CN115830908B - A method and system for collaborative lane changing of driverless vehicle queues in mixed traffic flow - Google Patents

Abstract

The present invention discloses a method and system for collaborative lane changing of an unmanned vehicle fleet in a mixed traffic flow. When the target gap cannot accommodate the entire fleet changing lanes at the same time, the lane changing process of the fleet can be completed step by step, and Consider the uncertainty of manned vehicle driving behavior and the safety and time efficiency in the action planning process of unmanned vehicles. This method is closer to reality than the existing vehicle lane-changing method in a pure unmanned vehicle environment, and more efficient than the single-vehicle lane-changing method. The method proposed by the present invention for autonomous driving fleets to collaboratively change lanes in mixed traffic flow can recommend the latest lane-changing starting position for autonomous driving fleets under different traffic conditions, remind the fleets to change lanes in time, and prevent the fleets from missing the latest lane changes. Missing the opportunity to get off the ramp provides technical support for autonomous driving fleets to change lanes in mixed traffic.

Description

A method and system for collaborative lane changing of driverless vehicle queues in mixed traffic flow

Technical field

The invention belongs to the field of road traffic control, and specifically relates to a method and system for collaborative lane changing of a queue of unmanned vehicles in mixed traffic flow.

Background technique

Advances in driverless technology are beneficial to improving the efficiency and safety of the entire transportation system. With the ability of vehicle communication, driverless cars are expected to be able to travel in a platoon, because the platoon can shorten the gap between vehicles and improve traffic capacity. Moreover, queuing 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, driverless vehicles will be mixed with manned vehicles for a long time to come. Because of the uncertainty in the driving behavior of manned vehicles, it becomes difficult for unmanned vehicles to change lanes in mixed traffic. Moreover, in order to ensure safety during the interaction with manned vehicles, unmanned vehicles often adopt more conservative actions when changing lanes. Most of the existing research focuses on the environment of purely unmanned vehicles and does not consider the impact of nearby manned vehicles on lane changes 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 applied to fleets changing lanes, and it is difficult to ensure the success rate of fleets changing lanes. Therefore, there is still a lack of effective solutions to the lane changing problem of driverless vehicle queues in mixed flows.

Contents of the invention

The purpose of the present invention is to provide a method and system for cooperative lane changing of a queue of unmanned vehicles in mixed traffic flow, so as to overcome the problems of existing methods with regard to the success rate of lane changing of unmanned vehicles and the low efficiency in the lane changing process.

A method for cooperative lane changing of driverless vehicle queues in mixed traffic flow, including the following steps:

S1, obtain all vehicle information within the cooperative interaction radius of the unmanned vehicle queue, and establish an unmanned vehicle mixed flow lane changing model based on the unmanned vehicle queue and all acquired vehicle information;

S2, according to the established mixed-flow lane-changing model of unmanned vehicles, 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 If the position of the vehicle is determined to be within the longitudinal range of the target gap, the unmanned vehicle is confirmed to be an unmanned vehicle that changes lanes for the first time. When the target gap meets the conditions for safe entry of an unmanned vehicle that changes lanes for the first time, it will Lane Changing The unmanned vehicle changes lanes and drives into the target gap;

S3, build an autonomous vehicle longitudinal trajectory planning model based on the idea of rolling optimization. After the autonomous vehicle changes lanes for the first time, obtain the trajectories of all vehicles currently in the autonomous vehicle queue within a rolling time domain. Based on the longitudinal trajectory of the autonomous vehicle, Planning model, determine the target gap of the remaining driverless vehicles, and sequentially change the lanes of the remaining driverless vehicles and drive into the target gap.

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

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. If the driverless vehicle currently switches into the target gap and the reaction acceleration of the vehicle behind the target gap is greater than or equal to the maximum deceleration of the vehicle behind the target gap, then the driverless vehicle switching requirements are met.

Preferably, 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 bicycle, the gap between the bicycle and the vehicle in front of it is adjusted so that the gap satisfies the remaining driverless vehicles. If the vehicle can safely drive into the target gap, one of the remaining unmanned vehicles will change lanes and drive into the target gap.

Preferably, when the autonomous vehicle that changes lanes for the first time is a fleet of multiple autonomous vehicles, the gap between the two autonomous vehicles in the fleet of autonomous vehicles is adjusted so that the gap meets the remaining The target gap that the unmanned vehicle can safely drive into, then one of the remaining unmanned vehicles will change lanes and drive into the target gap.

