Decision-making and planning method for network-connected automatic driving vehicle in expressway shunting area

Abstract

The invention discloses a network-connected automatic driving vehicle decision-making and planning method for an expressway shunting area. The method comprises the following steps: 1, establishing a basic scene of application of the method; 2, constructing a fuzzy logic reasoning system, outputting a lane changing willingness coefficient, and making a decision scheme; 3, performing risk assessment by adopting TTC (residual collision time); 4, introducing a risk field concept, performing modeling on a vehicle potential field, a road boundary potential field, a lane division line potential field and a static roadblock potential field, and further constructing a multi-source superposition risk potential field model in an expressway diversion area scene by superposing the risk potential fields; 5, generating a lane changing trajectory curve by adopting a quintic polynomial; and 6, constructing a multi-target comprehensive evaluation function considering safety, efficiency and comfort, and screening out an optimal lane changing track of the networked automatic driving vehicle. According to the method, the automatic driving vehicle can be endowed with intelligent decision-making ability and accurate path planning ability while dealing with variable traffic conditions, so that traffic congestion can be effectively relieved, and the traffic capacity of a road is improved.

Description

Technical Field

[0001] The present invention belongs to the field of intelligent traffic safety, and in particular to the field of decision-making and planning of networked autonomous driving vehicles in expressway diversion areas, and specifically is a decision-making and planning method for networked autonomous driving vehicles in expressway diversion areas. Background Art

[0002] With the rapid development of intelligent transportation systems, autonomous driving technology has become a hot topic in the field of modern transportation. In a complex urban traffic environment, the expressway diversion area is a key node connecting different traffic flows, and its traffic conditions have a significant impact on the operating efficiency of the entire road network. However, the existing decision-making and planning methods for autonomous vehicles in expressway diversion areas have several limitations.

[0003] Traditional autonomous vehicles mainly rely on preset routes and fixed rules for navigation, and lack the ability to dynamically respond to real-time traffic conditions. This is particularly evident in expressway diversion areas where traffic flow changes frequently, resulting in vehicles being unable to make optimal decisions in a timely manner, which in turn affects traffic efficiency. In addition, the traffic environment in expressway diversion areas is complex and changeable, including multiple factors such as multiple lane changes, emergency handling, and climate conditions. Existing autonomous vehicles often need to rely on complex algorithms and a large amount of computing resources when dealing with these complex situations, which not only increases the complexity of the system, but also limits the real-time nature of decision-making.

[0004] Therefore, the present invention proposes a decision-making and planning method for networked autonomous driving vehicles in expressway diversion areas, aiming to achieve rapid response to the complex traffic environment of expressway diversion areas and provide a safer and more efficient driving experience for autonomous driving vehicles. Summary of the invention

[0005] The present invention aims to address the deficiencies of the above-mentioned prior art and proposes a decision-making and planning method for networked autonomous driving vehicles in expressway diversion areas, aiming to cope with changeable traffic conditions and give autonomous driving vehicles intelligent decision-making power and precise path planning capabilities, thereby ensuring that autonomous driving vehicles can achieve safe and efficient driving even in complex traffic environments, thereby effectively alleviating traffic congestion and improving road capacity.

[0006] In order to achieve the above-mentioned purpose, the present invention adopts the following technical scheme:

[0007] The feature of the decision-making and planning method for a networked autonomous driving vehicle in a freeway diversion area of ​​the present invention is that it is applied to a freeway diversion area road with three lanes in a one-way direction, and the three lanes in the same direction on the freeway diversion area road are respectively recorded as the first lane, the second lane, and the third lane, the first lane is provided with a static roadblock, and an exit ramp is provided on the outer side of the third lane, and at least one networked autonomous driving vehicle and one manually driven vehicle are included on the one-way three lanes, and a plane coordinate system is established with the length of the upstream section of the exit ramp L0 as the origin, the driving direction of the vehicle as the positive direction of the X-axis, and the direction perpendicular to the X-axis as the Y-axis direction; the lane change trajectory planning method for the networked autonomous driving vehicle is performed according to the following steps;

[0008] Step 1: Use fuzzy logic reasoning method to make lane change decision for connected autonomous driving vehicle;

[0009] Step 1.1: Construct a fuzzy logic system, including input layer, fuzzy layer, rule layer, and output layer;

