CN114495578B - Non-signal lamp crossing vehicle scheduling method of multiple virtual fleets based on conflict points - Google Patents
Non-signal lamp crossing vehicle scheduling method of multiple virtual fleets based on conflict points Download PDFInfo
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Abstract
The invention discloses a method for dispatching vehicles at a signal lamp-free intersection of multiple virtual vehicle fleets based on conflict points, which relates to the field of automatic driving. The method is oriented to the signal lamp-free intersection, and the optimal vehicle scheduling strategy for maximizing the throughput of the signal lamp-free intersection is found on the premise of ensuring the driving safety.
Description
Technical Field
The invention relates to the field of linear programming, in particular to a conflict point-based method for dispatching vehicles at a multi-virtual vehicle fleet at a signal lamp-free intersection.
Background
With the development of urbanization and the improvement of living standard of people, the unbalanced increase of the number of automobiles and traffic capacity brings unprecedented challenges to traffic systems. The automatic driving vehicle gradually realizes the complete control of the vehicle and can interact necessary vehicle information with adjacent vehicles in real time, which lays a technical foundation for the efficiency improvement of a future traffic system, and the automatic driving vehicle also becomes one of the core units for the scheduling optimization of the future traffic system.
The traffic crossroads are used as a pre-planned traffic flow intersection area, congestion is easily caused due to complex topological relations and variable road conditions, and meanwhile, in the United states, traffic accidents of nearly four components are related to the crossroads, so that the problem of how to realize safe and efficient traffic of the traffic crossroads is a common problem in all countries in the world. In order to solve the problems, one type of research focuses on optimizing scheduling under the traditional signal lamp intersection, and the optimization scheduling is mainly divided into two directions, wherein one direction is based on a fixed signal lamp period, and whether an automatic driving vehicle passes through the current intersection or not is scheduled, so that the maximum benefit under the residual time of the current signal lamp is met. The other direction is based on dynamic signal lamps, and the intelligent signal lamps at the intersection dynamically adjust the period of the signal lamps in real time according to the collected intersection vehicle information to meet the preset passing target.
However, the optimization of the traditional signal lamp still has unnecessary waste of signal lamp time and unnecessary fuel consumption caused by frequent start and stop before the intersection, so the optimization scheduling of the intersection without the signal lamp gradually becomes the core of the research of the future traffic scheduling. Scheduling methods related to this kind are mainly classified into three categories. The first type is a reservation-based method, in which an autonomous vehicle near an intersection sends a traffic request to a centralized solver (controller) device at the intersection, and the solver reserves corresponding space-time resources for each vehicle at the intersection according to the vehicle conditions at the intersection, so that the vehicles can safely and efficiently pass through the system. The second method is based on an optimization model, the safe and efficient traffic problem of the intersection without the signal lamp can be abstracted into an accurate optimization model, a centralized high-calculation solver placed at the intersection is used for solving the problem in real time, vehicle collision can be avoided through a plurality of constraints in the model, and finally different optimization purposes are achieved by setting an optimization objective function.
The third category of methods is based on a single virtual fleet of vehicles and is the most similar implementation of the present invention. Different from the two methods, the method is a distributed control method, lanes of a two-dimensional signal-lamp-free intersection are uniformly converted to a one-dimensional virtual lane by equidistant rotation transformation with the center of the intersection as a fulcrum, then the sequence of vehicles in the virtual fleet entering the intersection is constructed by a spanning tree method and a first-in first-out criterion according to the conflict relationship between the lanes, finally the safe and efficient traffic problem of the vehicles at the two-dimensional intersection is converted into the control problem of forming a stable virtual fleet by scheduling the vehicles on the one-dimensional virtual lane, and the effect of centralized optimization is achieved by using a distributed control strategy. Based on the thought of a single virtual motorcade, some researches optimize the vehicle sequence in the single virtual motorcade, and aim to achieve higher traffic efficiency. However, since the scheme is a single virtual vehicle fleet constructed according to lanes, there is an upper limit on the improvement of the utilization rate of the intersection, because the scheme only allows vehicles on compatible lanes to simultaneously travel at the same time, while vehicles on conflicting lanes travel in two batches, but the collision of the vehicles only occurs at the intersection of the conflicting lanes, not the whole conflicting lanes, the intersection space utilization efficiency of the single virtual vehicle fleet scheme still has a certain optimization space.
