CN114030469B - Multi-vehicle collaborative trajectory planning and path tracking method - Google Patents
Multi-vehicle collaborative trajectory planning and path tracking method Download PDFInfo
- Publication number
- CN114030469B CN114030469B CN202110675043.XA CN202110675043A CN114030469B CN 114030469 B CN114030469 B CN 114030469B CN 202110675043 A CN202110675043 A CN 202110675043A CN 114030469 B CN114030469 B CN 114030469B
- Authority
- CN
- China
- Prior art keywords
- vehicle
- lane
- distance
- representing
- acceleration
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 34
- 230000001133 acceleration Effects 0.000 claims description 53
- 230000008859 change Effects 0.000 claims description 29
- 230000006870 function Effects 0.000 claims description 15
- 230000008901 benefit Effects 0.000 claims description 14
- 230000008569 process Effects 0.000 claims description 9
- 230000000694 effects Effects 0.000 claims description 5
- 238000001514 detection method Methods 0.000 claims description 3
- 230000003044 adaptive effect Effects 0.000 claims description 2
- 230000004888 barrier function Effects 0.000 claims description 2
- 238000006073 displacement reaction Methods 0.000 claims description 2
- 230000007935 neutral effect Effects 0.000 claims description 2
- 238000000926 separation method Methods 0.000 claims description 2
- 230000006855 networking Effects 0.000 abstract description 4
- 238000004088 simulation Methods 0.000 description 5
- 230000007704 transition Effects 0.000 description 5
- 238000004891 communication Methods 0.000 description 3
- 238000013461 design Methods 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000000007 visual effect Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000036461 convulsion Effects 0.000 description 1
- 238000005265 energy consumption Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000008450 motivation Effects 0.000 description 1
- 230000010355 oscillation Effects 0.000 description 1
Images
Classifications
-
- 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
- B60W30/10—Path keeping
- B60W30/12—Lane keeping
-
- 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
- B60W2552/00—Input parameters relating to infrastructure
- B60W2552/50—Barriers
Landscapes
- Engineering & Computer Science (AREA)
- Automation & Control Theory (AREA)
- Transportation (AREA)
- Mechanical Engineering (AREA)
- Traffic Control Systems (AREA)
Abstract
The invention discloses a multi-vehicle cooperative track planning and path tracking method, relates to the technical field of intelligent traffic, and solves the technical problem of low lane changing efficiency of a single vehicle in the prior art. The method can realize intelligent networking automobile collaborative real-time trajectory re-planning and global path tracking, and has strong practicability and wide commercial application prospect.
Description
Technical Field
The disclosure relates to the technical field of intelligent transportation, in particular to a multi-vehicle cooperative track planning and path tracking method.
Background
The intelligent and networking development is the development trend of the current automobile industry and is also an industry hotspot traced by automobile manufacturers. Under the environment of intelligent network connection, different vehicles are communicated through Communication means such as 5G and Dedicated Short Range Communication (DSRC) and the like, so that the vehicles equipped with the Communication equipment can communicate with surrounding vehicles even outside the sight line, and acquire information such as positions, speeds, accelerations and driving intentions of the vehicles, and therefore not only can the driving safety and comfort be improved, but also the energy consumption can be reduced, and the problem of traffic jam is solved.
Most of the current multi-vehicle lane changing algorithms are non-cooperative lane changing, other vehicles around the main vehicle do not participate in the speed adjusting process when the lane changing, and the cooperative lane changing has better effect in the environment of intelligent network connection. At this time, the problem of planning the track of a single vehicle under the mixed traffic flow is converted into the problem of planning the coordinated track of multiple vehicles, and how to realize the coordinated track planning of multiple vehicles is the problem to be solved by the application.
Disclosure of Invention
The invention provides a multi-vehicle cooperative track planning and path tracking method, which aims to realize cooperative lane change through multi-vehicle cooperation in an intelligent networking environment.
The technical purpose of the present disclosure is achieved by the following technical solutions:
a multi-vehicle collaborative trajectory planning and path tracking method comprises the following steps:
the main car detects current lane and adjacent lane, judges whether current lane and adjacent lane have the barrier, includes:
s1: when no obstacle exists in the current lane, the steering angle delta of the front wheel is calculated according to the speed of the main vehicle and the transverse deviation of the main vehicle and the target path f According to said front wheel steering angle delta f Continuing to drive in the current lane and continuing to track the initial target track;
s2: when the obstacle exists in the current lane and no obstacle exists in the adjacent lane, the current path is replanned, and the transverse direction of the replanned track and the initial target track is obtainedDistance e des According to said transverse distance e des Performing lane changing;
s3: when the obstacle exists in the current lane and the obstacle also exists in the adjacent lane, the lane changing process is divided into an adjusting stage and a lane changing stage, and the adjusting stage and the lane changing stage comprise the following steps:
s31: in the adjusting stage, different overall longitudinal positions of the vehicle after adjustment are obtained through different accelerated speeds, and the optimal acceleration/deceleration is selected for adjustment;
s32: in the lane change stage, lane change is performed according to the step S1 or the step S2.
