CN115830908A - Cooperative lane change method and system for unmanned vehicle platoon in mixed traffic flow - Google Patents

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

Cooperative lane change method and system for unmanned vehicle platoon in mixed traffic flow

technical field

The invention belongs to the field of road traffic control, and in particular relates to a method and system for cooperatively changing lanes of an unmanned vehicle queue in a mixed traffic flow.

Background technique

Advances in driverless technology are beneficial to improve the efficiency and safety of the entire transportation system. With the ability of vehicles to communicate, driverless cars are expected to be able to travel in convoys, which can reduce gaps between vehicles and increase traffic capacity. And the queue can reduce the air resistance of the car and reduce energy consumption. However, due to technical limitations and the lack of corresponding policies and regulations, unmanned vehicles will be mixed with manned vehicles for a long time to come. Because of the uncertain driving behavior of manned vehicles, it becomes difficult for unmanned vehicles to change lanes in mixed traffic. Moreover, in order to ensure the safety of driverless vehicles during the interaction process, unmanned vehicles often adopt more conservative actions when changing lanes. Most of the existing research focuses on the environment of pure unmanned vehicles, without considering the influence of nearby manned vehicles on the lane change of unmanned vehicles. In practical applications, these methods are difficult to implement. There are also some studies that focus on the single-vehicle problem. This type of method is relatively inefficient when the team changes lanes, and it is difficult to guarantee the success rate of the team's lane change. Therefore, there is still a lack of effective solutions to the lane-changing problem of unmanned vehicle platoons in mixed flows.

Contents of the invention

The purpose of the present invention is to provide a method and system for cooperatively changing lanes of unmanned vehicle platoons in mixed traffic flow, so as to overcome the problems of the success rate of unmanned vehicle lane changing and the low efficiency in the lane changing process of existing methods.

A method for cooperatively changing lanes in a mixed traffic flow in a platoon of unmanned vehicles, comprising the following steps:

S1. Obtain all vehicle information within the cooperative interaction radius of the unmanned vehicle platoon, and establish a mixed traffic lane change model for unmanned vehicles based on the unmanned vehicle platoon and all the vehicle information obtained;

S2, according to the established unmanned vehicle mixed flow lane change model, determine the maximum gap on the target lane within the longitudinal range of the lane where the unmanned vehicle is located, and use the maximum gap as the target gap for the first lane change; according to the unmanned vehicle The position of the vehicle is determined as the unmanned vehicle within the longitudinal range of the target gap, and it is confirmed that the unmanned vehicle is the unmanned vehicle for the first lane change. When the target gap meets the safe entry conditions for the first lane change unmanned vehicle, Changing lanes The driverless vehicle changes lanes and enters the target gap;

S3. Construct the longitudinal trajectory planning model of unmanned vehicles based on the idea of rolling optimization. After the unmanned vehicle changes lanes for the first time, obtain the trajectories of all vehicles in the current unmanned vehicle queue in a rolling time domain, based on the longitudinal trajectory of unmanned vehicles The planning model determines the target gap of the remaining unmanned vehicles, and sequentially changes lanes of the remaining unmanned vehicles to enter the target gap.

Preferably, the interaction between the roadside unit and the vehicle communication unit is used to obtain the information of all vehicles within the communication radius of the unmanned vehicle platoon.

Preferably, the real-time distance between the unmanned vehicle and the vehicle in front of the target gap is obtained, 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 between the unmanned vehicle and the vehicle in front of the target gap at the current moment The safe distance, and the reaction acceleration of the vehicle behind the target gap after the unmanned vehicle is currently switched into the target gap is greater than or equal to the maximum deceleration of the vehicle behind the target gap, then the unmanned vehicle’s switching requirements are met.

Preferably, after the unmanned vehicle changes lanes for the first time, it enters the target vehicle queue. When the unmanned vehicle that changes lanes for the first time is a single vehicle, the gap between the single vehicle and the vehicle in front is adjusted so that the gap meets the requirements of the remaining unmanned vehicles. The target gap that the vehicle safely enters, and one of the remaining unmanned vehicles is changed lanes and driven into the target gap.

