Network connection type automatic driving vehicle ramp confluence passing order cooperative game method

Abstract

The invention provides a network connection type automatic driving vehicle ramp confluence passing order cooperative game method. The method comprises the following steps: establishing a ramp confluence order cooperative game model; solving a ramp confluence sequence cooperative game problem with transferable utility; and obtaining an optimal passing sequence according to the optimal time sequence of the vehicles arriving at the confluence point. According to the method, the dynamic interaction characteristics of the converging vehicles are fully considered, multi-vehicle cooperative driving in the ramp converging area can be realized, and the road traffic efficiency in the ramp converging area is improved on the premise of ensuring the driving safety.

Description

Technical Field

[0001] This invention relates to the field of ramp merging control technology, and in particular to a cooperative game theory method for the merging order of connected autonomous vehicles. Background Technology

[0002] Merging ramp areas are characterized by frequent traffic accidents and congestion, and are considered typical traffic bottlenecks. When traffic flows from ramps and main lanes merge, if vehicles in the main lane do not yield, ramp vehicles may have to wait a long time for a suitable merging distance; if ramp vehicles force their way into the main lane, it can easily cause sudden deceleration of vehicles in the main lane, potentially leading to uncomfortable driving experiences, traffic congestion, or even traffic accidents. Connected autonomous vehicles equipped with C-V2X communication technology can achieve real-time information interaction between vehicles and between vehicles and other facilities, thus possessing great potential to improve road traffic safety, increase road efficiency, and reduce vehicle fuel consumption. Therefore, focusing on connected autonomous vehicles, exploring a reasonable method for optimizing the traffic order of vehicles merging on ramps to achieve coordinated vehicle merging is beneficial for solving problems such as road congestion and traffic accidents in merging ramp areas, and has strong practical significance and research value.

[0003] Currently, some studies on ramp merging control employ rule-based vehicle traffic order decision-making methods. For example, Chinese Patent Publication No. CN 111710191 A discloses a ramp merging control method and system for urban expressways, which assumes that vehicle traffic order follows a First-in-First-out (FIFO) rule. Patent Publication No. CN 112750318 A discloses an edge cloud-based ramp merging control method that uses a FIFO strategy to obtain an initial merging order and then obtains a corrected merging order through a road priority adjustment strategy.

[0004] The rule-based traffic order decision-making methods mentioned above have the advantage of being simple and easy to implement; however, they are poorly adaptable to complex and ever-changing scenarios. Furthermore, most existing studies on ramp merging vehicle traffic order do not fully consider the dynamic interactions between vehicles in different lanes and the performance differences between vehicles, making it difficult to adapt to dynamic changes in the scenario and achieve global optimization, and also making it difficult to guarantee the fairness of the traffic order results.

[0005] In summary, existing ramp merging vehicle traffic order optimization technologies have not yet achieved fair ramp merging control by considering demand differentiation. Therefore, this paper proposes a cooperative game theory method for ramp merging traffic order of connected autonomous vehicles with transferable utility, taking into account the characteristics of heterogeneous vehicles and the traffic demands of different vehicles. This method can provide a reference solution for addressing the bottleneck technical problem of ramp merging control for connected autonomous vehicles. Summary of the Invention

[0006] The purpose of this invention is to overcome the shortcomings of the existing technology by providing a cooperative game theory method and system for the merging order of connected autonomous vehicles on ramps. This method optimizes the merging order of connected autonomous vehicles on ramps, ensuring the safety of vehicles in the merging area and improving road traffic efficiency. This objective can be achieved through the following technical solutions:

[0007] A cooperative game theory method for determining the merging order of connected autonomous vehicles on ramps includes the following steps:

[0008] Establish a cooperative game model for the merging order of ramps;

[0009] Solve the cooperative game problem of ramp merging order with transferable utility;

[0010] The optimal passage order is obtained by sorting the vehicles according to their optimal arrival time at the merging point.

[0011] Preferably, the steps for establishing a cooperative game model for the merging order of ramps specifically include:

[0012] Step 3.1.1: Define the player set N:

[0013] N = {1, 2, ..., n} represents the set of all vehicles participating in the cooperative game in the ramp merging control area;

[0014] n is the total number of vehicles;

[0015] Step 3.1.2: Model the shortest time and the longest time for vehicle i to reach the merging point;

[0016] Step 3.1.3: Define the total vehicle cost function;

[0017] Step 3.1.4: Establish the characteristic function V(S).

