CN115830908B - Method and system for cooperatively changing lanes of unmanned vehicle queues in mixed traffic flow - Google Patents
Method and system for cooperatively changing lanes of unmanned vehicle queues in mixed traffic flow Download PDFInfo
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Abstract
The application discloses a method and a system for cooperatively changing lanes of an unmanned vehicle queue in a mixed traffic flow, which can finish lane changing by stepping the lane changing process of a vehicle queue when a target gap cannot accommodate the whole vehicle queue and simultaneously change lanes, and consider uncertainty of driving behaviors of the unmanned vehicle and safety and time efficiency in the planning process of the actions of the unmanned vehicle. The method is closer to reality than the existing vehicle lane changing method under the pure unmanned vehicle environment, and is more efficient than the single vehicle lane changing method. The method for the unmanned vehicle team to cooperatively change the lanes in the mixed traffic flow can recommend the latest lane change starting position for the automatic driving vehicle team under different traffic conditions, remind the vehicle team to change lanes in time, prevent the vehicle team from missing the latest opportunity to slide to miss the opportunity of the down-turn lane, and provide technical guarantee for the automatic driving vehicle team to change lanes in the mixed traffic flow.
Description
Technical Field
The application belongs to the field of road traffic control, and particularly relates to a method and a system for cooperatively changing lanes in a mixed traffic flow by using an unmanned vehicle queue.
Background
Advances in unmanned technology are beneficial to improving the safety of the efficiency of the overall traffic system. With the ability of vehicles to communicate, unmanned vehicles are expected to be able to travel in the form of fleets of vehicles, as fleets of vehicles can shorten the gap between vehicles, improving traffic ability. And the queues can reduce the air resistance of the automobile and reduce the energy consumption. However, due to technical limitations and lack of corresponding policy regulations, unmanned vehicles will be mixed with manned vehicles for a long time in the future. Because of the uncertainty of the driving behavior of the unmanned vehicle, lane changing of the unmanned vehicle in the mixed stream becomes difficult. In addition, in order to ensure the safety in the interaction process of the manned vehicle, the unmanned vehicle often adopts a conservative action when changing lanes. The existing researches are mostly directed to the environment of the pure unmanned vehicle, the influence of the nearby unmanned vehicle on the lane change 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 car, and when the method is applied to changing the lane of the motorcade, the efficiency is low, and the success rate of changing the lane of the motorcade is difficult to ensure. Therefore, the lane change problem of the unmanned vehicle train in the mixed stream is not yet an effective solution.
Disclosure of Invention
The application aims to provide a method and a system for cooperatively changing lanes of an unmanned vehicle queue in a mixed traffic flow, so as to solve the problems of the conventional method that the lane changing success rate of the unmanned vehicle is high and the efficiency is low in the lane changing process.
A method for collaborative lane change of an unmanned vehicle queue in a mixed traffic stream, comprising the steps of:
s1, acquiring all vehicle information in the 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 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 taking the maximum gap as the target gap for first lane change; determining an unmanned vehicle within the longitudinal range of a target gap according to the position of the unmanned vehicle, confirming that the unmanned vehicle is a first lane change unmanned vehicle, and when the target gap meets the safe driving condition of the first lane change unmanned vehicle, driving the first lane change unmanned vehicle into the target gap;
s3, constructing a longitudinal track planning model of the unmanned vehicle based on the idea of rolling optimization, acquiring tracks of all vehicles in a current unmanned vehicle queue in a rolling time domain after the unmanned vehicle changes lanes for the first time, determining target gaps of the rest unmanned vehicles based on the longitudinal track planning model of the unmanned vehicle, and sequentially changing lanes of the rest unmanned vehicles to drive into the target gaps.
Preferably, the interaction between the road side unit and the vehicle communication unit is adopted to acquire the information of all vehicles within the communication radius range of the unmanned vehicle queue.
Preferably, the real-time distance between the unmanned vehicle and the target gap front vehicle is obtained, and if the distance between the unmanned vehicle and the target gap front vehicle at the current moment is greater than or equal to the minimum lane change safety distance between the unmanned vehicle and the target gap front vehicle at the current moment, and the reaction acceleration of the unmanned vehicle after the target gap is greater than or equal to the maximum deceleration of the target gap rear vehicle after the unmanned vehicle is currently changed into the target gap, the requirement of changing the unmanned vehicle is met.
