CN111445692B - Speed collaborative optimization method for intelligent networked automobile at signal-lamp-free intersection - Google Patents
Speed collaborative optimization method for intelligent networked automobile at signal-lamp-free intersection Download PDFInfo
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Abstract
The invention provides a speed collaborative optimization method for an intelligent networked automobile at a signal-lamp-free intersection, which comprises the following steps: at the beginning time of the current collaborative optimization period, selecting each first vehicle which is not subjected to coordinated optimization in all paths with conflict points in other paths within the intersection range, solving a hybrid integer linear programming problem according to respective position and initial speed information of each first vehicle, and obtaining the expected passing speed of each vehicle and the relative passing sequence among the vehicles; and then planning the acceleration process of each selected vehicle, and ensuring that the vehicles of the wheels all drive into the intersection after the vehicles of the upper wheels drive out of the intersection. In addition, in each round of optimization, a subset is dynamically extracted and is used as an optimization object to indirectly delete vehicles relatively far away from the intersection, and efficiency loss is avoided. The invention can ensure that the upper limit of the scale of the problem solving is the number of the controlled lanes, thereby reducing the requirements on the computing power and the communication frequency of the server and being suitable for crossing scenes of vehicles which dynamically drive in and out.
Description
Technical Field
The invention belongs to the technical field of vehicle cooperative control at a traffic intersection, and particularly relates to a speed cooperative optimization method for an intelligent networked automobile at a signal-lamp-free intersection.
Background
The development of automobile intelligentization and networking technologies can help traffic intersections to reduce accidents and improve efficiency. When all vehicles passing through the intersection are connected with the intelligent network, the intersection can cancel traffic signal lamps, and conflict-free efficient passing is performed by means of the network connection road cooperation technology. The safety is ensured, the efficiency is improved, and the key of the vehicle cooperation technology at the signal lamp-free intersection is realized; meanwhile, the practical application puts real-time requirements on the technology.
The existing signal lamp-free intersection vehicle cooperation method mainly comprises two types. One type is based on a simple principle of 'first arrival first service', the time when the vehicle arrives at the intersection is estimated, and the passing sequence of the vehicle is determined according to the estimated time; and the other type of the method considers the motion states of all vehicles, constructs an optimization problem which aims to improve the efficiency and takes no collision as a constraint condition, and solves the optimization problem. The first method only carries out simple rule limitation on the passing sequence level, does not carry out real-time planning on the vehicle speed, and has limited improvement on the efficiency; the second method can theoretically obtain the optimal efficiency, but the problem scale is large, and the instantaneity is difficult to guarantee. Further, since the vehicle population at the intersection is a dynamic set in nature, the variables included in the optimization problem also change, and thus it is difficult to apply the optimization to practical use.
Disclosure of Invention
The invention aims to solve the problem of cooperative control of the intelligent networked automobile to efficiently pass at the signal-lamp-free intersection, and provides a speed cooperative optimization method of the intelligent networked automobile at the signal-lamp-free intersection.
In order to achieve the purpose, the invention adopts the following technical scheme:
the invention provides a speed collaborative optimization method for an intelligent networked automobile at a signal-lamp-free intersection, which is characterized by comprising the following steps of:
1) at the current collaborative optimization starting moment, respectively selecting a first intelligent networked automobile which is not subjected to speed collaborative optimization from each path with conflict in the intersection range, constructing a vehicle passing strategy model based on a hybrid integer optimization method, and determining the expected speed and the relative passing sequence of each selected vehicle entering the intersection by taking the maximum and minimum speed which can be reached by the vehicles without collision as constraint conditions;
2) sequencing the selected vehicles in an ascending order according to the ratio of the expected speed to the current speed, and defining that the selected vehicles uniformly accelerate from the current speed to the respective expected speed and then drive into the intersection at a constant speed; accelerating the vehicle arranged at the last position from the current speed to the expected speed according to the maximum acceleration which can be achieved by the vehicle, and obtaining the acceleration time of the vehicle; for any selected other vehicle, obtaining the acceleration time of the vehicle according to the condition that the ratio of the travel distance of the vehicle arranged at the last position to the travel distance of the vehicle is equal to the ratio of the expected speed of the vehicle arranged at the last position to the expected speed of the vehicle in the same time, thereby obtaining the acceleration time of all the selected vehicles; determining the time of each vehicle reaching the intersection according to the selected acceleration time and the expected speed of each vehicle and the distance between the vehicle and the nearest intersection stop line;
3) judging whether the earliest time of arriving at the intersection in the vehicles selected by the current round is later than the latest time of leaving the intersection in the previous round of cooperative optimization vehicles, if so, enabling the vehicles of the current round to pass through the intersection according to the determined acceleration time, the expected speed and the relative passing sequence; if not, the acceleration time of each vehicle is proportionally prolonged on the premise that the expected speed of the vehicle is not changed until the earliest time of the vehicles selected by the current wheel to arrive at the intersection is later than the latest time of the vehicles to leave the intersection in the upper wheel cooperative optimization vehicle, and each vehicle selected by the current wheel passes through the intersection according to the expected speed, the relative passing sequence and the prolonged acceleration time; and recording the latest driving time of each vehicle selected in the current round from the intersection, and waiting for the start of the next round of cooperative optimization.