Preferably, when the unmanned vehicle that changes lanes for the first time is a convoy of multiple unmanned vehicles, the distance between two adjacent unmanned vehicles in the convoy of multiple unmanned vehicles that changes lanes for the first time is expanded. gap so that the remaining driverless vehicles can safely switch into the gap.

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

Preferably, the trajectory in the future rolling time domain is continued to be planned based on the position of each vehicle in the mixed-flow lane-changing model of unmanned vehicles at the current moment, until the target gap is enlarged to the target gap length.

A system for collaborative lane changing of driverless vehicle queues in mixed traffic flow, including an information collection module and a processing module;

The information collection module obtains all vehicle information within the cooperative interaction radius of the unmanned vehicle queue, and establishes an unmanned vehicle mixed flow lane changing model based on the unmanned vehicle queue and all acquired vehicle information;

The processing module determines the maximum gap on the target lane within the longitudinal range of the lane where the driverless vehicle is located based on the established mixed-flow lane-changing model of the driverless vehicle, and uses the maximum gap as the target gap for the first lane change; according to the The position of the driving vehicle is determined to be an unmanned vehicle within the longitudinal range of the target gap, and the unmanned vehicle is confirmed to be an unmanned vehicle that changes lanes for the first time. When the target gap meets the conditions for safe entry of an unmanned vehicle that changes lanes for the first time, The unmanned vehicle changes lanes for the first time and drives into the target gap; a longitudinal trajectory planning model of the unmanned vehicle is built based on the idea of rolling optimization. After the unmanned vehicle changes lanes for the first time, the current unmanned vehicle queue in the rolling time domain is obtained Based on the trajectories of all vehicles in the unmanned vehicle longitudinal trajectory planning model, the target gaps of the remaining unmanned vehicles are determined, and the remaining unmanned vehicles are sequentially changed lanes and driven 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. If the driverless vehicle currently switches into the target gap and the reaction acceleration of the vehicle behind the target gap is greater than or equal to the maximum deceleration of the vehicle behind the target gap, then the driverless vehicle switching requirements are met.

Compared with the existing technology, the present invention has the following beneficial technical effects:

The present invention is a collaborative lane-changing method for unmanned vehicle queues in mixed traffic flow. When the target gap cannot accommodate the entire fleet to change lanes at the same time, the lane-changing process of the fleet can be completed step by step, and manned vehicles can be taken into consideration. Driving behavior uncertainty and safety and time efficiency in the action planning process of autonomous vehicles. This method is closer to reality than the existing vehicle lane-changing method in a pure unmanned vehicle environment, and more efficient than the single-vehicle lane-changing method. The method proposed by the present invention for autonomous driving fleets to collaboratively change lanes in mixed traffic flow can recommend the latest lane-changing starting position for autonomous driving fleets under different traffic conditions, remind the fleets to change lanes in time, and prevent the fleets from missing the latest lane changes. Missing the opportunity to get off the ramp provides technical support for autonomous driving fleets to change lanes in mixed traffic.

Description of the drawings

Figure 1 is a flow chart of a team's coordinated lane change in a mixed flow in an embodiment of the present invention.

Figure 2 is a schematic diagram of cooperative trajectory planning of unmanned vehicles in the embodiment of the present invention.

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

Figure 4 is a trajectory diagram of coordinated lane changing of the fleet 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 accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only These are some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts should fall within the scope of protection of the present invention.

It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It is to be understood that the data so used are interchangeable under appropriate circumstances so that the embodiments of the invention described herein are capable of being practiced in sequences other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, e.g., a process, method, system, product, or apparatus that encompasses a series of steps or units and need not be limited to those explicitly listed. Those steps or elements may instead include other steps or elements not expressly listed or inherent to the process, method, product or apparatus.

The present invention is a method for cooperative lane changing of unmanned vehicle queues in mixed traffic flow, which includes the following steps:

S1, obtain all vehicle information within the cooperative interaction radius of the unmanned vehicle queue, and establish an unmanned vehicle mixed flow lane changing model based on the unmanned vehicle queue and all acquired vehicle information;

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 Able to communicate and obtain vehicle information, including vehicle location information, vehicle speed and acceleration information.