The beneficial effect of this disclosure lies in: according to the multi-vehicle cooperative track planning and path tracking method, cooperative lane change between vehicles is divided into two processes of vehicle speed adjustment and lane change convergence, factors such as safety, comfort and traffic efficiency of vehicle running are comprehensively considered in an adjustment stage, and optimal acceleration/deceleration of each vehicle is obtained and longitudinal vehicle speed adjustment is carried out. The method can realize intelligent networking automobile collaborative real-time trajectory re-planning and global path tracking, 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 diagram of a pure tracking algorithm designed in the present application;
FIG. 3 is a schematic view of traffic efficiency gains from different accelerations for the present design;
FIG. 4 is a graphical illustration of comfort gains from different accelerations for the present design;
FIG. 5 is a diagram of a simulation path of two vehicles traveling at the same position and at the same speed according to the present application;
FIG. 6 is a graph of simulated speed of two vehicles traveling at the same position and at the same speed according to the present application.
Detailed Description
The technical scheme of the disclosure will be described in detail with reference to the accompanying drawings.
Fig. 1 is a flowchart of a method according to the present application, and as shown in fig. 1, when performing trajectory planning and path tracking, a host vehicle first detects a current lane and an adjacent lane, and determines whether there is an obstacle in the current lane and the adjacent lane, including:
s1: when no obstacle exists in the current lane, the steering angle delta of the front wheel is calculated according to the speed of the main vehicle and the transverse deviation of the main vehicle and the target path f According to said front wheel steering angle delta f Continuing to drive in the current lane and continuing to track the initial target track, wherein the method comprises the following steps:
wherein, L represents the foresight distance, L represents the wheelbase of the main vehicle, theta represents the included angle between the foresight direction and the main vehicle course angle, and the selection of L comprises:
wherein, V x Indicating the longitudinal speed of the tractor, e y Indicating a lateral deviation from the target path, L adapt Indicating an adaptive look-ahead distance, L, taking into account the effects of velocity and lateral errors min Represents a preset minimum forward looking distance, k v Denotes the velocity coefficient, k e Denotes the coefficient of lateral deviation, and k v Is a positive number, k e Is a negative number.
Specifically, when global path tracking is performed, a pure tracking algorithm with high real-time performance is adopted, as shown in fig. 2, a reference path is given, the reference path is global coordinate points, a proper forward-looking distance L is selected, a tracking point is selected on a target path according to L, a transition circular arc at one end is planned through the pose state of the main vehicle and the geometric relation with the target path to reach the tracking point, and the radius of the transition circular arc is used for deducing a required front wheel steering angle delta f . The geometrical relationships include:
(16) (ii) a In the formula (11), theta is an included angle between the forward-looking direction and the vehicle heading angle, namely heading deviation; r represents a transition arcRadius (i.e., turning radius).
From equation (11), the curvature λ of the transition arc to the tracking point can be calculated as:
when the wheel base l of the vehicle is far smaller than the turning radius R and the vehicle mass center side deflection angle beta is small, according to the Ackerman steering geometric relation, the following can be approximately obtained:
the front wheel steering angle δ can be calculated from the equation (18) f ：
Different front sight distances can obtain transition arcs with different curvatures, and the steering angle of the front wheel is greatly influenced. The too small front visual distance can cause the vehicle to vibrate excessively along the tracking curve, and the too large front visual distance can greatly reduce the tracking performance, so that in order to improve the tracking effect, the influence of the vehicle speed and the transverse deviation on the pure tracking effect is considered in the method, and the existing pure tracking algorithm is improved. Setting a minimum forward looking distance L at lower speeds min The speed coefficient k is used to account for the fact that a vehicle with greater speed is more likely to take greater forward range to track a more distant path, whereas a vehicle with greater lateral error is more likely to track a closer waypoint to reduce the lateral error first v And coefficient of lateral deviation k e To adjust the total forward looking distance, and set k v Is a positive number, k e Is a negative number. The forward looking distance is selected as shown in the formula (2), the finally selected forward looking distance used for calculating the steering angle of the front wheel is the larger value of the self-adaptive forward looking distance and the minimum forward looking distance, and serious oscillation of a tracking track caused by the fact that the forward looking distance of a vehicle is too small when the vehicle speed is too small and the transverse deviation is too large is prevented.
