CN115830885A - Vehicle ramp confluence cooperative control method considering multi-vehicle type energy consumption - Google Patents
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Abstract
The invention belongs to the field of intelligent vehicles and traffic, and particularly relates to a ramp confluence cooperative control method combining vehicle type information. The method comprises the following steps: (1) Setting a confluence control area, and distributing serial numbers to vehicles entering the control area according to an entering sequence; (2) The roadside central controller receives vehicle information, including the running speed, the acceleration and the vehicle type information of a vehicle, and forms a corresponding data packet; (3) Considering different characteristics of different vehicle types, such as mass, air resistance and the like, establishing an energy consumption function, and constructing a vehicle control method based on the vehicle energy consumption, comfort and passing time targets; and (4) reducing the complexity of sequence optimization by using a pruning strategy. The invention can improve the passing efficiency of vehicles passing through the ramp junction and reduce the energy consumption.
Description
Technical Field
The invention belongs to the field of intelligent vehicles and traffic, and particularly relates to a vehicle ramp confluence cooperative control method considering multi-vehicle type energy consumption.
Background
With the intellectualization becoming one of the important development directions of automobiles, intelligent networked automobiles are widely researched. The vehicle-mounted communication system carried by the intelligent networking automobile realizes information exchange between vehicles and between the vehicles and roadside infrastructure, and based on the information exchange, optimal control of ramp confluence of multi-vehicle type vehicles becomes possible.
At present, partial patents exist to solve the problem of the merging of the vehicle ramps. For example, ZL202010040259.4 discloses a ramp confluence cooperative control method and system based on confluence time optimization, wherein the method calculates and broadcasts the reference time of a vehicle reaching a confluence point, and continuously updates the actual confluence time of the vehicle by solving the confluence optimization problem to realize confluence optimization; ZL202010211956.1 discloses an intelligent networking vehicle cooperative confluence control method for an entrance of a high-speed ramp, and the method uploads vehicle information to a central control system through V2I communication and performs confluence control optimization calculation by combining traffic management information, so that adverse effects of the high-speed entrance ramp on main road traffic are reduced; ZL201810317854.0 discloses a traffic control method for interwoven vehicles in a road confluence area, which divides a vehicle confluence speed control area to determine a confluence area traffic rule; ZL202010736981 invents a ramp confluence control method facing an urban expressway, and the method constructs a vehicle confluence control optimization model to circularly optimize the motion state of a vehicle, so that the economy and the safety of confluence are improved.
The method has positive significance for safe and efficient vehicle confluence. However, at present, the vehicles are mostly considered as the same node in related research, and the influence of the dynamic characteristics of different vehicles on the optimization of the confluence is not considered, and factors such as different vehicle dynamic characteristics and qualities greatly influence the energy consumption when the vehicles merge, so that the optimal control quantity and merging sequence of the vehicles merge are influenced.
Disclosure of Invention
The invention aims to provide a vehicle ramp confluence cooperative control method considering multi-vehicle type energy consumption to solve the confluence optimization control problem of different vehicle dynamics characteristics, and the method can further improve the traffic speed of vehicle ramp confluence and reduce the energy consumption.
The technical solution for realizing the purpose of the invention is as follows: a vehicle ramp confluence cooperative control method considering multi-vehicle type energy consumption comprises the following steps:
step (1): and setting a confluence control area, formulating a confluence rule, and distributing serial numbers i to newly-entered vehicles on the main road and the ramp according to a first-in first-out rule.
Step (2): for vehicles i and i +1, the roadside central controller receives vehicle-related information based on the state information and the model information of the vehicle transmitted by the vehicle-mounted V2X communication device.