Therefore, the invention constructs a multi-virtual vehicle team consisting of a plurality of virtual lanes based on all conflict points in the intersection, converts the scheduling problem of the signal-free intersection into the problem of multi-virtual vehicle team packing, and provides an optimized model and a solving method thereof based on the problem, thereby effectively solving the problems of difficult control, large communication load and the like of the signal-free intersection, improving the space utilization rate of the intersection, and increasing the throughput of the intersection.
Disclosure of Invention
In view of the above defects in the prior art, the technical problem to be solved by the present invention is to find an optimal vehicle scheduling strategy that maximizes the throughput at the signal-free intersection on the premise of ensuring driving safety. The problem that the utilization rate of the intersection is low due to the fact that only the conflict relationship between lanes is considered is mainly solved; meanwhile, the problem that communication and calculation loads are too high due to frequent calculation of a centralized solver at the intersection is solved, and the real-time calculation requirement of high traffic flow is met; finally, the invention can complete the centralized solution of the problem only by the lane and the relative position of the vehicle, thereby protecting the privacy data of the vehicles at the intersection.
In order to achieve the purpose, the invention provides a conflict point-based method for dispatching vehicles at a signal lamp-free intersection of multiple virtual vehicle fleets, which is characterized in that the vehicle dispatching method is based on multiple conflict points of an intersection, an integral system consisting of the multiple virtual vehicle fleets is constructed, the conflict relationship of the vehicles at the intersection is converted into the distance relationship in the multiple virtual vehicle fleets, the throughput of the intersection is converted into the maximum external rectangle size in the multiple virtual vehicle fleets, and the conflict-free optimal dispatching problem of a two-dimensional signal lamp-free intersection is converted into a one-dimensional boxing problem through the multiple virtual vehicle fleets.
Further, the method comprises the steps of:
step 2, constructing a boxing problem, namely the boxing problem under the background of multiple virtual fleets at the intersection without the signal lamp, which is the boxing problem of one-dimensional irregular graphs;
step 3, constructing an optimization model;
s.t.p(x i )≥0
p(x i )≤d max
|p(x j )-p(x i )|≥d s ,i＜j
wherein the safe following distance of the vehicle is d s Is represented by d max Overall vehicle maximum separation, p (x), representing intersection throughput or intersection utilization i ) Representing the position of the vehicle i on the multiple virtual lanes;
step 4, heuristic improvement;
in order to remove some redundant constraints in the optimization model, the invention provides three corresponding heuristic improvement strategies to remove the redundant constraints:
strategy one: the first vehicle on each actual lane entering the coordination area must satisfy a first constraint and the last vehicle must satisfy a second constraint. The other vehicles only need to compare whether the relative distance between the adjacent vehicles on the same lane meets the safety distance, and other vehicles on the same lane do not need to be compared through the third constraint;
and (2) strategy two: when the lanes where the vehicle i and the vehicle j are located are compatible lanes, the third constraint does not need to be compared;
and (3) strategy three: specifying a passing threshold
When the number of overtaking vehicles i on a certain lane is higher than the threshold value
The threshold and subsequent vehicles must not overtake vehicle i; in particular, equal to a threshold value
The vehicle(s) need to satisfy a third constraint after the absolute value is removed, and the vehicle(s) larger than the threshold value do not need to compare with the vehicle i for the third constraint; in particular when
Then, the representation optimization method is based on the criterion of first-in first-out;
step 5, forming discrete time distributed control of the virtual motorcade;
when the centralized solver at the intersection obtains the optimal vehicle following relationship when the motorcade is in a steady state, the solver sends information to the vehicles in the coordination area through a V2I communication technology, and after the vehicles receive the information of the vehicles needing to follow in the virtual motorcade and the expected vehicle following distance, the vehicles do not communicate with the centralized solver at the intersection any more, but communicate with the adjacent vehicles to obtain the information of the target vehicle in real time and control the self input so as to ensure that the vehicles reach the steady state of the motorcade before reaching the intersection area;
at this time, a plurality of vehicles in the virtual fleet are likely to need to follow, but strict verification shows that the vehicles only need to follow any one of the vehicles to perform a following process, and the final multi-virtual fleet system can also reach a desired fleet steady state.