The mixed-flow lane-changing model of autonomous vehicles is constructed to determine whether a certain gap can allow autonomous vehicles to safely change in;

The mixed-flow lane-changing model for driverless vehicles is used to calculate the distance between the driverless vehicle and the target gap, and determine whether the distance is greater than the set safety distance. The set safety distance is calculated by the Gipps model. The reaction time and maximum acceleration and deceleration of the driverless vehicle need to be determined.

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

In the formula: ——The minimum safe distance for lane change between the unmanned vehicle (CAV) and the target vehicle in front (pre) at time t, unit: meter;

v _CAV (t)——The speed of the unmanned vehicle at time t, unit: meters/second;

v _pre (t)——The speed of the vehicle in front at time t, unit: meters/second;

——Maximum deceleration of unmanned vehicles, unit: m/s2;

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

τ _CAV ——Reaction time of unmanned vehicle, unit: seconds;

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

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

h——The length of the unmanned vehicle, unit: meters.

if Then the safe lane change standard for unmanned vehicles is met. 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. Its calculation formula is as follows:

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

The unmanned vehicle preparing to change lanes changes into the target gap. The reaction acceleration of the vehicle (fol) behind 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. The calculation shows that the IDM model needs to determine the minimum following distance (taken as 3 meters), the acceleration coefficient (taken as 2), and the limit range of the vehicle's acceleration and deceleration.

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

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

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;

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

s ₀ ——minimum following distance, taken as 3 meters;

S ^* ——The desired distance between the driverless vehicle and the vehicle behind it, unit: meters;

Δt——discrete time interval, taken as 0.1 seconds;

δ——Acceleration coefficient, generally taken as 2.

Satisfy at the same time Then the unmanned vehicle changes lanes and enters the target gap.

S2, according to the established mixed-flow lane-changing model of unmanned vehicles, 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 The position of the vehicle is determined to be an unmanned vehicle within the longitudinal range of the target gap, and the unmanned vehicle is confirmed to be an unmanned vehicle that changes lanes for the first time.

Assume that there are n vehicles in the unmanned vehicle fleet, and the k and m values in {k,m|k＝1,...,n,m＝1,...,n-k} are brought into the following inequality in turn, Find the k and m values 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 driverless vehicles changing lanes for the first time;

k——The kth vehicle in the fleet.

t ₀ ——The time when lane changing starts.

S3, build an autonomous vehicle longitudinal trajectory planning model based on the idea of rolling optimization. After the autonomous vehicle changes lanes for the first time, obtain the trajectories of all vehicles currently in the autonomous vehicle queue within a rolling time domain. Based on the longitudinal trajectory of the autonomous vehicle, Planning model, determine the target gap of the remaining driverless vehicles, and sequentially change the lanes of the remaining driverless vehicles and drive into the target gap.

When the driverless vehicle changes lanes for the first time, it enters the target vehicle queue. When the driverless vehicle that changes lanes for the first time is a bicycle, the gap between the bicycle and the vehicle in front of it is adjusted so that the gap meets the safety requirements of the remaining driverless vehicles. If the driver enters the target gap, one of the remaining driverless vehicles will change lanes and drive into the target gap.

When the driverless vehicle that changes lanes for the first time is a fleet of multiple driverless vehicles, the gap between the two driverless vehicles in the fleet of driverless vehicles is adjusted so that the gap satisfies the remaining driverless vehicles. If the vehicle can safely drive into the target gap, one of the remaining unmanned vehicles will change lanes and drive into the target gap.

As shown in Figure 1 and Figure 2, this application takes an unmanned vehicle queue consisting of six vehicles as an example, and splits the unmanned vehicle queue into three small fleets. One of the small fleets changes lanes for the first time, and the remaining ones have not changed lanes. The two small teams in the lane are divided into Team 1 and Team 2 according to their front and rear positions. The small team that has changed lanes forms Team 3.

The collaborative method in the collaborative trajectory planning stage is to expand the gap between two adjacent unmanned vehicles in fleet 3 so that vehicles in fleet 1 and fleet 2 can safely switch in. At the same time, fleet 1 and fleet 2 need to be based on fleet 3. to adjust its position in real time according to the position of its target gap. The specific details are as follows:

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

Where L ₁ and L ₂ are the lane changing distances required by Team 1 and Team 2 respectively.