Further, before the ascending ranking of the ratio of the expected speed to the current speed of the vehicle in step 2), the method further comprises the following steps:
according to the expected speeds and relative passing sequence of all the vehicles determined in the step 1), removing the vehicles meeting any one of the following conditions, and taking the rest vehicles as processing objects in the step 2):
condition 1: the expected speed of the vehicle determined according to the step 1) is the maximum speed which can be reached in the range of the intersection, and the passing order of other vehicles has priority over the vehicle;
condition 2: the expected speed of the vehicle determined according to the step 1) is the maximum speed which can be reached within the range of the intersection, and the passing order of the vehicle is prior to all other vehicles which conflict with the vehicle, but another vehicle meeting the two requirements exists, and the distance from the intersection to the another vehicle is shorter than that of the vehicle;
condition 3: the passing order of the vehicle lags behind the vehicle satisfying the condition 1 or the condition 2.
The invention has the following characteristics and beneficial effects:
the invention can ensure that the upper limit of the scale of the problem solving is the number of the controlled lanes, thereby reducing the requirements on the computing power and the communication frequency of the server and being suitable for crossing scenes of vehicles which dynamically drive in and out. On one hand, the problem is benefited from the characteristic of bounded scale, and on the other hand, the solving method adopts the mixed integer linear programming, so that the solving efficiency and the solving quality of the problem can be simultaneously ensured. Therefore, the invention can improve the traffic efficiency of the intersection on the premise of ensuring the driving safety.
The invention provides a method for dynamically determining and optimizing vehicle objects, which indirectly deletes vehicles relatively far away from an intersection, thereby avoiding the situation that vehicles approaching the intersection on a congested lane need to wait for vehicles far away from the intersection on a sparse lane sometimes, and effectively avoiding efficiency loss; meanwhile, a specific method for determining the vehicle group processed by each round of optimization problem is provided, and the problem of dynamic change of the optimization object in the technical background is solved.
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FIG. 1 is an overall flow diagram of an embodiment of the present invention;
FIG. 2 is a schematic diagram of an application scenario of an embodiment of the present invention;
FIG. 3 is a schematic diagram of the process of the present invention for dynamically determining a set of optimization target vehicles at a current time;
fig. 4 (a) and (b) are schematic diagrams of safety constraints in the vehicle speed optimization problem according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the detailed description and specific examples, while indicating the scope of the invention, are intended for purposes of illustration only and are not intended to limit the scope of the invention.
In order to better understand the invention, an application example of the speed collaborative optimization method of the intelligent networked automobile at the intersection without the signal lamp is explained in detail below.
Referring to fig. 1, the speed collaborative optimization method for the intelligent networked automobiles at the signal lamp-free intersection, provided by the invention, solves the hybrid integer linear programming problem according to the respective positions and the current speed information of the automobiles, obtains the expected passing speed of each automobile and the relative passing sequence among the automobiles, and plans the acceleration curve of each automobile which is adjusted from the initial speed to the expected speed. In each round of optimization, a subset is dynamically extracted from a set formed by the first vehicles which are not subjected to speed planning on each running path by combining a graph theory method, and the subset is used as an optimized object set to indirectly delete the vehicles which are relatively far away from the intersection, so that the condition that the vehicles approaching the intersection on a congested lane need to wait for the vehicles which are far away from the intersection on a sparse lane is avoided, and the efficiency loss is effectively avoided.