Step S2: when the obstacle exists in the current lane and no vehicle passes through the adjacent lane, the current path is processedRe-planning to obtain the transverse distance e between re-planned track and initial target track des According to said transverse distance e des The lane change is carried out, and the lane change is carried out,
the method comprises the following steps: e.g. of the type des (σ)＝c 0 +c 1 σ+c 2 σ 2 +c 3 σ 3 ； (3)；
Where σ denotes the longitudinal displacement from the starting point of the lane change, c 0 ,c 1 ,c 2 ,c 3 Representing the control coefficient.
Considering that the planned track of the main vehicle is tangent to the initial target track at the initial position of lane change, namely the transverse deviation and the initial course angle are 0, the target of the lane change of the main vehicle is the central line of the adjacent lane of the obstacle-free vehicle, and the transverse position of the main vehicle after lane change is the width W from the current lane to the target lane Lane The longitudinal distance of the whole lane change is X avo The course angle after lane change is the same as the initial course angle and is 0, the formulas (4) to (7) can be obtained, and the control coefficient c can be obtained by combining the formulas (4) to (7) 0 ,c 1 ,c 2 ,c 3 The method comprises the following steps:
e des (0)＝c 0 ＝0； (4)；
e des (X avo )＝c 0 +c 1 X avo +c 2 X avo 2 +c 3 X avo 3 ＝W Lane ； (6)；
the four linearly independent equations (4) to (7) can solve four control coefficients, so that the transverse position e of any point on the re-planned track can be determined des Only the point on the original straight path is needed to be taken to carry out the deviation of the transverse distance value, and the point after the deviation is taken as the pre-aiming pointThe vehicle travels to gradually increase the offset value to W Lane And keeping the deviation value, so that the tracking of the lane changing track can be completed and the vehicle can run along the lane after lane changing.
When planning a transverse lane change track, not only the driving safety but also the personalized subjective risk feeling of the driver and passengers to the obstacle need to be considered, which is an important factor influencing the comfort of the driver in the unmanned vehicle. The main lane change trajectory is determined by two parameters, W Lane And X avo ，W Lane Indicating the width of the current lane to the target lane, in relation to the selection of the target safe lane. X avo Longitudinal distance representing the entire lane change, i.e. obstacle avoidance distance of the host vehicle, from the host vehicle longitudinal speed V x And the lane change time T expected by the driver Lc Determination of X avo ＝V x *T Lc . The method selects proper obstacle avoidance time T Lc If an obstacle avoidance track meeting the individual subjective feeling of the driver is planned, the obstacle avoidance track has
Wherein, T 0 For neutral driver obstacle avoidance time at current vehicle speed, P e [ -1,1 [ ]]Is the driver style factor. The larger the P is, the stronger the aggressiveness of a driver is, the shorter the obstacle avoidance distance of the main vehicle is, and the higher the obstacle avoidance efficiency is; the smaller the P is, the more conservative the driver is, and the longer the obstacle avoidance distance is.
S3: when the obstacle exists in the current lane and the moving vehicle also exists in the adjacent lane, the lane changing process is divided into an adjusting stage and a lane changing stage, and the adjusting stage and the lane changing stage comprise the following steps:
s31: in the adjusting stage, different global longitudinal positions of the vehicle after adjustment are obtained through different accelerations, and the optimal acceleration/deceleration is selected for adjustment, wherein the adjusting stage comprises the following steps:
wherein A represents the host vehicle in the current lane, and B represents the vehicle in the adjacent lane;
and
respectively representing the relative adjustment distances of the host vehicle A and the vehicle B;
and
respectively representing the global longitudinal positions of the host vehicle A and the vehicle B after the adjustment phase is finished;
indicating the initial velocity of the host vehicle a,
represents the initial speed of the vehicle B; t is ad Indicating the adjustment time of each acceleration/deceleration of the host vehicle A and the vehicle B; a is A And a B The accelerations of the host vehicle a and the vehicle B are respectively indicated.
Specifically, in the adjustment stage, two vehicles perform cooperative adjustment of longitudinal speed, for example, a vehicle in the right lane is defined as a vehicle a (i.e. the host vehicle in the current lane), a vehicle in the left lane is defined as a vehicle B (i.e. the vehicle in the adjacent lane), when the vehicle a runs on a straight lane, an obstacle in front is detected by a laser radar, and the effective distance between the vehicle a and the obstacle is detected by the laser radar as D det And after the obstacle enters the detection area of the laser radar of the vehicle A, the vehicle A and the vehicle B communicate to change lanes cooperatively.
