CN115830908A - Collaborative lane changing method and system of unmanned vehicle queue in mixed traffic flow - Google Patents
Collaborative lane changing method and system of unmanned vehicle queue in mixed traffic flow Download PDFInfo
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Abstract
The invention discloses a method and a system for cooperatively changing lanes of an unmanned vehicle queue in a mixed traffic flow, which can complete lane changing by stepping the lane changing process of a motorcade when a target gap cannot accommodate the whole motorcade and simultaneously change lanes, and consider the uncertainty of the driving behavior of a manned vehicle and the safety and the time efficiency in the action planning process of the unmanned vehicle. Compared with the existing vehicle lane changing method under the pure unmanned vehicle environment, the method is closer to reality and more efficient than the single-vehicle lane changing method. The method for the collaborative lane change of the unmanned motorcade in the mixed traffic flow can recommend the latest lane change starting position for the automatic motorcade under different traffic conditions, remind the motorcade to change lanes in time, prevent the motorcade from missing the opportunity of sliding to the latest and missing the off-ramp opportunity, and provide technical support for the lane change of the automatic motorcade in the mixed flow.
Description
Technical Field
The invention belongs to the field of road traffic control, and particularly relates to a method and a system for collaborative lane changing of an unmanned vehicle queue in mixed traffic flow.
Background
Advances in unmanned technology are beneficial to improving the safety of the efficiency of the overall traffic system. By virtue of the ability of vehicles to communicate, unmanned automobiles are expected to be able to travel in a fleet of vehicles, as fleets of vehicles can shorten the gaps between vehicles, improving traffic capacity. And the air resistance of the automobile can be reduced and the energy consumption can be reduced. However, due to technical limitations and lack of corresponding policy and regulations, unmanned vehicles and manned vehicles can run in a mixed mode for a long time in the future. Because the driving behavior of the manned vehicle has uncertainty, lane changes of the unmanned vehicle in the mixed flow become difficult. In addition, in order to ensure the safety of the unmanned vehicle in the interaction process of the manned vehicle, conservative actions are often adopted during lane changing. Many existing researches are directed to the environment of a pure unmanned vehicle, the influence of nearby manned vehicles on lane changing of the unmanned vehicle is not considered, and in practical application, the methods are difficult to implement. Some researches aim at the problem of single vehicle, and when the method is applied to lane changing of the motorcade, the efficiency is low, and the success rate of lane changing of the motorcade is difficult to ensure. Therefore, an effective solution to the lane change problem of the unmanned vehicle fleet in the mixed flow is still lacking.
Disclosure of Invention
The invention aims to provide a method and a system for cooperatively changing lanes of an unmanned vehicle queue in a mixed traffic flow, which aim at solving the problems of the existing method that the lane changing success rate of unmanned vehicles and the efficiency in the lane changing process are low.
A method for collaboratively changing lanes of an unmanned vehicle queue in a mixed traffic flow comprises the following steps:
s1, acquiring all vehicle information in a cooperative interaction radius of an unmanned vehicle queue, and establishing an unmanned vehicle mixed flow lane changing model according to the unmanned vehicle queue and the acquired all vehicle information;
s2, determining the maximum gap on a target lane in the longitudinal range of the lane where the unmanned vehicle is located according to the established unmanned vehicle mixed flow lane change model, and taking the maximum gap as the target gap for changing the lane for the first time; determining the unmanned vehicle in the longitudinal range of the target gap according to the position of the unmanned vehicle, confirming that the unmanned vehicle is the unmanned vehicle for changing the lane for the first time, and when the target gap meets the safe driving condition of the unmanned vehicle for changing the lane for the first time, enabling the unmanned vehicle for changing the lane for the first time to drive into the target gap;
and S3, constructing a longitudinal track planning model of the unmanned vehicle based on a rolling optimization idea, acquiring tracks of all vehicles in a current unmanned vehicle queue in a rolling time domain after the unmanned vehicle changes the track for the first time, determining target gaps of the remaining unmanned vehicles based on the longitudinal track planning model of the unmanned vehicle, and sequentially changing the tracks of the remaining unmanned vehicles to drive into the target gaps.
Preferably, the information of all vehicles in the communication radius range of the unmanned vehicle queue is acquired by adopting the interaction between the road side unit and the vehicle communication unit.