Further, in the step 1, the multi-virtual vehicle fleet forming method is that all vehicles passing through the lanes of the two conflict points are located, and the absolute positions of the vehicles in the virtual lanes are determined according to the distances from the vehicles to the conflict points;
the position of the vehicle in the virtual lane is obtained by equidistant transformation of the actual driving distance between the vehicle and the conflict point, and all vehicles on each virtual lane form a stable virtual vehicle fleet through a following model, so that the multi-virtual vehicle fleet is formed;
further, an absolute position of the vehicle in the virtual lane
Can be obtained by the following formula:
wherein p is i Is the distance of vehicle i to the intersection junction boundary,
is a lane l in the intersection area j The margin distance to the conflict point k, which can be derived by off-line methods through geometric or physical measurements.
Further, the objective of the optimization model in step 3 is to minimize the maximum distance difference between the front vehicle and the rear vehicle in the virtual fleet, the first two constraints are used to limit all vehicles within the maximum distance difference, the third constraint is to ensure that the following distances of any two vehicles in the virtual fleet are greater than the safe distance when the fleet is in a steady state, but the constraint is a non-convex constraint and is not beneficial to solving the problem, so that boolean quantities are introduced to scale the non-convex constraint, and finally the optimization model is converted into a form of mixed integer linear programming which is easier to solve by a solver.
Further, the objective of solving the optimization model is to find the optimal following relationship in a multi-virtual fleet of vehicles, where each vehicle represents x i On the premise of maximizing intersection space utilization, the relative position of the vehicle i in the multi-virtual vehicle fleet in the steady state of the vehicle fleets is shown, and at the moment, the front vehicles of the vehicle i in any virtual vehicle fleet are the optimal following objects of the vehicle i.
Further, the three heuristic improvement strategies in step 4 are based on the following three criteria:
criterion one is as follows: two adjacent vehicles on the same lane can ensure that the two vehicles do not collide when the integral virtual vehicle team passes through the intersection only by keeping a safe following distance on any virtual lane; because the virtual lanes where two adjacent vehicles on the same lane are located are completely the same and overtaking is completed before all vehicles enter the coordination area, the vehicles only need to keep a safe following distance on any virtual lane, and the vehicles are ensured to be at the same distance in other virtual fleets;
criterion two: the vehicles on the compatible lane without conflict points can not collide;
criterion three: when a vehicle j in a lane has determined that it will not overtake vehicle i at the fleet steady state, then, according to criterion one, a vehicle in the same lane behind vehicle j will never overtake vehicle i.
Furthermore, only a centralized solver is needed to solve the optimal following relationship once, and the actual control process is realized through the distributed control and communication of the vehicle.
Further, in step 5, because of the unavoidable time interval of V2V communication, it is difficult to ensure continuity in actual vehicle control, and the state space expression of the vehicle i at discrete time is as follows:
wherein x is i (k)＝[p i (k),v i (k),u i (k T ) Respectively representing the displacement, velocity and acceleration of the vehicle i, t s Is the communication time interval of the system, and in the present invention
u i (k) The vehicle executes the input amount of the following model.
Further, the vehicle executes the input amount u of the following model i (k) The method comprises the following steps That is, the acceleration of the vehicle i is expressed by a linear combination of the speed difference Δ v and the desired distance difference Δ p with the preceding vehicle j as follows:
u i (k+1)＝k p Δp+k v Δv
＝k p (p j (k)-p i (k)-D des )
+k v (v j (k)-v i (k))
wherein k is p And k v Are feedback coefficients for the distance difference and the speed difference, respectively, and the same feedback coefficient is used for all vehicles of the system.
The invention has the following technical effects:
1. different from the traditional virtual vehicle fleet method based on the lane topological relation, the invention constructs a plurality of virtual vehicle fleets based on the conflict points which are possible to collide in the intersection, maximizes the utilization efficiency of the intersection and also improves the throughput of the intersection to the greatest extent under the condition of ensuring the safety.
2. The invention converts the maximized throughput problem of the two-dimensional intersection into the one-dimensional boxing problem on the multi-virtual fleet, abstracts the optimal scheduling strategy of the intersection vehicles by using an optimized model, and converts the optimal scheduling strategy into a mixed integer programming form easy to solve by a solver.