Firstly, longitudinal vehicle speed adjustment is carried out, the longitudinal adjustment stage is divided into two parts, two vehicles on left and right lanes are subjected to acceleration/deceleration for distance separation once, then acceleration/deceleration adjustment is carried out for one time to keep the original vehicle speed, and the two times of adjustment are assumed to be T ad A, B initial longitudinal positions of two vehicles in a global coordinate system are
Initial velocities are respectively
In order to ensure that the adjustment process has better comfort, the constant acceleration is adopted in the adjustment process, so that the jerk during adjustment is 0.
Assume that the first adjusted acceleration is a A 、a B Since the adjustment time is the same, in order to make the adjusted vehicle speed return to the original vehicle speed, the acceleration of the second adjustment is-a A 、-a B Assuming that the maximum acceleration of the vehicle is a max A, B both cars are in the interval [ -a ] max ,a max ]An acceleration is selected to form an acceleration combination for adjusting the longitudinal speed, and different adjustment results, namely, the formula (8) to the formula (9), can be obtained.
Further, in step S31, a multi-objective gain function may be established according to traffic efficiency, comfort and driver style factor, and then an optimal acceleration/deceleration may be selected according to the global longitudinal position and the multi-objective gain function, including:
C＝w G C G +w a C a +w P C P ； (14)；
wherein G represents the distance between the host vehicle A and the vehicle B after adjustment, and L A Indicates the length of the main car A, L B Indicates the length of the vehicle B, D s Indicating the safe distance that the vehicle keeps with the rear vehicle after adjusting; d det Representing the effective detection distance of the sensor, C representing the total yield, C G Indicates the value of the income of the traffic efficiency term C brought by the adjusted distance G between the two vehicles a Representing a comfort benefit function, C P Representing a driver style revenue function; w is a G 、w a 、w P And weight coefficients respectively representing the traffic efficiency benefit item, the comfort benefit item and the driver style benefit item.
Specifically, the acceleration combination is evaluated by comprehensively considering several aspects such as safety, comfort, traffic efficiency and driver motivation characteristics, so as to measure the excellent degree of the acceleration combination. Firstly, to ensure the safety during the lane change and the following driving, the vehicle needs to keep a certain distance with the following vehicle after adjustmentFixed safety distance D s The higher the speed of the rear vehicle, the greater the safety distance, as shown in the following equation:
wherein, V r To select the speed of the following vehicle after the adjustment,
the maximum acceleration of the vehicle during emergency braking. Equation (19) ensures that the rear vehicle can be braked suddenly to prevent accidents in case of sudden failure of the front vehicle. If the distance between the two vehicles after adjustment is G, equation (12) can be obtained.
According to the formula (12), in order to ensure that the vehicle A has enough distance to change lanes, the maximum adjustment distance of the vehicle A needs to be calculated, and the obstacle avoidance distance of the vehicle A can be obtained from the previous step and is X avo The reserved adjustment distance can be calculated as equation (13).
However, in addition to the need for ensuring safety, in order to improve traffic efficiency, G is not as large as possible, and an excessively large space between vehicles may result in inefficient road utilization, and thus it is only necessary to adjust the distance between two vehicles to be greater than a safe distance without being excessively large. The traffic efficiency that distance G that this application had designed a normal distribution can promote is estimated to the income function.
Wherein, C G The income value of traffic efficiency term, sigma, brought by adjusting the distance between two vehicles 1 The rate at which the yield curve drops may be adjusted.
When in use
σ 1 The yield when 6 is shown in fig. 3. When the distance is opened to be about 9m, the utilization rate of the road can be greatly improved, thereby obtaining higher income,the larger the distance is, the lower the income is, when the distance is 30m, other vehicles can also be inserted between two vehicles at this moment, the road utilization rate is low, and the income is almost 0.
Secondly, considering the riding comfort of the vehicle during adjustment, the two vehicles are respectively in the region [ -a ] max ,a max ]An acceleration is selected to adjust the longitudinal speed, the smaller the selected acceleration is, the more comfortable driving experience can be obtained, and the comfort benefit function item C is obtained at the moment a Comprises the following steps:
wherein, a RMS Is the root mean square value of the two-time acceleration of the two vehicles,
a, B is the acceleration of two vehicles when accelerating and decelerating twice. Sigma a As shown in fig. 4, the benefit image when the overall acceleration root mean square value is closer to 0, the higher the comfort benefit is obtained.