Preferably, when the unmanned vehicle that changes lanes for the first time is a fleet of multiple unmanned vehicles, then adjust the gap between the two unmanned vehicles in the fleet of unmanned vehicles so that the gap meets the remaining The target gap that the unmanned vehicle safely enters, and one of the remaining unmanned vehicles changes lanes and drives into the target gap.

Preferably, when the unmanned vehicle that changes lanes for the first time is a fleet composed of multiple unmanned vehicles, expand the distance between two adjacent unmanned vehicles in the fleet composed of multiple unmanned vehicles that change lanes for the first time. gaps so that the remaining unmanned vehicles can safely swap into the gaps.

Preferably, the remaining unmanned vehicles select the gap closest to them as the target gap.

Preferably, according to the position of each vehicle in the unmanned vehicle mixed flow lane change model at the current moment, continue to plan the trajectory in a rolling time domain in the future until the target gap is enlarged to the target gap length.

An unmanned vehicle platoon cooperatively changing lanes in a mixed traffic flow, including an information collection module and a processing module;

The information collection module acquires all vehicle information within the cooperative interaction radius of the unmanned vehicle platoon, and establishes a mixed traffic lane change model for unmanned vehicles based on the unmanned vehicle platoon and all the vehicle information obtained;

The processing module determines the maximum gap on the target lane within the longitudinal range of the lane where the unmanned vehicle is located according to the established unmanned vehicle mixed flow lane change model, and takes the maximum gap as the target gap for the first lane change; The position of the driving vehicle determines the unmanned vehicle within the longitudinal range of the target gap, and confirms that the unmanned vehicle is the unmanned vehicle for the first lane change. When the target gap meets the safe entry conditions for the first lane change unmanned vehicle, The unmanned vehicle changes lanes for the first time and enters the target gap; the longitudinal trajectory planning model of unmanned vehicles is constructed based on the idea of rolling optimization. After the unmanned vehicles change lanes for the first time, the current unmanned vehicle queue in the rolling time domain Based on the trajectory planning model of the longitudinal trajectory of unmanned vehicles, determine the target gaps of the remaining unmanned vehicles, and then change lanes and 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 obtained, 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 between the unmanned vehicle and the vehicle in front of the target gap at the current moment The safe distance, and the reaction acceleration of the vehicle behind the target gap after the unmanned vehicle is currently switched into the target gap is greater than or equal to the maximum deceleration of the vehicle behind the target gap, then the unmanned vehicle’s switching requirements are met.

Compared with the prior art, the present invention has the following beneficial technical effects:

The present invention provides a method for cooperative lane change of unmanned vehicle platoons in mixed traffic flow. When the target gap cannot accommodate the entire fleet to change lanes at the same time, the lane change process of the fleet can be completed step by step, and manned vehicles can be considered. Uncertainty in driving behavior and safety and time efficiency during motion planning for unmanned vehicles. This method is closer to reality than the existing vehicle lane changing method in pure unmanned vehicle environment, and is more efficient than the single vehicle lane changing method. The method proposed by the present invention for the coordinated lane change of driverless fleets in mixed traffic flows can recommend the latest lane change start position for autonomous driving fleets under different traffic conditions, remind the fleet to change lanes in time, and prevent the fleet from missing the latest lane change. The opportunity to miss the off-ramp opportunity provides a technical guarantee for the autonomous vehicle fleet to change lanes in the mixed flow.

Description of drawings

Fig. 1 is a flow chart of coordinated lane changing of fleets in a mixed flow in an embodiment of the present invention.

Fig. 2 is a schematic diagram of collaborative planning of unmanned vehicle trajectories in an embodiment of the present invention.

Fig. 3 is a schematic diagram of a traffic scene in an embodiment of the present invention.

Fig. 4 is a track diagram of team coordinated lane change in the embodiment of the present invention.

Detailed ways

In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only It is an embodiment of a part of the present invention, but not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts shall fall within the protection scope of the present invention.

It should be noted that the terms "first" and "second" in the description and claims of the present invention and the above drawings are used to distinguish similar objects, but not necessarily used to describe a specific sequence or sequence. It is to be understood that the data so used are interchangeable under appropriate circumstances such that the embodiments of the invention described herein can be practiced in sequences other than those illustrated or described herein. Furthermore, the terms "comprising" and "having", as well as any variations thereof, are intended to cover a non-exclusive inclusion, for example, a process, method, system, product or device comprising a sequence of steps or elements is not necessarily limited to the expressly listed instead, may include other steps or elements not explicitly listed or inherent to the process, method, product or apparatus.

