A road path dynamic control method based on risk optimization

Abstract

This invention discloses a method for dynamic road path control based on risk optimization. The method comprises the following steps: 1. calculating conflict risk; 2. calculating collision risk; 3. calculating lane restriction risk; 4. integrating risks; and 5. calculating the optimal vehicle path. This method categorizes risk into three types: conflict risk, collision risk, and lane restriction risk. It then combines the magnitude of risk with the probability of risk occurrence, constructs a risk integration mechanism, calculates the optimal risk point, and fits it to a line, providing a vehicle with a minimal-risk driving trajectory, thereby improving road safety and traffic efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent traffic control, and in particular to a road path dynamic control method based on risk optimization. Background Art

[0002] In modern society, with the rapid increase in the number of vehicles, road traffic faces unprecedented challenges. Especially during peak hours in the morning and evening, vehicle density on roads increases significantly, and with it, various risk factors. These risk factors include, but are not limited to, conflict risk, collision risk, and road marking restriction risk. These can accumulate rapidly within a short period of time, leading to traffic accidents and seriously impacting road safety and traffic efficiency.

[0003] Existing research often focuses on analyzing a single risk factor, such as conflict risk or collision risk, while rarely considering these risk factors comprehensively. This isolated approach ignores the interactions and influences between different risk factors, resulting in incomplete and inaccurate assessment and control of road traffic risks. Many field theory-based methods have been introduced to intuitively express the magnitude of risk in the driving environment. However, these methods directly express the magnitude of risk without further calculating the actual probability of risk occurrence. In this case, the risk assessment model may display a risk area with high field intensity, which intuitively gives the impression of high risk. However, even with a high field intensity, if the probability of risk occurrence is actually low, this high-risk area may not be as dangerous as it appears. In other words, a high field intensity risk area does not always indicate a high collision probability, as the actual collision probability depends on a variety of other factors, such as the dynamic behavior of the vehicle and the complexity of the traffic environment.

[0004] Furthermore, existing traffic management methods often rely on traditional road markings to guide vehicles, which lacks flexibility and adaptability. On roads with heavy traffic and high vehicle density, fixed marking restrictions often fail to meet actual traffic needs, leading to traffic congestion and safety hazards. Summary of the Invention

[0005] The present invention overcomes the shortcomings of the existing technology and proposes a road path dynamic control method based on risk optimization, in order to provide vehicles with a driving trajectory with minimal risk, thereby improving road safety and traffic efficiency.

[0006] In order to achieve the above-mentioned object of the invention, the present invention adopts the following technical solutions:

[0007] The present invention provides a method for dynamic control of a road path based on risk optimization, which comprises the following steps:

[0008] Step 1: Establish a rectangular coordinate system for the road, with the endpoint on the road boundary as the origin, the driving direction of the vehicle on the road as the x-axis, and the axis perpendicular to the driving direction of the vehicle as the y-axis;

[0009] The roads are numbered from the inside out according to the number of lanes, and the lane lines are marked as ,in, represents the bth lane line, and n represents the total number of lanes;

[0010] The vertical coordinates of the n+1 lane lines are recorded as ;in, Indicates lane line b The vertical coordinate of lane line b The vertical coordinate and lane line b+1 The vertical coordinate Any coordinate between the vertical coordinates is marked as any coordinate of lane b ;

[0011] The definition divides the risk factors on the road into three categories: conflict risk, collision risk and lane restriction risk;

[0012] Define any vehicle i and vehicle j, and , using the intelligent roadside detector set on the road to obtain the angle between vehicle i and vehicle j ; Get the size of the vehicle, and Represent the length and width of vehicle i respectively; get the coordinates of the midpoint of each vehicle, and Represent the midpoint coordinates of vehicle i and vehicle j respectively, and calculate the distance between vehicle i and vehicle j ; Get the steering angle of each vehicle, Indicates the steering angle of vehicle i; obtains the speed of vehicle i ; Get the speed difference between vehicle i and vehicle j ; Get the acceleration difference between vehicle i and vehicle j ; and count the total number of vehicles on the road c;

[0013] Step 2: Calculate the virtual volume of vehicle i ;