[0010] Assume that the expected speed of the connected autonomous driving vehicle is , and the expected vehicle distance is , the input layer includes: speed difference coefficient membership function , membership function of vehicle distance expectation coefficient , as shown in formula (1) and (2) respectively;

[0011]

[0012] In formula (1), is the speed of the connected autonomous vehicle, The speed of the manually driven vehicle;

[0013]

[0014] In formula (2), The distance between the connected autonomous vehicle and the manually driven vehicle;

[0015] The fuzzy layer includes: velocity difference coefficient fuzzy subset {small, smaller, medium, larger, large}, fuzzy subset of vehicle distance expectation coefficient {small, smaller, medium, larger, large}, fuzzy subset of lane change intention {weak, weaker, medium, stronger, strong};

[0016] The rule layer includes: lane change fuzzy rules ;

[0017] The output layer includes: lane change willingness coefficient ;

[0018] Step 1.2: Set up a decision plan , , The specific implementation decisions are as follows:

[0019] :If the lane change willingness coefficient satisfy: , then the decision plan is to keep going straight;

[0020] :If the lane change willingness coefficient satisfy: , then the decision plan is to wait for lane change and go to step 2;

[0021] :If the lane change willingness coefficient satisfy: , then the decision is to change lanes immediately and go to step 3. , , are all constants;

[0022] Step 2: Calculate any connected autonomous driving vehicle CAV according to formula (3): j HV h Remaining collision time , and conduct risk assessments;

[0023]

[0024] The risk assessment methodology described is as follows:

[0025] like , it is determined that there is a collision risk in the scene, and the decision is changed to keep going straight;

[0026] like , it is determined that there is no collision risk in the scenario, and the vehicle changes lanes and proceeds to step 3;

[0027] Step 3: Use formula (4) to construct the artificial driving vehicle HV h , road boundary line, lane dividing line, static roadblock at any point in the plane coordinate system Multi-source superposition risk field model ;

[0028]

[0029] In formula (4), , , , Any point Vehicle potential field at , road boundary line potential field , lane dividing line potential field , Static roadblock potential field The weight parameter of

[0030] Step 4: Assume any connected autonomous driving vehicle CAV j The starting point of the lane change is A j , the end point of lane change is F j , and the fifth-order polynomial curve fitting method is used at the lane change starting point A j and lane change end point F j Generate a connected autonomous vehicle CAV j The lane-changing trajectory curve cluster is a lane-changing trajectory curve cluster. Assume that there are m lane-changing trajectory curves in the lane-changing trajectory curve cluster, respectively. , , , let the jth lane change trajectory curve The polynomial fitting curve equation is , 1≤j≤m;

[0031] Step 5: Construct the jth lane change trajectory curve using formula (5): Multi-objective comprehensive evaluation function ;

[0032]

[0033] In formula (5), , , They are safety evaluation functions , efficiency evaluation function , comfort evaluation function The weight parameter of

[0034] Step 6: Use formula (5) to solve the multi-objective comprehensive evaluation value of the m trajectory curves, and select the lane change trajectory curve corresponding to the minimum comprehensive objective evaluation value That is, the connected autonomous vehicle CAV j Optimal lane change trajectory.

[0035] The method for decision-making and planning of a networked autonomous driving vehicle in a freeway diversion area according to the present invention is also characterized in that step 3 comprises:

[0036] Step 3.1: Assume that any human-driven vehicle HV hThe centroid coordinates are , and the vehicle potential field is modeled according to formula (6) to obtain the artificially driven vehicle HV h Any point in the plane coordinate system The potential field strength generated at ;

[0037]

[0038] In formula (6), is the direction of the vehicle potential field and the direction of the artificial driving vehicle HV h The angle between the driving directions, R is the complexity coefficient of the operating environment, 0<R≤1, is the potential field parameter value of the manually driven vehicle;

[0039] Step 3.2: Let the center coordinates of the i-th road boundary line be According to formula (7), the potential field of the road boundary line on both sides of the one-way three-lane road is modeled, and the potential field of the road boundary line at any point in the plane coordinate system is obtained. The potential field strength generated at ;

[0040]

[0041] In formula (5), is the potential field parameter value of the road boundary line;

[0042] Step 3.3: Let the center coordinate of the jth lane separator be According to formula (8), the potential field of the lane separation line of a one-way three-lane vehicle is modeled, and the potential field of the lane separation line at any point in the plane coordinate system is obtained. The magnitude of the potential field generated at ;

[0043]

[0044] Step 3.4: Assume the centroid coordinates of the static roadblock in the scene to be , and the static roadblock potential field is modeled according to formula (9), and the static roadblock at any point in the plane coordinate system is obtained The potential field strength generated at ;

[0045] .