3. The invention provides three heuristic improvement strategies by combining the actual conditions and the driving rules of the traffic intersection, removes the redundant constraint in the proposed mixed integer programming problem, and greatly improves the calculation efficiency of the optimization method.
4. The invention provides the distributed control of the vehicle in discrete time, which is closer to the actual control and is easier to realize.
The conception, the specific structure and the technical effects of the present invention will be further described with reference to the accompanying drawings to fully understand the objects, the features and the effects of the present invention.
Drawings
FIG. 1 is a schematic illustration of an intersection in accordance with a preferred embodiment of the present invention;
FIG. 2 is a system block diagram of a preferred embodiment of the present invention;
FIG. 3 is a block diagram of a preferred embodiment of the present invention for constructing multiple virtual lanes;
FIG. 4 is a virtual fleet of vehicles on multiple virtual lanes in accordance with a preferred embodiment of the present invention;
FIG. 5 illustrates the fleet steady-state bin packing problem of a preferred embodiment of the present invention.
Detailed Description
The technical contents of the preferred embodiments of the present invention will be more clearly and easily understood by referring to the drawings attached to the specification. The present invention may be embodied in many different forms of embodiments and the scope of the invention is not limited to the embodiments set forth herein.
In the drawings, structurally identical elements are represented by like reference numerals, and structurally or functionally similar elements are represented by like reference numerals throughout the several views. The size and thickness of each component shown in the drawings are arbitrarily illustrated, and the present invention is not limited to the size and thickness of each component. The thickness of the components may be exaggerated where appropriate in the figures to improve clarity.
The invention takes the intersection with four directions and three lanes as an example and explains the scheme in detail by taking the intersection with four directions and three lanes as an example as shown in figure 1. The crossroad has four vehicle entering directions and vehicle exiting directions, each direction is divided into a straight lane, a left-turn lane and a right-turn lane, and all the lanes are named as l in the clockwise direction 0 -l 11 . Two mutually conflicting lanes can meet at a conflict point in the intersection, and as the conflict point of the left-turn lane is very close to the conflict point of the straight lane, a plurality of conflict points which are very close are unified into one conflict point, so that 8 conflict points c shown in the figure are formed 0 -c 7 For the purpose of traffic safety at intersections, a plurality of vehicles cannot simultaneously appear at the same conflict point. In addition, the intersection is also divided into two areas, namely an intersection area of a plurality of lanes in the intersection and an area which is most likely to cause vehicle collision, and a coordination area which is a coordination buffer area designed for vehicles for dispatching vehicles to safely pass through the intersection and has a size related to the communication range of intersection facilities.
The system structure of the invention is shown in fig. 2, all vehicles in the invention are fully automatic driving vehicles, and are equipped with V2V (Vehicle-to-Vehicle) and V2I (Vehicle-to-Infrastructure) communication technologies without delay and packet loss problems, and overtaking and lane changing are already completed when entering a coordination area. The invention relates to a conflict point-based vehicle scheduling method for multiple virtual fleets, which mainly comprises the following steps:
By the conflict point c 3 c 4 Two virtual lanes constructed
And with
For example, as shown in fig. 3, all vehicles passing through the lanes of two conflict points determine the absolute position of the vehicle in the virtual lane according to the distance from the vehicle to the conflict point. It should be noted that the method for constructing the virtual lane in the present invention is different from the conventional single virtual lane method, in which the vehicle is equidistantly rotated and converted onto the virtual lane, and the position of the vehicle in the virtual lane in the present invention is not the straight distance from the vehicle to the corresponding conflict point, but the actual driving distance between the vehicle and the conflict point is obtained by equidistant conversion. Meanwhile, the end point of each virtual lane in the invention is a conflict point according to which the lane is established, and is not the center of the traditional intersection.
After the construction of the virtual lanes is known, each conflict point in the intersection is correspondingly changed, so that 8 virtual lanes shown in fig. 4 are obtained, all vehicles on each virtual lane form a stable virtual fleet through a following model, and 8 virtual fleets, namely the multiple virtual fleets in the invention, are constructed. Absolute position of vehicle in virtual lane
Can be obtained by the following formula:
wherein p is i Is the distance of vehicle i to the intersection junction boundary,
is a lane l in the intersection area j The distance to the conflict point k, which can be determined off-line by geometric or physical measurements.