The embodiment of the invention is applied to a bidirectional six-lane intersection shown in fig. 2, a plurality of intelligent networked automobiles (shown as reference numerals 1-5 in the figure) drive into the intersection without signal lamps, the road side is provided with a computing device 6 and a communication device 7, and the method is solidified in the road side computing device 6 through a conventional programming technology. When the intelligent network-connected automobile enters the intersection, the communication equipment 7 transmits the information of the lane where the automobile is located and the information of the distance from the automobile to the intersection central point to the computing equipment 6, the computing equipment 6 plans the speed of each automobile by the method of the invention, transmits the obtained speed planning instruction to the corresponding intelligent network-connected automobile through the communication equipment 7, and the automobile runs according to the planned speed, thereby realizing the safe and efficient passing through the intersection. The method specifically comprises the following steps:
1) when an intelligent networked vehicle which is not subjected to speed collaborative optimization enters a collaborative control range, the moment becomes a new round of collaborative optimization starting moment. The cooperative control range D is determined according to the communication range of the communication equipment, and a value larger than or equal to 200m is taken under the condition of ensuring reliable communication. Respectively selecting a first intelligent networked automobile without speed collaborative optimization from each path with conflict in the intersection range, constructing a vehicle passing strategy model based on a hybrid integer optimization method, and determining the expected speed and the relative passing sequence of each selected vehicle entering the intersection by taking the maximum and minimum speeds which can be reached by the vehicles and the vehicles without collision as constraint conditions; the specific implementation process is as follows:
1-1) recording the number of all paths with conflict points with other paths in the intersection range as N, the total number of the conflict points existing among the N paths as P, selecting a first intelligent networked automobile without speed planning from each path in the N paths, and taking the speed of the selected N automobiles in the intersection range as an objective function of a vehicle passing strategy model to the maximum extent, wherein the expression is as follows:
in the formula, viThe desired speed of each vehicle selected for the current wheel;
the constraint conditions of the vehicle passing strategy model are set as follows:
a) speed constraint:
vi∈[vmin,vmax] (2)
in the formula, vmin,vmaxThe minimum speed and the maximum speed which can be reached by the vehicle within the range of the intersection are respectively set according to the maximum idling speed of all vehicles and the maximum speed limit allowed by the intersection.
b) And (3) collision restraint:
Ax≤b (3)
in the formula:
a is a matrix A of collision-free velocity coefficients1And a collision-free traffic sequence coefficient matrix A2And (3) forming a collision-free traffic coefficient matrix, wherein the expressions are respectively as follows:
b is a collision-free traffic constant vector, and the expression is as follows:
wherein A is1kA non-collision speed coefficient submatrix at the k-th collision point, wherein only the ith column and the jth column are non-zero columns, and k is 1,2, …, P; 1,2, …, N; j ═ 1,2, …, N; i is not equal to j; l isenterDefining the distance between the center of the vehicle and the conflict point at the moment when the vehicle front end reaches the conflict area, and taking the value as half of the sum of the length of the vehicle and the width of other vehicles; l issafeDefined as the distance from the center of the car to cross the conflict point when the front end of the car reaches the conflict area, the value must satisfy L or moreenterAnd the larger the value is, the larger the safety margin is, but the optimization result tends to be conservative. L'jAnd L'iThe distance between each of the two intelligent networked automobiles with the path conflict and the conflict point is represented, and the path of the vehicle is fixed and can be converted from the distance between the vehicle and the central point of the intersection. M is a constant with a value greater than D.vmaxWherein D is the cooperative control range, once an unplanned vehicle enters the distance range D, the unplanned vehicle can go from N pathsThe first vehicle which has not been subjected to the collaborative optimization process is selected in each path, the distance from the first vehicle to the intersection central point O is collected, and the calculation is carried out.
x is the traffic strategy vector of each vehicle selected by the current wheel within the range of the intersection, and the expected speed v of all the vehicles selected by the current wheeliAnd relative passage order bijComposition, dimension 1 × (N + P), expression as follows:
wherein, b ij0 or 1, when bij1, representing that vehicle i passes through a conflict point before vehicle j; when b isij0, representing that vehicle j passes the conflict point before vehicle i.