And (3):
constructing a control method taking vehicle energy consumption and confluence passing time as targets, aiming at the wind resistance coefficient C of different vehicles Di Frontal area A i M is different in mass i Considering that the types of vehicles are greatly different and the energy sources of power units are different, a comprehensive energy consumption function C (t) of the traditional vehicle and the new energy vehicle is established i ,u i (t),v i (t)), wherein t i Is the travel time of vehicle i, u i (t) is a control input amount of a vehicle i, v i (t) the speed of the vehicle i is combined with the principle of minimum passing time during running, and the vehicle confluence cooperative control is excellentThe overall goal is to
Wherein alpha is 1 +α 2 =1 is a weight coefficient, u i (t) is a control input to the controller,
the time when the vehicle i travels to the merge point,
time of entry of vehicle i into confluence control zone, u max For maximum control input of the vehicle, u min Is the minimum control input for the vehicle. After the roadside central controller receives the vehicle related information, the vehicle information is input into the controller, confluence optimization of the vehicle i and the vehicle i +1 is carried out according to the current passing order, and a combined target of energy consumption and passing time in the vehicle confluence process under the current order is calculated
And (4): optimizing the vehicle passing sequence, and respectively calculating the optimal control input quantity of vehicle confluence under the preferential passing of the vehicle i and the vehicle i +1 for the vehicle i and the vehicle i +1
Storing the corresponding control target amount, and using the sum of the two combined targets J = J i +J i+1 The minimum is a rule that determines the vehicle merging order.
And (5): and traversing all vehicles entering the ramp control area until all vehicles complete sequence and track optimization, and reading the optimal control input corresponding to the optimal sequence as the final control input of the vehicles.
Further, the confluence control zone in the step (1) is specifically: a merging coordinate system is established with a vehicle merging point as a center, an area with a radius of 500m as a merging control area, a starting point of the merging control area as an original point and a vehicle running direction as a positive direction.
Further, the confluence rule in the step (1) is specifically as follows: the overtaking is not allowed in the same lane, and the safety interval between vehicles is ensured by the rear vehicles.
Further, the "allocation number" in the step (1) is specifically: all vehicles on the main road and the ramp are numbered uniformly according to the time sequence of entering the confluence control area.
Further, the "vehicle-related information" in step (2) is specifically: including speed v of vehicle i and vehicle i +1 i Acceleration a i Retardation coefficient τ i Frontal area of vehicle A i And the speed v of the vehicle i +1 i+1 Acceleration a i+1 Retardation coefficient τ i+1 Frontal area of vehicle A i+1 And so on.
Further, the "control method with the vehicle energy consumption and the confluence passing time as targets" in the step (3) specifically comprises the following steps:
step (31): and modeling vehicle dynamics. For a vehicle i, analyzing the stress resistance of the vehicle in the running process
Wherein m is i Is vehicle mass, f is rolling resistance coefficient, g is gravitational acceleration, k is gradient, C D Is the coefficient of air resistance of the vehicle, A i Is the frontal area of the vehicle, v i (t) is the traveling speed of vehicle i; under the condition of normal running and no slip, the relation between the power and the resistance of the vehicle is as follows:
wherein r is i Is the wheel radius, a i (t) is vehicle travel acceleration;
step (32): vehicle dynamics model:
wherein
As the speed of the vehicle, is,
for vehicle acceleration, T i (t) engine output torque;
after feedback linearization, the third order state space equation of the vehicle longitudinal dynamics:
wherein:
step (33): and establishing an optimization target.
Target 1: minimum transit time. Moment when vehicle i enters confluence control area
To the moment of departure from the merging point
The time required for the process is minimal:
target 2: minimum energy consumption. Namely, the energy consumption index J i (t i ,u i (t),v i (t)) is minimal.
Wherein C (t) i ,u i (t),v i (t)) is the energy consumption function:
C(t i ,u i (t),v i (t))＝C acc (t i ,u i (t),v i (t))+C cruse (t i ,u i (t),v i (t))
dividing the energy consumption of the vehicle into acceleration consumption and constant speed driving consumption, and obtaining the energy consumption rate C when the vehicle accelerates acc The calculation is as follows:
C acc (t i ,u i (t),v i (t))＝m i a i (t)v i (t)
energy consumption rate C when the vehicle is running at a constant speed cruse The following were used:
wherein C is Di Is the vehicle air resistance coefficient.