Preferably, after the first lane change of the unmanned vehicles, the unmanned vehicles enter the target vehicle queue, and when the first lane change of the unmanned vehicles is a single vehicle, the gap between the single vehicle and the front vehicle is adjusted to enable the gap to meet the target gap for the safe driving of the rest unmanned vehicles, and one of the rest unmanned vehicles is lane-changed to drive into the target gap.
Preferably, when the first lane change unmanned vehicle is a fleet of a plurality of unmanned vehicles, a gap between two unmanned vehicles in the fleet of unmanned vehicles is adjusted to enable the gap to meet a target gap for safe driving of the remaining unmanned vehicles, and one of the remaining unmanned vehicles is lane-changed to drive into the target gap.
Preferably, when the unmanned vehicle for the first lane change is a vehicle team consisting of a plurality of unmanned vehicles, a gap between two adjacent unmanned vehicles in the vehicle team consisting of the plurality of unmanned vehicles for the first lane change is enlarged, so that the remaining unmanned vehicles can be safely changed into the gap.
Preferably, the remaining unmanned vehicles select the gap closest to them as the target gap.
Preferably, the track in a rolling time domain is planned continuously according to the positions of all vehicles in the unmanned vehicle mixed flow lane model at the current moment until the target gap is increased to the target gap length.
A cooperative lane change system 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 changing 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 change model, and taking the maximum gap as the target gap for first lane change; determining an unmanned vehicle within the longitudinal range of a target gap according to the position of the unmanned vehicle, confirming that the unmanned vehicle is a first lane change unmanned vehicle, and when the target gap meets the safe driving condition of the first lane change unmanned vehicle, driving the first lane change unmanned vehicle into the target gap; and constructing a longitudinal track planning model of the unmanned vehicle based on the idea of rolling optimization, acquiring tracks of all vehicles in a current unmanned vehicle queue in a rolling time domain after the unmanned vehicle changes lanes for the first time, determining a target gap of the rest unmanned vehicles based on the longitudinal track planning model of the unmanned vehicle, and sequentially changing lanes of the rest unmanned vehicles to drive into the target gap.
Preferably, the real-time distance between the unmanned vehicle and the target gap front vehicle is obtained, and if the distance between the unmanned vehicle and the target gap front vehicle at the current moment is greater than or equal to the minimum lane change safety distance between the unmanned vehicle and the target gap front vehicle at the current moment, and the reaction acceleration of the unmanned vehicle after the target gap is greater than or equal to the maximum deceleration of the target gap rear vehicle after the unmanned vehicle is currently changed into the target gap, the requirement of changing the unmanned vehicle is met.
Compared with the prior art, the application has the following beneficial technical effects:
according to the method for cooperatively changing the lanes of the unmanned vehicle queues in the mixed traffic flow, when the target clearance cannot accommodate the whole vehicle queue and simultaneously change lanes, lane changing is completed by stepping the lane changing process of the vehicle queue, and uncertainty of driving behaviors of the unmanned vehicles and safety and time efficiency in the unmanned vehicle action planning process are considered. The method is closer to reality than the existing vehicle lane changing method under the pure unmanned vehicle environment, and is more efficient than the single vehicle lane changing method. The method for the unmanned vehicle team to cooperatively change the lanes in the mixed traffic flow can recommend the latest lane change starting position for the automatic driving vehicle team under different traffic conditions, remind the vehicle team to change lanes in time, prevent the vehicle team from missing the latest opportunity to slide to miss the opportunity of the down-turn lane, and provide technical guarantee for the automatic driving vehicle team to change lanes in the mixed traffic flow.
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FIG. 1 is a flow chart of a co-channel change of a fleet of vehicles in a mixed stream in an embodiment of the present application.
Fig. 2 is a schematic diagram of a collaborative planning of a trajectory of an unmanned vehicle according to an embodiment of the present application.
Fig. 3 is a schematic view of a traffic scene in an embodiment of the application.
