learning algorithm and an artificial potential field method. In the prior art, the obstacle avoidance study is limited to a single passive obstacle avoidance track, and lacks of study on planning the obstacle avoidance track in advance for the emergency lane change problem.
Disclosure of Invention
The invention aims to provide a network-connected vehicle lane change track planning method based on reachability analysis, which aims to solve the problem that the reachability planning is not considered in the CAV vehicle lane change obstacle avoidance in the prior art.
The network-connected vehicle lane change track planning method based on reachability analysis comprises the following steps:
s1, establishing a lane change track function of a vehicle;
s2, adding constraint conditions to the lane change track function of the vehicle, wherein the constraint conditions comprise minimum safety distance constraint, safety constraint and comfort constraint;
s3, accessibility analysis of the obstacle;
s4, vehicle lane change track planning is conducted.
S1 comprises the following steps:
the lane change track function of the vehicle is as follows:
wherein x (t), y (t) are the longitudinal displacement and the transverse displacement of the vehicle in the course of lane change respectively;
A＝(a 6 a 5 a 4 a 3 a 2 a 1 a 0 ) T 、B＝(b 5 b 4 b 3 b 2 b 1 b 0 ) T is a coefficient vector to be determined of the polynomial, and t is time.
The minimum safe distance constraint includes: minimum safety distance MSS between vehicle M and front vehicle L0 running on original lane at initial time M，L0 The method comprises the following steps:
wherein alpha is the yaw angle of the lane-changing vehicle, t c Is critical collision time, V M 、a L0 、a M 、a L0 The speed and acceleration of the self-vehicle and the front vehicle, L is the vehicle length, W is the vehicle width, t 0 Is the initial time and τ is the intermediate variable.
The security constraints include: lateral displacement constraints and safe distance constraints of the vehicle;
where y (t) is the lateral displacement of the vehicle, h=3.75 is the lane width, t 1 Is the channel change ending time;
the minimum initial distance calculated according to the minimum safe distance model is added with the minimum longitudinal distance d kept between the vehicle and surrounding vehicles when the vehicle is running 0 As a safe distance D (M，L0) ：
D (M，L0) ＞MSS (M，L0) +d 0 。
Comfort constraints include:
wherein a is x For longitudinal acceleration, a y Is the transverse acceleration, j x For longitudinal jerk, j y Is a lateral jerk.
S2 comprises the following steps:
critical collision time t c The coordinates of the right front point of the CAV vehicle at the critical collision time are：
(X m (t c )-diga cos(θ+α)+L cosα，Y m (tc)-diga sin(θ+α)+L sinα)；
Wherein, diga is the distance from the center point of the CAV vehicle to the front right point of the CAV vehicle, X m (t c ) The critical collision moment CAV vehicle center point abscissa, theta is the acute included angle between the straight line where diga is located and the x axis, alpha is the vehicle yaw rate, Y m (t c ) The ordinate of the central point of the CAV vehicle at the critical collision moment;
the coordinates of the left rear point of the HV vehicle at the critical collision time are divided into:
X L0 (t c ) The abscissa of the center point of the vehicle at the critical collision moment HV;
in the course of changing lane, the vehicle yaw rate alpha is less than theta, and the coordinates of two vehicle collision positions of the critical collision point of changing lane are considered to be equal, so as to obtain the following critical collision constraint equation:
and combining the critical collision constraint equation and the vehicle lane change track function, and solving a lane change transverse and longitudinal track function by combining the known lane change initial time and the lane change end time vehicle state.
S3 comprises the following steps:
the model of the power system is defined as m= (f, X) 0 ，U)，f，X 0 U represents the boundary constraints of the powertrain, powertrain states and inputs, respectively, and the reachable set R (M, R) at time t=r is:
where X (t) is the state, u (t) is the input, t is the time, f (X (t), u (t)) represents the powertrain, X (0) represents the initial state, X (0) ∈X 0 ，
The reachable sets within time interval t e [0, r ] are:
R＝(M，[0，r])＝∪ t∈[0，r] R(M，t)；
with proj (x) = [ x ] 1 ，x 2 ，x 3 ] T Representing occupancy set x of a vehicle 1 And x 2 Representing the transverse and longitudinal position of the vehicle x 3 Representing the direction of the vehicle, proj (R (M, t)) = { proj (x) |x e R (M, t) } representing a set of vehicle positions and directions, calculating the possible occupied areas of other vehicles; model of the power system of the CAV vehicle is M 0 And M i Wherein M is i Is a model M 0 Is a overapproximation model of M i The occupied set contains M 0 Occupied set of (1)Expressed as:
s3 comprises the following steps:
the occupation set is replaced by a circle with the center of c (t) and the radius of r (t), and the longitudinal displacement s is set x =0, lateral displacement s y Longitudinal velocity v =1 x =v and transverse velocity v y =0, fitting the region boundaries of each step of the circular occupancy set with a curve yields a two-dimensional function of occupancy boundaries [ b ] x (t)，b y (t)] T Wherein b x (t) and b y (t) is:
in the formula, v 0 For the speed of the initial moment, a max Is the maximum acceleration.