[0014] Step 3: Calculate the collision risk of vehicle i ;

[0015] Step 4: Calculate any coordinate on the road Conflict risk ;

[0016] Step 5: Calculate any coordinate on the road Collision risk at ;

[0017] Step 6: Divide the road into n+1 lanes to get n lanes, and calculate any coordinate on each lane The restricted risk of the lane line at the location, and the restricted risk of the lane line on each road constitutes the restricted risk of any coordinate on the road Lane restriction risk ;

[0018] Step 7: Calculate any coordinate on the road according to formula (30) Total risk ;

[0019] (30)

[0020] In formula (30), m represents the risk dimension index;

[0021] Step 8: Based on total risk , calculate the coordinates of the risk optimal point on the road, which is used to fit the optimal driving path of the vehicle.

[0022] The risk optimization-based road path dynamic control method of the present invention is also characterized in that step 2 includes the following steps:

[0023] Step 2.1: Calculate the directional effectiveness factor of vehicle i according to formula (1): ;

[0024] (1)

[0025] In formula (1), represents the underlying risk factor;

[0026] Step 2.2: Calculate the virtual volume of vehicle i according to formula (2) ;

[0027] (2)

[0028] In formula (2), represents the vehicle volume conversion factor, Represents the velocity influence weighting factor in the virtual volume.

[0029] Furthermore, step 3 includes the following steps:

[0030] Step 3.1: Calculate the steering angle of the left boundary of vehicle i according to formula (3): ;

[0031] (3)

[0032] In formula (3), A constant representing the uncertainty of the steering angle, and Indicates the left and right fluctuation range thresholds related to the steering angle;

[0033] Step 3.2: Calculate the radius of the left boundary of vehicle i according to formula (4): ;

[0034] (4)

[0035] Step 3.3: Calculate the steering angle of the right boundary of vehicle i according to formula (5): ;

[0036] (5)

[0037] Step 3.4: Calculate the radius of the left boundary of vehicle i according to formula (6): ;

[0038] (6)

[0039] Step 3.5: Calculate the path travel area of ​​vehicle i according to formula (7) ;

[0040] (7)

[0041] Step 3.6: Calculate the estimated left driving area of ​​vehicle i according to formula (8): ;

[0042] (8)

[0043] Step 3.7: Calculate the right side estimated driving area of ​​vehicle i according to formula (9): ;

[0044] (9)

[0045] Step 3.8: Calculate the safe operating area of ​​vehicle i according to formula (10): ;

[0046] (10)

[0047] Step 3.9: Calculate the safe operating area of ​​vehicle j according to the process from step 3.1 to step 3.8. ;

[0048] Step 3.10: Calculate the intersection of the collision areas of vehicle i and vehicle j according to formula (11): ;

[0049] (11)

[0050] Step 3.11: Calculate the collision probability of vehicle i according to formula (12): ;

[0051] (12)

[0052] In formula (12), represents the maximum intersection of the conflict areas of vehicle i and other vehicles;

[0053] Step 3.12: Calculate the collision risk field strength of vehicle i according to formula (13): ;

[0054] (13)

[0055] In formula (13), Indicates the impact weight factor of the conflict risk size, Speed ​​impact weighting factor in conflict risk; is the virtual volume of vehicle j, The modulus of the relative distance;

[0056] Step 3.13: Calculate the collision risk of vehicle i according to formula (14): ;

[0057] (14).

[0058] Furthermore, step 4 includes the following steps:

[0059] Step 4.1: Calculate the coordinates of vehicle i on the road according to formula (15): Horizontal risk attenuation factor ;

[0060] (15)

[0061] In formula (15), Indicates the degree of influence of the relative distance between other vehicles and vehicle i on the longitudinal risk, represents the speed of vehicle i The degree of impact on vertical risk;

[0062] Step 4.2: Calculate the coordinates of vehicle i on the road according to formula (16): Longitudinal risk attenuation factor ;

[0063] (16)

[0064] In formula (16), Indicates the degree of influence of the relative distance between other vehicles and vehicle i on the lateral risk, represents the speed of vehicle i The degree of impact on horizontal risks;

[0065] Step 4.3: Calculate the coordinates of vehicle i on the road according to formula (17): The risk decay function at ;

[0066] (17)

[0067] Step 4.4: Calculate any coordinate on the road according to formula (18) Conflict risk ;

[0068] (18).