Preferably, the real-time distance between the unmanned vehicle and the vehicle in front of the target gap is acquired, and if the distance between the unmanned vehicle and the vehicle in front of the target gap at the current time is greater than or equal to the minimum lane-changing safety distance between the unmanned vehicle and the vehicle in front of the target gap at the current time, and the reaction acceleration of the vehicle after the unmanned vehicle is currently changed into the target gap is greater than or equal to the maximum deceleration of the vehicle after the target gap, the change requirement of the unmanned vehicle is met.
Preferably, after the unmanned vehicle changes lanes for the first time, the unmanned vehicle enters the target vehicle queue, and when the unmanned vehicle changing lanes for the first time is a single vehicle, the gap between the single vehicle and the front vehicle is adjusted to meet the target gap for the remaining unmanned vehicles to safely drive in, and one of the remaining unmanned vehicles is changed lanes to drive in the target gap.
Preferably, when the unmanned vehicle changing lanes for the first time is a fleet of multiple unmanned vehicles, adjusting a gap between two unmanned vehicles in the fleet of unmanned vehicles to meet a target gap for the remaining unmanned vehicles to safely enter, and changing lanes of one of the remaining unmanned vehicles to enter the target gap.
Preferably, when the unmanned vehicle changing lanes for the first time is a fleet of multiple unmanned vehicles, a gap between two adjacent unmanned vehicles in the fleet of the multiple unmanned vehicles changing lanes for the first time is enlarged, so that the remaining unmanned vehicles can safely change into the gap.
Preferably, the remaining unmanned vehicles select the gap closest to them as the target gap.
Preferably, the trajectory in a future rolling time domain is continuously planned according to the position of each vehicle in the unmanned vehicle mixed flow lane changing model at the current moment until the target gap is enlarged to reach the target gap length.
A system for collaboratively changing lanes of an unmanned vehicle queue in a mixed traffic flow comprises an information acquisition module and a processing module;
the information acquisition module is used for acquiring all vehicle information in the cooperative interaction radius of the unmanned vehicle queue and establishing an unmanned vehicle mixed flow lane change model according to the unmanned vehicle queue and the acquired all vehicle information;
the processing module is used for determining the maximum gap on a target lane in the longitudinal range of the lane where the unmanned vehicle is located according to the established unmanned vehicle mixed flow lane changing model, and taking the maximum gap as the target gap for changing the lane for the first time; determining the unmanned vehicle in the longitudinal range of the target gap according to the position of the unmanned vehicle, confirming that the unmanned vehicle is the unmanned vehicle for changing the lane for the first time, and when the target gap meets the safe driving condition of the unmanned vehicle for changing the lane for the first time, enabling the unmanned vehicle for changing the lane for the first time to drive into the target gap; the method comprises the steps of constructing a longitudinal track planning model of the unmanned vehicle based on the rolling optimization idea, obtaining tracks of all vehicles in a current unmanned vehicle queue in a rolling time domain after the unmanned vehicle changes the track for the first time, determining target gaps of the remaining unmanned vehicles based on the longitudinal track planning model of the unmanned vehicle, and sequentially changing the track of the remaining unmanned vehicles to drive the remaining unmanned vehicles into the target gaps.
Preferably, the real-time distance between the unmanned vehicle and the vehicle in front of the target gap is acquired, and if the distance between the unmanned vehicle and the vehicle in front of the target gap at the current time is greater than or equal to the minimum lane-changing safety distance between the unmanned vehicle and the vehicle in front of the target gap at the current time, and the reaction acceleration of the vehicle after the unmanned vehicle is currently changed into the target gap is greater than or equal to the maximum deceleration of the vehicle after the target gap, the change requirement of the unmanned vehicle is met.
Compared with the prior art, the invention has the following beneficial technical effects:
the cooperative lane changing method of the unmanned vehicle queue in the mixed traffic flow can complete lane changing by stepping the lane changing process of the motorcade when a target gap cannot accommodate the whole motorcade and simultaneously change lanes, and considers the uncertainty of the driving behavior of the unmanned vehicles and the safety and the time efficiency in the action planning process of the unmanned vehicles. Compared with the existing vehicle lane changing method under the pure unmanned vehicle environment, the method is closer to reality and more efficient than the single-vehicle lane changing method. The method for the collaborative lane change of the unmanned motorcade in the mixed traffic flow can recommend the latest lane change starting position for the automatic motorcade under different traffic conditions, remind the motorcade to change lanes in time, prevent the motorcade from missing the opportunity of sliding to the latest and missing the off-ramp opportunity, and provide technical support for the lane change of the automatic motorcade in the mixed flow.
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FIG. 1 is a flow chart of the cooperative lane change of the fleet in the mixed stream according to the embodiment of the present invention.