Only plan the trajectory within the next rolling time domain (p). After executing a trajectory in a time domain, continue to plan a future trajectory in a rolling time domain based on the position of each vehicle in the current driverless vehicle mixed flow lane changing model until the target gap is enlarged to the target gap length. At the same time, the planning model should consider passenger comfort and trajectory adjustment time, 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 —the weight of comfort;

ω ₂ —weight of time efficiency;

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

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

n—the number of vehicles in the unmanned fleet;

p—is the rolling prediction time domain;

L ₁ ——The minimum required distance for lane change of team 1, unit: meters;

L ₂ ——The minimum required distance for lane change of team 2, unit: meters;

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

The goal of Team 1 in the trajectory coordination phase is that when Team 3 adjusts the gap, Team 1 is in a position where it can safely switch 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 uses this safe distance as the target for the actual distance; in addition, the comfort of Team 1 passengers should also be considered. To this end, establish the following The objective function of the fleet 1 trajectory planning model shown in Equation (19). Also based on the idea of rolling optimization, the model only plans a trajectory of fleet 1 in a rolling time domain (p) at a time. After executing the trajectory in a rolling time domain, it recalculates the safety between the vehicle in front and the target gap. Change the lane gap and re-plan the trajectory of a rolling time domain in the future. The model is detailed as follows:

Objective function:

Constraints: Formula (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 in a position where it can safely switch into its target gap in Team 3. To this end, Team 2 calculates the safe lane-changing distance between it and the vehicle in front of the target gap in real time, and uses this safe distance as the target of the actual distance; in addition, the comfort of the passengers in Team 2 should also be considered. To this end, establish such as The objective function of the fleet 2 trajectory planning model shown in Equation (20). Also based on the idea of rolling optimization, the model only plans the trajectory of fleet 2 in a rolling time domain (p) at a time. After executing the trajectory in a rolling time domain, it recalculates the safety between the vehicle in front and the target gap. Change the lane gap and re-plan the trajectory of a rolling time domain in the future. The model is detailed as follows:

Objective function:

constraint:

Formula (14)-(18) and Formula (21), And i=k+m.

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

Table 1 Model parameter settings

The collaborative lane changing trajectory diagram in this scenario is shown in Figure 4. It can be seen from Figure 4 that using the method proposed by the present invention, a fleet of 10 unmanned vehicles in a traffic environment of 1500veh/h, All lane changes for 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 fleet lane changing in mixed flow. 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 significantly improved the lane changing success rate, and at the same time, the fleet lane changing execution time has been significantly reduced.

The method proposed by the present invention for cooperative lane changing of an unmanned vehicle fleet in mixed traffic flow is reasonable, reliable, simple and easy to implement. The method proposed by the present invention for a fleet of vehicles to change lanes can be used by changing the lane of the fleet when the target gap cannot accommodate the entire fleet of vehicles changing lanes at the same time. The lane change is completed in a step-by-step process, taking into account the uncertainty in the driving behavior of manned vehicles and the safety and time efficiency in the action planning process of unmanned vehicles. This method is closer to reality than the existing vehicle lane-changing method in a pure unmanned vehicle environment, and more efficient than the single-vehicle lane-changing method. The method proposed by the present invention for autonomous driving fleets to collaboratively change lanes in mixed traffic flow can recommend the latest lane-changing starting position for autonomous driving fleets under different traffic conditions, remind the fleets to change lanes in time, and prevent the fleets from missing the latest lane changes. Missing the opportunity to get off the ramp provides technical support for autonomous driving fleets to change lanes in mixed traffic.

It should be pointed out that for those of ordinary skill in the art, several improvements and modifications can be made without departing from the principles of the present invention, and these improvements and modifications should also be regarded as the protection scope of the present invention. All components not specified in this embodiment can be implemented using existing technologies.

Claims (10)