[0069] Furthermore, step 5 includes the following steps:

[0070] Step 5.1: Calculate the vehicle i’s time interval according to formula (19): Driving length within ;

[0071] (19)

[0072] Step 5.2: Calculate the vehicle i’s time interval according to formula (20): Driving range within ;

[0073] ϕ i ( D t) = [ x i , x i + L i ( D t) ] (20)

[0074] Step 5.3: Calculate the time interval of vehicle j according to the process from step 5.1 to step 5.2. Driving range within ϕ j ( D t) = [ x j , x j + L j ( D t) ] ;

[0075] Step 5.4: Calculate the time interval between vehicle i and vehicle j according to formula (21): Intersection of inner driving areas ;

[0076] (twenty one)

[0077] Step 5.5: Calculate the collision probability of vehicle i according to formula (22): ;

[0078] (twenty two)

[0079] In formula (22), Indicates that vehicle i and other vehicles are The maximum length of the travel area within the time period;

[0080] Step 5.6: Calculate the collision risk field strength of vehicle i according to formula (23): ;

[0081] (twenty three)

[0082] In formula (23), Indicates the impact weight factor of the collision risk, Indicates the acceleration impact weight factor in collision risk;

[0083] Step 5.7: Calculate the collision risk of vehicle i according to formula (24) ;

[0084] (twenty four)

[0085] Step 5.8: Calculate any coordinate on the road according to formula (25) Collision risk at ;

[0086] (25).

[0087] Furthermore, step 6 includes the following steps:

[0088] Step 6.1: Calculate the line limit risk according to formula (26) ;

[0089] (26)

[0090] In formula (26), represents a fixed risk value for road markings;

[0091] Step 6.2: Calculate any coordinate on lane b according to formula (27): The attenuation factor of the lane line restriction risk at ;

[0092] (27)

[0093] In formula (27), represents the risk diffusion weight of the marking line;

[0094] Step 6.3: Calculate any coordinate on lane b according to formula (28): The attenuation function of the lane line restriction risk at ;

[0095] (28)

[0096] Step 6.4: Calculate any coordinate on lane b according to formula (29): Lane markings limit risk ;

[0097] (29)

[0098] Step 6.5: Follow the process from step 6.1 to step 6.4 to calculate the coordinates of any lane. The lane lines at each location limit the risk and constitute the risk of the entire road .

[0099] Furthermore, step 8 includes the following steps:

[0100] Step 8.1: Calculate any coordinate on the road according to formula (31) Total risk Gradient ;

[0101] (31)

[0102] Step 8.2, define the number of iterations as ,initialization ;

[0103] Step 8.3, select another coordinate on the road as the first Coordinates of the iteration ,in, , ;

[0104] Step 8.4: Calculate the first Coordinates of the iteration ;

[0105] (32)

[0106] In formula (32), Indicates the The step size of the iteration, Representing coordinates Total risk gradient;

[0107] Step 8.5: When equation (33) holds true, As the coordinate of the optimal point of risk;

[0108] (33)

[0109] In formula (33), Indicates the convergence threshold;

[0110] Step 8.6: After assigning k + 1 to k, return to step 8.4 until the number of risk-optimal point coordinates reaches p. Obtain all risk-optimal point coordinates and perform second-order polynomial fitting to obtain the curve equation with minimum risk, which is the optimal driving path for vehicles on the road.

[0111] The 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 road path dynamic control method, and the processor is configured to execute the program stored in the memory.

[0112] The present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program executes the steps of the road path dynamic control method when the computer program is executed by a processor.

[0113] Compared with the existing technology, the beneficial technical effects of the present invention are embodied in:

[0114] 1. This invention classifies risks into three types: conflict risk, collision risk, and road marking restriction risk, and combines the magnitude of risk with the probability of risk occurrence, providing a comprehensive risk assessment model that is more consistent with actual traffic scenarios. This makes the risk assessment results more comprehensive and accurate, thereby more realistically reflecting the risks that may be encountered during road driving.

