Multi-objective optimization control method and system for cooperative ramp merging of connected vehicles on highway

Patent metadata

Field	Value
Publication number	US-12400541-B2
Application number	US-202318112541-A
Country	US
Kind code	B2
Filing date	Feb 22, 2023
Priority date	Feb 23, 2022
Publication date	Aug 26, 2025
Grant date	Aug 26, 2025

Abstract

State data of vehicles in a control area is collected, then time of starting control and selection of controlled vehicles are decided according to a position of a ramp merging vehicle, information is transmitted into a traffic control module by means of a data transmission module, and an artificial intelligence based ramp merging multi-objective control model ensures efficient, safe, and energy-saving operation of overall traffic of a road while completing ramp merging by means of a vehicle traveling track in the cooperative ramp control area. Compared with a traditional method, the present invention greatly promotes merging of the ramp vehicles, and different from other methods having the defect of transforming the problem of ramp merging into the problem of vehicle sequencing, the present invention greatly improves efficiency of ramp merging.

First claim

What is claimed is:

1. A multi-objective optimization control method for cooperative ramp merging of connected vehicles on a highway, comprising:
step 1, collecting state data of vehicles in a control area of the highway, and analyzing and processing the state data, the control area comprising an intersection of a main road and a ramp, a merging area, and partial road segments of the main road, the ramp and an acceleration lane of the highway, a range of the control area being a communication range of a road side unit, the road side unit being arranged at the intersection of the main road and the ramp of the highway, and the merging area being a preselected area comprising the partial road segments of the acceleration lane and road segments of the main road parallel to the partial road segments of the acceleration lane;
step 2, constructing a set of alternatives for a ramp merging vehicle, an auxiliary vehicle, and a guidance vehicle according to the state data of the vehicles in the control area;
step 3, inputting the set of alternatives into an artificial intelligence based ramp merging multi-objective control model separately, and further determining selection of the auxiliary vehicle and the guidance vehicle by means of an optimal value strategy; wherein
the optimal value strategy selected for the ramp merging vehicle, the guidance vehicle, and the auxiliary vehicle is as follows:
i) selecting the merging vehicle: setting a vehicle, of which a front bumper is closest to a ramp exit line, on the ramp as the merging vehicle, and obtaining state information of all the vehicles in the control area in first t time steps;
ii) according to position information of all the vehicles, preliminary selecting each z main road vehicles in the rear of and in front of the merging vehicle as alternative vehicles of the auxiliary vehicle and the guidance vehicle on the basis of front and rear relations between main line vehicles and the merging vehicle;
iii) in all the alternative vehicles, selecting two vehicles that are adjacent to each other as a group of guidance vehicle and auxiliary vehicle, to construct an alternative set AL of the guidance vehicle and the auxiliary vehicle of the merging vehicle; and
iv) using a traversal method to substitute combinations in the alternative set AL into the artificial intelligence based ramp merging multi-objective control model separately, and determining the merging vehicle, the auxiliary vehicle, and the guidance vehicle that are finally selected on the basis of a value function Q π of the model;

step 4, according to the auxiliary vehicle, the guidance vehicle, and the merging vehicle that are selected, controlling and adjusting accelerations of the auxiliary vehicle and the guidance vehicle, to ensure safe merging of the ramp merging vehicle from the acceleration lane into the main road in the selected merging area; and
step 5, collecting state data of the ramp merging vehicle and the auxiliary vehicle, of which the accelerations are adjusted, and returning to step 4 to adjust accelerations at a next moment.

2. The multi-objective optimization control method for cooperative ramp merging of connected vehicles on a highway of claim 1 , wherein in step 1, the state data of the vehicles comprises positions, speeds, power battery states, and corresponding moment information of the vehicles in the control area.

3. The multi-objective optimization control method for cooperative ramp merging of connected vehicles on a highway of claim 1 , wherein in step 1, the analyzing and processing the state data comprises: analyzing the data, extracting features, and fusing information.

4. The multi-objective optimization control method for cooperative ramp merging of connected vehicles on a highway according to claim 1 , wherein the state information of all the vehicles in the control area in the first t time steps comprises speeds, positions, and accelerations.