The present invention relates to a method for cooperatively changing lanes of an unmanned vehicle platoon in a mixed traffic flow, comprising the following steps:

S1. Obtain all vehicle information within the cooperative interaction radius of the unmanned vehicle platoon, and establish a mixed traffic lane change model for unmanned vehicles based on the unmanned vehicle platoon and all the vehicle information obtained;

Specifically, the interaction between the roadside unit and the vehicle communication unit is used to obtain the information of all vehicles within the communication radius of the unmanned vehicle queue. Each vehicle is equipped with a vehicle communication unit, and the roadside unit and the vehicle communication unit It can communicate and obtain vehicle information, including vehicle location information, vehicle speed and acceleration information.

The purpose of constructing the mixed lane change model of unmanned vehicles is to judge whether a certain gap can allow unmanned vehicles to switch in safely;

The unmanned vehicle mixed flow lane change model is used to calculate the distance between the unmanned vehicle and the target gap, and judge whether the distance is greater than the set safety distance, which is calculated by the Gipps model. The reaction time and maximum acceleration and deceleration of the autonomous vehicle need to be determined.

Gipps calculates the safe distance from the vehicle in front of the target gap:

——无人驾驶车辆(CAV)与目标间隙前车(pre)在时刻t的最小换道安全间距，单位：米；In the formula:

——The minimum lane-changing safety distance between the unmanned vehicle (CAV) and the vehicle in front of the target gap (pre) at time t, unit: meter;

v _CAV (t)——the speed of the unmanned vehicle at time t, unit: m/s;

v _pre (t)——the speed of the vehicle in front at time t, unit: m/s;

——无人驾驶车辆的最大减速度，单位：米/秒2；

——The maximum deceleration of the unmanned vehicle, unit: m/s2;

——目标间隙前车的最大减速度，单位：米/秒2；

——The maximum deceleration of the vehicle in front of the target gap, unit: m/s2;

τ _CAV - reaction time of unmanned vehicle, unit: second;

x _CAV (t)——the position of the unmanned vehicle at time t, unit: meter;

x _pre (t)——the position of the vehicle in front of the target gap at time t, unit: meter;

h——the length of the unmanned vehicle, unit: meter.

则满足无人驾驶车辆的安全换道标准，s_CAV,pre(t)是无人驾驶车辆(CAV)与目标间隙前车(pre)在时刻t的实际距离，其计算公式如下：if

Then meet the safe lane-changing standard of unmanned vehicles, s _CAV,pre (t) is the actual distance between the unmanned vehicle (CAV) and the vehicle in front of the target gap (pre) at time t, and its calculation formula is as follows:

s _CAV,pre (t)=x _pre (t)-x _CAV (t)-h (2)

The unmanned vehicle that is about to change lanes changes into the target gap. The reaction acceleration generated by the vehicle (fol) after the target gap cannot exceed the acceleration/deceleration limit of the vehicle itself. The reaction acceleration of the vehicle (fol) after the target gap is determined by the IDM model It is calculated that the IDM model needs to determine the minimum car-following distance (take 3 meters), the acceleration coefficient (take 2), and the limit range of the acceleration and deceleration of the vehicle.

The formula for calculating the safety judgment of the vehicle behind the target gap is as follows:

—目标间隙后车的最大减速度，单位：米/秒²；

—The maximum deceleration of the vehicle behind the target gap, unit: m/s ² ;

a _fol,CAV (t) is the reaction acceleration of the vehicle behind the target gap after the unmanned vehicle switches into the target gap at time t, unit: m/s2;

——目标间隙后车的最大加速度，单位：米/秒²；

——The maximum acceleration of the vehicle behind the target gap, unit: m/ ^s2 ;

s ₀ —— the minimum car-following distance, take 3 meters;

S ^* ——the expected distance between the unmanned vehicle and the rear vehicle, unit: meter;

Δt——discrete time interval, take 0.1 second;

δ——acceleration coefficient, generally 2.

则无人驾驶车辆进行换道进入目标间隙。satisfy at the same time

Then the unmanned vehicle changes lanes and enters the target gap.