[0018] Preferably, solving the cooperative game problem of ramp merging order with transferable utility specifically includes the following steps:

[0019] Step 3.2.1: Establish a sub-alliance for vehicles in the same lane;

[0020] Step 3.2.2: Select the first sub-alliance of different lanes to establish a large alliance;

[0021] Step 3.2.3: Solve the large caravan optimization problem to obtain the arrival time of all vehicles in the large caravan at the ramp merging point;

[0022] Step 3.2.4: Distribute the revenue of each vehicle in the sub-alliance according to its marginal contribution;

[0023]

[0024] in, and Assigned to sub-alliance G after cooperation among sub-alliances A and G B The benefits;

[0025] V(G A ) and V(G B ) for sub-alliance G A and G B The characteristic function is calculated by defining it in step 3.1.4;

[0026] V(G A ∪G B The revenue generated from the cooperation between the two sub-alliances;

[0027] t A1 and t B1 The sub-alliance G is defined as follows, without forming a grand alliance. A and G B The time it takes for the lead vehicle to arrive at the merging point on the ramp;

[0028] Step 3.2.5: Obtain the adjustment time Δt for the utility transfer strategy of the leading vehicle in the sorted sub-alliance. G ;

[0029] Step 3.2.6: Based on the time of the lead vehicle of the large alliance passing through the ramp merging point obtained in Step 3.2.3 and the adjustment time Δt obtained in Step 3.2.5. G Calculate the optimal time for the lead vehicle of the large alliance to arrive at the ramp merging point and the optimal time for all vehicles in each sub-alliance to arrive at the ramp merging point;

[0030] Step 3.2.7: Select the next sub-alliance that is in a different lane from the sub-alliance that was ranked after the previous major alliance and establish a major alliance. Repeat steps 3.2.3-3.2.5 to perform repeated cooperative game until the optimal time for all vehicles in player set N to reach the merging point is found.

[0031] Preferably, step 3.2.3 specifically includes the following steps:

[0032]

[0033] Among them, t A and t B The first sub-alliance G of the ramp A And the first sub-alliance G of the main lane B The time it takes for the lead vehicle of each vehicle to arrive at the merging point on the ramp;

[0034] τ safe This indicates the expected safe inter-vehicle distance between the two vehicles;

[0035] ΔT intrrnal The time difference between the last and first cars of the subsequent sub-alliance passing through the ramp merging point;

[0036] and Sub-alliance G A The lead car and the sub-alliance G B The shortest / longest time for the lead vehicle to reach the merging point of the ramp;

[0037] a I and a J Sub-alliance G A The lead car and the sub-alliance G B The acceleration of the lead vehicle;

[0038] τ m This refers to the time interval between vehicles in front and behind each other in the same lane.

[0039] Compared with the prior art, the present invention has the following advantages:

[0040] 1) It fully considers the dynamic interaction characteristics of merging vehicles, can adapt to different ramp merging scenarios, realize multi-vehicle cooperative driving in the ramp merging area, and improve the road traffic efficiency in the ramp merging area.

[0041] 2) It integrates the differences in power performance of different vehicles and the different vehicle preferences for traffic needs such as efficiency, safety and comfort, so as to achieve a fair and reasonable allocation of traffic order and improve the acceptance of the allocation results by vehicles. Attached Figure Description

[0042] Figure 1 This is a schematic diagram of a ramp merging scenario for connected autonomous vehicles in an embodiment of the present invention;

[0043] Figure 2 This is a flowchart of the method for optimizing the merging traffic order of connected autonomous vehicles on ramps in an embodiment of the present invention;

[0044] Figure 3 This is a flowchart of the cooperative game theory method for the merging order of connected autonomous vehicles on ramps, as described in an embodiment of the present invention.

[0045] Figure 4 This is a schematic diagram of the sub-alliances divided in step 3.2.1. Detailed Implementation

[0046] The following will describe in more detail the cooperative game theory method for the merging order of connected autonomous vehicles on ramps according to the present invention, with reference to the schematic diagrams. Preferred embodiments of the invention are shown. It should be understood that those skilled in the art can modify the invention described herein while still achieving its advantageous effects. Therefore, the following description should be understood as being of general knowledge to those skilled in the art and is not intended to limit the invention.