Since the same lane may pass different conflict points, vehicles on the lane may also appear in different virtual lanes. However, since the driving route of the lane is fixed, and thus the conflict point is also fixed, strict derivation proves that the difference between the positions of the same vehicle in different virtual lanes is fixed. In conclusion, the positions of the same vehicle in different virtual lanes can be any position
To uniquely illustrate, for convenience, the present invention uses the absolute position of the vehicle on the virtual lane to its first conflict point to uniquely identify the position of the vehicle on the multiple virtual lanes.
Step 2, constructing a packing problem
The purpose of constructing multiple virtual lanes is to make the vehicles on each virtual lane form a stable vehicle fleet before entering the intersection junction, i.e. multiple virtual vehicle fleets. The virtual vehicle fleet is formed by the same process of forming the virtual vehicle fleet that the individual vehicles achieve the same speed with the whole vehicle fleet and the stable expected following distance D des As shown in formula (2). We refer to the state of the resulting stable multi-virtual fleet as fleet steady state.
And re-seeing the intersection from the perspective of the multi-virtual fleet. According to the stable state of the fleet, all vehicles have uniform speed, so that the time difference of the vehicles passing through the same conflict point in the stable state of the fleet can be linearly represented by the distance difference of the front vehicle and the rear vehicle in the virtual fleet according to the physical relation of the distance, the speed and the time. And the conflict points at the intersection can know that a plurality of vehicles passing through a certain conflict point have time difference, so that the vehicles do not collide at the conflict point. By popularizing the above idea, if the time difference of the vehicles passing through each conflict point exists, the process that all the vehicles pass through the intersection is completely collision-free, namely safe intersection passing. In other words, when each vehicle in the virtual fleet maintains a safe following distance from the preceding vehicle, then all vehicles will not collide at the intersection.
The efficient traffic problem of looking at the intersection again from the perspective of the multi-virtual fleet. The time of all vehicles passing through the intersection is the difference between the time of the first vehicle and the time of the last vehicle, and the speed of the vehicles is the same when the vehicle group is in a steady state, so the time difference can be reduced to the difference between the time of the first vehicle reaching the first conflict point and the time of the last vehicle reaching the last conflict point, namely the difference between the distance between the front vehicle and the rear vehicle in the virtual vehicle group. In other words, the smaller the maximum distance between the whole vehicles in the virtual fleet, the higher the utilization rate of the intersection, the shorter the transit time of the vehicles, and the higher the throughput of the intersection.
In summary, the required safe following distance of each vehicle is taken as a part of the vehicle to represent the vehicle, and as shown in fig. 5, the safe following distance of the vehicle is d s The maximum distance d of the vehicle as a whole representing the crossing throughput or crossing utilization max And (4) showing.
Therefore, the safe and efficient traffic problem of the intersection is converted into the problem of non-overlapping and small-distance arrangement of vehicles in the virtual fleet. This is similar to the classical two-dimensional packing problem description, namely arranging several rectangles of different lengths and widths, arranged in a rectangular container of fixed width, minimizing the container length while ensuring that all the rectangular packing arrangements are non-overlapping.
The two-dimensional bin packing problem is a classic NP problem, but the problem addressed by the present invention is two-point specific, the first is that the lane in which the vehicle is located is fixed, so the width of the rectangle represented by each vehicle is fixed, so the vehicle can only be optimized by lateral movement, but not longitudinal movement. The second point is that the distance difference of the same vehicle in different virtual lanes is fixed, so the movement of the same vehicle in the container is a unified whole composed of a plurality of same rectangles as a basic unit of movement, and the minimum unit of scheduling is an irregular figure composed of a plurality of rectangles. Therefore, the problem of boxing under the background of multiple virtual fleets at the intersection without the signal lamp is the problem of boxing of one-dimensional irregular graphs, and the problem can be solved by an optimization method to obtain a global optimal solution.