[0046] Further, the step 5 comprises:

[0047] Step 5.1: Construct the jth lane change trajectory curve according to formula (10): The safety evaluation function ;

[0048]

[0049] Step 5.2: Construct the jth lane change trajectory curve according to formula (11): The efficiency evaluation function ;

[0050]

[0051] Step 5.3: Construct the jth lane change trajectory curve according to formula (12): Comfort evaluation function ;

[0052]

[0053] In formula (12), is the number of lane-changing points on the j-th lane-changing trajectory curve, , The polynomial fitting curve equations are First and second order derivatives.

[0054] An electronic device of the present invention includes a memory and a processor, wherein the memory is used to store a program that supports the processor to execute the method for making and planning a networked autonomous driving vehicle in a freeway diversion area, and the processor is configured to execute the program stored in the memory.

[0055] The present invention provides a computer-readable storage medium, and the computer-readable storage medium stores a computer program, wherein when the computer program is run by a processor, the steps of the method for decision-making and planning of a networked autonomous driving vehicle in a freeway diversion area are executed.

[0056] Compared with the prior art, the beneficial technical effects of the present invention are embodied in:

[0057] 1. Traditional methods often ignore the uncertainty and complexity of environmental factors when making decisions. The present invention uses fuzzy logic reasoning technology to process uncertain information, simulate the decision-making process of human drivers, and improve the robustness of the system. Fuzzy logic represents the uncertainty of input variables through membership functions, so that the system can make reasonable decisions under incomplete information.

[0058] 2. In terms of trajectory screening for autonomous vehicles, traditional screening methods often focus on a single performance indicator, such as safety or efficiency, while ignoring other key factors. In contrast, the present invention adopts a comprehensive approach when screening trajectories, taking into account the three dimensions of safety, efficiency and comfort. By constructing a multi-objective optimization function, the present invention achieves the best balance between these objectives, ensuring optimal driving performance under different conditions. In addition, the present invention also introduces the innovative concept of risk field as a key indicator for evaluating the safety of autonomous vehicles. The introduction of the risk field enables the system to more accurately predict and evaluate potential dangerous situations, thereby paying more attention to safety during the trajectory screening process. This method not only improves the safety of autonomous vehicles in various traffic environments, but also enhances the overall driving experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 It is a scene schematic diagram of the present invention;

[0060] Figure 2 It is the overall flow chart of the present invention.

[0061] Figure 3 is a flow chart of the fuzzy logic system of the present invention;

[0062] Figure 4 It is a schematic diagram of the trajectory planning result of the present invention. DETAILED DESCRIPTION

[0063] In this example, in the ever-improving field of autonomous driving, this innovative technology breaks through the limitations of traditional technology and carefully designs a set of decision-making and planning solutions for networked autonomous driving vehicles in the expressway diversion area. It uses advanced decision-making technology and path planning algorithms to take into account the uncertainty factors in the external environment, so that the autonomous driving system can make reasonable decisions. In addition, it comprehensively considers safety, efficiency, comfort and other indicators to ensure that autonomous driving vehicles can also drive stably in complex and changing traffic environments. The application of this technology is not limited to improving the performance of a single vehicle, but also focuses on the optimization of the entire traffic network. It relieves traffic pressure and improves the road's traffic capacity by intelligently regulating the traffic flow in the diversion area. Specifically, the method is applied to a one-way three-lane expressway diversion area road, and the three lanes in the same direction on the expressway diversion area road are respectively recorded as the first lane, the second lane, and the third lane. The first lane is provided with a static roadblock, and an exit ramp is provided on the outer side of the third lane. The one-way three-lane includes at least one connected autonomous driving vehicle and one manually driven vehicle. The length of the upstream section of the exit ramp is L0 as the origin, the vehicle's driving direction is the positive direction of the X-axis, and the direction perpendicular to the X-axis is the Y-axis direction. A plane coordinate system is established. The scene schematic diagram is shown as follows: Figure 1 As shown; the flow chart of the lane change trajectory planning method for a networked autonomous driving vehicle is shown Figure 2 As shown, the following steps are followed;