Step (34): in order to ensure that the driving safety and the control input of the vehicle are suitable for the performance requirement of the vehicle, the following constraint conditions are established:
constraint 1: the vehicle confluence control firstly ensures the driving safety of the vehicle, the driving safety is ensured by the safety time of the vehicle in the confluence process, and the vehicles in the same lane need to be kept to have enough safety time interval t s ：
t i -t i+1 >t s
Constraint 2: and (6) safely merging. The vehicle is driven to a merging point with a certain safe time t ms 。
Constraint 3: the vehicle has limited self-driving braking capability and limited vehicle running speed, and in order to accord with the acceleration and deceleration capability of the vehicle, the vehicle should be restrained on the speed:
v min ≤v i ≤v max
controlling input constraint:
u min ≤u i (t)≤u max
the acceleration and braking capabilities of different vehicles are different, and therefore the maximum acceleration and deceleration will also be different.
Step (35): converting the multi-vehicle confluence problem into an optimal control problem in multi-vehicle speed planning, and converting a state equation into the following constraint equation form:
the control optimization target is as follows:
wherein alpha is 1 +α 2 =1 is a weight coefficient.
Construct the Hamilton function:
the canonical equation is:
the Euler-Lagrange equation is:
the optimal solution is as follows:
p i (t)＝c 1 t+c 2
wherein c is 1 ,c 2 ,c 3 ,c 4 All constants have not been determined. The optimal solution of the vehicle trajectory can be found from the following conditions
Initial conditions
Terminal conditions
Boundary condition
And cross-sectional conditions
Further, the "optimizing the vehicle passing order" in the step (4) specifically includes:
optimizing the sequence between the main road vehicle i and the ramp vehicle i +1, and respectively calculating the optimal control input of the main road vehicle under the condition of preferential passage
And confluence passage time t i Optimal control input under preferential passage with ramp vehicles
And confluence passage time t i+1 Storing the optimal control input, and determining the traffic sequence according to the minimum principle of the confluence traffic performance index.
And optimizing the sequence between the main road vehicle i and the main road vehicle i +1, and keeping the first-in first-out sequence unchanged according to the rule that the same lane does not allow overtaking in confluence. Optimal control input under calculation order
And confluence passage time t i ，t i+1 . Storing optimal control inputs
And optimizing the sequence between the ramp vehicle i and the ramp vehicle i +1, and keeping the first-in first-out sequence unchanged according to the rule that the same lane does not allow overtaking in confluence. Optimal control input under calculation order
And confluence passage time t i ，t i+1 . Storing optimal control inputs
And further, the step (5) traverses all vehicles entering the ramp control area until all vehicles complete the sequence and the track optimization.
Compared with the prior art, the invention has the remarkable advantages that:
(1) The multi-vehicle information ramp confluence cooperative control based on the invention considers the vehicle dynamics characteristics of different vehicle types and carries out multi-vehicle cooperative control under the condition of considering different vehicle acceleration and braking capacities.
(2) The controller of the invention calculates the corresponding energy consumption function according to the vehicle information, thereby obtaining the optimal confluence control and confluence sequence of the comprehensive vehicle type factors, fully considering the power performance and the quality of different vehicles in the vehicle control optimization calculation, and the acceleration and deceleration of the heavy-duty vehicle means the factor of larger energy consumption, realizing the improvement of the running efficiency under the confluence control and further reducing the energy consumption.
(3) The invention reduces the sequence optimization calculation, for the general vehicle sequence optimization problem, the common method is to traverse all the vehicle confluence sequences, the optimal control quantity corresponding to the confluence process can be calculated by each traversal, the calculation complexity is exponential, and the invention reduces the calculation complexity in the vehicle confluence sequence optimization process by dynamic sequence optimization.
Drawings
Fig. 1 is a flow merging control area division diagram of the present invention.
FIG. 2 is a flow chart of the optimization control of the present invention.
FIG. 3 is a flow chart of the vehicle merge global optimization of the present invention.
Detailed Description
The present invention is described in further detail below with reference to the attached drawing figures.