Step 3, construction of optimization model
The optimization model is as follows:
s.t.p(x i )≥0
p(x i )≤d max
|p(x j )-p(x i )|≥d s ,i＜j
wherein p (x) i ) Indicating the position of vehicle i on the multiple virtual lanes. The optimization aims at minimizing the maximum distance difference of front and rear vehicles in a virtual fleet, the first two constraints are used for limiting all vehicles within the maximum distance difference, the third constraint is used for ensuring that the following distance of any two vehicles in the virtual fleet is larger than a safe distance when the fleet is in a steady state, but the constraint is a non-convex constraint and is not beneficial to solving the problem, so that Boolean quantity is introduced to scale the non-convex constraint, and finally, an optimization model is converted into a mixed integer linear programming form which is easier to solve by a solver.
The purpose of solving the optimization model is to find the optimal following relation in the multi-virtual fleet, and each vehicle represents x i Under the premise of maximizing intersection space utilization rate, the relative position of the vehicle i in the multi-virtual fleet in the steady state of the fleet is shown, and at the moment, the front vehicle of the vehicle i in any virtual fleet is the optimal following object.
It can be seen from the parameters of the optimization model that the solution of the optimization model only uses the lane where the vehicle is and the relative sequence of the vehicle entering the intersection coordination area, but does not use any privacy information such as the position, the speed and the like of the individual vehicle, so that the scheme can effectively prevent the vehicle information from being collected in a centralized and large-scale manner on the aspect of centralized solution, and the privacy information of the vehicle is protected.
Step 4, heuristic improvement
However, when the scale of the problem to be solved is too large, the above model generates a phenomenon of constraint explosion, so that the complexity of the solving time is very high. In order to remove some redundant constraints in the optimization model, the invention combines the actual intersection condition and the driving requirement to summarize three-point criteria of intersection vehicle driving:
criterion one is as follows: two vehicles close to the same lane only need to keep a safe following distance on any virtual lane, and the two vehicles can be ensured not to collide when the whole virtual vehicle team drives through the intersection. Because the virtual lanes where two adjacent vehicles on the same lane are located are completely the same and overtaking is completed before all vehicles enter the coordination area, the vehicles only need to keep a safe following distance on any virtual lane, and the same distance is also ensured among other virtual motorcades.
The second criterion is as follows: vehicles on compatible lanes without conflict points will not collide.
The third criterion is that: when a vehicle j in a lane has determined that it will not overtake vehicle i at the fleet steady state, then, according to criterion one, a vehicle in the same lane behind vehicle j will never overtake vehicle i.
Aiming at the above criteria, the invention provides three corresponding heuristic improvement strategies to remove the redundancy constraint:
strategy one: the first vehicle on each actual lane entering the coordination area must satisfy a first constraint and the last vehicle must satisfy a second constraint. The other vehicles only need to compare whether the relative distance between the adjacent vehicles on the same lane meets the safety distance, and the other vehicles on the same lane do not need to be compared through the third constraint.
And (2) strategy two: when the lanes where the vehicle i and the vehicle j are located are compatible lanes, the third constraint does not need to be compared.
Strategy three: specifying a passing threshold
When the number of overtaking vehicles i on a certain lane is higher than the threshold value
The threshold and subsequent vehicles must not overtake vehicle i. In particular, equal to a threshold value
The vehicle of (2) has to satisfy the third constraint after the absolute value is removed, and the vehicle greater than the threshold value has no need to compare the third constraint with the vehicle i. In particular when
The representation optimization method is based on first-in-first-out criteria.
By combining the heuristic improvement strategies, the redundancy constraint of the optimization model can be greatly reduced, and the solving time of the optimization method is saved.
Step 5, forming discrete time distributed control of virtual motorcade
When the optimal vehicle following relationship of the motorcade in the steady state is obtained by the centralized solver at the intersection, the solver sends information to the vehicles in the coordination area through a V2I communication technology, and after the vehicles receive the information of the vehicles which need to follow in the virtual motorcade and the expected vehicle following distance, the vehicles do not communicate with the centralized solver at the intersection any more, but communicate with the adjacent vehicles to obtain the information of the target vehicle in real time and control the self input so as to enable the vehicles to reach the steady state of the motorcade before reaching the intersection area. It should be noted that, at this time, there are likely a plurality of vehicles in the virtual fleet that need to follow, but it is determined by strict verification that the vehicles only need to follow any one of the vehicles to perform the following process, and the final multi-virtual fleet system will also reach the desired fleet steady state.