[0047] This embodiment presents a cooperative game theory method for determining the merging order of connected autonomous vehicles on ramps. For example... Figure 1 As shown, the ramp merging control area can be set to a certain range before the end of the ramp. The center point of the main lane connected at the end of the ramp is the merging point. It is assumed that the connected autonomous vehicle obeys cloud scheduling after entering the ramp merging control area.

[0048] like Figure 2 As shown, the method in this embodiment includes the following steps:

[0049] Step 1: Connected autonomous vehicles entering the ramp merging control area serialize information such as position, attitude, maximum acceleration and deceleration, and traffic demand parameters into Protobuf protocol messages, and broadcast them to cloud communication devices through on-board unit (OBU).

[0050] Among them, the maximum acceleration and deceleration are mainly used to distinguish the acceleration performance of different vehicles;

[0051] The parameters for travel demand mainly include the sensitivity to time and comfort (weighting coefficients), the expected minimum safe vehicle distance, etc.

[0052] Step 2: The cloud communication device subscribes to and receives messages from each vehicle, and transmits them to the cloud computing device.

[0053] Step 3: The cloud computing device deserializes the information obtained through interaction and uses a utility-transferable cooperative game method to dynamically decide the passage order of each vehicle leaving the merging area of ​​the ramp.

[0054] Step 4: The cloud communication device obtains the passage order decided by the cloud computing device and broadcasts it in the ramp merging control area;

[0055] Step 5: Each vehicle communication unit (OBU) subscribes to cloud messages and transmits them to the vehicle controller;

[0056] Step 6: Each vehicle controller deserializes the message to obtain its own passage order, plans the vehicle merging trajectory according to the sequence, and controls the vehicle to follow the trajectory until it leaves the ramp merging control area.

[0057] Step 1 involves serializing the data into Protobuf protocol messages and broadcast messages, which specifically includes the following sub-steps:

[0058] Step 1.1: Define the message structure shared by the sender and receiver;

[0059] Step 1.2: Generate the underlying source code of the Protobuf protocol target;

[0060] Step 1.3: Serialize vehicle data such as position and speed based on the source code;

[0061] Step 1.4: The vehicle-mounted OBU sends a serialized message to the cloud via the MQTT protocol.

[0062] It should be noted that the underlying source code of the target system may differ depending on the system and language, but all of them can be generated by the corresponding compiler.

[0063] Steps 1.1 to 1.4 are all existing technologies.

[0064] like Figure 3 As shown, step 3 specifically includes the following sub-steps:

[0065] Step 3.1: Establish a cooperative game model for the merging order of ramps;

[0066] Step 3.2: Solve the cooperative game problem of ramp merging order with transferable utility;

[0067] Step 3.3: Sort the optimal passage order according to the optimal arrival time of each vehicle at the merging point.

[0068] Step 3.1 includes the following sub-steps:

[0069] Step 3.1.1: Define the player set N:

[0070] Let N = {1,2,...,n} represent the set of all vehicles participating in the cooperative game in the ramp merging control area;

[0071] n is the total number of vehicles. For each vehicle i∈N, the smaller the value, the earlier it enters the merging area of ​​the ramp.

[0072] Step 3.1.2: Model the shortest time and the longest time for vehicle i to reach the merging point to form the time constraint in step 3.2.3.

[0073] in and Let be the shortest time and the longest time for vehicle i to reach the merging point, respectively. Their calculation expressions are as follows:

[0074]

[0075] In the formula, and Let be the maximum deceleration and maximum acceleration that vehicle i can achieve, respectively, and these are known quantities;

[0076] p i and v i Let i represent the current position and current speed of vehicle i, respectively, which are known quantities;

[0077] p mp This indicates the location of the ramp merging point, and is a known quantity.

[0078] Time t for vehicles to arrive at the merging point of the ramp i And it satisfies the reachability window constraint.

[0079] Step 3.1.3: Define the total vehicle cost function:

[0080] The expression for the total cost function C for all vehicles is:

[0081]

[0082] In the formula, the cost function c for each vehicle i i Includes traffic efficiency costs and comfort costs Its expression is:

[0083]

[0084] In the formula, a i Let be the current acceleration of vehicle i, which is a known quantity;

[0085] and These are the weighting coefficients for the traffic efficiency cost and comfort cost of vehicle i, respectively. They are known quantities and depend on vehicle i's traffic needs for time and comfort. They are also key parameters that determine utility transfer.