The invention relates to a vehicle ramp confluence cooperative control method considering multi-vehicle type energy consumption, and confluence optimization control is shown in figure 1. The confluence control method comprises the following steps: establishing a vehicle ramp confluence model; and setting a ramp confluence control area, and setting an area with a radius of 500m away from a confluence point as the confluence control area.
Establishing a vehicle driving rule, rule 1: vehicles in the same lane are not allowed to overtake; rule 2: the safe interval of the vehicle is ensured by the rear vehicle. The vehicle transmits the vehicle running state including speed, acceleration, vehicle type information, etc. through the V2I communication device. The roadside infrastructure receives vehicle information entering the merge control area. And (4) counting and calculating information, and numbering the vehicles entering the confluence control area by a first-in first-out principle. For the vehicle i, the roadside central controller receives the relevant information of the vehicle i based on the state information and the model information of the vehicle transmitted by the vehicle-mounted V2X communication equipment. The vehicle-related information is specifically: including the speed v of vehicle i and vehicle i +1 i Acceleration a i Retardation coefficient τ i Wheel output torque T i Vehicle air resistance coefficient C Di Vehicle windwardArea A i And the speed i +1 and the acceleration a of the vehicle i +1 i+1 Retardation coefficient τ i+1 Wheel output torque T i+1 Vehicle air resistance coefficient C Di+1 Vehicle windward area A i+1 And so on.
After receiving the vehicle related information, the roadside central controller starts to perform multi-vehicle speed optimization as shown in fig. 2, inputs the vehicle information into the controller, performs confluence optimization of the vehicle i and the vehicle i +1 according to the current passing order, and calculates the energy consumption and confluence passing time in the vehicle confluence process under the current passing order. For a vehicle i, analyzing the stress resistance of the vehicle in the running process
Wherein m is i Is vehicle mass, f is rolling resistance coefficient, g is gravitational acceleration, k is gradient, C D Is the coefficient of air resistance of the vehicle, A i Is the frontal area of the vehicle, v i (t) is the traveling speed of vehicle i; under the condition of normal running and no slip, the relation between the power and the resistance of the vehicle is as follows:
vehicle dynamics model:
wherein
As the speed of the vehicle, is,
for vehicle acceleration, T i (t) engine output torque;
after feedback linearization, the third order state space equation of the vehicle longitudinal dynamics:
wherein:
in order to ensure that the driving safety and the control input of the vehicle are suitable for the performance requirements of the vehicle, a constraint condition 1 is established: the vehicle confluence control firstly ensures the driving safety of the vehicle, the driving safety is ensured by the safety time of the vehicle in the confluence process, and the vehicles in the same lane need to be kept to have enough safety time interval t s ：t i -t i+1 >t s (ii) a Constraint 2: when the vehicles reach the confluence point, in order to realize safe confluence, a safe time interval t is required to be met between all vehicles ms (ii) a Constraint 3: the vehicle has limited self-driving braking capability and limited vehicle running speed, and in order to accord with the acceleration and deceleration capability of the vehicle, the control input is restricted: v. of min ≤v i ≤v max And controlling input constraint: u. of min ≤u i (t)≤u max (ii) a The acceleration and braking capabilities of different vehicles are different, and therefore the maximum acceleration and deceleration will also be different.
And establishing an optimization target. Target 1: minimum transit time. Moment when vehicle i enters confluence control area
To the moment of departure from the merging point
The time required for the process is minimal:
target 2: minimum energy consumption.
Namely, the energy consumption index J i (t i ,u i (t),v i (t)) is minimal.
Wherein C (t) i ,u i (t),v i (t)) is the energy consumption function:
C(t i ,u i (t),v i (t))＝C acc (t i ,u i (t),v i (t))+C cruse (t i ,u i (t),v i (t))
dividing the energy consumption of the vehicle into acceleration consumption and constant speed running consumption, and dividing the energy consumption C when the vehicle accelerates acc The calculation is as follows:
C acc (t i ,u i (t),v i (t))＝m i a i (t)v i (t)t i
wherein a is i The acceleration of the vehicle during running.
Energy consumed during constant speed running of vehicle C cruse The following were used:
wherein C is Di Is the vehicle air resistance coefficient.