The method only needs a centralized solver to solve the optimal following relation once, and the actual control process is realized through the distributed control and communication of the vehicle. The problems of large communication load and high computational requirement caused by continuous communication with vehicles in the traditional centralized method are reduced to a certain extent.
Because of the unavoidable time interval of V2V communication, it is difficult to ensure continuity in actual vehicle control, and the state space expression of the vehicle i at discrete time is as follows:
wherein x i (k)＝[p i (k),v i (k),u i (k)T]Respectively representing the displacement, velocity and acceleration of the vehicle i, t s Is the communication time interval of the system, and in the present invention
Input quantity u of vehicle execution following model i (k) That is, the acceleration of the vehicle i is expressed by a linear combination of the velocity difference Δ v and the desired distance difference Δ p with the preceding vehicle j as follows:
u i (k+1)＝k p Δp+k v Δv
＝k p (p j (k)-p i (k)-D des )+k v (v j (k)-v i (k))
wherein k is p And k is v Are feedback coefficients for the distance difference and the speed difference, respectively, and the same feedback coefficient is used for all vehicles of the system.
The foregoing detailed description of the preferred embodiments of the invention has been presented. It should be understood that numerous modifications and variations could be devised by those skilled in the art in light of the present teachings without departing from the inventive concepts. Therefore, the technical solutions available to those skilled in the art through logic analysis, reasoning and limited experiments based on the prior art according to the concept of the present invention should be within the scope of protection defined by the claims.
Claims (6)
1. A method for dispatching vehicles at a signal-lamp-free intersection of multiple virtual vehicle fleets based on conflict points is characterized in that an integral system consisting of the multiple virtual vehicle fleets is constructed based on the multiple conflict points of an intersection, the conflict relationship of the vehicles at the intersection is converted into the distance relationship among the multiple virtual vehicle fleets, the throughput of the intersection is converted into the maximum external rectangle size of the multiple virtual vehicle fleets, and then the conflict-free optimal dispatching problem of a two-dimensional signal-lamp-free intersection is converted into a one-dimensional boxing problem through the multiple virtual vehicle fleets; the method comprises the following steps:
step 1, constructing multiple virtual lanes to form a virtual vehicle fleet;
step 2, constructing a boxing problem, namely a boxing problem under the background of multiple virtual fleets at the intersection without the signal lamp, which is a boxing problem of one-dimensional irregular graphs;
step 3, constructing an optimization model;
s.t.p(x i )≥0
p(x i )≤d max
|p(x j )-p(x i )|≥d s ,i＜j
wherein d is used for the safe following distance of the vehicle s Is represented by d max Overall vehicle maximum separation, p (x), representing intersection throughput or intersection utilization i ) Representing the position of the vehicle i on the multiple virtual lanes;
step 4, heuristic improvement;
in order to remove some redundant constraints in the optimization model, three corresponding heuristic improvement strategies are proposed to remove the redundant constraints:
strategy one: the first vehicle entering the coordination area on each actual lane needs to meet a first constraint, the last vehicle needs to meet a second constraint, and the rest vehicles only need to compare whether the relative distance between the adjacent vehicles on the same lane meets the safety distance or not, and do not need to compare other vehicles on the same lane through a third constraint;
and (2) strategy two: when the lanes where the vehicle i and the vehicle j are located are compatible lanes, the third constraint does not need to be compared;
strategy three: specifying a passing threshold
When the number of overtaking vehicles i on a certain lane is higher than the threshold value
The threshold and subsequent vehicles must not overtake vehicle i; in particular, equal to a threshold value
The vehicle(s) need to satisfy a third constraint after the absolute value is removed, and the vehicle(s) larger than the threshold value do not need to compare with the vehicle i for the third constraint; in particular when
Then, the representation optimization method is based on the criterion of first-in first-out;
step 5, forming discrete time distributed control of the virtual motorcade;
when the centralized solver at the intersection obtains the optimal vehicle following relationship when the motorcade is in a steady state, the solver sends information to the vehicles in the coordination area through a V2I communication technology, and after the vehicles receive the information of the vehicles needing to follow in the virtual motorcade and the expected vehicle following distance, the vehicles do not communicate with the centralized solver at the intersection any more, but communicate with the adjacent vehicles to obtain the information of the target vehicle in real time and control the self input so as to ensure that the vehicles reach the steady state of the motorcade before reaching the intersection area;