5. The multi-objective optimization control method for cooperative ramp merging of connected vehicles on a highway according to claim 1 , wherein in step 4) of the optimal value strategy selected for the merging vehicle, the auxiliary vehicle, and the guidance vehicle, constructing an objective function and constraints of the artificial intelligence based ramp merging multi-objective control model successfully merged into a moment comprises:
i) setting t as a safe merging moment to construct a position and speed relation to be satisfied by the guidance vehicle, the auxiliary vehicle, and the ramp merging vehicle at the safe merging moment:
xm ( t )< x l ( t )−( L 1 +s 0 )−( m 1( ) C 1)  (1)

xm ( t )> x f ( t )+( L 1 +so)+ Tvf ( t )  (2)

1′ m ( t )< v 1 ( )  (3)

vo m ( t );>  (4)

xm ( t ) E[d min, d max]  (5)

x l , v, and a l representing a position, a speed, and an acceleration of the guidance vehicle;
x f , v f and a f representing a position, a speed, and an acceleration of the auxiliary vehicle;
x m , v m and a m representing a position, a speed, and an acceleration of the ramp merging vehicle; r being a constant time interval, L 1 being a length of the vehicle, so being a pause gap, d min and d max being a start point and an end point of the merging area respectively, and the merging area having a length of dm ax −d min ; and the equation sequentially representing, from top to bottom, that the ramp merging vehicle is at the rear of the guidance vehicle, the ramp merging vehicle is in front of the auxiliary vehicle, the ramp merging vehicle and the guidance vehicle have a consistent speed, the ramp merging vehicle and the auxiliary vehicle have a consistent speed, and the ramp merging vehicle is safely merged from the acceleration lane into the main road in the selected merging area; and
ii) under the condition of satisfying 1), further constructing an objective function C comprising, but not limited to, objectives of driving comfort, vehicle energy consumption, traffic efficiency, etc.;
min C ( {circumflex over (t)} )=min f ( c 1 C 1 ( f )+ c 2 C 2 ( f )++ C n ( f ))  (6)

C n ({circumflex over (t)}) representing cost functions of different objectives, and c n representing parameters.

6. The multi-objective optimization control method for cooperative ramp merging of connected vehicles on a highway according to claim 1 , wherein in step 4) of the optimal value strategy selected for the merging vehicle, the guidance vehicle and the auxiliary vehicle, the artificial intelligence based ramp merging multi-objective control model is solved by using a reinforcement learning actor-critic algorithm, which specifically comprises:
i) establishing a state space S and a behavior space A: selecting, according to the state data of the guidance vehicle, the auxiliary vehicle, and the ramp merging vehicle, six-dimensional state information s={x l , X m ,x f , V l , v m , v f ) to represent the most relevant influence factor in an environment, sE S, and selecting a control behavior strategy a={a m , a f ), a E A on the basis of a control object;
ii) establishing an optimal objective: according to a vehicle constraint relation at a safe merging moment t, constructing an optimal objective space set g*={g* 1 , g* 2 , g* 3 , g* 4 , g*} for ramp merging, g*Eg,g being an objective space set, g* 1 representing that equation (1) that the merging vehicle is at the rear of the guidance vehicle is satisfied, g* representing that equation (2) that the ramp merging vehicle is in front of the auxiliary vehicle is satisfied, g* representing that equation (3) that the ramp merging vehicle and the guidance vehicle have a consistent speed is satisfied, g* representing that equation (4) that the ramp merging vehicle and the auxiliary vehicle have a consistent speed is satisfi

Assignees

• Univ Southeast

Inventors

• Dong Hanxuan
• Ding Fan
• Zhang Hailong
• Tan Huachun
• Ye Linhui
• Dai Yunqi

Classifications

• G06Q50/40 Primary

  Business processes related to the transportation industry (shipping G06Q10/083) · CPC title

• G08G1/052

  with provision for determining speed or overspeed {(speed measuring in general G01P)} · CPC title

• G08G1/207

  with respect to certain areas, e.g. forbidden or allowed areas with possible alerting when inside or outside boundaries · CPC title

• G06Q30/0207

  Discounts or incentives, e.g. coupons or rebates · CPC title

• G08G1/0137

  for specific applications · CPC title