1-2) solving the model to determine the expected speeds and the relative traffic sequence of N intelligent networked automobiles in the intersection range, namely a traffic strategy vector x.
The collision-free velocity coefficient matrix A is combined with FIG. 21As explained below: FIG. 2 shows xiO′xjRectangular coordinate system, wherein xiThe axis is superposed with the driving path of the vehicle i, and the positive direction of the axis is opposite to the driving direction of the vehicle i; x is the number ofjThe axis coincides with the travel path of the vehicle j and its positive direction is opposite to the travel direction of the vehicle j. According to the definition, the abscissa of the star point in the graph is the position of the vehicle j, and the ordinate is the position of the vehicle i; the star points corresponding to the vehicle positions at different moments constitute the combined trajectory line of the vehicle. If the trajectory line passes through the origin of coordinates O' (the point O in fig. 2 is the intersection center point), it indicates that the vehicle i and the vehicle j are simultaneously present at the same position, i.e., a collision occurs. Therefore, the collision avoidance constraint of the vehicle can be equivalently expressed that the track line needs to satisfy a certain distance from the coordinate origin O'. Assuming that the vehicles run at a constant speed at the same time and the trajectory line is a straight line, the trajectory line is located at the coordinate (-L)safe,Lenter) Or under point 1 of coordinate (L), or at coordinate (L)enter,-Lsafe) Above point 2, no collision occurs. For vehicles with curved paths, it is not possibleA rectangular coordinate system is established, but can still be for L'jAnd L'iSimilar collision avoidance constraints are performed.
2) According to the traffic strategy vector x determined in the step 1), removing vehicles meeting any one of the following conditions, and taking the remaining N' intelligent networked automobiles as a cooperative optimization object of the round:
condition 1: the expected speed of the vehicle determined according to step 1) is the maximum speed v that can be reached within the range of the intersectionmaxAnd there is a priority in the order of passage of other vehicles over the vehicle;
condition 2: the expected speed of the vehicle determined according to step 1) is the maximum speed v that can be reached within the range of the intersectionmaxAnd the order of passage of the vehicle is prior to all other vehicles with which there is a conflict, but there is another vehicle that meets both requirements and that is closer to the center of the intersection than the vehicle.
Condition 3: the passing order of the vehicle lags behind the vehicle satisfying the condition 1 or the condition 2.
Specifically, the present embodiment rejects vehicles satisfying any of the above conditions by constructing a directed graph according to the relative traffic order between the vehicles, see fig. 3. The figure shows an example of the traffic strategy of each vehicle which is not planned within the range of the intersection determined by the vehicle traffic strategy model in the step 1) under the condition that only one lane is arranged in each direction and each lane only allows straight going. In the figure, the solid circles E1, S1, W1 and N1 represent the first intelligent networked automobile without speed planning in the paths in the east, south, west and north directions, respectively, and correspond to an optimal passing speed (i.e. the expected speed determined in step 1); the arrows between the solid circles represent the relative order of passage of two vehicles, with the order of passage of the arrow pointing to the vehicle lagging the order of passage of the arrow away from the vehicle. The solid vehicles in the dashed line frame represent vehicles which do not belong to the optimization target of the current round after the condition judgment. The open circles in the figure represent vehicles that have completed the speed plan and are heading to the intersection at the desired speed. The vehicle represented by the solid circle outside the dashed box in the figure will continue to perform the determination, selection and optimization before the next optimization process begins.
In FIG. 4 (a), the directed edge points from the node W1 to the node S1 (each node corresponds to a smart networked automobile), and the expected speed v of the node S1 ismaxThat is, when the condition 1 is satisfied, the node S1 (condition 1 requirement) and the directed spanning tree node E1 (condition 3 requirement) having the node S1 as the root node need to be deleted from all the current N nodes, and are considered in the next round of optimization. Here, the directed spanning tree refers to all nodes that can be reached in the direction of the arrow with a certain node as a root node, and therefore, there is not necessarily a conflict relationship between vehicles corresponding to the directed spanning tree nodes, but there is a definite passing order.
In fig. 4 (b), the desired vehicle speed v at the node S1maxThere is no directed edge pointing to node S1. The desired vehicle speed at node N1 is also vmaxAlso, there is no directed edge pointing to node N1, and node N1 is closer to the center of the intersection than node S1. In this case, the node S1 (conditional 2 requirement) and all nodes of the directed spanning tree having the node S1 as the root node (conditional 3 requirement) are deleted.