S3 comprises the following steps:
the vehicle is rectangular with length L and width WOccupancy O 1 The coordinates of each point of the occupied area are expressed by a polygon q:
wherein q is 1 、q 2 、q 3 、q 4 、q 5 、q 6 For vertices of polygon q, c x (t k )，r(t k )，c y (t k )，b x (t k )，c y (t k+1 )，r(t k+1 )，c x (t)、c y (t) and r (t) represent the abscissa and the ordinate of the circle center of the occupation set and the radius, t k 、t k+1 Representing predicted time steps, b x (t k )，b y (t k ) The lateral boundary coordinates fitted by the two-step circular occupancy set are represented by p for the vehicle and all the areas of the vehicles nearby, p 1 、p 2 、p 3 、p 4 、p 5 、p 6 Representing the vertex of p.
Compared with the prior art, the invention has the following beneficial effects: the invention uses a mixed polynomial of fifth and sixth orders and a lane change critical collision model to plan a lane change track meeting the safety and comfort constraints; considering the safety problem caused by the emergency lane change of the front vehicle, a set-based prediction tool SPOT is used for searching a safety area, part of comfort is sacrificed, and a safety avoidance track is planned. The reaction time of the internet-connected vehicle in the emergency is reduced; and solving various parameters during the network vehicle lane change 3S by using a lane change track function through the designed simulated traffic scene. Solving an avoidance track function by combining the searched safe positions, and splicing the two sections of tracks into one track, so that reasonable connection of the track changing track and the avoidance track can be realized; the research on the lane change track planning aspect does not deeply consider the influence of track tracking control on the actual track of the vehicle; the influence of track tracking control on the running track of the vehicle is further researched in future research, so that a safer and more reliable track changing rule model is constructed.
Drawings
FIG. 1 is an occupancy set diagram of the present invention;
FIG. 2 is an occupancy set map irrespective of vehicle size;
FIG. 3 is an occupancy set map that accounts for vehicle size;
FIG. 4 is a track change scene and track change diagram;
FIG. 5 is a longitudinal displacement diagram;
FIG. 6 is a curvature map;
FIG. 7 is a longitudinal velocity map;
FIG. 8 is a lateral velocity diagram;
FIG. 9 is a longitudinal acceleration diagram;
FIG. 10 is a lateral acceleration diagram;
FIG. 11 is a longitudinal acceleration diagram;
fig. 12 is a lateral jerk diagram.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, the technical solutions in the present invention will be clearly and completely described below, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The network-connected vehicle lane change track planning method based on reachability analysis comprises the following steps:
s1, establishing a lane change track function of a vehicle;
s2, adding constraint conditions to the lane change track function of the vehicle, wherein the constraint conditions comprise minimum safety distance constraint, safety constraint and comfort constraint;
s3, accessibility analysis of the obstacle;
s4, vehicle lane change track planning is conducted.
S1 comprises the following steps:
the lane change track function of the vehicle is as follows:
wherein x (t), y (t) are the longitudinal displacement and the transverse displacement of the vehicle in the course of lane change respectively;
A＝(a 6 a 5 a 4 a 3 a 2 a 1 a 0 ) T 、B＝b 5 b 4 b 3 b 2 b 1 b 0 ) T is a coefficient vector to be determined of the polynomial, and t is time.
The minimum safe distance constraint includes: minimum safety distance MSS between vehicle M and front vehicle L0 running on original lane at initial time M，L0 The method comprises the following steps:
wherein alpha is the yaw angle of the lane-changing vehicle, t c Is critical collision time, V M 、V L0 、a M 、a L0 The speed and acceleration of the self-vehicle and the front vehicle, L is the vehicle length, W is the vehicle width, t 0 Is the initial time and τ is the intermediate variable.