Fig. 4 is a schematic diagram of a cooperative lane change track of a fleet in accordance with an embodiment of the present application.
Detailed Description
In order that those skilled in the art will better understand the present application, a technical solution in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present application without making any inventive effort, shall fall within the scope of the present application.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present application and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the application described herein may be implemented in sequences other than those illustrated or otherwise 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 application discloses a method for cooperatively changing lanes in a mixed traffic flow by using an unmanned vehicle queue, which comprises the following steps of:
s1, acquiring all vehicle information in the 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, interaction between the road side unit and the vehicle communication unit is adopted to acquire information of all vehicles in the communication radius range of the unmanned vehicle queue, each vehicle is provided with the vehicle communication unit, the road side unit and the vehicle communication unit can communicate, and the vehicle information comprising the position information, the vehicle speed and the acceleration information of the vehicle is acquired.
The method comprises the steps of constructing a mixed flow lane changing model of the unmanned vehicle to judge whether a certain gap can allow safe lane changing of the unmanned vehicle;
the unmanned vehicle mixed flow lane change 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 interval or not, and the set safety interval is calculated 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 from the vehicle in front of the target gap:
wherein:-minimum lane change safety distance between the unmanned vehicle (CAV) and the target gap front vehicle (pre) at time t, unit: rice;
v CAV (t) -the speed of the unmanned vehicle at time t, unit: rice/sec;
v pre (t) -the speed of the preceding vehicle at time t, in units of: rice/sec;
-maximum deceleration of the unmanned vehicle, unit: meter/second 2;
-maximum deceleration of the vehicle before the target gap, unit: meter/second 2;
τ CAV reaction time of unmanned vehicle, unit: second, wherein the second is;
x CAV (t) -the position of the unmanned vehicle at time t, unit: rice;
x pre (t) -the position of the target gap front truck at the time t, unit: rice;
h-length of unmanned vehicle, unit: and (5) rice.
If it isThen the safety lane changing standard of the unmanned vehicle is satisfied s CAV,pre (t) is the actual distance between the unmanned vehicle (CAV) and the target gap front vehicle (pre) at the time t, and the calculation formula is as follows:
s CAV,pre (t)＝x pre (t)-x CAV (t)-h (2)
the unmanned vehicle ready for lane change is changed into a target gap, the reaction acceleration generated by the vehicle (fol) after the target gap cannot exceed the acceleration/deceleration limit of the vehicle, the reaction acceleration of the vehicle (fol) after the target gap is calculated by an IDM model, and the IDM model needs to determine the minimum following distance (taking 3 meters), the acceleration coefficient (taking 2) and the limit range of the acceleration and deceleration of the vehicle.
The calculation formula of the safety judgment of the vehicle after the clearance with the target is as follows:
-maximum deceleration of the vehicle after the target clearance, unit: rice/second 2 ；
a fol,CAV And (t) is the reaction acceleration of the vehicle after the target gap after the unmanned vehicle is changed into the target gap at the time t, and the unit is: meter/second 2;
-maximum acceleration of the vehicle after the target gap, unit: rice/second 2 ；
s 0 -minimum heel-to-heel spacing, taking 3 meters;
S * -desired spacing of unmanned vehicles and rear vehicles, units: rice;
Δt, discrete time interval, taking 0.1 seconds;
delta-acceleration coefficient, generally taken as 2.
At the same time satisfyThe unmanned vehicle makes a lane change into the target gap.
S2, determining the maximum gap on a 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 taking the maximum gap as the target gap for first lane change; and determining the unmanned vehicle within the longitudinal range of the target gap according to the position of the unmanned vehicle, and confirming that the unmanned vehicle is the first lane-changing unmanned vehicle.
Providing n vehicles in the unmanned vehicle team, and bringing k, m values in { k, m|k=1, n, m=1, n-k } into the following inequality in sequence, finding k, m values which enable the following inequality to be met, and confirming the unmanned vehicle or the unmanned vehicle set for the first lane change to be { k, k+1, n, k+m-1}.
Wherein: m-the number of unmanned vehicles for the first lane change;
k-kth vehicle in fleet.
t 0 -the time at which the lane change starts.