Fig. 2 is a schematic diagram of collaborative planning of a trajectory of an unmanned vehicle in an embodiment of the present invention.
Fig. 3 is a schematic view of a traffic scene in an embodiment of the invention.
FIG. 4 is a track diagram of fleet collaborative lane change in an embodiment of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
The invention discloses a cooperative lane changing method of an unmanned vehicle queue in a mixed traffic flow, which comprises the following steps:
s1, acquiring all vehicle information in a cooperative interaction radius of an unmanned vehicle queue, and establishing an unmanned vehicle mixed flow lane changing model according to the unmanned vehicle queue and the acquired all vehicle information;
specifically, the information of all vehicles in the communication radius range of the unmanned vehicle train is acquired by adopting interaction between the road side unit and the vehicle communication unit, each vehicle is provided with the vehicle communication unit, and the road side unit and the vehicle communication unit can communicate to acquire the vehicle information, including the position information, the vehicle speed and the acceleration information of the vehicle.
The method comprises the following steps of constructing an unmanned vehicle mixed flow lane changing model to judge whether a certain gap can allow safe switching of the unmanned vehicle;
the unmanned vehicle mixed flow lane changing model is used for calculating the distance between the unmanned vehicle and the target gap, judging whether the distance is larger than a set safety distance or not, and calculating the set safety distance by the Gipps model. It is necessary to determine the reaction time and the maximum acceleration and deceleration of the unmanned vehicle.
Gipps calculates the safe distance to the target gap lead vehicle:
in the formula:
-minimum lane change safety distance, unit, of unmanned vehicle (CAV) to target gap lead vehicle (pre) at time t: rice;
v CAV (t) -the speed of the unmanned vehicle at time t, in units: m/s;
v pre (t) -speed of the leading vehicle at time t, in units: m/s;
τ CAV -reaction time of the unmanned vehicle, in units: second;
x CAV (t) -position of the unmanned vehicle at time t, in units: rice;
x pre (t) -position of the vehicle ahead of the target gap at time t, in units: rice;
h-length of unmanned vehicle, unit: and (4) rice.
If it is not
The safe lane change standard of the unmanned vehicle is satisfied, s CAV,pre (t) is the actual distance of the unmanned vehicle (CAV) from the target gap leading vehicle (pre) at time t, and is calculated as follows:
s CAV,pre (t)＝x pre (t)-x CAV (t)-h (2)
the unmanned vehicle to be lane-changed is changed into the target gap, the reaction acceleration generated by the vehicle (fol) behind the target gap cannot exceed the acceleration/deceleration limit of the vehicle, the reaction acceleration of the vehicle (fol) behind the target gap is calculated by an IDM model, and the IDM model needs to determine the minimum following distance (3 meters), the acceleration coefficient (2), and the limit range of the acceleration and deceleration of the vehicle.
The safety judgment calculation formula of the vehicle after the vehicle is in the target clearance is as follows:
a fol,CAV (t) is the reaction acceleration of the unmanned vehicle after the target gap at time t after the unmanned vehicle is switched into the target gap, unit: 2 m/s;
s 0 -taking 3 meters for the minimum following distance;
S * -desired separation of unmanned vehicle and rear vehicle, unit: rice;
Δ t-discrete time interval, 0.1 second;
delta-the acceleration coefficient, typically taken as 2.
At the same time satisfy
The unmanned vehicle makes a lane change to enter the target gap.
S2, determining the maximum gap on a target lane in the longitudinal range of the lane where the unmanned vehicle is located according to the established unmanned vehicle mixed flow lane change model, and taking the maximum gap as the target gap for changing the lane for the first time; and determining the unmanned vehicle in the longitudinal range of the target gap according to the position of the unmanned vehicle, and confirming that the unmanned vehicle is the unmanned vehicle for changing the lane for the first time.
Assuming that the unmanned vehicle fleet has n vehicles in total, sequentially substituting the values of k and m in { k, m | k =1,.. Multidot.n, m =1,.. Multidot.n-k } into the following inequalities, finding the values of k and m that satisfy the following inequalities, and confirming that the unmanned vehicle or the unmanned vehicle set of the first lane change is { k, k +1,. Multidot.k + m-1}.
In the formula: m is the number of unmanned vehicles changing lanes for the first time;
k-the kth vehicle in the fleet.
t 0 -the time at which the lane change starts.