The security constraints include: lateral displacement constraints and safe distance constraints of the vehicle;
where y (t) is the lateral displacement of the vehicle, h=3.75 is the lane width, t 1 Is the channel change ending time;
the minimum initial distance calculated according to the minimum safe distance model is added with the minimum longitudinal distance d kept between the vehicle and surrounding vehicles when the vehicle is running 0 As a safe distance D (M，L0) ：
D (M，L0) ＞MSS (M，L0) +d 0 。
Comfort constraints include:
wherein a is x For longitudinal acceleration, a y Is the transverse acceleration, j x For longitudinal jerk, j y Is a lateral jerk.
S2 comprises the following steps:
critical collision time t c The coordinates of the front right point of the CAV vehicle at the critical collision time are:
(X m (t c )-diga cos(θ+α)+L cosα，Y m (t c )-diga sin(θ+α)+L sinα)；
wherein, diga is the distance from the center point of the CAV vehicle to the front right point of the CAV vehicle, X m (t c ) The critical collision moment CAV vehicle center point abscissa, theta is the acute included angle between the straight line where diga is located and the x axis, alpha is the vehicle yaw rate, Y m (t c ) The ordinate of the central point of the CAV vehicle at the critical collision moment;
the coordinates of the left rear point of the HV vehicle at the critical collision time are divided into:
X L0 (t c ) The abscissa of the center point of the vehicle at the critical collision moment HV;
in the course of changing lane, the vehicle yaw rate alpha is less than theta, and the coordinates of two vehicle collision positions of the critical collision point of changing lane are considered to be equal, so as to obtain the following critical collision constraint equation:
and combining the critical collision constraint equation and the vehicle lane change track function, and solving a lane change transverse and longitudinal track function by combining the known lane change initial time and the lane change end time vehicle state.
S3 comprises the following steps:
the model of the power system is defined as m= (f, X) 0 ，U)，f，X 0 U represents the boundary constraints of the powertrain, powertrain states and inputs, respectively, and the reachable set R (M, R) at time t=r is:
where X (t) is the state, u (t) is the input, t is the time, f (X (t), u (t)) represents the powertrain, X (0) represents the initial state, X (0) ∈X 0 ，Time interval t E [0, r]The reachable sets within are:
R＝(M，[0，r])＝∪ t∈[0，r] R(M，t)；
with proj (x) = [ x ] 1 ，x 2 ，x 3 ] T Representing occupancy set x of a vehicle 1 And x 2 Representing the transverse and longitudinal position of the vehicle x 3 Representing the direction of the vehicle, proj (R (M, t)) = { proj (x) |x e R (M, t) } representing a set of vehicle positions and directions, calculating the possible occupied areas of other vehicles; model of the power system of the CAV vehicle is M 0 And M i Wherein M is i Is a model M 0 Is a overapproximation model of M i The occupied set contains M 0 Occupied set of (1)Expressed as:
s3 comprises the following steps:
the occupation set is replaced by a circle with the center of c (t) and the radius of r (t), and the longitudinal displacement s is set x =0, lateral displacement s y =0, longitudinal velocity v x =v and transverse velocity v y =0, fitting the region boundaries of each step of the circular occupancy set with a curve yields a two-dimensional function of occupancy boundaries [ b ] x (t)，b y (t)] T Which is provided withB in (b) x (t) and b y (t) is:
in the formula, v 0 For the speed of the initial moment, a max Is the maximum acceleration.
S3 comprises the following steps:
the vehicle is rectangular with length L and width W, and the occupancy rate O 1 The coordinates of each point of the occupied area are expressed by a polygon q:
wherein q is 1 、q 2 、q 3 、q 4 、q 5 、q 6 For vertices of polygon q, c x (t k )，r(t k )，c y (t k )，b x (t k )，c y (t k+1 )，r(t k+1 )，c x (t k )、c y (t k ) R (t) represents the abscissa and the ordinate of the circle center of the occupation set and the radius, t k 、t k+1 Representing predicted time steps, b x (t k )，b y (t k ) The lateral boundary coordinates fitted by the two-step circular occupancy set are represented by p for the vehicle and all the areas of the vehicles nearby, p 1 、p 2 、p 3 、p 4 、p 5 、p 6 Representing the vertex of p.