S3, constructing a longitudinal track planning model of the unmanned vehicle based on the idea of rolling optimization, acquiring tracks of all vehicles in a current unmanned vehicle queue in a rolling time domain after the unmanned vehicle changes lanes for the first time, determining target gaps of the rest unmanned vehicles based on the longitudinal track planning model of the unmanned vehicle, and sequentially changing lanes of the rest unmanned vehicles to drive into the target gaps.
When the unmanned vehicle is a single vehicle, the gap between the single vehicle and the front vehicle is adjusted to enable the gap to meet the target gap for the safe driving of the rest unmanned vehicles, and one of the rest unmanned vehicles is driven into the target gap after the lane change.
When the first unmanned vehicle for lane changing is a vehicle team consisting of a plurality of unmanned vehicles, the gap between two unmanned vehicles in the vehicle team consisting of the unmanned vehicles is adjusted, so that the gap meets the target gap for safe driving of the rest unmanned vehicles, and one unmanned vehicle in the rest unmanned vehicles is lane-changed and driven into the target gap.
As shown in fig. 1 and 2, the application takes an example of an unmanned vehicle queue formed by six vehicle groups, divides the unmanned vehicle queue into three vehicle queues, wherein one vehicle queue changes lanes for the first time, two vehicle queues which are not changed lanes are divided into a vehicle queue 1 and a vehicle queue 2 according to the front and back positions, and the vehicle queues which are changed lanes form a vehicle queue 3.
The collaborative way of the collaborative trajectory planning phase is: the gap between two adjacent unmanned vehicles in the motorcade 3 is enlarged, so that the vehicles in the motorcade 1 and the motorcade 2 can be safely changed, and meanwhile, the positions of the motorcade 1 and the motorcade 2 need to be adjusted in real time according to the positions of the target gaps in the motorcade 3, and the specific details are as follows:
the fleet 3 needs to determine which vehicles in the fleet 3 to enlarge the gap between and the target size of the enlarged gap. To this end, first, target gaps of the fleet 1 and the fleet 2 are determined, and in order to reduce the time for adjusting the positions of the fleet 1 and the fleet 2, the fleet 1 and the fleet 2 select the gap closest to the fleet 1 and the fleet 2 respectively in the fleet 3 as the target gap; next, the required pitch sizes of the fleet 1 and the fleet 2 are calculated according to the formulas (9) - (12), respectively.
Wherein L is 1 And L 2 Lane change pitch required for fleet 1 and fleet 2, respectively.
Only the trajectory in the next scroll horizon (p) is planned. After the track in one time domain is executed, the track in one rolling time domain is planned continuously according to the positions of the vehicles in the mixed flow channel model of the unmanned vehicle at the current moment until the target gap is increased to the target gap length. At the same time, the planning model should consider the comfort of passengers and the time of track adjustment, so the following track planning model is built for the motorcade 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)
wherein: omega 1 -a weight of comfort;
ω 2 -weight of time efficiency;
I 3 -a collection of unmanned vehicles within a fleet 3;
minimum safe lane change distance required for the unmanned vehicle k-1 to the following vehicle k, unit: and (5) rice.
n-number of vehicles in the unmanned fleet;
p-is the rolling prediction horizon;
L 1 minimum lane change required pitch of fleet 1, unit: rice;
L 2 minimum lane change required pitch of fleet 2, unit: rice;
-minimum safe lane change distance required by unmanned vehicle k-1 and rear vehicle k, unit: and (5) rice.
The targets of fleet 1 at the track co-ordination stage are: when the fleet 3 is adjusted for clearance, the fleet 1 is in a position where it can safely be swapped into its target clearance in the fleet 3. For this purpose, the fleet 1 calculates the safe lane change distance between the fleet and the front vehicle in the target clearance in real time, and takes the safe distance as the target of the actual distance; in addition, comfort of the passengers of the fleet 1 should be considered, and for this purpose, a fleet 1 trajectory planning model objective function as shown in formula (19) is established. 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, and after the track in one rolling time domain is executed, the safe lane change gap between the motorcade 1 and the front vehicle with the target gap is recalculated, and the track in one rolling time domain is planned in the future. The model is specifically as follows:
objective function:
constraint: formulas (14) - (18),and i=1.