And S3, constructing a longitudinal track planning model of the unmanned vehicle based on the rolling optimization idea, acquiring tracks of all vehicles in the current unmanned vehicle queue in a rolling time domain after the unmanned vehicle firstly changes the track, determining target gaps of the rest unmanned vehicles based on the longitudinal track planning model of the unmanned vehicle, and sequentially changing the tracks of the rest unmanned vehicles to drive in the target gaps.
When the unmanned vehicles change lanes for the first time, entering a target vehicle queue, and when the unmanned vehicles change lanes for the first time are single vehicles, adjusting a gap between the single vehicle and a front vehicle thereof to enable the gap to meet a target gap for the safe driving of the remaining unmanned vehicles, and changing lanes of one of the remaining unmanned vehicles to drive into the target gap.
When the unmanned vehicle changing the lane for the first time is a fleet consisting of a plurality of unmanned vehicles, adjusting a gap between two unmanned vehicles in the fleet consisting of the unmanned vehicles to enable the gap to meet a target gap for the rest unmanned vehicles to safely drive in, and changing the lane of one unmanned vehicle in the rest unmanned vehicles to drive in the target gap.
As shown in fig. 1 and fig. 2, in the present application, an unmanned vehicle queue composed of six vehicles is taken as an example, and the unmanned vehicle queue is split into three small vehicle fleets, wherein one small vehicle fleet switches lanes for the first time, the remaining two vehicle fleets not switching lanes are divided into a vehicle fleet 1 and a vehicle fleet 2 according to the front and back positions, and the vehicle fleet that has switched lanes forms a vehicle fleet 3.
The collaborative mode in the collaborative trajectory planning stage is as follows: the gap between two adjacent unmanned vehicles in the fleet 3 is enlarged, so that the vehicles in the fleet 1 and the fleet 2 can be safely switched in, and meanwhile, the fleet 1 and the fleet 2 need to adjust the positions of the target gaps in the fleet 3 in real time, and the specific details are as follows:
the fleet 3 needs to determine which vehicles in the fleet 3 to pull up the gaps between, and the target size of the pulled up gaps. For this purpose, the target gaps of the motorcade 1 and the motorcade 2 are determined firstly, and in order to reduce the time for adjusting the positions of the motorcade 1 and the motorcade 2, the gap closest to the motorcade 1 and the motorcade 2 is respectively selected as the target gap in the motorcade 3; next, the required pitch sizes of the fleet 1 and the fleet 2 are calculated according to equations (9) to (12), respectively.
Wherein L is 1 And L 2 The lane change spacing required for fleet 1 and fleet 2, respectively.
Only the trajectory within the next rolling horizon (p) is planned. And after executing the track in one time domain, continuing to plan a track in a future rolling time domain according to the position of each vehicle in the current unmanned vehicle mixed flow lane change model until the target gap is enlarged to reach the target gap length. Meanwhile, the planning model should consider the comfort of passengers and the time of track adjustment, so that the following track planning model is established for the fleet 3:
an objective function:
and (3) constraint:
v i (t)＝v i (t-1)+a i (t-1)Δt,i∈I 3 (15)
x i (t)-x i+1 (t)≥s 0 ,i∈I 3 (18)
in the formula: omega 1 -weight of comfort;
ω 2 -weight of time efficiency;
I 3 -is a collection of unmanned vehicles within the fleet 3;
n is the number of vehicles in the unmanned fleet;
p-is the rolling prediction time domain;
L 1 the minimum required lane change interval of the fleet 1 is as follows: rice;
L 2 the minimum required lane change interval of the fleet 2, unit: rice;
The fleet 1 targets at the track coordination stage: when the vehicle platoon 3 has adjusted the gap, the vehicle platoon 1 is in a position where it is safe to swap in its target gap in the vehicle platoon 3. Therefore, the motorcade 1 calculates the safe lane changing distance between the motorcade and a front vehicle in the target gap in real time, and the safe distance is taken as a target of the actual distance; in addition, the comfort of the vehicle fleet 1 passengers should be considered, and for this purpose, a vehicle fleet 1 trajectory planning model objective function is established as shown in formula (19). And based on the idea of rolling optimization, the model only plans the track of the motorcade 1 in one rolling time domain (p) at a time, recalculates the safe lane changing gap between the front vehicle and the target gap after the track in one rolling time domain is executed, and replans the track of a future rolling time domain. The model is as follows:
an objective function:
and (3) constraint: the formulae (14) to (18),
and i =1.