[0086] Let be the expected time for vehicle i to reach the merging point of the ramp, and its expression is:

[0087]

[0088] t i - The optimal time for vehicle i to arrive at the ramp merging point.

[0089] It should be noted that the cost function can also be calculated in different ways, such as using jerk to calculate the cost of comfort; or a cost function with different objectives can be considered, such as considering fuel economy and safety objectives in the cost function.

[0090] In steps 3.1.2 and 3.1.3, 'i' refers to any vehicle in the player set N.

[0091] For example, the "i" in steps 3.1.2 and 3.1.3 can refer to the "i" in step 3.1.4, or it can refer to the "j" in step 3.1.4.

[0092] Similarly, i in steps 3.1.2 and 3.1.3 can refer to I and J in step 3.2.3 and O in step 6.2.

[0093] Step 3.1.4: Establish the characteristic function V(S):

[0094] V(S) is defined as the alliance formed by vehicle cooperation. The maximum total benefit that can be guaranteed, i.e., the minimum total cost, is calculated by solving the optimization problem:

[0095]

[0096] In the formula,

[0097] t j ,j∈S represents the time when vehicle j in alliance S arrives at the merging point, which is an unknown quantity;

[0098] τ safe The expected safe time interval between the two vehicles is the rear vehicle, and its value is determined according to the traffic needs of different vehicles; it is a set value.

[0099] t s Let be a set containing t. j That is, t S For t j A set of.

[0100] Alliance S applies to sub-alliances and major alliances.

[0101] Step 3.2.3 Solve for the unknown quantity t J t I Then, in step 3.1.4, the feature function values ​​of the sub-alliance are obtained.

[0102] That is, step 3.1.4 is used to solve for the sub-alliance revenue in step 3.2.4.

[0103] Step 3.2 includes the following sub-steps:

[0104] Step 3.2.1: Simplify the problem-solving scale by dividing all ramp sub-alliances and main lane sub-alliances for adjacent vehicles on ramps or main lanes respectively;

[0105] Among them, the sub-alliance closest to the merging point of the ramp is the first sub-alliance, and the second sub-alliance, the third sub-alliance, and so on are ordered in sequence.

[0106] If there are multiple main lanes, assuming that vehicles autonomously select the optimal lane before entering the ramp merging control area, this can be considered a single main lane and single ramp scenario. Furthermore, it is assumed that vehicles in the same lane cannot overtake. Therefore, the problem size can be simplified by establishing possible ramp sub-alliances or main lane sub-alliances for adjacent vehicles on the ramp or main lane respectively.

[0107] The simplification includes: 1. Adjacent vehicles form sub-alliances; 2. The scenario is assumed to be a single main lane + a single ramp; 3. Two sub-alliances form a larger alliance to engage in game theory.

[0108] The conditions for establishing a sub-alliance are:

[0109] If the time interval between adjacent vehicles in the same lane (ramp or main lane) is less than the expected safe time interval between the following vehicle, the following vehicle joins the preceding vehicle to form a sub-alliance; otherwise, the preceding and following vehicles each form a sub-alliance.

[0110] "Same lane" means being in the same lane;

[0111] The conditional expression for determining whether two adjacent vehicles in the same lane form a sub-alliance is:

[0112]

[0113] In the formula, q∈{1,2,...,n} r} and z∈{1,2,...,n} m} represent the vehicle numbers on the ramp and the main lane, respectively, and n r +n m =n;

[0114] The superscript 'm' indicates a main lane; the superscript 'r' indicates a ramp.

[0115] and These represent ramp sub-alliance and main lane sub-alliance, respectively.

[0116] This indicates the time interval between vehicles in front and behind in the same lane; it is a set value.

[0117] p z - The current position of vehicles in the main lane;

[0118] l z-1 - Vehicle length, a known quantity, measured value;

[0119] η is the scaling factor, which is a set value;

[0120] {} represents a set, that is, a sub-alliance;

[0121] If the preceding vehicle is in a sub-alliance and meets the conditions for establishing a sub-alliance with the following vehicle, then the following vehicle joins the sub-alliance to which the preceding vehicle belongs, and so on.

[0122] Step 3.2.2: Select the first sub-alliance G of the ramp. A And the first sub-alliance G of the main lane B Establish a major alliance;

[0123] "Establishing a grand alliance" refers to cooperation between two sub-alliances.