at the moment, a plurality of vehicles are likely to be required to follow in the virtual fleet, but strict verification shows that the vehicles only need to follow any one of the vehicles to execute the following process, and the final multi-virtual fleet system can also achieve the expected fleet stable state;
the optimization model in the step 3 aims to minimize the maximum distance difference between the front vehicle and the rear vehicle in the virtual fleet, the first two constraints are used for limiting all vehicles within the maximum distance difference, the third constraint is used for ensuring that the following distances of any two vehicles in the virtual fleet are greater than the safe distance when the fleet is in a steady state, but the constraint is a non-convex constraint and is not beneficial to solving the problem, so that Boolean quantity is introduced to zoom the non-convex constraint, and finally the optimization model is converted into a form of mixed integer linear programming which is easier to solve by a solver;
in the step 5, because of the unavoidable time interval of V2V communication, it is difficult to ensure continuity in actual vehicle control, so the state space expression of the vehicle i in discrete time is as follows:
wherein x i (k)＝[p i (k),v i (k),u i (k)] T Respectively representing the displacement, velocity and acceleration of the vehicle i, t s Is a communication time interval of the system, and
u i (k) The vehicle executes the input quantity of the following model;
the input quantity u of the vehicle execution following model i (k) That is, the acceleration of the vehicle i is expressed by a linear combination of the speed difference Δ v and the desired distance difference Δ p with the preceding vehicle j as follows:
u i (k+1)＝k p Δp+k v Δv
＝k p (p j (k)-p i (k)-D des )+k v (v j (k)-v i (k))
wherein k is p And k v Feedback coefficients for the distance difference and speed difference, respectively, and using the same feedback coefficient for all vehicles of the system, D des Is the desired following distance.
2. The method for dispatching vehicles at the signal-lamp-free intersection of multiple virtual vehicle fleets based on conflict points as claimed in claim 1, wherein the multiple virtual vehicle fleets are formed by determining the absolute position of all the vehicles passing through the two conflict points in the virtual lanes according to the distance from the vehicles to the conflict points in step 1;
the positions of the vehicles in the virtual lanes are obtained by equidistant transformation of the actual driving distances of the vehicles from the conflict points, and all the vehicles on each virtual lane form a stable virtual vehicle fleet through a following model, so far, the virtual vehicle fleet is constructed.
3. The method of claim 2, wherein the absolute position of the vehicle in the virtual lane is determined by the method of dispatching vehicles at the beacon-less intersection of multiple virtual fleets based on the conflict points
Can be obtained by the following formula:
wherein p is i Is the distance of vehicle i to the intersection junction boundary,
is a lane l in the intersection area j The distance to the conflict point k, which can be determined off-line by geometric or physical measurements.
4. The method as claimed in claim 3, wherein the purpose of solving the optimization model is to find the optimal following relationship in the multiple virtual vehicle fleets, and each vehicle represents x i Representation maximizationOn the premise of intersection space utilization rate, the relative position of the vehicle i in the multi-virtual vehicle fleet in the steady state of the vehicle fleet, and at the moment, the front vehicles of the vehicle i in any virtual vehicle fleet are the optimal following objects of the vehicle i.
5. The method for the signal-free intersection vehicle dispatching based on the conflict point multi-virtual vehicle fleet is characterized in that the three heuristic improvement strategies in the step 4 are based on the following three criteria:
criterion one is as follows: two adjacent vehicles on the same lane can ensure that the two vehicles do not collide when the integral virtual vehicle team drives through the intersection only by keeping a safe following distance on any virtual lane; because the virtual lanes where two adjacent vehicles on the same lane are located are completely the same and overtaking is completed before all vehicles enter the coordination area, the vehicles only need to keep a safe following distance on any virtual lane, and the vehicles are ensured to be at the same distance in other virtual fleets;
the second criterion is as follows: the vehicles on the compatible lane without conflict points can not collide;
the third criterion is that: when a vehicle j in a lane has determined that it will not overtake vehicle i at the fleet steady state, then, according to criterion one, a vehicle in the same lane behind vehicle j will never overtake vehicle i.
6. The method as claimed in claim 5, wherein the centralized solver only needs to solve the optimal following relationship once, and the actual control process is realized by distributed control and communication of vehicles.
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