[0064] Step 1: Use fuzzy logic reasoning method to make lane change decision for connected autonomous driving vehicle;

[0065] Step 1.1: Construct a fuzzy logic system. The flowchart of the system is as follows: Figure 3 As shown, it includes: input layer, fuzzy layer, rule layer and output layer;

[0066] Assume that the expected speed of the connected autonomous driving vehicle is , and the expected vehicle distance is , the input layer includes: speed difference coefficient membership function , membership function of vehicle distance expectation coefficient , as shown in formula (1) and (2) respectively;

[0067] (1)

[0068] In formula (1), is the speed of the connected autonomous vehicle, The speed of the manually driven vehicle;

[0069] (2)

[0070] In formula (2), It is the distance between the connected autonomous driving vehicle and the manually driven vehicle.

[0071] The fuzzy layer includes: velocity difference coefficient fuzzy subset {small, smaller, medium, larger, large}, fuzzy subset of vehicle distance expectation coefficient {small, smaller, medium, larger, large}, fuzzy subset of lane change intention {weak, weaker, medium, stronger, strong};

[0072] Rule layer, including: lane change fuzzy rules ,The fuzzy rules are shown in Table 1;

[0073] Table 1

[0074]

[0075] Output layer, including: lane change willingness coefficient ;

[0076] Step 1.2: Set up a decision plan , , The specific implementation decisions are as follows:

[0077] :If the lane change willingness coefficient satisfy: , then the decision plan is to keep going straight;

[0078] :If the lane change willingness coefficient satisfy: , then the decision plan is to wait for lane change and go to step 2;

[0079] :If the lane change willingness coefficient satisfy: , then the decision is to change lanes immediately and go to step 3. , , are all constants.

[0080] Step 2: Calculate any connected autonomous driving vehicle CAV according to formula (3): j HV h Remaining collision time , and conduct risk assessments;

[0081] (3)

[0082] The risk assessment method is as follows:

[0083] like , it is determined that there is a collision risk in the scene, and the decision is changed to keep going straight;

[0084] like , it is determined that there is no collision risk in the scene, and the vehicle changes lanes and proceeds to step 3.

[0085] Step 3: Construct a multi-source superposition risk field model, including vehicle potential field, road boundary line potential field, and lane dividing line potential field;

[0086] Step 3.1: Assume any human-driven vehicle HV in the scene h The centroid coordinates are , and the vehicle potential field is modeled according to formula (4) to obtain the artificially driven vehicle HV h Any point in the plane coordinate system The potential field strength generated at ;

[0087] (4)

[0088] In formula (4), is the direction of the vehicle potential field and the direction of the artificial driving vehicle HV hThe angle between the driving directions, R is the complexity coefficient of the operating environment, 0<R≤1, is the potential field parameter value of the manually driven vehicle.

[0089] Step 3.2: Let the center coordinates of the i-th road boundary line be According to formula (5), the potential field of the road boundary line on both sides of the one-way three-lane road is modeled, and the potential field of the road boundary line at any point in the plane coordinate system is obtained. The potential field strength generated at ;

[0090] (5)

[0091] In formula (5), is the parameter value of the road boundary line potential field.

[0092] Step 3.3: Let the center coordinate of the jth lane separator be According to formula (6), the potential field of the lane separation line of a one-way three-lane vehicle is modeled, and the potential field of the lane separation line at any point in the plane coordinate system is obtained. The magnitude of the potential field generated at ;

[0093] (6)

[0094] Step 3.4: Assume the centroid coordinates of the static roadblock in the scene to be , and the static roadblock potential field is modeled according to formula (7), and the static roadblock at any point in the plane coordinate system is obtained The potential field strength generated at ;

[0095] (7)

[0096] Step 3.5: Obtain any point in the plane coordinate system by superimposing the vehicle potential field, the road boundary line potential field, and the lane dividing line potential field The superposition potential field of multi-source risk fields at , as shown in formula (8);

[0097] (8)

[0098] In formula (8), , , , They are the weight parameters of the vehicle potential field, road boundary line potential field, lane dividing line potential field, and static roadblock potential field respectively.