In the method for deleting the nodes, vehicles relatively far away from the intersection are indirectly deleted, so that the condition that vehicles approaching the intersection on a congested lane need to wait for vehicles far away from the intersection on a sparse lane is avoided, and the efficiency loss is avoided (in fig. 4 (b), if the vehicle E1 is not deleted, the vehicle N2 needs to wait for the vehicle E1 to pass through the intersection and then enter the intersection); meanwhile, a specific method for determining the vehicle group processed by each round of optimization problem is provided, and the problem of dynamic change of the optimization object in the technical background is solved. In addition, the deletion of the vehicles according to the 3 conditions provided by the invention does not affect the optimization result of the retained vehicles, namely the retained N 'vehicles do not need to recalculate the optimized vehicle speed, and the original optimization result is still optimal for the N' vehicles at the moment.
It should be noted that, in the present invention, 3 conditions are not considered in the case that there is a directed loop in the traffic policy, for example, multiple intelligent networked automobiles completely and synchronously pass through the intersection, and each automobile is earlier than the left colliding automobile and later than the right colliding automobileConflicting vehicle traffic, a directed closed loop is formed in fig. 4. At this time, if the above criteria are followed, 4 nodes will be deleted at the same time. In the situation, the distance between the vehicles is relatively short, and the safety risk is relatively high in practical application; so that L can be increasedsafeAnd (3) reducing a feasible domain generated by collision avoidance constraint of the optimization model until a closed loop is not generated. If the screening method provided in step 2) is not adopted, LsafeCan still be according to L or moreenterAnd (6) carrying out value taking.
So far, according to three criteria, from the input N intelligent networked automobiles, N' automobiles are selected as the optimization objects of the current round, and the respective optimized passing speeds are determined and recorded as vopt,i′(1≤i′≤N′)。
3) And determining the speed change process of each selected intelligent networked automobile passing through the intersection. The speed planning of the stage has the main task of ensuring the acceleration and deceleration process of each vehicle, and can be equivalent to the process of driving at a constant speed according to the respective expected speed. In the formula (3), Ax is less than or equal to b, and practically all N vehicles are driven according to the respective expected vehicle speeds (recorded as v) from the current timeopt,i′) Under the assumed conditions of driving, the restraint required for preventing collision is ensured. Since the vehicle must actually go through the acceleration or deceleration process to reach the optimal vehicle speed, the vehicle speed is planned in this stage to compensate the unrealistic actual situation caused by the acceleration and deceleration process. The method specifically comprises the following steps:
step 2) sequencing the selected vehicles in an ascending order according to the ratio of the expected speed to the current speed (namely the speed at the starting moment of the cooperative optimization of the current round), and defining that the selected vehicles uniformly accelerate from the current speed to the respective expected speed and then drive into the intersection at a constant speed; after the sequence is set, the following formula is satisfied:
in the formula, vopt,i′,v0,i′The expected speed and the current speed of the vehicle ranked at the ith ' position are sorted in ascending order according to the ratio of the expected speed to the initial speed of the vehicle, i ' is 1,2, …, N '；
Let the last vehicle follow the maximum acceleration a that it can achievemaxFrom the current speed v0,N′Accelerating to its desired velocity vopt,N′And obtaining the acceleration time t of the vehicleacc,N′Calculated according to the following formula:
for any selected other vehicle, obtaining the acceleration time of any vehicle according to the condition that the ratio of the travel distance of the vehicle arranged at the last position to the travel distance of any vehicle in the same time is equal to the ratio of the expected speed of the vehicle arranged at the last position to the expected speed of any vehicle, thereby obtaining the acceleration time of all vehicles, wherein the specific calculation formula is as follows:
determining the time t of each vehicle reaching the intersection according to the acceleration time, the current speed, the expected speed and the distance from the vehicle to the corresponding intersection stop linearrive,i′The calculation formula is as follows:
tarrive,i′＝tacc,i′+(Li′-0.5W-0.5tacc,i′(v0,i′+vopt,i′))/vopt,i′ (12)
in the formula, Li′Indicating the distance from the vehicle at the i' th position to the intersection center point O; w represents the distance between two stop lines oppositely arranged in the intersection range, namely (L)i′0.5W) represents the distance from the vehicle at the i' th position to the intersection stop line corresponding thereto, i.e., the time when the vehicle reaches the intersection.