S2, according to the established unmanned vehicle mixed flow lane change model, determine the maximum gap on the target lane within the longitudinal range of the lane where the unmanned vehicle is located, and use the maximum gap as the target gap for the first lane change; according to the unmanned vehicle The position of the vehicle is determined to be an unmanned vehicle within the longitudinal range of the target gap, and it is confirmed that the unmanned vehicle is the unmanned vehicle for the first lane change.

Assuming that there are n vehicles in the unmanned vehicle team, the values of k and m in {k,m|k=1,...,n,m=1,...,n-k} are brought into the following inequality in turn, Find the values of k and m that satisfy the following inequalities, and confirm the unmanned vehicle or the set of unmanned vehicles that change lanes for the first time, which is {k, k+1,...,k+m-1}.

In the formula: m—the number of unmanned vehicles that change lanes for the first time;

k——the kth car in the fleet.

t ₀ ——the time at which the lane change starts.

S3. Construct the longitudinal trajectory planning model of unmanned vehicles based on the idea of rolling optimization. After the unmanned vehicle changes lanes for the first time, obtain the trajectories of all vehicles in the current unmanned vehicle queue in a rolling time domain, based on the longitudinal trajectory of unmanned vehicles The planning model determines the target gap of the remaining unmanned vehicles, and sequentially changes lanes of the remaining unmanned vehicles to enter the target gap.

When the unmanned vehicle changes lanes for the first time, it enters the target vehicle queue. When the unmanned vehicle that changes lanes for the first time is a single vehicle, the gap between the single vehicle and the vehicle in front is adjusted so that the gap meets the requirements for safe driving of the remaining unmanned vehicles. Enter the target gap, and change lanes of one of the remaining unmanned vehicles to enter the target gap.

When the unmanned vehicle that changes lanes for the first time is a convoy of multiple unmanned vehicles, adjust the gap between two unmanned vehicles in the convoy of unmanned vehicles so that the gap meets the requirements of the remaining unmanned vehicles. The target gap that the vehicle safely enters, and one of the remaining unmanned vehicles is changed lanes and driven into the target gap.

As shown in Figure 1 and Figure 2, this application takes an unmanned vehicle platoon composed of six vehicles as an example, and splits the unmanned vehicle platoon into three small fleets. One of the small fleets changed lanes for the first time, and the rest did not. The two small convoys on the road are divided into convoy 1 and convoy 2 according to their front and rear positions, and the small convoys that have changed lanes form convoy 3.

The coordination method in the collaborative trajectory planning stage is: to expand the gap between two adjacent unmanned vehicles in fleet 3, so that the vehicles in fleet 1 and fleet 2 can safely switch in, and fleet 1 and fleet 2 need to follow the The position of its target gap is adjusted in real time, the details are as follows:

Fleet 3 needs to determine which vehicles in Fleet 3 to widen the gap between, and the target size of the widened gap. To this end, first determine the target gaps of the team 1 and the team 2, in order to reduce the time for the position adjustment of the team 1 and the team 2, the team 1 and the team 2 respectively select the gap closest to them in the team 3 as the target gap; then, according to Equations (9)-(12) calculate the size of the required spacing between fleet 1 and fleet 2.

Among them, L ₁ and L ₂ are the required lane-changing distances of fleet 1 and fleet 2, respectively.

Only trajectories within the next rolling horizon (p) are planned. After executing a trajectory in the time domain, continue to plan the trajectory in the future rolling time domain according to the position of each vehicle in the mixed flow lane change model of unmanned vehicles at the current moment, until the target gap is enlarged to the target gap length. At the same time, the planning model should consider the comfort of passengers and the time of trajectory adjustment, so the following trajectory planning model is established for fleet 3:

Objective function:

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: ω ₁ —weight of comfort degree;

ω ₂ —weight of time efficiency;

I ₃ —is the collection of unmanned vehicles in fleet 3;

—无人驾驶车辆k-1与后车k所需的最小安全换道间距，单位：米。

—The minimum safe lane-changing distance between unmanned vehicle k-1 and the following vehicle k, unit: meter.

n—the number of vehicles in the unmanned fleet;

p—is the rolling forecast time domain;

L ₁ ——the distance required for the minimum lane change of team 1, unit: meter;

L ₂ ——the distance required for the minimum lane change of team 2, unit: meter;

——无人车k-1与后车k所需的最小安全换道间距，单位：米。

——The minimum safe lane-changing distance between unmanned vehicle k-1 and the following vehicle k, unit: meter.