[0115] 2. This invention proposes the concept of a risk dimension index and establishes a risk fusion mechanism. This approach reveals the coupling relationships and interaction mechanisms between different risk factors, enabling risk assessment to transcend a single dimension and instead form a multi-dimensional, three-dimensional risk assessment system. This fusion mechanism makes risk assessment more realistic and provides more scientific decision-making support for traffic management and road safety.

[0116] 3. This invention considers the vehicle's directional effectiveness factor and constructs a virtual volume indicator based on it. The introduction of virtual volume provides drivers with a more intuitive risk perception, helping to improve driving safety and reduce the occurrence of traffic accidents.

[0117] 4. This method calculates the optimal risk point and fits it to a line, providing vehicles with the lowest-risk driving trajectory. This approach not only minimizes risk for individual vehicles but also optimizes the entire traffic flow, allowing vehicles to travel along the optimal path while ensuring safety, thereby improving road safety and traffic efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0118] Figure 1 It is the overall flow chart of the present invention;

[0119] Figure 2 This is the cycle graph for calculating the optimal path points of the present invention. DETAILED DESCRIPTION

[0120] In this embodiment, Figure 1 As shown in the figure, a road path dynamic control method based on risk optimization divides risks into three types: conflict risk, collision risk, and road marking restriction risk. The risk magnitude is combined with the probability of risk occurrence, and a risk fusion mechanism is constructed. The optimal risk point is calculated and fitted into a line, thereby providing a driving trajectory with the least risk for the vehicle. Specifically, the following steps are included:

[0121] Step 1: Establish a rectangular coordinate system for the road, with the endpoint on the road boundary as the origin, the driving direction of the vehicle on the road as the x-axis, and the axis perpendicular to the driving direction of the vehicle as the y-axis;

[0122] The roads are numbered from the inside out according to the number of lanes, and the lane lines are marked as ,in, represents the bth lane line, and n represents the total number of lanes;

[0123] The vertical coordinates of the n+1 lane lines are recorded as ;in, Indicates lane line b The vertical coordinate of lane line b The vertical coordinate and lane line b+1 The vertical coordinate Any coordinate between the vertical coordinates is marked as any coordinate of lane b ;

[0124] The definition divides the risk factors on the road into three categories: conflict risk, collision risk and lane restriction risk;

[0125] Define any vehicle i and vehicle j, and , using the intelligent roadside detector set on the road to obtain the angle between vehicle i and vehicle j ; Get the size of the vehicle, and Represent the length and width of vehicle i respectively; get the coordinates of the midpoint of each vehicle, and Represent the midpoint coordinates of vehicle i and vehicle j respectively, and calculate the distance between vehicle i and vehicle j ; Get the steering angle of each vehicle, Indicates the steering angle of vehicle i; obtains the speed of vehicle i ; Get the speed difference between vehicle i and vehicle j ; Get the acceleration difference between vehicle i and vehicle j ; and count the total number of vehicles on the road c;

[0126] Step 2: Calculate virtual volume;

[0127] Step 2.1: Calculate the directional effectiveness factor of vehicle i according to formula (1): ;

[0128] (1)

[0129] In formula (1), represents the underlying risk factor;

[0130] Step 2.2: Calculate the virtual volume of vehicle i according to formula (2) ;

[0131] (2)

[0132] In formula (2), represents the vehicle volume conversion factor, represents the velocity influence weight factor in the virtual volume;

[0133] Step 3: Calculate conflict risk;

[0134] The collision risk involves the potential for collisions with other vehicles or traffic participants that could occur when a vehicle attempts to change lanes. This risk can be calculated by taking the product of the collision risk field strength and the collision probability.