On the basis of considering the safety, comfort and lane changing efficiency of the lane changing track, the emergency lane changing of the front vehicle to the target lane is further considered. The road area possibly occupied by the front vehicle is predicted in advance through reachability analysis, and the conditions can be divided into three types, wherein the first condition is that after the front vehicle changes lanes, the vehicle distance between the two vehicles is enough for the self-vehicle to return to the original lane; secondly, according to the fact that the two workshops do not meet the lane changing requirement, the front car can be avoided by decelerating and stopping the vehicle; thirdly, the vehicle distance does not meet the requirement of deceleration avoidance, and avoidance is realized through a steering acceleration and deceleration strategy. The track planning flow is shown in fig. 1, and the obstacle avoidance safety area is searched in advance when the track is planned. In the emergency, the reaction time of the internet-connected vehicle can be reduced, and the traffic safety problem is treated by adopting the most suitable strategy.
In the prior art, under the traffic scene of double lanes, only one obstacle vehicle is assumed, when the network-connected vehicle changes lanes, the distance between two vehicles is smaller when the front vehicle suddenly changes lanes, and the network-connected vehicle selects a strategy of steering and braking to avoid the front vehicle. The invention relates to a method for planning a lane change track by using a polynomial, searching a safety area by using reachability analysis, and planning a safety avoidance track by using the polynomial. The road can be kept smooth on the premise of ensuring the safety.
The vehicle model of the lane change track is established, the lane change process can be accurately simulated, and the calculation process can be simplified. Among the vehicle models that are currently in common use are elliptical vehicle models, dynamic circular vehicle models, and rectangular vehicle models. Wherein the rectangular model has the highest similarity with the actual vehicle, and the error of reflecting the motion of the vehicle is the smallest.
During lane changing, three collision accidents, namely rear-end collision, corner collision and side collision, can occur with surrounding vehicles. And establishing a minimum safety distance model MSS, and simultaneously establishing channel changing constraint according to the minimum safety distance model, so that the safety in the channel changing process can be ensured.
The critical point of the collision between the net-connected vehicle and the front vehicle of the original lane is that the left front point collides with the left rear point of the front vehicle when the CAV vehicle changes lanes. MSS (Mobile station) M，L0 Is the critical minimum safety distance X of the collision between the CAV vehicle and the front vehicle M Is the longitudinal displacement of CAV vehicle at the time of collision, X L0 Is the longitudinal displacement of the HV vehicle before a collision occurs.
When the CAV vehicle exits the original lane, the car collides with the left rear point of the front car from the left front point of the car, and the critical moment is assumed to bet c . The lateral displacement of the left front point of the CAV vehicle at the critical moment is as follows: y (t) c )＝W cosα。
In order to realize autonomous lane change of a networked vehicle, a lane change track conforming to the dynamics and kinematic constraint of the vehicle needs to be planned. Because the motion direction of the constant-speed offset model and the Dubins curve model suddenly changes in the course of changing lanes, the requirement of curvature continuity is not met, and the maximum value exists in the start point and the end point of changing lanes of the sine-cosine lane changing model and the Bezier curve model, so that the steering wheel is not regulated after the vehicle runs to the lane combining point, and the vehicle runs to deviate from the center line of the lane. Polynomial models do not suffer from these drawbacks, so polynomial models are chosen to construct the track change and safety trajectories.
In order to improve the accuracy and continuity of the lane change track in the longitudinal direction, the degree of the longitudinal displacement polynomial is increased to describe the vehicle lane change track in the complex dynamic traffic scene.
The time matrix is marked as T A 、T B ：
Matrix expression of the horizontal and vertical equations to the start and end of the lane change:
the model has a total of 13 unknowns. And at the starting point t=t 0 And endpoint t=t 1 Is known to the position, velocity, acceleration, at t 0 And t 1 The total of 12 equations can be obtained in the transverse direction and the longitudinal direction at the moment, the equations cannot be solved, critical constraint conditions are required to be established, and simultaneous solving is required.
Based on the trajectory function and the minimum safe distance model, the kinematics and dynamics constraint in the course of vehicle lane change are carried out. The constraint brought by the road needs to be considered, and the maximum speed and the acceleration need to be limited in order to prevent the vehicle from rollover. Finally, to meet comfort requirements, the maximum jerk needs to be limited.