The targets of the fleet 2 in the track coordination phase are: the platoon 2 is positioned in a position where it is possible to safely swap in its target gap in the platoon 3 when the platoon 3 has adjusted the gap. Therefore, the motorcade 2 calculates the safe lane changing distance between the motorcade and a front vehicle in the target gap in real time, and the safe distance is taken as a target of the actual distance; in addition, the comfort of the vehicle fleet 2 passengers should be considered, and for this purpose, a vehicle fleet 2 trajectory planning model objective function is established as shown in equation (20). The method is also based on the idea of rolling optimization, the model only plans the track of the motorcade 2 in one rolling time domain (p) at a time, after the track in one rolling time domain is executed, the safe lane changing gap between the motorcade and the front vehicle of the target gap is recalculated, and the track of the future rolling time domain is replanned. The model is as follows:
an objective function:
and (3) constraint:
formulae (14) to (18) and formula (21),
and i = k + m.
Taking the section of a section of two lanes on the highway as an example, 10 unmanned vehicle teams need to change lanes to outer lanes before reaching the ramp junction. The traffic volume of the outer lane is 1500veh/h, the manned vehicle on the target lane runs according to the IDM model, and the initial position is randomly generated according to Poisson arrival. The initial velocity was 30m/s. The fleet receives a lane change start signal at a location 1500 upstream of the turnstile. The scene diagram is shown in fig. 3. The model parameters are shown in Table 1 below
TABLE 1 model parameter settings
The coordinated lane change trajectory diagram under the scene is shown in fig. 4, and it can be seen from fig. 4 that by using the method provided by the invention, a fleet composed of 10 unmanned vehicles can complete all lane changes of the fleet within 20 seconds under the traffic environment of 1500 veh/h. Compared with two classical lane changing models, i.e. Gipps and MOBIL, the method disclosed by the invention has obvious advantages in the problem of lane changing of the fleet in mixed flow. The test results of a plurality of times under the scene also prove the superiority of the invention. The test results are shown in table 2:
TABLE 2 comparison of Process Performance results
Compared with the traditional classical lane change model, the method provided by the invention has the advantages that the lane change success rate is obviously improved, and the execution time of the lane change of the motorcade is greatly reduced.
The method for changing lanes in cooperation with the unmanned fleet in the mixed traffic flow is reasonable, reliable, simple and feasible, and can complete lane changing by steps in the fleet lane changing process when the whole fleet cannot be accommodated in a target gap and the lanes can be changed simultaneously, and the uncertainty of the driving behavior of the manned vehicle and the safety and the time efficiency in the unmanned vehicle action planning process are considered. Compared with the existing vehicle lane changing method under the pure unmanned vehicle environment, the method is closer to reality and more efficient than the single-vehicle lane changing method. The method for the collaborative lane change of the unmanned motorcade in the mixed traffic flow can recommend the latest lane change starting position for the automatic motorcade under different traffic conditions, remind the motorcade to change lanes in time, prevent the motorcade from missing the opportunity of sliding to the latest and missing the off-ramp opportunity, and provide technical support for the lane change of the automatic motorcade in the mixed flow.
It should be noted that, for those skilled in the art, without departing from the principle of the present invention, several improvements and modifications can be made, and these improvements and modifications should also be construed as the protection scope of the present invention. All the components not specified in the present embodiment can be realized by the prior art.
Claims (10)
1. A method for cooperatively changing lanes of an unmanned vehicle queue in a mixed traffic flow is characterized by comprising the following steps:
s1, acquiring all vehicle information in a cooperative interaction radius of an unmanned vehicle queue, and establishing an unmanned vehicle mixed flow lane changing model according to the unmanned vehicle queue and the acquired all vehicle information;
s2, determining the maximum gap on a target lane in the longitudinal range of the lane where the unmanned vehicle is located according to the established unmanned vehicle mixed flow lane change model, and taking the maximum gap as the target gap for changing the lane for the first time; determining the unmanned vehicle in the longitudinal range of the target gap according to the position of the unmanned vehicle, confirming that the unmanned vehicle is the unmanned vehicle for changing the lane for the first time, and when the target gap meets the safe driving condition of the unmanned vehicle for changing the lane for the first time, enabling the unmanned vehicle for changing the lane for the first time to drive into the target gap;
and S3, constructing a longitudinal track planning model of the unmanned vehicle based on a rolling optimization idea, acquiring tracks of all vehicles in a current unmanned vehicle queue in a rolling time domain after the unmanned vehicle changes the track for the first time, determining target gaps of the remaining unmanned vehicles based on the longitudinal track planning model of the unmanned vehicle, and sequentially changing the tracks of the remaining unmanned vehicles to drive into the target gaps.