Converting the multi-vehicle confluence problem into an optimally controlled multi-vehicle speed planning problem, and converting a state equation into the following constraint equation form:
the control optimization target is as follows:
wherein alpha is 1 +α 2 =1 is a weight coefficient.
Construct the Hamilton function:
the canonical equation is:
the Euler-Lagrange equation is:
the optimal solution is as follows:
p i (t)＝c 1 t+c 2
wherein c is 1 ,c 2 ,c 3 ,c 4 All constants have not been determined. The optimal solution of the vehicle trajectory can be found from the following conditions
Initial conditions
Terminal conditions
Boundary condition
And cross-sectional conditions
Optimizing the sequence between the main road vehicle i and the ramp vehicle i +1, and respectively calculating the optimal control input of the main road vehicle under the condition of preferential passage
And confluence passage time t i Optimal control input under preferential passage with ramp vehicles
And confluence passage time t i+1 Storing the optimal control input, and determining the traffic sequence according to the minimum principle of the confluence traffic performance index.
And optimizing the sequence between the main road vehicle i and the main road vehicle i +1, and keeping the first-in first-out sequence unchanged according to the rule that the same lane does not allow overtaking in confluence. Optimal control input under calculation order
And confluence passage time t i ，t i+1 . Storing optimal control inputs
And optimizing the sequence between the ramp vehicle i and the ramp vehicle i +1, and keeping the first-in first-out sequence unchanged according to the rule that the same lane does not allow overtaking in confluence. Optimal control input under calculation order
And confluence passage time t i ，t i+1 . Storing optimal control inputs
If the adjacent vehicles are vehicles in the same lane, the merging control optimization is carried out according to the rule that the overtaking is not allowed, if the adjacent vehicles are vehicles in different lanes, the passing sequence of the adjacent serial number vehicles is exchanged, and in order to reduce the merging optimization calculation under the meaningless vehicle sequence, the vehicle merging sequence is pruned and optimized as shown in figure 3.
Let S i And (m, n) is the optimized passing sequence of the ith step, m is a prior passing vehicle, and n is a rear passing vehicle. Step 1, carrying out control optimization of the vehicle 1 and the vehicle 2 in different orders to obtain a converging order S of the vehicles 1 and 2 with a smaller objective function 1 (2, 1), and then storing the corresponding control input
According to the optimization result of the step 1, performing optimization of the step 2, performing analysis by adding the vehicle 3 in the step 2, analyzing the merging sequence of the vehicle 1 and the vehicle 3 because the vehicle 1 is a rear vehicle in the optimization result of the step 1, and keeping the merging sequence unchanged because the vehicle 1 and the vehicle 3 are vehicles in the same lane, namely the vehicle sequence S 2 (1, 3) storing the corresponding control input
Step 3 and step 2, the merging sequence of the vehicles in the same lane is kept unchanged to obtain an optimized sequence S 3 (3, 4) and optimal control input
Step 4 is the same as step 1, and the optimization sequence is S 4 (5, 4) storing the optimal control input
And for the i vehicles, traversing all the vehicles entering the ramp control area until all the vehicles complete the sequence and track optimization, and reading the optimal control input corresponding to the optimal sequence as the final control input of the vehicles.