The targets of fleet 2 at the track co-ordination stage are: fleet 2 when fleet 3 adjusts for clearance, fleet 2 is located in a position that enables safe replacement into fleet 3 for its target clearance. For this purpose, the motorcade 2 calculates the safe lane change distance between the motorcade and the front vehicle of the target gap in real time, and takes the safe distance as the target of the actual distance; in addition, comfort of the passengers of the fleet 2 should be considered, and for this purpose, a fleet 2 trajectory planning model objective function as shown in formula (20) is established. 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, and after the track in one rolling time domain is executed, the safe lane change gap between the motorcade 2 and the front vehicle with the target gap is recalculated, and the track in one rolling time domain is planned in the future. The model is specifically as follows:
objective function:
constraint:
formulas (14) - (18) and formula (21),and i=k+m.
The above-mentioned required section of two lanes on the expressway is exemplified by that one 10 unmanned vehicle fleets need to change lanes to outside lanes before reaching the turn road junction. The traffic volume of the outside 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 the lane change start signal at a location upstream 1500 from the turn junction. A schematic view of the scene is shown in fig. 3. Model parameters are shown in Table 1 below
TABLE 1 model parameter settings
The collaborative lane change track diagram in the scene is shown in fig. 4, and it can be seen from fig. 4 that by using the method provided by the application, a fleet of 10 unmanned vehicles can complete the entire lane change of the fleet within 20 seconds under a traffic environment of 1500 veh/h. By comparing with two classical lane changing models Gipps and MOBIL, the method of the application has obvious advantages in the problem of lane changing of a vehicle team in a mixed flow. The multiple test results in this scenario also confirm the superiority of the present application. The test results are shown in table 2:
TABLE 2 comparison of Process Performance results
As can be seen from simulation results, compared with the conventional channel changing model, the channel changing success rate of the method provided by the application is obviously improved, and meanwhile, the channel changing execution time of a motorcade is greatly reduced.
The method for the cooperative lane change of the unmanned vehicle fleet in the mixed traffic flow is reasonable, reliable, simple and easy to implement, can finish lane change by step-by-step lane change process when the whole vehicle fleet cannot be accommodated in a target gap and meanwhile the lane is changed, and considers uncertainty of driving behavior of the unmanned vehicle and safety and time efficiency in the planning process of the actions of the unmanned vehicle. The method is closer to reality than the existing vehicle lane changing method under the pure unmanned vehicle environment, and is more efficient than the single vehicle lane changing method. The method for the unmanned vehicle team to cooperatively change the lanes in the mixed traffic flow can recommend the latest lane change starting position for the automatic driving vehicle team under different traffic conditions, remind the vehicle team to change lanes in time, prevent the vehicle team from missing the latest opportunity to slide to miss the opportunity of the down-turn lane, and provide technical guarantee for the automatic driving vehicle team to change lanes in the mixed traffic flow.
It should be noted that modifications and adaptations to the present application may occur to one skilled in the art without departing from the principles of the present application and are intended to be comprehended within the scope of the present application. The components not explicitly described in this embodiment can be implemented by using the prior art.