4) Judging whether the time of the N' intelligent networked automobiles selected by the current wheel arriving at the intersection is later than the time of the latest driving off the intersection in the upper wheel collaborative optimization vehicle, if so, the time is min (t)arrive,1,…,tarrive,N′)+Tclock>Tleave,last，TclockAnd Tleave,lastRespectively representing the starting moment of the cooperative optimization of the current round and the moment when the vehicle of the previous round leaves the intersection at the latest, each vehicle selected by the current round can directly pass through the intersection according to the acceleration time, the expected speed and the relative passing sequence calculated in the step 3). If not, i.e. min (t)arrive,1,…,tarrive,N′)+Tclock≤Tleave,lastAnd on the premise that the expected speed of the vehicle is not changed, the acceleration time of each vehicle is proportionally prolonged until the time that the vehicle of the wheel arrives at the intersection at the earliest is later than the time that the vehicle of the wheel drives away from the intersection at the latest in the upper wheel cooperative optimization vehicle, and each vehicle of the wheel passes through the intersection according to the expected speed, the relative passing sequence and the prolonged acceleration time. Finally, the time t of the latest driving from the intersection in the current wheel of the vehicle is calculatedleave,i′For the next vehicle arrival time test, the calculation formula is as follows:
in the formula (I), the compound is shown in the specification,
for the actual acceleration time of the vehicle in position i' of the current bank, i.e. equal to tacc,i′Or an extended acceleration time. Δ Li′Is a conversion constant of the actual distance of the vehicle from the intersection, and for a straight-going vehicle, Δ Li′0.5W; for left-turn vehicles,. DELTA.Li′-0.5W + 0.5R, where R is the vehicle left turn radius.
It is added that, considering the economy and comfort of the vehicle, optimizing the uniform acceleration curve to the variable acceleration is an extension of the method and cannot be regarded as a new method.
In addition, step 2) in the method can be defaulted, and the implementation of the subsequent steps is not influenced.
The present invention and its embodiments have been described above schematically, without limitation, and what is shown in the drawings is only one of the embodiments of the present invention and is not actually limited thereto. Therefore, if the person skilled in the art receives the teaching, it is within the scope of the present invention to design the similar manner and embodiments without departing from the spirit of the invention.
Claims (4)
1. A speed collaborative optimization method for intelligent networked automobiles at signal lamp-free intersections is characterized by comprising the following steps:
1) at the current collaborative optimization starting moment, respectively selecting a first intelligent networked automobile which is not subjected to speed collaborative optimization from each path with conflict in the intersection range, constructing a vehicle passing strategy model based on a hybrid integer optimization method, and determining the expected speed and the relative passing sequence of each selected vehicle entering the intersection by taking the maximum and minimum speed which can be reached by the vehicles without collision as constraint conditions;
2) sequencing the selected vehicles in an ascending order according to the ratio of the expected speed to the current speed, and defining that the selected vehicles uniformly accelerate from the current speed to the respective expected speed and then drive into the intersection at a constant speed; accelerating the vehicle arranged at the last position from the current speed to the expected speed according to the maximum acceleration which can be achieved by the vehicle, and obtaining the acceleration time of the vehicle; for any selected other vehicle, obtaining the acceleration time of the vehicle according to the condition that the ratio of the travel distance of the vehicle arranged at the last position to the travel distance of the vehicle is equal to the ratio of the expected speed of the vehicle arranged at the last position to the expected speed of the vehicle in the same time, thereby obtaining the acceleration time of all the selected vehicles; determining the time of each vehicle reaching the intersection according to the selected acceleration time and the expected speed of each vehicle and the distance between the vehicle and the nearest intersection stop line;
3) judging whether the earliest time of arriving at the intersection in the vehicles selected by the current round is later than the latest time of leaving the intersection in the previous round of cooperative optimization vehicles, if so, enabling the vehicles of the current round to pass through the intersection according to the determined acceleration time, the expected speed and the relative passing sequence; if not, the acceleration time of each vehicle is proportionally prolonged on the premise that the expected speed of the vehicle is not changed until the earliest time of the vehicles selected by the current wheel to arrive at the intersection is later than the latest time of the vehicles to leave the intersection in the upper wheel cooperative optimization vehicle, and each vehicle selected by the current wheel passes through the intersection according to the expected speed, the relative passing sequence and the prolonged acceleration time; recording the latest driving time of each vehicle selected by the current round from the intersection, and waiting for the start of the next round of collaborative optimization;