1. A method for collaborative lane changing of driverless vehicle queues in mixed traffic flow, which is characterized by including the following steps: S1, obtain all vehicle information within the cooperative interaction radius of the unmanned vehicle queue, and establish an unmanned vehicle mixed flow lane changing model based on the unmanned vehicle queue and all acquired vehicle information; The mixed-flow lane-changing model of driverless vehicles is used to calculate the distance between the driverless vehicle and the target gap, and determine whether the distance is greater than the set safety distance. The set safety distance is calculated by the Gipps model to determine the driverless vehicle. reaction time and maximum acceleration and deceleration: Gipps calculates the safe distance from the vehicle in front of the target gap: In the formula: ——The minimum safe distance for lane change between the unmanned vehicle (CAV) and the target vehicle in front (pre) at time t, unit: meter; v _CAV (t)——The speed of the unmanned vehicle at time t, unit: meters/second; v _pre (t)——The speed of the vehicle in front at time t, unit: meters/second; ——Maximum deceleration of unmanned vehicles, unit: m/s2; ——The maximum deceleration of the vehicle in front of the target gap, unit: m/s2; τ _CAV ——Reaction time of unmanned vehicle, unit: seconds; x _CAV (t)——The position of the unmanned vehicle at time t, unit: meters; x _pre (t)——The position of the vehicle in front of the target gap at time t, unit: meters; h——The length of the unmanned vehicle, unit: meters; if Then the safe lane change standard for unmanned vehicles is met. 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. Its calculation formula is as follows: s _CAV,pre (t)＝x _pre (t)-x _CAV (t)-h (2) The unmanned vehicle preparing to change lanes switches into the target gap. The reaction acceleration of 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 calculated by the IDM model. The IDM model needs to determine the minimum following distance of 3 meters, the acceleration coefficient of 2, and the vehicle's acceleration and deceleration limit range; The calculation formula for safety judgment of the vehicle behind the target gap is as follows: —The maximum deceleration of the vehicle behind the target gap, unit: m/ ^s2 ; 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 ; ——The maximum acceleration of the vehicle behind the target gap, unit: m/ ^s2 ; s ₀ ——minimum following distance, taken as 3 meters; S ^* ——The desired distance between the driverless vehicle and the vehicle behind it, unit: meters; Δt——discrete time interval, taken as 0.1 seconds; δ——The acceleration coefficient is 2; Satisfy at the same time Then the unmanned vehicle changes lanes and enters the target gap; S2, according to the established mixed-flow lane-changing model of unmanned vehicles, 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 If the position of the vehicle is determined to be within the longitudinal range of the target gap, the unmanned vehicle is confirmed to be an unmanned vehicle that changes lanes for the first time. When the target gap meets the conditions for safe entry of an unmanned vehicle that changes lanes for the first time, it will Lane Changing The unmanned vehicle changes lanes and drives into the target gap; S3, build an autonomous vehicle longitudinal trajectory planning model based on the idea of rolling optimization. After the autonomous vehicle changes lanes for the first time, obtain the trajectories of all vehicles currently in the autonomous vehicle queue within a rolling time domain. Based on the longitudinal trajectory of the autonomous vehicle, Planning model, determines the target gap of the remaining driverless vehicles, and sequentially changes lanes for the remaining driverless vehicles and drives into the target gap; Taking a six-vehicle driverless vehicle queue as an example, the driverless vehicle queue is split into three small fleets. One of the small fleets changes lanes for the first time, and the remaining two small fleets that have not changed lanes are divided into three groups according to their front and rear positions. For Team 1 and Team 2, the small teams that have changed lanes form Team 3; The collaborative method in the collaborative trajectory planning stage is to expand the gap between two adjacent unmanned vehicles in fleet 3 so that vehicles in fleet 1 and fleet 2 can safely switch in. At the same time, fleet 1 and fleet 2 need to be based on fleet 3. to adjust its position in real time according to the position of its target gap, specifically: First determine the target gaps of Team 1 and Team 2. Team 1 and Team 2 respectively select the gap closest to them in Team 3 as the target gap. Then, calculate the distances of Team 1 and Team 2 according to equations (9)-(12) respectively. Required spacing size: Where L ₁ and L ₂ are the lane changing distances required by Team 1 and Team 2 respectively; Plan the trajectory in the next rolling time domain p. After executing the trajectory in one time domain, continue to plan the trajectory in the next rolling time domain based on the position of each vehicle in the driverless vehicle mixed flow lane changing model 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 —the weight of comfort; ω ₂ —weight of time efficiency; I ₃ —is the collection of unmanned vehicles in fleet 3; —The minimum safe lane-changing distance required between the unmanned vehicle k-1 and the vehicle behind it k, unit: meter; n—the number of vehicles in the unmanned fleet; p—is the rolling prediction time domain; L ₁ ——The minimum required distance for lane change of team 1, unit: meters; L ₂ ——The minimum distance required for lane change of team 2, unit: meters. 