Claims (7)
1. A vehicle ramp confluence cooperative control method considering multi-vehicle type energy consumption is characterized by comprising the following steps:
step (1): setting a confluence control area, formulating a confluence rule, and distributing serial numbers i to newly-entered vehicles on a main road and a ramp according to a first-in first-out rule;
step (2): for a vehicle i, receiving vehicle-related information by a roadside central controller based on state information and vehicle type information of the vehicle sent by an on-board V2X communication device;
and (3): constructing a control method taking vehicle energy consumption and confluence passing time as targets, aiming at the wind resistance coefficient C of different vehicles Di Windward area A i Mass of m is different i Considering that the types of vehicles are greatly different and the energy sources of power units are different, a comprehensive energy consumption function C (t) of the traditional vehicle and the new energy vehicle is established i ,u i (t),v i (t)), wherein t i Is the travel time of vehicle i, u i (t) is a control input amount of a vehicle i, v i (t) the speed of the vehicle i is combined with the minimum principle of the transit time during running, and the vehicle confluence cooperative control optimizes the overall target
Wherein alpha is 1 +α 2 =1 is a weight coefficient, u i (t) is a control input to the controller,
the time when the vehicle i travels to the merge point,
time of entry of vehicle i into confluence control zone, u max As maximum control input of the vehicle, u min A vehicle minimum control input; after the roadside central controller receives the vehicle related information, the vehicle information is input into the controller, confluence optimization of the vehicle i and the vehicle i +1 is carried out according to the current passing order, and a combined target of energy consumption and passing time in the vehicle confluence process under the current order is calculated
And (4): optimizing the vehicle passing sequence, and respectively calculating the optimal control input quantity of vehicle confluence under the preferential passing of the vehicle i and the vehicle i +1 for the vehicle i and the vehicle i +1
Storing the corresponding control target amount, and using the sum of the two combined targets J = J i +J i+1 Determining a vehicle confluence sequence according to a minimum principle;
and (5): and traversing all vehicles entering the ramp control area until all vehicles complete sequence and track optimization, and reading the optimal control input corresponding to the optimal sequence as the final control input of the vehicles.
2. The method according to claim 1, wherein the confluence rule in step (1) is specifically: and (4) not allowing overtaking in the same lane, and establishing a confluence coordinate system with the starting point of the confluence control area as the original point and the driving direction of the vehicle as the positive direction.
3. The method according to claim 2, wherein the "assignment number" in step (1) is specifically: all vehicles on the main road and the ramp are numbered uniformly according to the time of entering the confluence control area.
4. The method according to claim 3, wherein the "vehicle-related information" in the step (2) includes a vehicle number i, an entry time
Velocity v i Acceleration a i Retardation coefficient τ i Wheel output torque T i Frontal area of vehicle A i 。
5. The method according to claim 4, wherein the "confluence control method" in the step (3) is specifically:
step (31): modeling vehicle dynamics; for vehicleVehicle i, analyzing the stress resistance of the vehicle in the driving process
Wherein m is i Is vehicle mass, f is rolling resistance coefficient, g is gravitational acceleration, k is gradient, C D Is the air resistance coefficient, v, of the vehicle i (t) is the traveling speed of vehicle i; under the condition of normal running and no slip, the relation between the power and the resistance of the vehicle is as follows:
wherein r is i Is the radius of the wheel, a i (t) is vehicle travel acceleration;
step (32): vehicle dynamics model:
wherein
As the speed of the vehicle, is,
for vehicle acceleration, T i (t) engine output torque;
after feedback linearization, the third order state space equation of the vehicle longitudinal dynamics:
wherein:
step (33): establishing an optimization target;
target 1: a minimum transit time; time when vehicle i enters confluence control area
To the moment of departure from the merging point
The time required for the process is minimal:
target 2: minimum energy consumption; namely, the energy consumption index J i (t i ,u i (t),v i (t)) minimum;
wherein C (t) i ,u i (t),v i (t)) is the energy consumption function:
C(t i ,u i (t),v i (t))＝C acc (t i ,u i (t),v i (t))+C cruse (t i ,u i (t),v i (t))
dividing the energy consumption of the vehicle into acceleration consumption and constant-speed running consumption;
specific energy consumption C at the time of vehicle acceleration acc The calculation is as follows:
C acc (t i ,u i (t),v i (t))＝m i a i (t)v i (t)
energy consumption rate C when the vehicle is running at a constant speed cruse The following were used:
wherein C is Di Is the vehicle air resistance coefficient;
step (34): in order to ensure that the driving safety and the control input of the vehicle are suitable for the performance requirement of the vehicle, the following constraint conditions are established:
constraint 1: the vehicle confluence control firstly ensures the driving safety of the vehicle, the driving safety is ensured by the vehicle safety time in the confluence process, and the vehicles in the same lane need to be kept to have enough safety time interval t for the adjacent vehicles in the same lane s ：
t i -t i+1 >t s
Constraint 2: safe combination; the vehicle is driven to a merging point with a certain safe time t ms ；
Constraint 3: the vehicle has limited self-driving braking capability and limited vehicle running speed, in order to meet the acceleration and deceleration capability of the vehicle,
the speed is constrained:
v min ≤v i ≤v max
controlling input constraint:
u min ≤u i (t)≤u max
for different vehicles, the acceleration capacity and the braking capacity are different, so the maximum acceleration and deceleration are also different;
step (35): converting the multi-vehicle confluence problem into an optimal control problem in multi-vehicle speed planning, and converting a state equation into the following constraint equation form:
the control optimization target is as follows:
wherein alpha is 1 +α 2 =1 is a weight coefficient;
construct the Hamilton function:
the canonical equation is:
the Euler-Lagrange equation is:
the optimal solution is as follows:
p i (t)＝c 1 t+c 2
wherein c is 1 ,c 2 ,c 3 ,c 4 Are all constants not yet determined(ii) a The optimal solution of the vehicle trajectory can be found from the following conditions
Initial conditions
Terminal conditions
Boundary condition
Cross section Condition
6. The method according to claim 5, wherein step (4) is specifically:
optimizing the sequence between the main road vehicle i and the ramp vehicle i +1, and respectively calculating the optimal control input of the main road vehicle under the condition of preferential passage
And confluence passage time t i Optimal control input under preferential passage with ramp vehicles
And confluence passage time t i+1 Storing optimal control input, and determining a traffic sequence according to a minimum principle of a confluence traffic performance index;
optimizing the sequence between the main road vehicle i and the main road vehicle i +1, and keeping the first-in first-out sequence unchanged according to the rule that the same lane does not allow overtaking in confluence; optimal control input under calculation order
And confluence passage time t i ，t i+1 (ii) a Storing optimal control inputs
Optimizing the sequence between the ramp vehicle i and the ramp vehicle i +1, and keeping the first-in first-out sequence unchanged according to the rule that the same lane does not allow overtaking during confluence; optimal control input under calculation order
And confluence passage time t i ，t i+1 (ii) a Storing optimal control inputs
7. The method of claim 6, wherein step 5 is: and traversing all vehicles entering the ramp control area until all vehicles complete the sequence and track optimization.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20170160745A1 (en) * | 2015-12-08 | 2017-06-08 | Ford Global Technologies, Llc | Deceleration determination of a vehicle |
| CN112750318A (en) * | 2020-12-30 | 2021-05-04 | 山东高速信息集团有限公司 | Ramp confluence control method and system based on edge cloud |
| CN113362619A (en) * | 2021-06-04 | 2021-09-07 | 东南大学 | Intelligent network vehicle ramp cooperative confluence control method and device under mixed traffic environment |
| CN114973650A (en) * | 2022-04-13 | 2022-08-30 | 东南大学 | Vehicle ramp entrance confluence control method, vehicle, electronic device, and storage medium |
-
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- 2022-11-29 CN CN202211509503.2A patent/CN115830885B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20170160745A1 (en) * | 2015-12-08 | 2017-06-08 | Ford Global Technologies, Llc | Deceleration determination of a vehicle |
| CN112750318A (en) * | 2020-12-30 | 2021-05-04 | 山东高速信息集团有限公司 | Ramp confluence control method and system based on edge cloud |
| CN113362619A (en) * | 2021-06-04 | 2021-09-07 | 东南大学 | Intelligent network vehicle ramp cooperative confluence control method and device under mixed traffic environment |
| CN114973650A (en) * | 2022-04-13 | 2022-08-30 | 东南大学 | Vehicle ramp entrance confluence control method, vehicle, electronic device, and storage medium |
Non-Patent Citations (1)
| Title |
|---|
| 杨澜 等: "智能网联汽车协同生态驾驶策略综述", 《交通运输工程学报》, vol. 20, no. 5, 31 October 2020 (2020-10-31), pages 58 - 72 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116189461A (en) * | 2023-04-28 | 2023-05-30 | 安徽大学 | Intersection traffic control method, system and storage medium considering carbon emission |
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