Then, in the stage of longitudinal speed adjustment, the personalized aggressive driving style of the two drivers is innovatively introduced, the setting is the same as that of the driving style planned in the previous section of track, and the style coefficients of the two drivers in the A, B cars are respectively set as
The closer the style coefficient of the driver is to 1, the more aggressive the driver is, and the closer the style coefficient is to-1, the more conservative the driver is, the aggressive driver usually selects overtaking adjustment as much as possible during adjustment, and the conservative driver is more likely to perform following adjustment. Based on the method, the application sets a driver style revenue function C P To evaluate how well the two-vehicle speed adjustment conforms to the driver's preferences.
The profit is the sum of products of the driver style coefficients of the two vehicles and the relative adjustment distance of the corresponding vehicle, when the driving style of the driver is aggressive, the driving style coefficient is positive, the higher the relative adjustment distance is, the higher the profit is, the driver is more willing to perform acceleration adjustment on the original speed and then return to the original speed, thereby achieving the expectation of overtaking. Conversely, if the relative adjustment distance is positive due to vehicle acceleration when the driver style coefficient is negative, the obtained benefit is negative, i.e., the acceleration and deceleration will result in a decrease in the benefit and will not meet the driver's expectation. The income item is used as the sum of the income of two vehicles, the maximum income can be obtained when the acceleration and deceleration are consistent with the driving styles of two drivers, if the driving styles of the drivers are not met, the income is negative, the final total income can be greatly reduced, and if the driving preferences of one driver are only met, the income is more general.
Finally, on the premise of ensuring safety, a total revenue function C comprehensively considering traffic efficiency, comfort and driving style of a driver is shown as a formula (14).
By the above method, in [ -a ] max ,a max ]And a proper acceleration combination of the A vehicle and the B vehicle is found, so that the total profit C is maximized. The longitudinal speed is adjusted through the acceleration combination, so that a proper distance can be pulled, and the driving safety of two vehicles after the lane changing process is ensured.
In the lane change phase in step S32, lane change is continued according to step S1 or step S2.
As a specific embodiment, in order to verify the effectiveness of the method described in the present application, the present application compares the influence caused by the personalized driving style change of the drivers of the two cars when the vehicle a detects that the obstacle is to be changed when the vehicle b A, B has the same position and the same speed. A. And the initial speed of the two vehicles B is 40km/h, and the vehicle A detects an obstacle in the second, starts to perform cooperative adjustment with the vehicle B and then merges into a lane.
When T is ad ＝2s，a max ＝2.5m/s 2 ，
σ 1 ＝10，σ a ＝0.8，D det ＝100m，w G ＝0.5,w a ＝1,w P The different driver style coefficients were studied 0.1.
If P A ＝0.5，P B At-0.5, the results of the algorithmic simulation are shown in fig. 5 (a) and fig. 6 (a), where the same graph in fig. 5 (a) represents the positions of two vehicles at the same time, and each 2s pass represents the position of the vehicle at that time by one shape. It can be seen that, in the case that the driver of the vehicle a is aggressive and the driver of the vehicle B is conservative, the vehicle a accelerates and the vehicle B decelerates to pull away the safe distance, so that the value of the obtained driver benefit term is the largest, and from (a) in fig. 6, A, B the two vehicles just use opposite acceleration and deceleration to perform acceleration and deceleration, which is caused by the fact that the driving styles of the two vehicles are opposite and the initial position is the same as the initial speed.
If P A ＝-0.5，P B At this time, the algorithm simulation results are shown in fig. 5 (B) and fig. 6 (B), the acceleration and deceleration conditions of the two vehicles are just opposite to those of the last group of simulation, the vehicle a is conservative and the vehicle B is aggressive, and the simulation conditions are the vehicle a is decelerated and the vehicle B is accelerated, which fully explains the influence of the driving style of the driver.
If P A ＝0.6，P B If both drivers are in aggressive driving styles, the algorithm will meet the requirement of a vehicle with a more aggressive driving style under the condition that the reserved obstacle avoidance distance is sufficient, that is, the driver income item is guaranteed to be a positive value. As can be seen from fig. 6 (c), the acceleration and deceleration of the vehicle a and the vehicle B are not opposite numbers, because the driver benefit term of the vehicle B is negative, in order to ensure that the total driver benefit is relatively highIn the high-speed vehicle B, a small acceleration and deceleration combination is adopted, and in order to pull the safety distance, the vehicle a is decelerated by the same acceleration and deceleration as that in (a) in fig. 6, so that a high driver profit value is obtained.