[0135] Step 3.1: Calculate the steering angle of the left boundary of vehicle i according to formula (3): ;

[0136] (3)

[0137] In formula (3), A constant representing the uncertainty of the steering angle, and Indicates the left and right fluctuation range thresholds related to the steering angle;

[0138] Step 3.2: Calculate the radius of the left boundary of vehicle i according to formula (4): ;

[0139] (4)

[0140] Step 3.3: Calculate the steering angle of the right boundary of vehicle i according to formula (5): ;

[0141] (5)

[0142] Step 3.4: Calculate the radius of the left boundary of vehicle i according to formula (6): ;

[0143] (6)

[0144] Step 3.5: Calculate the path travel area of ​​vehicle i according to formula (7) ;

[0145] (7)

[0146] Step 3.6: Calculate the estimated left driving area of ​​vehicle i according to formula (8): ;

[0147] (8)

[0148] Step 3.7: Calculate the right side estimated driving area of ​​vehicle i according to formula (9): ;

[0149] (9)

[0150] Step 3.8: Calculate the safe operating area of ​​vehicle i according to formula (10): ;

[0151] (10)

[0152] Step 3.9: Calculate the safe operating area of ​​vehicle j according to the process from step 3.1 to step 3.8. ;

[0153] Step 3.10: Calculate the intersection of the collision areas of vehicle i and vehicle j according to formula (11): ;

[0154] (11)

[0155] Step 3.11: Calculate the collision probability of vehicle i according to formula (12): ;

[0156] (12)

[0157] In formula (12), represents the maximum intersection of the conflict areas of vehicle i and other vehicles;

[0158] Intuitively, the conflict level between vehicles i and j is related to the degree of overlap in their trajectory distributions. The overlap in the spatial trajectories of the two vehicles indicates the likelihood of a conflict between them. When the trajectories of the two vehicles do not overlap, the estimated conflict probability is zero, and the conflict risk field strength is also zero. The conflict probability increases with increasing overlap in the geometric spatial trajectories of the two vehicles.

[0159] Step 3.12: Calculate the collision risk field strength of vehicle i according to formula (13): ;

[0160] (13)

[0161] In formula (13), Indicates the impact weight factor of the conflict risk size, Speed ​​impact weighting factor in conflict risk; is the virtual volume of vehicle j, The modulus of the relative distance;

[0162] Step 3.13: Calculate the collision risk of vehicle i according to formula (14): ;

[0163] (14)

[0164] Step 3.14: Calculate the coordinates of vehicle i on the road according to formula (15): Horizontal risk attenuation factor ;

[0165] (15)

[0166] In formula (15), Indicates the degree of influence of the relative distance between other vehicles and vehicle i on the longitudinal risk, represents the speed of vehicle i The degree of impact on vertical risk;

[0167] Step 3.15: Calculate the coordinates of vehicle i on the road according to formula (16): Longitudinal risk attenuation factor ;

[0168] (16)

[0169] In formula (16), Indicates the degree of influence of the relative distance between other vehicles and vehicle i on the lateral risk, represents the speed of vehicle i The degree of impact on horizontal risks;

[0170] Step 3.16: Calculate the coordinates of vehicle i on the road according to formula (17): The risk decay function at ;

[0171] (17)

[0172] Step 3.17: Calculate any coordinate on the road according to formula (18) Conflict risk ;

[0173] (18)

[0174] Step 4: Collision risk;

[0175] Step 4.1: Calculate the vehicle i’s time interval according to formula (19): Driving length within ;

[0176] (19)

[0177] Step 4.2: Calculate the vehicle i’s time interval according to formula (20): Driving range within ;

[0178] ϕ i ( D t) = [ x i , x i + L i ( D t) ] (20)

[0179] Step 4.3: Calculate the time interval of vehicle j according to the process from step 4.1 to step 4.2. Driving range within ϕ j ( D t) = [ x j , x j + L j ( D t) ] ;

[0180] Step 4.4: Calculate the time interval between vehicle i and vehicle j according to formula (21): Intersection of inner driving areas ;

[0181] (twenty one)

[0182] Step 4.5: Calculate the collision probability of vehicle i according to formula (22) ;

[0183] (twenty two)

[0184] In formula (22), Indicates that vehicle i and other vehicles are The maximum length of the travel area within the time period;

[0185] Step 4.6: Calculate the collision risk field strength of vehicle i according to formula (23): ;

[0186] (twenty three)