The most basic constraint in the running process of the automobile is lane lines and lane boundaries and safety collision constraint:
in order to ensure the comfort of passengers in the lane changing process, the horizontal and longitudinal acceleration and the jerk of the planned lane changing track are required to be within a reasonable range, and the jerk has a larger influence on the comfort of the passengers. Maximum lateral acceleration of 2m/s 2 Maximum longitudinal acceleration of 2.5m/s 2 Maximum lateral jerk of 10m/s 2 . The shortest lane change time is used as a measure of lane change efficiency index, and in general, the shorter the lane change time of a vehicle is, the less delay is caused to traffic flows on the upstream of an original lane and a target lane, the less possibility of causing surrounding vehicle congestion is, and the lane change efficiency is higher. The channel change time is generally between 2s and 6s. The unmanned vehicle changes lanes from beginning to end and possibly collides with the front vehicle at an angle, so long as the unmanned vehicle does not collide at a certain moment, the whole lane changing process will not collide.
The internet protocol vehicle has superior computing power and does not make random mistakes, which is far more than human drivers in predicting and preventing collisions. From this point of view, many discussions about security verification have been raised, such as planning or verifying a security trajectory using reachability analysis, in the principle that a trajectory is secure if and only if it can be proven that the trajectory does not intersect with a spatiotemporal region that any dynamic or static obstacle may occupy. From this point forward, a set-based prediction tool SPOT was developed. The program calculates the future occupancy set of the vehicle in the scene by using the reachability analysis method.
And searching the front vehicle occupation set predicted by the SPOT tool to obtain a safety area, and further obtaining the final safety position of the safety track. Traffic scenes designed based on the reachability analysis tool SPOT. And MATLAB is used for obtaining the lane change track and the safety track data under the scene, and the designed lane change state vector is shown in tables 1 and 2.
Assuming that the initial time of the front vehicle is 10m in front of the automobile, driving at a constant speed of 7.0m/s (25.2 km/h), setting the lane change time of the vehicle to be 4.0s, and obtaining the critical collision time t according to the critical collision model type c =1.76S. The front vehicle suddenly changes lanes to the target lane at t=3s, and the searched safe area is predicted according to the traffic participant occupancy set, and (x, y) = (62,3.0) is selected as the safe position.
TABLE 1 State values at initial Change time
| Axial direction | Displacement X/m | Velocity V/ms -1 | Acceleration a/ms -2 |
| X | 0.0 | 10.0 | 0.0 |
| Y | 0.0 | 0.0 | 0.0 |
。
TABLE 2 State values at the end of a lane change
| Axial direction | Displacement X/m | Velocity V/ms -1 | Acceleration a/ms -2 |
| X | 45.0 | 10.0 | 0.0 |
| Y | 1.875 | 0.0 | 0.0 |
。
The final track change function and the safety track function are as follows:
in the present invention, the occupancy set is shown in fig. 1, the occupancy set irrespective of the vehicle size is shown in fig. 2, and the occupancy set irrespective of the vehicle size is shown in fig. 3. The established scene, the track changing scene and the track changing track in the track changing process are shown in fig. 4, the longitudinal displacement is shown in fig. 5, the curvature is shown in fig. 6, the longitudinal speed is shown in fig. 7, the transverse speed is shown in fig. 8, the longitudinal acceleration is shown in fig. 9, the transverse acceleration is shown in fig. 10, the longitudinal acceleration is shown in fig. 11, and the transverse acceleration is shown in fig. 12. The solid line is the lane change trajectory and the dashed line is the safety avoidance trajectory. From the graph, the planned lane change track curve is continuous and smooth, and the curvature is not suddenly changed. The transverse speed, the acceleration, the longitudinal speed and the acceleration at the initial time and the final time of channel change are all 0, and the actual situation is consistent. The variation range of the transverse and longitudinal acceleration and the jerk meets the comfort requirement. The transverse and longitudinal speeds meet safety requirements. The transverse and longitudinal speeds and the acceleration of the safety avoidance track also meet the safety requirements and meet the actual conditions. From the figure, it is clear that the vehicle can reach the position specified by the trajectory planning within a predetermined time. In conclusion, the planned lane change track and the planned safety track can realize safe and efficient lane change obstacle avoidance.