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 (3)
1. A multi-vehicle collaborative trajectory planning and path tracking method is characterized by comprising the following steps:
the main car detects current lane and adjacent lane, judges whether current lane and adjacent lane have the barrier, includes:
s1: when no obstacle exists in the current lane, the steering angle delta of the front wheel is calculated according to the speed of the main vehicle and the transverse deviation of the main vehicle and the target path f According to said front wheel steering angle delta f Continuing to drive in the current lane and continuing to track the initial target track, wherein the method comprises the following steps:
wherein, L represents the foresight distance, L represents the wheelbase of the main vehicle, theta represents the included angle between the foresight direction and the main vehicle course angle, and the selection of L comprises:
wherein, V x Indicating the longitudinal speed of the tractor, e y Indicating a lateral deviation from the target path, L adapt Indicating an adaptive look-ahead distance, L, taking into account the effects of velocity and lateral errors min Represents a preset minimum forward looking distance, k v Denotes the velocity coefficient, k e Denotes the coefficient of lateral deviation, and k v Is a positive number, k e Is a negative number;
s2: when the obstacle exists in the current lane and no obstacle exists in the adjacent lane, the current path is replanned, and the transverse distance e between the replanned track and the initial target track is obtained des According to said transverse distance e des Performing lane change, comprising: e.g. of the type des (σ)＝c 0 +c 1 σ+c 2 σ 2 +c 3 σ 3 ；(3)；
Where σ denotes the longitudinal displacement from the starting point of the lane change, c 0 ,c 1 ,c 2 ,c 3 Representing the control coefficient, and obtaining the control coefficient c by combining the formulas (4) to (7) 0 ,c 1 ,c 2 ,c 3 The method comprises the following steps:
e des (0)＝c 0 ＝0；(4)；
e des (X avo )＝c 0 +c 1 X avo +c 2 X avo 2 +c 3 X avo 3 ＝W Lane ；(6)；
wherein, W Lane Indicates the width from the current lane to the target lane, X avo Denotes the longitudinal distance, X, of the entire lane change avo ＝V x *T Lc ，T Lc Indicating the lane change time desired by the driver,
T 0 representing the obstacle avoidance time of a neutral driver at the current vehicle speed; p E [ -1,1 [ ]]Indicating driver style factor, the closer P is to-1, indicating drivingThe more conservative the driver, the closer P is to 1, the more aggressive the driver is;
s3: when the obstacle exists in the current lane and the obstacle also exists in the adjacent lane, the lane changing process is divided into an adjusting stage and a lane changing stage, and the adjusting stage and the lane changing stage comprise the following steps:
s31: in the adjusting stage, the longitudinal adjusting stage is divided into two parts, two vehicles on the left lane and the right lane firstly carry out acceleration/deceleration for distance separation, then carry out acceleration/deceleration adjustment for one time to keep the original vehicle speed, adopt constant acceleration in the adjusting process, have the same adjusting time for two times, and are both in the interval [ -a ] max ,a max ]Selecting an acceleration to form an acceleration combination for adjusting the longitudinal speed, obtaining different overall longitudinal positions of the vehicle after adjustment through different accelerations, and selecting the optimal acceleration/deceleration for adjustment, wherein the method comprises the following steps:
wherein A represents the host vehicle in the current lane, and B represents the vehicle in the adjacent lane;
and
phases of the host vehicle A and the vehicle B are respectively shownAdjusting the distance;
and
respectively representing the global longitudinal positions of the host vehicle A and the vehicle B after the adjustment phase is finished; v 0 A Indicating the initial velocity of the host vehicle a,
represents the initial speed of the vehicle B; t is ad Indicating the adjustment time of each acceleration/deceleration of the host vehicle A and the vehicle B; a is A And a B Representing the accelerations of the host vehicle a and the vehicle B, respectively;
representing the initial longitudinal position of the host vehicle A in the global coordinate system;
represents the initial longitudinal position of the vehicle B in the global coordinate system;
s32: in the lane change stage, lane change is performed according to the step S1 or the step S2.