2. The method for the cooperative lane change of the unmanned vehicle queue in the mixed traffic flow according to claim 1, wherein the information of all vehicles within the communication radius range of the unmanned vehicle queue is acquired by adopting the interaction between the road side unit and the vehicle communication unit.
3. The method for the cooperative lane change of the unmanned vehicle queue in the mixed traffic flow according to claim 1, wherein the real-time distance between the unmanned vehicle and the vehicle in front of the target gap is obtained, and if the distance between the unmanned vehicle and the vehicle in front of the target gap at the current moment is greater than or equal to the minimum lane change safety distance between the unmanned vehicle and the vehicle in front of the target gap at the current moment, and the reaction acceleration of the vehicle after the unmanned vehicle is currently changed into the target gap is greater than or equal to the maximum deceleration of the vehicle after the target gap, the requirement for the change of the unmanned vehicle is met.
4. The method for the cooperative lane change of the unmanned vehicle queue in the mixed traffic flow according to claim 1, wherein the unmanned vehicles enter the target vehicle queue after the first lane change, when the unmanned vehicles with the first lane change are single vehicles, the gap between the single vehicle and the front vehicle is adjusted to meet the target gap for the safe driving of the remaining unmanned vehicles, and one of the remaining unmanned vehicles is lane changed to drive into the target gap.
5. The method for the cooperative lane change of the unmanned vehicle queue in the mixed traffic flow according to claim 1, wherein when the unmanned vehicle for the first lane change is a fleet of a plurality of unmanned vehicles, the gap between two unmanned vehicles in the fleet of unmanned vehicles is adjusted to meet the target gap for the safe driving of the remaining unmanned vehicles, and one of the remaining unmanned vehicles is driven into the target gap for the lane change.
6. The method for changing lanes of unmanned vehicle queues in mixed traffic flow in a coordinated mode according to claim 5, is characterized in that when the unmanned vehicle changing lanes for the first time is a fleet of unmanned vehicles, the gap between two adjacent unmanned vehicles in the fleet of unmanned vehicles changing lanes for the first time is enlarged, so that the rest unmanned vehicles can safely change into the gap.
7. The method of claim 6, wherein the remaining unmanned vehicles select the gap nearest to them as the target gap.
8. The method of claim 1, wherein the trajectory in a future rolling time domain is continuously planned according to the position of each vehicle in the current unmanned vehicle mixed flow lane change model until the target gap is enlarged to the target gap length.
9. A collaborative lane changing system of an unmanned vehicle queue in a mixed traffic flow is characterized by comprising an information acquisition module and a processing module;
the information acquisition module is used for acquiring all vehicle information in the cooperative interaction radius of the unmanned vehicle queue and establishing an unmanned vehicle mixed flow lane change model according to the unmanned vehicle queue and the acquired all vehicle information;
the processing module is used for determining the maximum gap on a target lane in the longitudinal range of the lane where the unmanned vehicle is located according to the established unmanned vehicle mixed flow lane changing model, and taking the maximum gap as the target gap for changing the lane for the first time; determining an unmanned vehicle in the longitudinal range of a target gap according to the position of the unmanned vehicle, determining that the unmanned vehicle is a first lane-changing unmanned vehicle, and when the target gap meets the safe driving condition of the first lane-changing unmanned vehicle, driving the first lane-changing unmanned vehicle into the target gap in a lane-changing mode; the method comprises the steps of constructing a longitudinal track planning model of the unmanned vehicle based on the rolling optimization idea, obtaining tracks of all vehicles in a current unmanned vehicle queue in a rolling time domain after the unmanned vehicle changes the track for the first time, determining target gaps of the remaining unmanned vehicles based on the longitudinal track planning model of the unmanned vehicle, and sequentially changing the track of the remaining unmanned vehicles to drive the remaining unmanned vehicles into the target gaps.
10. The system of claim 9, wherein the real-time distance between the unmanned vehicle and the vehicle ahead of the target gap is obtained, and the change request of the unmanned vehicle is satisfied if the distance between the unmanned vehicle and the vehicle ahead of the target gap at the current time is greater than or equal to the minimum lane change safety distance between the unmanned vehicle and the vehicle ahead of the target gap at the current time, and the reaction acceleration of the vehicle behind the target gap after the unmanned vehicle is currently changed into the target gap is greater than or equal to the maximum deceleration of the vehicle behind the target gap.
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