[0099] Step 4: Assume any connected autonomous driving vehicle CAV j The starting point of the lane change is Aj , the end point of lane change is F j , and the fifth-order polynomial curve fitting method is used at the lane change starting point A j and lane change end point F j Generate a connected autonomous vehicle CAV j The lane-changing trajectory curve cluster is a lane-changing trajectory curve cluster. Assume that there are m lane-changing trajectory curves in the lane-changing trajectory curve cluster, respectively. , , , let the jth lane change trajectory curve The polynomial fitting curve equation is , 1≤j≤m.

[0100] Step 5: construct a multi-objective comprehensive evaluation function for the lane change trajectory curve;

[0101] Step 5.1: Construct the jth lane change trajectory curve according to formula (9): The safety evaluation function ;

[0102] (9)

[0103] Step 5.2: Construct the jth lane change trajectory curve according to formula (10): The efficiency evaluation function ;

[0104] (10)

[0105] Step 5.3: Construct the jth lane change trajectory curve according to formula (11): Comfort evaluation function ;

[0106] (11)

[0107] In formula (11), is the number of lane-changing points on the j-th lane-changing trajectory curve, , The polynomial fitting curve equations are First and second order derivatives.

[0108] Step 5.4: Construct the jth lane change trajectory curve using equation (12): Multi-objective comprehensive evaluation function ;

[0109] (12)

[0110] In formula (12), , , They are safety evaluation functions , efficiency evaluation function , comfort evaluation function The weight parameter of .

[0111] Step 6: Use formula (12) to solve the multi-objective comprehensive evaluation value of the m trajectory curves, and select the lane change trajectory curve corresponding to the minimum comprehensive evaluation value That is, the connected autonomous vehicle CAV j The optimal lane change trajectory and the trajectory planning result after screening are shown in the figure below: Figure 4 shown.

[0112] In this embodiment, an electronic device includes a memory and a processor. The memory is used to store a program that supports the processor to execute the above-mentioned expressway diversion area networked autonomous driving vehicle decision-making and planning method, and the processor is configured to execute the program stored in the memory.

[0113] In this embodiment, a computer-readable storage medium stores a computer program on the computer-readable storage medium, and when the computer program is executed by a processor, the steps of the above-mentioned expressway diversion area networked autonomous driving vehicle decision-making and planning method are executed.

Claims

1. A decision-making and planning method for a networked autonomous driving vehicle in a freeway diversion area, characterized in that: It is applied to a one-way three-lane expressway diversion area road, and the three lanes in the same direction on the expressway diversion area road are respectively recorded as the first lane, the second lane, and the third lane, the first lane is provided with a static roadblock, and an exit ramp is provided on the outer side of the third lane, and at least one networked autonomous driving vehicle and one manually driven vehicle are included on the one-way three-lane, and a plane coordinate system is established with the length of the upstream section of the exit ramp L0 as the origin, the driving direction of the vehicle as the positive direction of the X-axis, and the direction perpendicular to the X-axis as the Y-axis direction; the networked autonomous driving vehicle lane change trajectory planning method is performed according to the following steps; Step 1: Use fuzzy logic reasoning method to make lane change decision for connected autonomous driving vehicle; Step 1.1: Construct a fuzzy logic system, including input layer, fuzzy layer, rule layer, and output layer; Assume that the expected speed of the connected autonomous driving vehicle is , and the expected vehicle distance is , the input layer includes: speed difference coefficient membership function , membership function of vehicle distance expectation coefficient , as shown in formula (1) and (2) respectively; (1) In formula (1), is the speed of connected autonomous vehicles, The speed of manually driven vehicles; (2) In formula (2), The distance between the connected autonomous vehicle and the manually driven vehicle; The fuzzy layer includes: velocity difference coefficient fuzzy subset {small, smaller, medium, larger, large}, fuzzy subset of vehicle distance expectation coefficient {small, smaller, medium, larger, large}, fuzzy subset of lane change intention {weak, weaker, medium, stronger, strong}; The rule layer includes: lane change fuzzy rules ; The output layer includes: lane change willingness coefficient ; Step 1.2: Set up a decision plan , , The specific implementation decisions are as follows: :If the lane change willingness coefficient satisfy: , then the decision plan is to keep going straight; :If the lane change willingness coefficient satisfy: , then the decision plan is to wait for lane change and go to step 2; :If the lane change willingness coefficient satisfy: , then the decision is to change lanes immediately and go to step 3. , , are all constants; Step 2: Calculate any connected autonomous driving vehicle CAV according to formula (3): j Compared with manual driving vehicles HV h Remaining collision time , and conduct risk assessments; (3) The risk assessment methodology described is as follows: like , it is determined that there is a collision risk in the scene, and the decision is changed to keep going straight; like , it is determined that there is no collision risk in the scenario, and the vehicle changes lanes and proceeds to step 3; Step 3: Use formula (4) to construct the artificial driving vehicle HV h , road boundary line, lane dividing line, static roadblock at any point in the plane coordinate system Multi-source superposition risk field model ; (4) In formula (4), , , , Any point Vehicle potential field at , road boundary line potential field , lane dividing line potential field , Static roadblock potential field The weight parameter of Step 4: Assume any connected autonomous driving vehicle CAV j The starting point of lane change is A j , the end point of lane change is F j , and the fifth-order polynomial curve fitting method is used at the lane change starting point A j and the lane change end point F j Generate a connected autonomous vehicle CAV j The lane-changing trajectory curve cluster is a lane-changing trajectory curve cluster. Assume that there are m lane-changing trajectory curves in the lane-changing trajectory curve cluster, respectively. , , , let the jth lane change trajectory curve The polynomial fitting curve equation is , 1≤j≤m; Step 5: Construct the jth lane change trajectory curve using formula (5): Multi-objective comprehensive evaluation function ; (5) In formula (5), , , They are safety evaluation functions , efficiency evaluation function , comfort evaluation function The weight parameter of Step 6: Use formula (5) to solve the multi-objective comprehensive evaluation value of the m trajectory curves, and select the lane change trajectory curve corresponding to the minimum comprehensive objective evaluation value That is, the connected autonomous vehicle CAV j Optimal lane change trajectory.