[0187] In formula (23), Indicates the impact weight factor of the collision risk, Indicates the acceleration impact weight factor in collision risk;

[0188] Step 4.7: Calculate the collision risk of vehicle i according to formula (24) ;

[0189] (twenty four)

[0190] Step 4.8: Calculate any coordinate on the road according to formula (25) Collision risk at ;

[0191] (25)

[0192] Step 5: Divide the road into n+1 lanes to get n lanes, and calculate any coordinate on each lane The restricted risk of the lane line at the location, and the restricted risk of the lane line on each road constitutes the restricted risk of any coordinate on the road Lane restriction risk ;

[0193] In the transportation system, road markings, through their immutability, define the physical boundaries and regulatory constraints for vehicle movement. Their presence is constant and unchanging. Because these markings dictate the paths vehicles must maintain, any behavior traveling on these markings incurs varying degrees of road marking restriction risk. Given the immutability of road markings, we believe that as long as these markings exist, the occurrence of road marking restriction risk is certain, with a probability of 1.

[0194] Step 5.1: Calculate the line limit risk according to formula (26) ;

[0195] (26)

[0196] In formula (26), represents a fixed risk value for road markings;

[0197] Step 5.2: Calculate any coordinate on lane b according to formula (27): The attenuation factor of the lane line restriction risk at ;

[0198] (27)

[0199] In formula (27), represents the risk diffusion weight of the marking line;

[0200] Step 5.3: Calculate any coordinate on lane b according to formula (28): The attenuation function of the lane line restriction risk at ;

[0201] (28)

[0202] Step 5.4: Calculate any coordinate on lane b according to formula (29): Lane markings limit risk ;

[0203] (29)

[0204] Step 5.5: Follow the process from step 5.1 to step 5.4 to calculate the coordinates of any lane. The lane lines at each location limit the risk and constitute the risk of the entire road ;

[0205] Step 6: Calculate any coordinate on the road according to formula (30) Total risk ;

[0206] (30)

[0207] In formula (30), m represents the risk dimensionality index; m = 1 indicates that the risk is in one-dimensional space (linear superposition), that is, all risk factors are considered equally; m = 2 indicates that the risk is in two-dimensional space (square superposition), that is, the risk assessment is more influenced by risk factors that are close or have a greater direct impact; m = ∞ means that the risk assessment only considers the most direct and adjacent risk sources, ignoring all other risk factors. By carefully selecting the value of m, this formula can provide a comprehensive risk assessment based on different traffic environments and driving scenarios, even when various risks are intertwined, ensuring that the vehicle can take the most appropriate action.

[0208] Step 7: Calculate the optimal driving path of the vehicle; Figure 2 As shown, it is a cycle diagram for calculating the optimal path points for vehicle travel;

[0209] Step 7.1: Calculate any coordinate on the road according to formula (31) Total risk Gradient ;

[0210] (31)

[0211] Step 7.2, define the number of iterations as ,initialization ;

[0212] Step 7.3, select another coordinate on the road as the first Coordinates of the iteration ,in, , ;

[0213] Step 7.4: Calculate the first Coordinates of the iteration ;

[0214] (32)

[0215] In formula (32), Indicates the The step size of the iteration, Representing coordinates Total risk gradient;

[0216] Step 7.5: When equation (33) holds true, As the coordinate of the optimal point of risk;

[0217] (33)

[0218] In formula (33), Indicates the convergence threshold;

[0219] Step 7.6: After assigning k + 1 to k, return to step 7.4 until the number of risk-optimal point coordinates reaches p. Obtain all risk-optimal point coordinates and perform second-order polynomial fitting to obtain the curve equation with minimum risk, which is the optimal driving path for vehicles on the road.

[0220] In this embodiment, an electronic device includes a memory and a processor, wherein the memory is used to store a program that supports the processor to execute the above method, and the processor is configured to execute the program stored in the memory.

[0221] In this embodiment, a computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above method are executed.