[0124] Step 3.2.3: Obtain the first sub-alliance G of the ramp. A And the first sub-alliance G of the main lane B The time it takes for all vehicles to pass through the ramp merging point.

[0125] "First sub-alliance G on the ramp" A "It is a sub-alliance that is closest to the ramp merging point among all the ramp sub-alliances divided in step 3.2.1.

[0126] "Main lane first sub-alliance G" B The definition of "" is similar.

[0127] Get the first sub-alliance G of the ramp A And the first sub-alliance G of the main lane B The arrival times of all vehicles at the ramp merging point are obtained by solving the large carpooling optimization problem:

[0128]

[0129] In the formula, t A - First sub-alliance G of the ramp A The time it takes for the lead vehicle to arrive at the merging point on the ramp;

[0130] t B - First sub-alliance G on the main lane B The time it takes for the lead vehicle to arrive at the merging point on the ramp;

[0131] For example: the first sub-alliance G of the ramp A The lead car refers to the first sub-alliance G on the ramp. A The first vehicle closest to the merging point of the ramp.

[0132] "Main lane first sub-alliance G" B "The lead vehicle" is defined as above.

[0133] "First sub-alliance G of the ramp" A "Sub-alliance A" (hereinafter referred to as "Sub-alliance A"); "First Sub-alliance G of the main lane" BHereinafter referred to as "Sub-Alliance B".

[0134] τ safe This represents the desired safe headway between the rear vehicle and the other vehicle, and is a set value.

[0135] ΔT internal The time difference between the last and first vehicles of the subsequent sub-alliance passing through the ramp merging point is a set value.

[0136] First sub-alliance G of the ramp A The shortest / longest time for the lead vehicle to reach the merging point of the ramp;

[0137] The first sub-alliance G in the main lane B The shortest / longest time for the lead vehicle to reach the merging point of the ramp;

[0138] All of these are solved by step 3.1.2.

[0139] The first sub-alliance G in the main lane B The minimum / maximum acceleration of the lead car is a known quantity and depends on the vehicle's dynamic performance;

[0140] First sub-alliance G of the ramp A The minimum / maximum acceleration of the lead car is a known quantity and depends on the vehicle's dynamic performance;

[0141] a I - First sub-alliance G of the ramp A The acceleration of the lead car, known quantity, measured value;

[0142] a J - First sub-alliance G on the main lane B The acceleration of the lead car, known quantity, measured value;

[0143] τ m This is the set value for the time interval between vehicles in front and behind each other in the same lane. The time interval refers to the time interval between vehicles.

[0144] Solve for t A and t B After finding the optimal solution, based on t A optimal solution and t B optimal solution The size can be used to determine the passage order of sub-alliances and calculate sub-alliances (the first sub-alliance G in the main lane). B The first sub-alliance G of the ramp A The optimal time for each vehicle to arrive at the merging point of the ramp.

[0145] The optimal arrival times for all other vehicles at the ramp merging point include: and

[0146]

[0147] Specifically, after solving, if Then the first sub-alliance G in the main lane B The lead vehicle passes first, the first sub-alliance G on the ramp. A The lead vehicle passes behind it.

[0148] Step 3.2.4 Solve for the sub-alliance revenue and

[0149] Based on the Shapley value method, the revenue of each sub-alliance is allocated according to its marginal contribution:

[0150]

[0151] In the formula, and Assigned to the first sub-alliance G on the ramp after cooperation among sub-alliances A And the first sub-alliance G of the main lane B The benefits;

[0152] V(G A ) and V(G B ) is the first sub-alliance G of the ramp. A And the first sub-alliance G of the main lane B The characteristic function is calculated by defining it in step 3.1.4;

[0153] V(G A ∪G B The benefit derived from the cooperation between the two sub-alliances is defined as follows:

[0154]

[0155] t A1 and t B1 The sub-alliance G is defined as follows, without forming a grand alliance. A and G B The time it takes for the lead vehicle to arrive at the merging point on the ramp;

[0156] Regarding V(G) A ∪G B The solution process for ) is as follows:

[0157] Substitute the optimal solution (optimal time for each vehicle) from step 3.2.3 into the cost function formula from step 3.1.3 to obtain V(G A ∪G B ).

[0158] Step 3.25: Obtain the lead vehicle utility transfer strategy adjustment time Δt for the sorted sub-alliance. G .