2. A method for collaborative lane changing of an autonomous vehicle queue in a mixed traffic flow according to claim 1, characterized in that the interaction between the roadside unit and the vehicle communication unit is used to obtain the information on the autonomous vehicle queue. Information about all vehicles within the communication radius. 3. A method for collaborative lane changing of a queue of unmanned vehicles in mixed traffic flow according to claim 1, characterized in that the real-time distance between the unmanned vehicle and the vehicle in front of the target gap is obtained. If the unmanned vehicle and the vehicle in front of the target gap are The distance between the vehicle in front of the target gap at the current moment is greater than or equal to the minimum safe lane change 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 behind the target gap after the driverless vehicle currently switches into the target gap is greater than If it is equal to the maximum deceleration of the vehicle behind the target gap, the driverless vehicle switching requirements are met. 4. A method for collaborative lane changing of a queue of unmanned vehicles in mixed traffic flow according to claim 1, characterized in that when the unmanned vehicle changes lanes for the first time and enters the target vehicle queue, when the unmanned vehicle changes lanes for the first time, When the unmanned vehicle is a single vehicle, the gap between the single vehicle and the vehicle in front of it is adjusted so that the gap meets the target gap for the remaining unmanned vehicles to safely enter, and one of the remaining unmanned vehicles is moved Change lanes and drive into the target gap. 5. A method for collaborative lane changing of a queue of unmanned vehicles in mixed traffic flow according to claim 1, characterized in that 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 driverless vehicles in the fleet of driverless vehicles so that the gap meets the target gap for the remaining driverless vehicles to safely drive in, and remove one driverless vehicle from the remaining driverless vehicles. Drive the vehicle to change lanes and drive into the target gap. 6. A method for collaborative lane changing of a queue of unmanned vehicles in mixed traffic flow according to claim 5, characterized in that when the unmanned vehicle that changes lanes for the first time is a fleet of multiple unmanned vehicles. , Expand the gap between two adjacent autonomous vehicles in a convoy of multiple autonomous vehicles that change lanes for the first time, so that the remaining autonomous vehicles can safely switch into the gap. 7. A method for collaborative lane changing of a queue of unmanned vehicles in mixed traffic flow according to claim 6, characterized in that the remaining unmanned vehicles select the gap closest to them as the target gap. 8. A method for collaborative lane changing of a queue of unmanned vehicles in mixed traffic flow according to claim 1, characterized in that the future is continued to be planned based on the position of each vehicle in the mixed flow lane changing model of unmanned vehicles at the current moment. A trajectory in the rolling time domain until the target gap expands to the target gap length. 9. A system for cooperative lane changing of a queue of driverless vehicles in a mixed traffic flow based on the method of cooperative lane changing of a queue of driverless vehicles in a mixed traffic flow according to claim 1, characterized in that it includes an information collection module and a processing unit module; The information collection module obtains all vehicle information within the cooperative interaction radius of the unmanned vehicle queue, and establishes an unmanned vehicle mixed flow lane changing model based on the unmanned vehicle queue and all acquired vehicle information; The processing module determines the maximum gap on the target lane within the longitudinal range of the lane where the driverless vehicle is located based on the established mixed-flow lane-changing model of the driverless vehicle, and uses the maximum gap as the target gap for the first lane change; according to the The position of the driving vehicle is determined to be an unmanned vehicle within the longitudinal range of the target gap, and the unmanned vehicle is confirmed to be an unmanned vehicle that changes lanes for the first time. When the target gap meets the conditions for safe entry of an unmanned vehicle that changes lanes for the first time, The unmanned vehicle changes lanes for the first time and drives into the target gap; a longitudinal trajectory planning model of the unmanned vehicle is built based on the idea of rolling optimization. After the unmanned vehicle changes lanes for the first time, the current unmanned vehicle queue in the rolling time domain is obtained Based on the trajectories of all vehicles in the unmanned vehicle longitudinal trajectory planning model, the target gaps of the remaining unmanned vehicles are determined, and the remaining unmanned vehicles are sequentially changed lanes and driven into the target gaps. 10. A collaborative lane-changing system for a queue of unmanned vehicles in mixed traffic flow according to claim 9, characterized in that the real-time distance between the unmanned vehicle and the vehicle in front of the target gap is obtained. If the unmanned vehicle and the vehicle in front of the target gap are The distance between the vehicle in front of the target gap at the current moment is greater than or equal to the minimum safe lane change 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 behind the target gap after the driverless vehicle currently switches into the target gap is greater than If it is equal to the maximum deceleration of the vehicle behind the target gap, the driverless vehicle switching requirements are met.