2. The method as claimed in claim 1, wherein the step S31 of establishing a multi-objective gain function according to traffic efficiency, comfort and driver style factor, and selecting the optimal acceleration/deceleration according to the global longitudinal position and the multi-objective gain function comprises:
C＝w G C G +w a C a +w P C P ；(14)；
wherein G represents the distance between the host vehicle A and the vehicle B after adjustment, and L A Indicates the length of the main car A, L B Indicates the length of the vehicle B, D s Indicating the safe distance that the vehicle keeps with the rear vehicle after adjusting; d det Representing the effective detection distance of the sensor, C representing the total yield, C G Indicates the value of the income of the traffic efficiency term C brought by the adjusted distance G between the two vehicles a Representing a comfort benefit function, C P Representing a driver style revenue function; w is a G 、w a 、w P Respectively representing the weight coefficients of a traffic efficiency income item, a comfort income item and a driver style income item;
designing a normally distributed revenue function to evaluate the traffic efficiency that the distance G pulled apart can improve, expressed as:
wherein, C G Representing the value of the profit of the traffic efficiency term, sigma, brought by the adjusted distance between two vehicles 1 Indicating a rate at which the adjustable yield curve is declining;
comfort benefit function term C a Expressed as:
wherein, a RMS A root mean square value representing the two accelerations of the host vehicle a and the vehicle B,
respectively representAcceleration when the host vehicle a and the vehicle B perform acceleration and deceleration twice.
3. The method of claim 2, wherein the driver style revenue function includes
Wherein, P A Representing the driver style factor, P, of the host vehicle A B Representing the driver style factor, P, of the vehicle B A ,P B ∈[-1,1]。
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110675043.XA CN114030469B (en) | 2021-06-18 | 2021-06-18 | Multi-vehicle collaborative trajectory planning and path tracking method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110675043.XA CN114030469B (en) | 2021-06-18 | 2021-06-18 | Multi-vehicle collaborative trajectory planning and path tracking method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114030469A CN114030469A (en) | 2022-02-11 |
| CN114030469B true CN114030469B (en) | 2022-08-02 |
Family
ID=80134249
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110675043.XA Active CN114030469B (en) | 2021-06-18 | 2021-06-18 | Multi-vehicle collaborative trajectory planning and path tracking method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114030469B (en) |
Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE102006043149A1 (en) * | 2006-09-14 | 2008-03-27 | Bayerische Motoren Werke Ag | Integrated transverse and longitudinal guidance assistant for motor vehicle, has trajectory calculating unit to calculate lane change trajectory with given distance of vehicle traveling in front, during distance regulated travel of vehicle |
| CN103754224A (en) * | 2014-01-24 | 2014-04-30 | 清华大学 | Vehicle multi-target coordinating lane changing assisting adaptive cruise control method |
| CN104960524A (en) * | 2015-07-16 | 2015-10-07 | 北京航空航天大学 | Multi-vehicle coordinating lane changing control system and method based on vehicle-vehicle communication |
| CN107315411A (en) * | 2017-07-04 | 2017-11-03 | 合肥工业大学 | A kind of lane-change method for planning track based on automatic driving vehicle under collaborative truck |
| 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 |
| CN109144076A (en) * | 2018-10-31 | 2019-01-04 | 吉林大学 | A kind of more vehicle transverse and longitudinals coupling cooperative control system and control method |
| CN109712421A (en) * | 2019-02-22 | 2019-05-03 | 百度在线网络技术（北京）有限公司 | The speed planning method, apparatus and storage medium of automatic driving vehicle |
| WO2019132082A1 (en) * | 2017-12-29 | 2019-07-04 | 엘지전자(주) | V2x communication device and method for transmitting and receiving v2x message thereof |
| CN110329263A (en) * | 2019-07-05 | 2019-10-15 | 西南交通大学 | The adaptive lane-change method for planning track of automatic driving vehicle |
| CN111137288A (en) * | 2020-01-19 | 2020-05-12 | 江苏大学 | Multi-vehicle cooperative lane changing method under internet connection condition |
| CN111445697A (en) * | 2020-03-22 | 2020-07-24 | 华南理工大学 | Expressway cooperative vehicle lane changing control method under intelligent network connection condition |
| CN112109704A (en) * | 2020-09-22 | 2020-12-22 | 同济大学 | Vehicle collision avoidance dynamic safety path planning method based on accurate track prediction |
-
2021
- 2021-06-18 CN CN202110675043.XA patent/CN114030469B/en active Active