The above embodiments are only for illustrating the technical aspects of the present invention, not for limiting the same, and although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may be modified or some or all of the technical features may be replaced with other technical solutions, which do not depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (9)
1. The network-connected vehicle lane change track planning method based on reachability analysis is characterized by comprising the following steps of:
s1, establishing a lane change track function of a vehicle;
s2, adding constraint conditions to the lane change track function of the vehicle, wherein the constraint conditions comprise minimum safety distance constraint, safety constraint and comfort constraint;
s3, accessibility analysis of the obstacle;
s4, vehicle lane change track planning is conducted.
2. The network-connected vehicle lane-change trajectory planning method based on reachability analysis of claim 1, wherein S1 comprises:
the lane change track function of the vehicle is as follows:
wherein x (t), y (t) are the longitudinal displacement and the transverse displacement of the vehicle in the course of lane change respectively;
A＝(a 6 a 5 a 4 a 3 a 2 a 1 a 0 ) T 、B＝(b 5 b 4 b 3 b 2 b 1 b 0 ) T is a coefficient vector to be determined of the polynomial, and t is time.
3. The network-connected vehicle lane-change trajectory planning method based on reachability analysis of claim 2, wherein the minimum safe distance constraint comprises: minimum safety distance MSS between vehicle M and front vehicle L0 running on original lane at initial time M，L0 The method comprises the following steps:
wherein alpha is the yaw angle of the lane-changing vehicle, t c Is critical collision time, V M 、V L0 、a M 、a L0 The speed and acceleration of the self-vehicle and the front vehicle, L is the vehicle length, W is the vehicle width, t 0 Is the initial time and τ is the intermediate variable.
4. A method of networked vehicle lane-change trajectory planning based on reachability analysis as in claim 3 wherein the security constraints comprise: lateral displacement constraints and safe distance constraints of the vehicle;
where y (t) is the lateral displacement of the vehicle, h=3.75 is the lane width, t 1 Is the channel change ending time;
the best calculated according to the minimum safe distance modelSmall initial distance, plus minimum longitudinal distance d maintained from surrounding vehicles while the vehicle is traveling 0 As a safe distance D (M，L0) ：
D (M,L0) ＞MSS (M,L0) +d 0 。
5. The network-connected vehicle lane-change trajectory planning method based on reachability analysis of claim 4, wherein comfort constraints comprise:
wherein a is x For longitudinal acceleration, a y Is the transverse acceleration, j x For longitudinal jerk, j y Is a lateral jerk.
6. The network-connected vehicle lane-change trajectory planning method based on reachability analysis of claim 5, wherein S2 comprises:
critical collision time t c The coordinates of the front right point of the CAV vehicle at the critical collision time are:
(X m (t c )-diga cos(θ+α)+L cosα，Y m (t c )-diga sin(θ+α)+L sinα)；
wherein, diga is the distance from the center point of the CAV vehicle to the front right point of the CAV vehicle, X m (t c ) The critical collision moment CAV vehicle center point abscissa, theta is the acute included angle between the straight line where diga is located and the x axis, alpha is the vehicle yaw rate, Y m (t c ) The ordinate of the central point of the CAV vehicle at the critical collision moment;
the coordinates of the left rear point of the HV vehicle at the critical collision time are divided into:
X L0 (t c ) HV vehicle center for critical collision momentDot abscissa;
in the course of changing lane, the vehicle yaw rate alpha is less than theta, and the coordinates of two vehicle collision positions of the critical collision point of changing lane are considered to be equal, so as to obtain the following critical collision constraint equation:
and combining the critical collision constraint equation and the vehicle lane change track function, and solving a lane change transverse and longitudinal track function by combining the known lane change initial time and the lane change end time vehicle state.