the step 1) comprises the following steps:
1-1) recording the number of all paths with conflict points existing between the paths in the intersection range as N, the total number of the conflict points existing between the N paths as P, selecting a first vehicle without speed planning from each path in the N paths, and taking the speed of the selected N vehicles in the intersection range as an objective function of a vehicle passing strategy model to the maximum, wherein the expression is as follows:
in the formula, viThe desired speed of each vehicle selected for the current wheel;
the constraint conditions of the vehicle passing strategy model are set as follows:
a) speed constraint:
vi∈[vmin，vmax]
in the formula, vmin，vmaxRespectively the minimum and maximum speeds that the vehicle can reach within the range of the intersection;
b) and (3) collision restraint:
Ax≤b
in the formula:
a is a matrix A of collision-free velocity coefficients1And a collision-free traffic sequence coefficient matrix A2And (3) forming a collision-free traffic coefficient matrix, wherein the expressions are respectively as follows:
b is a collision-free traffic constant vector, and the expression is as follows:
wherein A is1kA non-collision speed coefficient submatrix at a kth collision point, wherein only the ith column and the jth column are non-zero columns, and k is 1, 2. 1,2, …, N; j ═ 1,2, …, N; i is not equal to j; l isenterDefining the distance between the center of the vehicle and the conflict point at the moment when the vehicle front end reaches the conflict area, and taking the value as half of the sum of the length of the vehicle and the width of other vehicles; l issafeIs defined as the distance from the center of the vehicle to cross the conflict point when the front end of the vehicle reaches the conflict area, and the value is equal to or more than Lenter；LjAnd LiRespectively representing the distance from each of the two intelligent networked automobiles with the path conflict to the conflict point; m is a constant with a value greater than D.vmaxD is a distance constant determined according to the communication range of the roadside setting communication equipment;
x is the traffic strategy vector of each selected vehicle in the range of the intersection and is determined by the expected speed v of each vehicleiAnd relative passage order bijThe formula is as follows:
wherein, bij0 or 1, when bij1, representing that vehicle i passes through a conflict point before vehicle j; when b isij0, representing that vehicle j passes the conflict point before vehicle i;
1-2) solving the vehicle passing strategy model to determine the expected speed and the relative passing sequence of N vehicles in the range of the intersection.
2. The speed collaborative optimization method according to claim 1, wherein the acceleration time of each vehicle in step 2) is calculated according to the following formula:
in the formula, tacc，i′For sorting the selected N 'vehicles in ascending order according to the ratio of the respective desired speed to the current speed, the acceleration time v of the vehicle arranged at the i' th position0，i′，vopt，i′The current speed and the desired speed of the i' th vehicle, respectively.
3. The speed collaborative optimization method according to any one of claims 1-2, characterized in that before the ascending ranking of the ratio of the desired speed to the current speed of the vehicle in step 2), the method further comprises the following steps:
according to the expected speeds and relative passing sequence of all the vehicles determined in the step 1), removing the vehicles meeting any one of the following conditions, and taking the rest vehicles as processing objects in the step 2):
condition 1: the expected speed of the vehicle determined according to the step 1) is the maximum speed which can be reached in the range of the intersection, and the passing order of other vehicles has priority over the vehicle;
condition 2: the expected speed of the vehicle determined according to the step 1) is the maximum speed which can be reached within the range of the intersection, and the passing order of the vehicle is prior to all other vehicles which conflict with the vehicle, but another vehicle meeting the two requirements exists, and the distance from the intersection to the another vehicle is shorter than that of the vehicle;
condition 3: the passing order of the vehicle lags behind the vehicle satisfying the condition 1 or the condition 2.
4. A speed co-optimization method according to claim 3, characterized in that step 3) is implemented by constructing a directed graph according to the relative traffic sequence between vehicles, the traffic sequence of the arrow pointing to the vehicle in the directed graph lags behind the traffic sequence of the arrow departing from the vehicle.
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