2. The method for decision-making and planning of a networked autonomous driving vehicle in a freeway diversion area according to claim 1, characterized in that: The step 3 comprises: Step 3.1: Assume that any human-driven vehicle HV h The centroid coordinates are , and the vehicle potential field is modeled according to formula (6) to obtain the artificially driven vehicle HV h Any point in the plane coordinate system The potential field strength generated at ; (6) In formula (6), is the direction of the vehicle potential field and the direction of the artificial driving vehicle HV h The angle between the driving directions, R is the complexity coefficient of the operating environment, 0<R≤1, is the potential field parameter value of the manually driven vehicle; Step 3.2: Let the center coordinates of the i-th road boundary line be According to formula (7), the potential field of the road boundary line on both sides of the one-way three-lane road is modeled, and the potential field of the road boundary line at any point in the plane coordinate system is obtained. The potential field strength generated at ; (7) In formula (5), is the potential field parameter value of the road boundary line; Step 3.3: Let the center coordinate of the jth lane separator be According to formula (8), the potential field of the lane separation line of a one-way three-lane vehicle is modeled, and the potential field of the lane separation line at any point in the plane coordinate system is obtained. The magnitude of the potential field generated at ; (8) Step 3.4: Assume the centroid coordinates of the static roadblock in the scene to be , and the static roadblock potential field is modeled according to formula (9), and the static roadblock at any point in the plane coordinate system is obtained The potential field strength generated at ; (9)。 3. The method for decision-making and planning of a networked autonomous driving vehicle in a freeway diversion area according to claim 2, characterized in that: The step 5 comprises: Step 5.1: Construct the jth lane change trajectory curve according to formula (10): The safety evaluation function ; (10) Step 5.2: Construct the jth lane change trajectory curve according to formula (11): The efficiency evaluation function ; (11) Step 5.3: Construct the jth lane change trajectory curve according to formula (12): Comfort evaluation function ; (12) In formula (12), is the number of lane-changing points on the j-th lane-changing trajectory curve, , The polynomial fitting curve equations are First and second order derivatives.

4. An electronic device, comprising a memory and a processor, characterized in that: The memory is used to store a program that supports the processor to execute the decision-making and planning method for a networked autonomous driving vehicle in a freeway diversion area as described in any one of claims 1-3, and the processor is configured to execute the program stored in the memory.

5. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, the steps of the method for decision-making and planning of a networked autonomous driving vehicle in a freeway diversion area described in any one of claims 1 to 3 are executed.

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