[0159] The utility transfer is achieved by solving the policy fine-tuning optimization problem to minimize the vehicle's corresponding revenue and the revenue allocated by the Shapley value method under the optimal solution in step 3.2.3, thus obtaining the optimal policy utility transfer adjustment time. The policy fine-tuning optimization problem is as follows:

[0160]

[0161] In the formula, Δt G - Utility transfer strategy adjustment time;

[0162] Subscript G - the sub-alliance sorted after the optimal solution in step 3.2.3;

[0163] Following the assumptions in step 3.2.3, the first sub-alliance G of the ramp... A The sub-alliance is sorted as follows.

[0164] μ - Regularization coefficient, which is a set value;

[0165] Step 3.2.3 Optimize the returns of the sorted sub-alliances in the solution;

[0166] The optimal time for the lead vehicle of the sub-alliance, after being sorted in the optimization solution of step 3.2.3, to reach the ramp merging point;

[0167] Continuing with the assumptions in step 3.2.3 First sub-alliance G of the ramp A For the sub-alliance ordered after [the specified order], therefore

[0168] c G - The cost value of the lead car in the sub-alliance after sorting; calculated from cost formula c in step 3.1.3. i calculate;

[0169] The shortest time for the lead car of the next sub-alliance to reach the merging point;

[0170] The longest time it takes for the lead car of the next sub-alliance to reach the merging point;

[0171] The first constraint indicates that the adjusted time must still meet the time constraint for the lead vehicle to reach the merging point of the ramp;

[0172] The time when the last car in the leading sub-alliance arrives at the ramp merging point.

[0173] τ safe -The expected safe time interval between the rear vehicle and the middle vehicle in the two vehicles, set value;

[0174] The second constraint defines the safe time interval between the last car of the first-order sub-coalition and the first car of the second-order sub-coalition.

[0175] This is the maximum allowed adjustment time for the lead car of the sub-alliance that is sorted after the current one, and is a set value.

[0176] The purpose of step 3.2.5 is to solve for the adjustment time of the utility transfer strategy of the lead car in the sub-alliance after sorting.

[0177] Step 3.2.6: Based on the time of the lead vehicle of the large alliance passing through the ramp merging point obtained in Step 3.2.3 and the utility transfer adjustment time Δt obtained in Step 3.2.5. G Calculate the optimal time for the lead vehicle of the large alliance to arrive at the ramp merging point, and the optimal time for all vehicles in each sub-alliance to arrive at the ramp merging point.

[0178] Specifically, based on the assumptions in step 3.2.3 but:

[0179] The optimal time for the lead car of the Grand Alliance to pass through the ramp merging point includes: and

[0180] The optimal time for all vehicles in each sub-alliance to pass through the ramp merging point includes: and

[0181]

[0182]

[0183] Step 3.2.7: Select the next sub-alliance that is in a different lane from the sub-alliance that was ranked after the previous major alliance and establish a major alliance. Repeat steps 3.2.3-3.2.5 to perform repeated cooperative game until the optimal time for all vehicles in player set N to reach the merging point is found.

[0184] Based on the assumptions in step 3.2.3 The large alliance established in step 3.2.8 includes:

[0185] The second sub-alliance of the main lane, the first sub-alliance G of the ramp A .

[0186] like Figure 4 As shown, sub-consortium A represents the first sub-consortium G of the ramp. AAnd so on.

[0187] The large alliance established in step 3.2.7 includes: sub-alliance C and sub-alliance A.

[0188] In the next round of cooperative game, sub-alliance C is the first sub-alliance of the main lane, and sub-alliance A is the first sub-alliance of the ramp.

[0189] Step 6 specifically includes the following sub-steps:

[0190] Step 6.1: Based on the passage order, identify the target vehicles that are in front of and behind your own vehicle;

[0191] Step 6.2: Use the Improved Intelligent Driver Model (IDM) to plan the longitudinal motion control acceleration of the autonomous vehicle. The desired acceleration of the connected autonomous vehicle i is:

[0192]

[0193] In the formula, s o,o-1 - The actual distance between vehicle o and the preceding vehicle o-1 is a known quantity, obtained by sensor measurement;

[0194] The expression for the expected following distance;

[0195] Δv o,o-1 - The relative speed between the two vehicles is a known quantity, obtained through sensor measurements;

[0196] v o - The current speed of vehicle o, a known quantity, is obtained from sensor measurements;

[0197] v odes -The desired vehicle speed is a set value that is dynamically adjusted based on the optimal time for vehicle o to pass through the ramp merging point;

[0198] This represents the maximum acceleration of vehicle o, which is a set value.