Claims

1. A road path dynamic control method based on risk optimization, characterized in that: The following steps are involved: Step 1: Establish a rectangular coordinate system for the road, with the endpoint on the road boundary as the origin, the driving direction of the vehicle on the road as the x-axis, and the axis perpendicular to the driving direction of the vehicle as the y-axis; The roads are numbered from the inside out according to the number of lanes, and the lane lines are marked as ,in, represents the bth lane line, and n represents the total number of lanes; The vertical coordinates of the n+1 lane lines are recorded as ;in, Indicates lane line b The vertical coordinate of lane line b The vertical coordinate and lane line b+1 The vertical coordinate Any coordinate between the vertical coordinates is marked as any coordinate of lane b ; The definition divides the risk factors on the road into three categories: conflict risk, collision risk and lane restriction risk; Define any vehicle i and vehicle j, and , using the intelligent roadside detector set on the road to obtain the angle between vehicle i and vehicle j ; Get the size of the vehicle, and Represent the length and width of vehicle i respectively; get the coordinates of the midpoint of each vehicle, and Represent the midpoint coordinates of vehicle i and vehicle j respectively, and calculate the distance between vehicle i and vehicle j ; Get the steering angle of each vehicle, Indicates the steering angle of vehicle i; obtains the speed of vehicle i ; Get the speed difference between vehicle i and vehicle j ; Get the acceleration difference between vehicle i and vehicle j ; and count the total number of vehicles on the road c; Step 2: Calculate the virtual volume of vehicle i ; Step 3: Calculate the collision risk of vehicle i ; Step 4: Calculate any coordinate on the road Conflict risk ; Step 5: Calculate any coordinate on the road Collision risk at ; Step 6: Divide the road into n+1 lanes to get n lanes, and calculate any coordinate on each lane The restricted risk of the lane line at the location, and the restricted risk of the lane line on each road constitutes the restricted risk of any coordinate on the road Lane restriction risk ; Step 7: Calculate any coordinate on the road according to formula (30) Total risk ; (30) In formula (30), m represents the risk dimension index; Step 8: Based on total risk , calculate the coordinates of the risk optimal point on the road, which is used to fit the optimal driving path of the vehicle.

2. The method for dynamic control of road paths based on risk optimization according to claim 1, characterized in that: Step 2 includes the following steps: Step 2.1: Calculate the directional effectiveness factor of vehicle i according to formula (1): ; (1) In formula (1), represents the underlying risk factor; Step 2.2: Calculate the virtual volume of vehicle i according to formula (2) ; (2) In formula (2), represents the vehicle volume conversion factor, Represents the velocity influence weighting factor in the virtual volume.

3. The method for dynamic control of road paths based on risk optimization according to claim 2, characterized in that: Step 3 includes the following steps: Step 3.1: Calculate the steering angle of the left boundary of vehicle i according to formula (3): ; (3) In formula (3), A constant representing the uncertainty of the steering angle, and Indicates the left and right fluctuation range thresholds related to the steering angle; Step 3.2: Calculate the radius of the left boundary of vehicle i according to formula (4): ; (4) Step 3.3: Calculate the steering angle of the right boundary of vehicle i according to formula (5): ; (5) Step 3.4: Calculate the radius of the right boundary of vehicle i according to formula (6): ; (6) Step 3.5: Calculate the path travel area of ​​vehicle i according to formula (7) ; (7) Step 3.6: Calculate the estimated left driving area of ​​vehicle i according to formula (8): ; (8) Step 3.7: Calculate the right side estimated driving area of ​​vehicle i according to formula (9): ; (9) Step 3.8: Calculate the safe operating area of ​​vehicle i according to formula (10): ; (10) Step 3.9: Calculate the safe operating area of ​​vehicle j according to the process from step 3.1 to step 3.