Claims (10)
1. A method for cooperatively changing lanes of an unmanned vehicle queue in a mixed traffic stream, comprising the steps of:
s1, acquiring all vehicle information in the 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;
the unmanned vehicle mixed flow lane change 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 interval, and calculating the reaction time and the maximum acceleration and deceleration of the unmanned vehicle by the set safety interval through the Gipps model:
gipps calculates the safe distance from the vehicle in front of the target gap:
wherein:-minimum lane change safety distance between the unmanned vehicle (CAV) and the target gap front vehicle (pre) at time t, unit: rice;
v CAV (t) -speed of unmanned vehicle at time t, singlyBits: rice/sec;
v pre (t) -the speed of the preceding vehicle at time t, in units of: rice/sec;
-maximum deceleration of the unmanned vehicle, unit: meter/second 2;
-maximum deceleration of the vehicle before the target gap, unit: meter/second 2;
τ CAV reaction time of unmanned vehicle, unit: second, wherein the second is;
x CAV (t) -the position of the unmanned vehicle at time t, unit: rice;
x pre (t) -the position of the target gap front truck at the time t, unit: rice;
h-length of unmanned vehicle, unit: rice;
if it isThen the safety lane changing standard of the unmanned vehicle is satisfied s CAV,pre (t) is the actual distance between the unmanned vehicle (CAV) and the target gap front vehicle (pre) at the time t, and the calculation formula is as follows:
s CAV,pre (t)＝x pre (t)-x CAV (t)-h (2)
the unmanned vehicle for lane change is prepared to be changed into a target gap, the reaction acceleration generated by the traffic fox after the target gap cannot exceed the acceleration/deceleration limit of the vehicle, the reaction acceleration of the traffic fox after the target gap is calculated by an IDM model, and the IDM model needs to determine the minimum following interval to be 3 meters, the acceleration coefficient to be 2 and the limit range of the acceleration and deceleration of the vehicle;
the calculation formula of the safety judgment of the vehicle after the clearance with the target is as follows:
-maximum deceleration of the vehicle after the target clearance, unit: rice/second 2 ；
a fol,CAV And (t) is the reaction acceleration of the vehicle after the target gap after the unmanned vehicle is changed into the target gap at the time t, and the unit is: rice/second 2 ；
-maximum acceleration of the vehicle after the target gap, unit: rice/second 2 ；
s 0 -minimum heel-to-heel spacing, taking 3 meters;
S * -desired spacing of unmanned vehicles and rear vehicles, units: rice;
Δt, discrete time interval, taking 0.1 seconds;
delta-acceleration coefficient is 2;
at the same time satisfyThe unmanned vehicle changes lanes to enter the target gap;
s2, determining the maximum gap on a 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 taking the maximum gap as the target gap for first lane change; determining an unmanned vehicle within the longitudinal range of a target gap according to the position of the unmanned vehicle, confirming that the unmanned vehicle is a first lane change unmanned vehicle, and when the target gap meets the safe driving condition of the first lane change unmanned vehicle, driving the first lane change unmanned vehicle into the target gap;
s3, constructing a longitudinal track planning model of the unmanned vehicle based on the idea of rolling optimization, acquiring tracks of all vehicles in a current unmanned vehicle queue in a rolling time domain after the unmanned vehicle changes lanes for the first time, determining a target gap of the rest unmanned vehicles based on the longitudinal track planning model of the unmanned vehicle, and sequentially changing lanes of the rest unmanned vehicles to drive into the target gap;
taking an unmanned vehicle queue formed by six vehicle groups as an example, dividing the unmanned vehicle queue into three teams, wherein one team changes lanes for the first time, and the two teams which are not changed lanes are divided into a team 1 and a team 2 according to the front and back positions, and the teams which are changed lanes form a team 3;
the collaborative way of the collaborative trajectory planning phase is: the gap between two adjacent unmanned vehicles in the motorcade 3 is enlarged, so that the vehicles in the motorcade 1 and the motorcade 2 can be safely changed, and meanwhile, the positions of the motorcade 1 and the motorcade 2 need to be adjusted in real time according to the positions of the target gaps in the motorcade 3, specifically:
firstly, determining target gaps of the motorcade 1 and the motorcade 2, wherein the gap closest to the motorcade 1 and the motorcade 2 in the motorcade 3 is selected as the target gap; next, the required distance magnitudes for fleet 1 and fleet 2 are calculated according to equations (9) - (12), respectively:
wherein L is 1 And L 2 Lane changing intervals required by the motorcade 1 and the motorcade 2 respectively;
planning a track in a rolling time domain p next, and after the track in the time domain is executed, continuing to plan the track in the rolling time domain in the future according to the positions of the vehicles in the unmanned vehicle mixed flow lane change model at the current moment until the target gap is increased to the target gap length; at the same time, the planning model should consider the comfort of passengers and the time of track adjustment, so the following track planning model is built for the motorcade 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)
wherein: omega 1 -a weight of comfort;
ω 2 -weight of time efficiency;
I 3 for unmanned vehicles in fleet 3A collection;
minimum safe lane change distance required for the unmanned vehicle k-1 to the following vehicle k, unit: rice;
n-number of vehicles in the unmanned fleet;
p-is the rolling prediction horizon;
L 1 minimum lane change required pitch of fleet 1, unit: rice;
L 2 minimum lane change required pitch of fleet 2, unit: and (5) rice.