[0199] δ - Acceleration exponent, set value;

[0200] b - Comfort deceleration, set value;

[0201] s odes Set a value for the minimum safe distance expected for vehicle o.

[0202] Step 6.3 Ramp vehicles use the Minimizing Overall Braking Induced by Lane Change (MOBIL) model to confirm lane change decisions and lateral target positions;

[0203] Step 6.4 Employs model predictive control to determine the front wheel steering angle for lateral motion control of the vehicle;

[0204] Step 6.5 Execute longitudinal and lateral motion control commands. Control commands include: acceleration and front wheel steering angle.

Claims

1. A cooperative game theory method for the merging traffic order of connected autonomous vehicles on ramps, characterized in that, Includes the following steps: Establish a cooperative game model for the merging order of ramps; Solve the cooperative game problem of ramp merging order with transferable utility; The optimal passage order is obtained by sorting the vehicles according to their optimal arrival time at the merging point.

2. The cooperative game theory method for the merging order of connected autonomous vehicles on ramps according to claim 1, characterized in that, The specific steps for establishing a cooperative game model for ramp merging order include: Step 3.1.1: Define the player set N: N = {1, 2, ..., n} represents the set of all vehicles participating in the cooperative game in the ramp merging control area; n is the total number of vehicles; Step 3.1.2: Model the shortest time and the longest time for vehicle i to reach the merging point; Step 3.1.3: Define the total vehicle cost function; Step 3.1.4: Establish the characteristic function V(S).

3. The cooperative game theory method for the merging order of connected autonomous vehicles on ramps according to claim 2, characterized in that, Solving the cooperative game problem of ramp merging order with transferable utility involves the following steps: Step 3.2.1: Establish a sub-alliance for vehicles in the same lane; Step 3.2.2: Select the first sub-alliance of different lanes to establish a large alliance; Step 3.2.3: Solve the large caravan optimization problem to obtain the arrival time of all vehicles in the large caravan at the ramp merging point; Step 3.2.4: Distribute the revenue of each vehicle in the sub-alliance according to its marginal contribution; in, and Assigned to sub-alliance G after cooperation among sub-alliances A and G B The benefits; V(G A ) and V(G B ) for sub-alliance G A and G B The characteristic function is calculated by defining it in step 3.1.4; V(G A ∪G B The revenue generated from the cooperation between the two sub-alliances; t A1 and t B1 The sub-alliance G is defined as follows, without forming a grand alliance. A and G B The time it takes for the lead vehicle to arrive at the merging point on the ramp; Step 3.2.5: Obtain the adjustment time Δt for the utility transfer strategy of the leading vehicle in the sorted sub-alliance. G ; Step 3.2.6: Based on the time of the lead vehicle of the large alliance passing through the ramp merging point obtained in Step 3.2.3 and the adjustment time Δt obtained in Step 3.2.

5. G Calculate the optimal time for the lead vehicle of the large alliance to arrive at the ramp merging point and the optimal time for all vehicles in each sub-alliance to arrive at the ramp merging point; Step 3.2.7: Select the next sub-alliance that is in a different lane from the sub-alliance that was ranked after the previous major alliance and establish a major alliance. Repeat steps 3.2.3-3.2.5 to perform repeated cooperative game until the optimal time for all vehicles in player set N to reach the merging point is found.

4. The cooperative game theory method for the merging order of connected autonomous vehicles on ramps according to claim 3, characterized in that, Step 3.2.3 specifically includes the following steps: Among them, t A and t B The first sub-alliance G of the ramp A And the first sub-alliance G of the main lane B The time it takes for the lead vehicle of each vehicle to arrive at the merging point on the ramp; τ safe This indicates the expected safe inter-vehicle distance between the two vehicles; ΔT internal The time difference between the last and first cars of the subsequent sub-alliance passing through the ramp merging point; and Sub-alliance G A The lead car and the sub-alliance G B The shortest / longest time for the lead vehicle to reach the merging point of the ramp; a I and a J Sub-alliance G A The lead car and the sub-alliance G B The acceleration of the lead car; τ m This refers to the time interval between vehicles in front and behind each other in the same lane.

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