7. The network-connected vehicle lane-change trajectory planning method based on reachability analysis of claim 6, wherein S3 comprises:
the model of the power system is defined as m= (f, X) 0 ，U)，f，X 0 U represents the boundary constraints of the powertrain, powertrain states and inputs, respectively, and the reachable set R (M, R) at time t=r is:
where x (t) is the state, u (t) is the input, t is the time, f (x (t), u (t)) represents the powertrain, x (0) represents the initial state,
the reachable sets within time interval t e [0, r ] are:
R＝(M，[0，r])＝U t∈[0，r] R(M，t)；
with proj (x) = [ x ] 1 ，x 2 ，x 3 ] T Representing occupancy set x of a vehicle 1 And x 2 Representing the transverse and longitudinal position of the vehicle x 3 Representing the direction of the vehicle, proj (R (M, t))= { proj (x) |x∈r (M, t) } representing the set of vehicle position and direction, calculating the possible occupancy of other vehiclesA use area; model of the power system of the CAV vehicle is M 0 And M i Wherein M is i Is a model M 0 Is a overapproximation model of M i The occupied set contains M 0 Occupied set of (1)Expressed as:
8. the network-connected vehicle lane-change trajectory planning method based on reachability analysis of claim 7, wherein S3 comprises:
the occupation set is replaced by a circle with the center of c (t) and the radius of r (t), and the longitudinal displacement s is set x =0, lateral displacement s y =0, longitudinal velocity v x =v and transverse velocity v y =0, fitting the region boundaries of each step of the circular occupancy set with a curve yields a two-dimensional function of occupancy boundaries [ b ] x (t)，b y (t)] T Wherein b x (t) and b y (t) is:
in the formula, v 0 For the speed of the initial moment, a max Is the maximum acceleration.
9. The network-connected vehicle lane-change trajectory planning method based on reachability analysis of claim 8, wherein S3 comprises:
the vehicle is rectangular with length L and width W, and the occupancy rate O 1 The coordinates of each point of the occupied area are expressed by a polygon q:
wherein q is 1 、q 2 、q 3 、q 4 、q 5 、q 6 For vertices of polygon q, c x (t k )，r(t k )，c y (t k )，b x (t k )，c y (t k+1 )，r(t k+1 )，c x (t)、c y (t) and r (t) represent the abscissa and the ordinate of the circle center of the occupation set and the radius, t k 、t k+1 Representing predicted time steps, b x (t k )，b y (t k ) The lateral boundary coordinates fitted by the two-step circular occupancy set are represented by p for the vehicle and all the areas of the vehicles nearby, p 1 、p 2 、p 3 、p 4 、p 5 、p 6 Representing the vertex of p.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109035862A (en) * | 2018-08-06 | 2018-12-18 | 清华大学 | A kind of more vehicles collaboration lane-change control method based on truck traffic |
| CN109501799A (en) * | 2018-10-29 | 2019-03-22 | 江苏大学 | A kind of dynamic path planning method under the conditions of car networking |
| CN112660124A (en) * | 2020-11-30 | 2021-04-16 | 吉林大学 | Collaborative adaptive cruise control method for lane change scene |
| CN115938106A (en) * | 2022-09-02 | 2023-04-07 | 吉林大学 | Automatic driving decision online verification method based on traffic participant accessibility analysis |
-
2023
- 2023-10-30 CN CN202311414963.1A patent/CN117445923A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109035862A (en) * | 2018-08-06 | 2018-12-18 | 清华大学 | A kind of more vehicles collaboration lane-change control method based on truck traffic |
| CN109501799A (en) * | 2018-10-29 | 2019-03-22 | 江苏大学 | A kind of dynamic path planning method under the conditions of car networking |
| CN112660124A (en) * | 2020-11-30 | 2021-04-16 | 吉林大学 | Collaborative adaptive cruise control method for lane change scene |
| CN115938106A (en) * | 2022-09-02 | 2023-04-07 | 吉林大学 | Automatic driving decision online verification method based on traffic participant accessibility analysis |
Non-Patent Citations (5)
| Title |
|---|
| 刘志强;张腾;: "基于车联网信息的自动换道方法研究", 重庆理工大学学报(自然科学), no. 04, 15 April 2020 (2020-04-15) * |
| 向勇;罗禹贡;曹坤;***;: "基于车-车通信的自动换道控制", 公路交通科技, no. 03, 15 March 2016 (2016-03-15), pages 121 - 126 * |
| 李玮;王晶;段建民;: "基于多项式的智能车辆换道轨迹规划", 计算机工程与应用, 31 December 2012 (2012-12-31) * |
| 杨刚;张东好;***;罗禹贡;: "基于车车通信的车辆并行协同自动换道控制", 公路交通科技, no. 01, 15 January 2017 (2017-01-15) * |
| 聂光明: "具有换道辅助功能的协同自适应巡航控制理论与方法研究", 中国博士学位论文全文数据库 (工程科技Ⅱ辑), no. 01, 15 January 2023 (2023-01-15), pages 63 - 67 * |
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