The goal of team 1 in the trajectory coordination phase is: when team 3 adjusts the gap, team 1 is at a position where it can safely change into its target gap in team 3. To this end, team 1 calculates the safe lane-changing distance between itself and the vehicle in front of the target gap in real time, and takes this safe distance as the target of the actual distance; in addition, the comfort of passengers in team 1 should also be considered. The objective function of the fleet 1 trajectory planning model shown in formula (19). Also based on the idea of rolling optimization, the model only plans the trajectory of fleet 1 in one rolling time domain (p) each time, and then recalculates the safety between the front vehicle and the target gap after executing a trajectory in the rolling time domain Change the lane gap, and re-plan the trajectory of a rolling time domain in the future. Its model is as follows:

Objective function:

且i＝1。Constraints: Formulas (14)-(18),

And i=1.

The goal of team 2 in the trajectory coordination phase is: when team 2 adjusts the gap in team 3, team 2 is at a position where it can safely change into its target gap in team 3. For this reason, the team 2 calculates the safe lane-changing distance between itself and the vehicle in front of the target gap in real time, and takes this safe distance as the target of the actual distance; in addition, the comfort of the passengers of the team 2 should also be considered. The objective function of the fleet 2 trajectory planning model shown in formula (20). Also based on the idea of rolling optimization, the model only plans the trajectory of fleet 2 in one rolling time domain (p) each time, and then recalculates the safety between the front vehicle and the target gap after executing a trajectory in the rolling time domain Change the lane gap, and re-plan the trajectory of a rolling time domain in the future. Its model is as follows:

Objective function:

constraint:

且i＝k+m。Formulas (14)-(18) and formula (21),

And i=k+m.

The above requirements take a two-lane road section on the expressway as an example. A fleet of 10 unmanned vehicles needs to change lanes to the outer lane before reaching the ramp. The traffic volume of the outer lane is 1500veh/h, the manned vehicles on the target lane travel according to the IDM model, and the initial position is randomly generated according to Poisson arrival. The initial speed is 30m/s. The convoy receives a lane change start signal at a position 1500 upstream of the ramp. The schematic diagram of the scene is shown in Figure 3. The model parameters are shown in Table 1 below

Table 1 Model parameter settings

The trajectory diagram of coordinated lane change in this scenario is shown in Figure 4. It can be seen from Figure 4 that using the method proposed in the present invention, a fleet of 10 unmanned vehicles can All lane changes of the convoy can be completed within 20 seconds. By comparing with two classic lane-changing models Gipps and MOBIL, it is found that the method of the present invention has obvious advantages in the problem of vehicle lane-changing in mixed flow. The multiple test results in this scenario also confirm the superiority of the present invention. The test results are shown in Table 2:

Table 2. Method performance comparison results

It can be seen from the simulation results that the method proposed by the present invention, compared with the current classic lane-changing model, has a significant improvement in the success rate of lane-changing, and at the same time, the team's lane-changing execution time is greatly reduced.

The method for coordinated lane change of unmanned fleets in the mixed traffic flow proposed by the present invention is reasonable, reliable, simple and easy to implement. The lane change is completed step by step, and the uncertainty of the driving behavior of the manned vehicle and the safety and time efficiency of the action planning process of the unmanned vehicle are considered. This method is closer to reality than the existing vehicle lane changing method in pure unmanned vehicle environment, and is more efficient than the single vehicle lane changing method. The method proposed by the present invention for the coordinated lane change of driverless fleets in mixed traffic flows can recommend the latest lane change start position for autonomous driving fleets under different traffic conditions, remind the fleet to change lanes in time, and prevent the fleet from missing the latest lane change. The opportunity to miss the off-ramp opportunity provides a technical guarantee for the autonomous vehicle fleet to change lanes in the mixed flow.

It should be pointed out that those skilled in the art can make some improvements and modifications without departing from the principle of the present invention, and these improvements and modifications should also be regarded as the protection scope of the present invention. All components that are not specified in this embodiment can be realized by existing technologies.

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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