2. The method for collaborative lane change in a mixed traffic flow in accordance with claim 1 wherein information about all vehicles within a communication radius of the unmanned vehicle queue is obtained using interactions between road side units and vehicle communication units.
3. The method for collaborative lane change in a mixed traffic flow according to claim 1, wherein a real-time distance between the unmanned vehicle and the target gap front vehicle is obtained, and the requirement for the change of the unmanned vehicle is satisfied if the distance between the unmanned vehicle and the target gap front vehicle at the current time is equal to or greater than a minimum lane change safety distance between the unmanned vehicle and the target gap front vehicle at the current time, and the reaction acceleration of the unmanned vehicle after the target gap is changed into the target gap is equal to or greater than a maximum deceleration of the target gap rear vehicle.
4. The method for collaborative lane change in a mixed traffic flow according to claim 1, wherein after the first lane change of the unmanned vehicles, the unmanned vehicles enter the target vehicle queue, and when the first lane change of the unmanned vehicles is a single vehicle, the gap between the single vehicle and the front vehicle is adjusted so that the gap meets the target gap for the safe entry of the remaining unmanned vehicles, and one of the remaining unmanned vehicles is lane-changed to enter the target gap.
5. The method for collaborative lane change in a mixed traffic flow according to claim 1, wherein when the first lane change of the unmanned vehicles is a fleet of a plurality of unmanned vehicles, a gap between two unmanned vehicles in the fleet of unmanned vehicles is adjusted to satisfy a target gap for safe entry of the remaining unmanned vehicles, and one of the remaining unmanned vehicles is lane-changed to enter the target gap.
6. The method for collaborative lane change in a mixed traffic flow according to claim 5, wherein when the first lane change of the unmanned vehicle is a fleet of a plurality of unmanned vehicles, a gap between two adjacent unmanned vehicles in the fleet of the first lane change of the plurality of unmanned vehicles is enlarged so that the remaining unmanned vehicles can safely be swapped into the gap.
7. A method of co-operating lane-changing in a mixed traffic stream in accordance with claim 6 wherein the remaining unmanned vehicles select the gap closest to them as the target gap.
8. The method for collaborative lane change in a mixed traffic flow of an array of unmanned vehicles according to claim 1, wherein the trajectory in a future rolling time domain is continuously planned according to the position of each vehicle in the mixed traffic flow lane change model of the unmanned vehicles at the current time until the target gap is increased to the target gap length.
9. A system for cooperatively changing lanes in a mixed traffic stream based on the method for cooperatively changing lanes in a mixed traffic stream in an unmanned vehicle train according to claim 1, which 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 changing 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 change model, and taking the maximum gap as the target gap for first lane change; determining an unmanned vehicle within the longitudinal range of a target gap according to the position of the unmanned vehicle, confirming that the unmanned vehicle is a first lane change unmanned vehicle, and when the target gap meets the safe driving condition of the first lane change unmanned vehicle, driving the first lane change unmanned vehicle into the target gap; and constructing a longitudinal track planning model of the unmanned vehicle based on the idea of rolling optimization, acquiring tracks of all vehicles in a current unmanned vehicle queue in a rolling time domain after the unmanned vehicle changes lanes for the first time, determining a target gap of the rest unmanned vehicles based on the longitudinal track planning model of the unmanned vehicle, and sequentially changing lanes of the rest unmanned vehicles to drive into the target gap.
10. The cooperative lane-changing system of the unmanned vehicle queue in the mixed traffic flow according to claim 9, wherein the real-time distance between the unmanned vehicle and the target gap front vehicle is obtained, and the requirement for the change of the unmanned vehicle is satisfied if the distance between the unmanned vehicle and the target gap front vehicle at the current time is equal to or greater than the minimum lane-changing safety distance between the unmanned vehicle and the target gap front vehicle at the current time, and the reaction acceleration of the unmanned vehicle after the target gap is equal to or greater than the maximum deceleration of the target gap rear vehicle after the unmanned vehicle is currently changed into the target gap.
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