8. ; Step 3.10: Calculate the intersection of the collision areas of vehicle i and vehicle j according to formula (11): ; (11) Step 3.11: Calculate the collision probability of vehicle i according to formula (12): ; (12) In formula (12), represents the maximum intersection of the conflict areas of vehicle i and other vehicles; Step 3.12: Calculate the collision risk field strength of vehicle i according to formula (13): ; (13) In formula (13), Indicates the impact weight factor of the conflict risk size, Speed ​​impact weighting factor in conflict risk; is the virtual volume of vehicle j, The modulus of the relative distance; Step 3.13: Calculate the collision risk of vehicle i according to formula (14): ; (14)。 4. The method for dynamic control of road paths based on risk optimization according to claim 3, characterized in that: Step 4 includes the following steps: Step 4.1: Calculate the coordinates of vehicle i on the road according to formula (15): Horizontal risk attenuation factor ; (15) In formula (15), Indicates the degree of influence of the relative distance between other vehicles and vehicle i on the longitudinal risk, represents the speed of vehicle i The degree of impact on vertical risk; Step 4.2: Calculate the coordinates of vehicle i on the road according to formula (16): Longitudinal risk attenuation factor ; (16) In formula (16), Indicates the degree of influence of the relative distance between other vehicles and vehicle i on the lateral risk, represents the speed of vehicle i The degree of impact on horizontal risks; Step 4.3: Calculate the coordinates of vehicle i on the road according to formula (17): The risk decay function at ; (17) Step 4.4: Calculate any coordinate on the road according to formula (18) Conflict risk ; (18)。 5. The method for dynamic control of road paths based on risk optimization according to claim 4, characterized in that: Step 5 includes the following steps: Step 5.1: Calculate the vehicle i’s time interval according to formula (19): Driving length within ; (19) Step 5.2: Calculate the vehicle i’s time interval according to formula (20): Driving range within ; (20) Step 5.3: Calculate the time interval of vehicle j according to the process from step 5.1 to step 5.

2. Driving range within ; Step 5.4: Calculate the time interval between vehicle i and vehicle j according to formula (21): Intersection of inner driving areas ; (21) Step 5.5: Calculate the collision probability of vehicle i according to formula (22): ; (22) In formula (22), Indicates that vehicle i and other vehicles are The maximum length of the travel area within the time period; Step 5.6: Calculate the collision risk field strength of vehicle i according to formula (23): ; (23) In formula (23), Indicates the impact weight factor of the collision risk, Indicates the acceleration impact weight factor in collision risk; Step 5.7: Calculate the collision risk of vehicle i according to formula (24) ; (24) Step 5.8: Calculate any coordinate on the road according to formula (25) Collision risk at ; (25)。 6. The method for dynamic control of road paths based on risk optimization according to claim 5, characterized in that: Step 6 includes the following steps: Step 6.1: Calculate the line limit risk according to formula (26) ; (26) In formula (26), represents a fixed risk value for road markings; Step 6.2: Calculate any coordinate on lane b according to formula (27): The attenuation factor of the lane line restriction risk at ; (27) In formula (27), represents the risk diffusion weight of the marking line; Step 6.3: Calculate any coordinate on lane b according to formula (28): The attenuation function of the lane line restriction risk at ; (28) Step 6.4: Calculate any coordinate on lane b according to formula (29): Lane markings limit risk ; (29) Step 6.5: Follow the process from step 6.1 to step 6.4 to calculate the coordinates of any lane. The restricted risk of the lane line at each location constitutes the restricted risk of the entire road .

7. The method for dynamic control of road paths based on risk optimization according to claim 6, characterized in that: Step 8 includes the following steps: Step 8.1: Calculate any coordinate on the road according to formula (31) Total risk Gradient ; (31) Step 8.2, define the number of iterations as ,initialization ; Step 8.3, select another coordinate on the road as the first Coordinates of the iteration ,in, , ; Step 8.4: Calculate the first Coordinates of the iteration ; (32) In formula (32), Indicates the The step size of the iteration, Representing coordinates Total risk gradient; Step 8.5: When equation (33) holds true, As the coordinate of the optimal point of risk; (33) In formula (33), Indicates the convergence threshold; Step 8.6: After assigning k + 1 to k, return to step 8.4 until the number of risk-optimal point coordinates reaches p. Obtain all risk-optimal point coordinates and perform second-order polynomial fitting to obtain the curve equation with minimum risk, which is the optimal driving path for vehicles on the road.

8. 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 road path dynamic control method according to any one of claims 1 to 7, and the processor is configured to execute the program stored in the memory.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the road path dynamic control method according to any one of claims 1 to 7 are executed.

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