Patent Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE102006043149A1 (en) * | 2006-09-14 | 2008-03-27 | Bayerische Motoren Werke Ag | Integrated transverse and longitudinal guidance assistant for motor vehicle, has trajectory calculating unit to calculate lane change trajectory with given distance of vehicle traveling in front, during distance regulated travel of vehicle |
| CN103754224A (en) * | 2014-01-24 | 2014-04-30 | 清华大学 | Vehicle multi-target coordinating lane changing assisting adaptive cruise control method |
| CN104960524A (en) * | 2015-07-16 | 2015-10-07 | 北京航空航天大学 | Multi-vehicle coordinating lane changing control system and method based on vehicle-vehicle communication |
| CN107315411A (en) * | 2017-07-04 | 2017-11-03 | 合肥工业大学 | A kind of lane-change method for planning track based on automatic driving vehicle under collaborative truck |
| WO2019132082A1 (en) * | 2017-12-29 | 2019-07-04 | 엘지전자(주) | V2x communication device and method for transmitting and receiving v2x message thereof |
| 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 |
| CN109144076A (en) * | 2018-10-31 | 2019-01-04 | 吉林大学 | A kind of more vehicle transverse and longitudinals coupling cooperative control system and control method |
| CN109712421A (en) * | 2019-02-22 | 2019-05-03 | 百度在线网络技术（北京）有限公司 | The speed planning method, apparatus and storage medium of automatic driving vehicle |
| CN110329263A (en) * | 2019-07-05 | 2019-10-15 | 西南交通大学 | The adaptive lane-change method for planning track of automatic driving vehicle |
| CN111137288A (en) * | 2020-01-19 | 2020-05-12 | 江苏大学 | Multi-vehicle cooperative lane changing method under internet connection condition |
| CN111445697A (en) * | 2020-03-22 | 2020-07-24 | 华南理工大学 | Expressway cooperative vehicle lane changing control method under intelligent network connection condition |
| CN112109704A (en) * | 2020-09-22 | 2020-12-22 | 同济大学 | Vehicle collision avoidance dynamic safety path planning method based on accurate track prediction |
Non-Patent Citations (2)
| Title |
|---|
| 面向无人驾驶的车辆协同换道轨迹规划;游峰等;《科学技术与工程》;20180528(第15期);155-161 * |
| 高速匝道入口多智能网联车协同合流控制;刘畅等;《东南大学学报(自然科学版)》;20200920(第05期);965-972 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114030469A (en) | 2022-02-11 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109035862B (en) | Multi-vehicle cooperative lane change control method based on vehicle-to-vehicle communication | |
| CN110286681B (en) | Dynamic automatic driving track-changing planning method for curvature-variable curve | |
| CN112677963B (en) | Intelligent networking four-wheel independent steering and independent driving electric automobile emergency obstacle avoidance system | |
| CN111338340B (en) | Model prediction-based local path planning method for unmanned vehicle | |
| CN109501799B (en) | Dynamic path planning method under condition of Internet of vehicles | |
| CN107315411B (en) | Lane changing track planning method for unmanned vehicle based on vehicle-vehicle cooperation | |
| CN108256233B (en) | Intelligent vehicle trajectory planning and tracking method and system based on driver style | |
| CN106114511B (en) | A kind of automobile cruise system core target identification method | |
| CN112258875B (en) | Intersection intelligent control method based on vehicle-road cooperation and automatic driving | |
| CN111137288B (en) | Multi-vehicle cooperative lane changing method under internet connection condition | |
| CN112965476B (en) | High-speed unmanned vehicle trajectory planning system and method based on multi-window model | |
| CN109649393A (en) | A kind of paths planning method and device of automatic Pilot changing Lane | |
| CN109324620A (en) | The dynamic trajectory planing method for carrying out avoidance based on lane line parallel offset and overtaking other vehicles | |
| US20200250991A1 (en) | Method for performing emergency braking in a motor vehicle and emergency braking system for performing the method | |
| CN112660124B (en) | Collaborative adaptive cruise control method for lane change scene | |
| CN111081069B (en) | Vehicle track control method for bottleneck area of expressway | |
| CN109131312A (en) | A kind of intelligent electric automobile ACC/ESC integrated control system and its method | |
| CN114987461A (en) | Intelligent passenger car dynamic lane change trajectory planning method under multi-car complex traffic environment | |
| WO2023024914A1 (en) | Vehicle avoidance method and apparatus, computer device, and storage medium | |
| CN109799821A (en) | A kind of automatic Pilot control method based on state machine | |
| CN115273450B (en) | Channel changing method for vehicles entering formation under network automatic driving environment | |
| CN114148330A (en) | Vehicle interchange lane method considering multi-vehicle game | |
| CN115320596A (en) | Intelligent internet motorcade plug-in cooperative lane change control method | |
| CN115489548A (en) | Intelligent automobile park road path planning method | |
| CN114889589A (en) | Intelligent automobile steering and braking cooperative collision avoidance control system and method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |