A method for establishing a vehicle following model on a curved road based on safety potential field theory

Abstract

The present invention discloses a method for establishing a two-dimensional plane following model of an autonomous driving vehicle at a road bend based on the safety potential field theory: obtaining road geometry information, autonomous driving vehicle information and vehicle information in the traffic flow at the road bend; establishing a physical and mechanical function of the autonomous driving vehicle at the road bend, constructing a two-dimensional plane autonomous driving vehicle potential field model and a road potential field model according to the safety potential field theory, obtaining the speed and acceleration of the autonomous driving vehicle and the vehicle in front at the bend through the physical and mechanical function, superimposing the vehicle potential field and the road potential field, and finally establishing a curve safety potential field following model of the autonomous driving vehicle. The present invention can effectively evaluate the driving risk of the following behavior of the autonomous driving vehicle at the road bend, and conduct safety hazard inspection for the promotion of autonomous driving vehicles and the actual operation of existing road infrastructure.

Description

Technical Field

[0001] The present invention relates to a method for establishing a car-following model of an automatic driving vehicle on a two-dimensional plane at a road curve based on safety potential field theory, and belongs to the technical field of road safety evaluation. Background Art

[0002] Autonomous driving vehicles are also called intelligent connected vehicles. Intelligent and connected vehicles (ICV) are vehicles equipped with advanced on-board sensors, controllers, actuators and other devices, and integrate modern communication and network technologies to achieve intelligent information exchange and sharing between vehicles and X (vehicles, roads, people, clouds, etc.). They have complex environment perception, intelligent decision-making, collaborative control and other functions, and can achieve safe, efficient, comfortable and energy-saving driving, and ultimately replace human operation. Intelligent connected vehicles can provide safer, more energy-saving, more environmentally friendly and more convenient travel methods and comprehensive solutions, and are internationally recognized as the future development direction and focus of attention.

[0003] In a complex traffic system, facing a changing road traffic environment, vehicles on the road make different driving behavior responses based on the traffic information they receive. The difference in driving behavior of different vehicles is usually the main factor affecting road traffic safety and road traffic efficiency. During the driving process, vehicles mainly choose different driving behaviors based on their own expected speed and the safe distance between them and surrounding vehicles. Among them, car following and lane changing are two basic driving behaviors during vehicle driving. Therefore, the car following model and lane changing model in traditional traffic flow theory are to achieve a detailed description of vehicle car following and lane changing behaviors by analyzing the longitudinal and lateral interactions of vehicles during road driving. During the driving process of an autonomous vehicle, the autonomous vehicle makes the choice of car following or lane changing based on its own expected speed and the information of surrounding vehicles (position, speed, etc.). Compared with the car following behavior of a driver-driven vehicle, its car following behavior process is more complicated, the running trajectory of surrounding vehicles has a more significant impact, and its safety research significance is more obvious. In recent years, the development of autonomous driving has provided new opportunities and challenges for traffic management and control. Self-driving cars can achieve information exchange between vehicles and complete car-following behavior through on-board sensors, controllers, and actuators. However, considering the changes in relevant road linear conditions and environment, as well as the interaction between intelligent networked vehicles and traffic flow, the safety of self-driving vehicles on the road is also the research object of many scholars. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide a two-dimensional plane following model of an autonomous driving vehicle at a road curve based on the safety potential field theory, which is helpful to evaluate the driving risk of the following behavior of an autonomous driving vehicle at a road curve, provide a theoretical reference for the design and optimization of the early warning function of a conditional autonomous driving system, and conduct safety hazard inspections for function testing of the conditional autonomous driving system and actual operation in existing road infrastructure.

[0005] The present invention adopts the following technical solutions to solve the above technical problems:

[0006] A risk model for an autonomous driving vehicle following a road curve based on a safety potential field theory includes the following steps:

[0007] Step 1, obtaining driving condition information of the autonomous driving vehicle in the traffic flow at a road curve, including road geometry information, front vehicle information, and autonomous driving vehicle information;

[0008] Step 2, establishing a two-dimensional spatial coordinate axis of the road cross section at the road bend to characterize the following behavior of the autonomous driving vehicle at the road bend;

[0009] Step 3, establishing the physical and mechanical function of the autonomous driving vehicle following behavior in the road cross section;

[0010] Step 4, according to the safety potential field theory, establish a road potential field model and a vehicle potential field model respectively;

[0011] Step 5: Establish a following model for an autonomous driving vehicle on a road curve based on safety potential field theory.

[0012] As a preferred solution of the present invention, the road geometric linear information described in step 1 includes: road curve turning radius, road surface width, wherein the road turning radius includes the inner road boundary radius and the outer road boundary radius; the front vehicle information includes the traction force of the front vehicle and the steering angle of the front vehicle; the autonomous driving vehicle information includes the traction force of the autonomous driving vehicle, the steering angle of the autonomous driving vehicle, and the relative distance between the center of mass of the autonomous driving vehicle and the center of mass of the front vehicle.

[0013] As a preferred solution of the present invention, the specific process of step 2 is as follows:

[0014] Step 21, according to the obtained geometric linear information of the road bend, the inner and outer curve linear shapes of the road are made, and the position of the road centerline is obtained according to the inner road radius of the road bend and the outer road boundary radius, and the starting point of the road centerline at the road bend is set as the coordinate origin, and the direction of the automatic driving vehicle and the front vehicle is the positive direction of the coordinate axis, and the two-dimensional space coordinate axis of the road cross section is established;

[0015] Step 22: Based on the coordinate axis established in step 21, mark the coordinate information of the front vehicle and the autonomous driving vehicle according to the positions of the center of mass of the front vehicle and the autonomous driving vehicle, and obtain the distance information between the center of mass of the two vehicles through the sensor on the autonomous driving vehicle. Set vehicle A as the front vehicle, vehicle B as the autonomous driving vehicle, d AB is the distance between the front vehicle and the autonomous driving vehicle.

[0016] As a preferred solution of the present invention, the specific process of step 3 is as follows:

[0017] Step 31, when the autonomous driving vehicle B is driving along a curve, according to the mechanical balance requirements of the two-dimensional space plane, the autonomous driving vehicle B is acted upon by traction and centripetal force, and its mechanical function expression is:

[0018] F1=M B a B

[0019]

[0020] Where: F1 is the traction of the autonomous driving vehicle B; F2 is the centripetal force on the autonomous driving vehicle B at the road bend; M B is the gravity of the autonomous vehicle B; a B is the acceleration of the autonomous driving vehicle B along the driving direction; v B is the speed of the autonomous driving vehicle B along the driving direction; R is the turning radius at the curve of the road;

[0021] Step 32, according to the magnitude of the traction force and the centripetal force on the autonomous driving vehicle B, the steering angle of the autonomous driving vehicle B along the curve is obtained as:

[0022]

[0023] Step 33, according to the steering angle of the autonomous driving vehicle B obtained by back-calculating the traction force and the centripetal force in step 32, the resultant force of the centripetal force and the traction force of the autonomous driving vehicle B at the road bend is obtained:

[0024]

[0025] Step 34, based on the resultant force magnitude of the autonomous driving vehicle B at the road bend obtained in step 33, the acceleration of the autonomous driving vehicle B in the direction of the resultant force at the bend is obtained as:

[0026]

[0027] Step 35: Similarly, the acceleration of the front vehicle A in the direction of the resultant force at the curve is obtained as:

[0028]

[0029] Where: F1′ is the traction of the front vehicle A; F2′ is the centripetal force on the front vehicle A at the road bend; M S is the gravity of the vehicle A in front; a A is the acceleration of the vehicle A in front along the travel direction; v A is the speed of the front vehicle A along the driving direction; θ′ is the steering angle of the front vehicle A along the curve; F 合 ′ is the resultant force of the centripetal force and traction of the front vehicle A at the road curve.

[0030] As a preferred solution of the present invention, the specific process of step 4 is as follows:

[0031] Step 41, road traffic is composed of basic elements such as people, vehicles, roads, and environment. The total safety potential field of the road is the superposition of the safety potential fields of many traffic elements. Since the vehicle is always in a following state during the turning process, the impact of the road boundary field is relatively small. The automatic driving vehicle is mainly affected by the superposition of the road potential field and the vehicle potential field at the road bend in the following state. Therefore, the potential field strength E of the total safety potential field in this scene is total The calculation is as follows:

[0032] |E total |=ω L |E L |+ω S |E S |+ω V |E V |

[0033] Where: E L is the potential field strength of the road line field; E S is the road boundary potential field strength; E V is the potential field strength of the vehicle potential field; ω L ,ω S ,ω V They correspond to the weights of the potential field strength of the road line field, the potential field strength of the road boundary field, and the potential field strength of the vehicle potential field.

[0034] Step 42: In an ideal car-following model, the autonomous driving vehicle B will not deviate from the center line of the road when driving in the middle of a single-lane road, and the effect of the road line potential field can be ignored. Therefore, the potential field strength expression of the safety total potential field in this scenario is as follows:

[0035] |E total |=ω S |E S |+ω V |E V |

[0036] Step 43, according to the expression of the safety total potential field in step 42, calculate the potential field size of the road boundary field:

[0037]

[0038] d S Aj =(y s,j -y A )

[0039] Where: d S Aj is the distance vector from the front vehicle A to the road boundary line; d S Aj =(y s,j -y A );y A is the y-axis coordinate of the front vehicle A; s,j is the y-axis coordinate of the road line field j; η is the road boundary field coefficient.

[0040] Step 44, based on the expression of the total safety potential field in step 42, calculate the potential field E of the autonomous driving vehicle V , assuming that the position coordinates of the center of mass of the autonomous driving vehicle B in the two-dimensional space coordinate system are (x0, y0), in the following scenario, the distance k from the two-dimensional plane space point to the vehicle has the following relationship:

[0041]

[0042] Step 46, based on the formula from the spatial point to the mass center of the autonomous driving vehicle in step 45, the following motion of the autonomous driving vehicle is considered to be realized only in the two-dimensional spatial plane, and the velocity component generated in the vertical direction is not considered. In order to better describe the change in the degree of risk faced by the autonomous driving vehicle B when approaching the front vehicle, the distance correction is performed on the actual two-dimensional space distance:

[0043]

[0044]

[0045] Where: k′ is the pseudo distance; θ is the steering angle when the vehicle turns (the counterclockwise direction of the steering angle is the positive direction of the steering angle); (x * ,y * ) is the corresponding deflection value after the original coordinate (x, y) is turned.

[0046] Step 47, according to the two-dimensional space plane pseudo-distance formula in step 46, due to the influence of the potential fields of other surrounding vehicles, the vehicle potential field formed by the autonomous driving vehicle has the following effect on the vehicles around the target vehicle: when the surrounding vehicles are a certain distance away from the autonomous driving vehicle, the vehicle potential field of the target vehicle will act on the surrounding vehicles, so the constructed autonomous driving vehicle potential field model is as follows:

[0047]

[0048] In the formula: λ, β1 are unknown coefficients; a 合 is the acceleration of the autonomous vehicle in the direction of travel; It is the angle between the driving direction of the autonomous vehicle and the line connecting the center of mass of the autonomous vehicle and the vehicle in front.

[0049] As a preferred embodiment of the present invention, the specific process of step 5 is as follows:

[0050] Step 51, according to the safety potential field theory, the magnitude of the potential field force of the vehicle in front of the autonomous driving vehicle can be expressed as:

[0051]

[0052] Where: Δx is the distance between the center of mass of the autonomous driving vehicle B and the center of mass of the vehicle in front A; E V ′ is the potential field strength of the vehicle ahead; M A 、M B are the masses of the autonomous driving vehicle and the vehicle in front, respectively; λ, β1, and β2 are all unknown coefficients.

[0053] Step 52, due to the field force F AB This is the reason for changing the acceleration and deceleration of the autonomous vehicle B during the following process. Therefore, the field force F AB The acceleration a generated by the action AB as follows:

[0054]

[0055] Step 53: The acceleration of the autonomous driving vehicle and the potential field force of the vehicle ahead generate a common acceleration a 场 (i.e. the vector sum in the driving direction of the autonomous driving vehicle B) is:

[0056]

[0057] Where: a 合 is the acceleration of the autonomous vehicle in the direction of travel; a AB is the acceleration generated by the potential field force of the front vehicle on the autonomous driving vehicle: is the angle between the driving direction of the autonomous vehicle and the line connecting the centers of mass of the two vehicles; is the angle between the driving direction of the front vehicle and the line connecting the centers of mass of the two vehicles; d AB is the distance between the autonomous driving vehicle and the center of mass of the vehicle in front; μ is the reduction coefficient of the acceleration generated by the potential field force as the distance changes.

[0058] Since the force generated by the safety potential field is a short-range force, that is, the force only works at a short distance, when the front and rear vehicles are far apart in the following state, the acceleration and deceleration of the vehicle is mainly expressed by the relationship between the current speed of the vehicle and the expected speed as follows:

[0059] a B =a max tanh[δ(v0 (A) -v B )]

[0060] Where: a B is the acceleration of the vehicle when the potential force of the road and vehicle does not work; a max is the maximum acceleration of the vehicle; v0 (A) is the expected speed of the vehicle under the current acceleration condition; δ is the coefficient value of the difference between the current speed of the vehicle and the expected speed.

[0061] Step 54, based on the above, a following model of the two-dimensional space plane of the autonomous driving vehicle on a curve is obtained based on the safety potential field theory:

[0062]

[0063]

[0064] Compared with the prior art solutions, the present invention has the following technical effects:

[0065] The present invention starts from the perspective of the impact of traffic flow conditions and road conditions on the safety of vehicles in traffic flow, combines the safety potential field theory to consider the following behavior of a single autonomous driving vehicle on a single lane at a curve, establishes the physical and mechanical functions of the autonomous driving vehicle, and integrates the potential field model of the autonomous driving vehicle under the safety potential field theory with the road potential field model to evaluate the speed and acceleration of the autonomous driving vehicle following behavior, thereby obtaining a complete autonomous driving vehicle following model under the safety potential field theory, providing a theoretical reference for the safety issues of autonomous driving vehicles in actual road scenarios. At the same time, the method disclosed in the present invention is used to obtain the risk of autonomous driving vehicles following, which is used to conduct safety hazard inspections for the promotion of autonomous driving vehicles and the actual operation of existing road infrastructure. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1This is a flow chart of a car-following model of an autonomous driving vehicle based on a two-dimensional plane at a road curve in the present invention based on the safety potential field theory;

[0067] Figure 2 It is a schematic diagram of the physical and mechanical functions of the car-following behavior of the autonomous driving vehicle of the present invention. DETAILED DESCRIPTION

[0068] The embodiments of the present invention are described in detail below, and examples of the embodiments are shown in the accompanying drawings. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be interpreted as limiting the present invention.

[0069] The present invention proposes a method for establishing a two-dimensional plane model of an autonomous driving vehicle following a curve on a road based on the safety potential field theory. Figure 1 As shown, the specific steps are as follows:

[0070] (1) Obtain road geometry information, autonomous driving vehicle information, and vehicle ahead information;

[0071] The road geometric line shape information at the curve includes: the turning radius of the road curve and the width of the road surface, where the road turning radius includes the inner road boundary radius and the outer road boundary radius; the front vehicle information includes the vehicle traction force, the steering angle of the front vehicle, and the position of the front vehicle in the coordinate axis; the autonomous driving vehicle information includes the autonomous driving vehicle traction force, the steering angle of the autonomous driving vehicle, the position of the autonomous driving vehicle in the coordinate axis and the relative distance from the center of mass of the front vehicle.

[0072] (2) Establishing the two-dimensional spatial coordinate axis of the road plane;

[0073] 1) The road curve information is used to obtain the position of the road centerline according to the inner and outer road radii of the road curve, and the starting point of the road centerline is set as the coordinate origin to establish the two-dimensional spatial coordinate axis of the road plane. Figure 2 shown.

[0074] 2) Based on the coordinate axis established in 1), the coordinate information of the front vehicle and the autonomous driving vehicle are marked according to the positions of the center of mass of the front vehicle and the autonomous driving vehicle, and the distance information between the center of mass of the two vehicles is obtained through the sensor on the autonomous driving vehicle.

[0075] (3) Establish the physical and mechanical functions between the autonomous driving vehicle and the vehicle ahead;

[0076] 1) When the autonomous driving vehicle B is driving on a curved road, according to the mechanical balance requirements of the two-dimensional plane, the autonomous driving vehicle B is subject to the effects of traction and centripetal force, and its mechanical function expression is:

[0077] F1=M B a B

[0078]

[0079] Where: F1 is the traction of the autonomous driving vehicle B; F2 is the centripetal force on the autonomous driving vehicle B at the road bend; M B is the gravity of the autonomous vehicle B; a B is the acceleration of the autonomous driving vehicle B along the driving direction; v is the speed of the autonomous driving vehicle B along the driving direction at that point; R is the turning radius at the curve;

[0080] 2) According to the magnitude of the traction and centripetal force received by the autonomous driving vehicle B, the steering angle of the autonomous driving vehicle B along the curve is obtained as:

[0081]

[0082] 3) The steering angle of the autonomous driving vehicle B is calculated by back-calculating the traction force and the centripetal force, and the resultant force of the centripetal force and the traction force of the autonomous driving vehicle B at the road bend is obtained:

[0083]

[0084] 4) According to the magnitude of the resultant force on the autonomous driving vehicle B at the bend, the acceleration of the autonomous driving vehicle B in the direction of the resultant force at the bend is obtained as:

[0085]

[0086] 5) Similarly, according to the above steps, the physical mechanical model of the front vehicle A is obtained. The acceleration of the front vehicle A in the direction of the resultant force at the curve is:

[0087]

[0088] (4) Establish road potential field and vehicle potential field models;

[0089] 1) Road traffic is composed of basic elements such as people, vehicles, roads, and environment. The total road safety potential field is the superposition of the safety potential fields of many traffic elements. Since the vehicle is always in a following state during the turning process, the impact of the road boundary field is small. When the autonomous driving vehicle is in the following state, it is mainly affected by the superposition of the road potential field and the vehicle potential field at the road bend. Therefore, the potential field strength E of the total safety potential field in this scene is total The calculation is as follows:

[0090] |E total |=ω L |E L |+ω S |E S |+ω V |E V |

[0091] Where: E L is the potential field strength of the road line field; E S is the road boundary potential field strength; E V is the potential field strength of the vehicle potential field; ω L ,ω S ,ω V They correspond to the weights of the potential field strength of the road line field, the potential field strength of the road boundary field, and the potential field strength of the vehicle potential field.

[0092] 2) In the ideal state of the car-following model, the autonomous driving vehicle B will not deviate from the center line of the road when driving in the middle of a single-lane road, and the effect of the road line field potential field can be ignored. Therefore, the potential field strength expression of the total safety potential field in this scenario is as follows:

[0093] |E total |=ω S |E S |+ω V |E V |

[0094] 3) According to the above safety total potential field expression, calculate the potential field size of the road boundary field:

[0095]

[0096] d S Aj =(y s,j -y A )

[0097] Where: The y-axis coordinate of the front vehicle A is y A ; The y-axis coordinate of the road line field j is y s ;d S Aj is the distance vector from the front vehicle A to the road boundary line; η is the road boundary field coefficient.

[0098] 4) According to the expression of the total safety potential field in step 42, calculate the potential field E of the autonomous driving vehicle V , assuming that the position coordinates of the center of mass of the autonomous driving vehicle B in the two-dimensional space coordinate system are (x0, y0), in the following scenario, the distance k from the two-dimensional plane space point to the vehicle has the following relationship:

[0099]

[0100] 5) According to the formula from the above spatial point to the center of mass of the autonomous driving vehicle, the following motion of the autonomous driving vehicle is considered to be realized only in the two-dimensional plane, and the velocity component generated in the vertical direction is not considered. In order to better describe the change in the degree of risk faced by the autonomous driving vehicle B when approaching the front vehicle, the distance correction is made to the actual two-dimensional space distance:

[0101]

[0102]

[0103] Where: k′ is the pseudo distance; θ is the steering angle when the vehicle turns (the counterclockwise direction of the steering angle is the positive direction of the steering angle); (x * ,y * ) is the corresponding deflection value after the original coordinate (x, y) is turned.

[0104] 6) According to the above two-dimensional space plane pseudo-distance formula, due to the influence of the potential fields of other surrounding vehicles, the vehicle potential field formed by the autonomous driving vehicle has the following effect on the vehicles around the target vehicle: when the surrounding vehicles are a certain distance away from the autonomous driving vehicle, the vehicle potential field of the target vehicle will act on the surrounding vehicles. Therefore, the constructed autonomous driving vehicle potential field model is as follows:

[0105]

[0106] Where: λ, β1 are unknown coefficients; a 合 is the acceleration of the autonomous vehicle in the direction of travel; It is the angle between the driving direction of the autonomous vehicle and the line connecting the center of mass of the autonomous vehicle and the vehicle in front.

[0107] (5) Establish a two-dimensional plane model of autonomous driving vehicles following a road curve based on safety potential field theory;

[0108] 1) According to the safety potential field theory, the magnitude of the potential field force on the autonomous driving vehicle from the vehicle in front can be expressed as:

[0109]

[0110] Where: E V ′ is the potential field strength of the vehicle ahead; M A 、M B are the masses of the autonomous driving vehicle and the vehicle in front, respectively; λ, β1, and β2 are all unknown coefficients.

[0111] 2) Due to the field force F AB This is the reason for changing the acceleration and deceleration of the autonomous vehicle B during the following process. Therefore, the field force F AB The acceleration a generated by the action AB as follows:

[0112]

[0113] 3) The acceleration of the autonomous driving vehicle and the potential field force of the vehicle in front generate a common acceleration a 场 (i.e. the vector sum in the driving direction of the autonomous driving vehicle B) is:

[0114]

[0115] Where: a 合 is the acceleration of the autonomous vehicle in the direction of travel; a AB is the acceleration generated by the potential field force of the front vehicle on the autonomous driving vehicle: is the angle between the driving direction of the autonomous vehicle and the line connecting the centers of mass of the two vehicles; is the angle between the driving direction of the front vehicle and the line connecting the centers of mass of the two vehicles; d AB is the distance between the autonomous driving vehicle and the center of mass of the vehicle in front; μ is the reduction coefficient of the acceleration generated by the potential field force as the distance changes.

[0116] Since the force generated by the safety potential field is a short-range force, that is, the force only works at a short distance, when the front and rear vehicles are far apart in the following state, the acceleration and deceleration of the vehicle is mainly expressed by the relationship between the current speed of the vehicle and the expected speed as follows:

[0117] a B =a max tanh[δ(v0 (a) -v B )]

[0118] Where: a B is the acceleration of the vehicle when the potential force of the road and vehicle does not work; a max is the maximum acceleration of the vehicle; v0 (a) is the expected speed of the vehicle under the current acceleration condition; δ is the coefficient value of the difference between the current speed of the vehicle and the expected speed.

[0119] 4) In summary, the following model of the two-dimensional plane of the autonomous driving vehicle on the curve based on the safety potential field theory is obtained:

[0120]

[0121]

[0122] In summary, the present invention starts from the perspective of the impact of traffic flow conditions and road conditions on the safety of vehicles in traffic flow, combines the safety potential field theory to consider the following behavior of a single autonomous driving vehicle on a single lane at a curve, establishes the physical and mechanical functions of the autonomous driving vehicle, and integrates the potential field model of the autonomous driving vehicle under the safety potential field theory with the road potential field model to evaluate the speed and acceleration of the autonomous driving vehicle following behavior, thereby obtaining a complete autonomous driving vehicle following model under the safety potential field theory, providing a theoretical reference for the safety issues of autonomous driving vehicles in actual road scenarios. At the same time, the method disclosed in the present invention is used to obtain the risk of autonomous driving vehicles following, which is used to conduct safety hazard inspections for the promotion of autonomous driving vehicles and the actual operation of existing road infrastructure.

[0123] The present invention also provides a vehicle lane change trajectory deviation calculation device based on traffic simulation, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the method for establishing a vehicle following model at a road curve based on the safety potential field theory are implemented.

[0124] The present invention also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of the method for establishing a vehicle following model at a road curve based on the safety potential field theory are implemented.

[0125] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0126] The above embodiments are only for illustrating the technical idea of ​​the present invention, and cannot be used to limit the protection scope of the present invention. Any changes made on the basis of the technical solution in accordance with the technical idea proposed by the present invention shall fall within the protection scope of the present invention.

Claims

1. A method for establishing a vehicle following model on a road curve based on safety potential field theory, characterized in that: The steps include: Step 1, obtaining driving condition information of the autonomous driving vehicle in the traffic flow at a road curve, including road geometry information, front vehicle information, and autonomous driving vehicle information; Step 2, establishing a two-dimensional spatial coordinate axis of the road cross section at the road bend to characterize the following behavior of the autonomous driving vehicle at the road bend; Step 3, establishing the physical and mechanical function of the autonomous driving vehicle following behavior in the road cross section; Step 4, according to the safety potential field theory, establish a road potential field model and a vehicle potential field model respectively; Step 5, establishing a following model of an autonomous driving vehicle on a road curve based on safety potential field theory; The specific process of step 2 is as follows: Step 21, according to the obtained geometric linear information of the road bend, the inner and outer curve linear shapes of the road are made, and the position of the road centerline is obtained according to the inner road radius of the road bend and the outer road boundary radius, and the starting point of the road centerline at the road bend is set as the coordinate origin, and the direction of the automatic driving vehicle and the front vehicle is the positive direction of the coordinate axis, and the two-dimensional space coordinate axis of the road cross section is established; Step 22, marking the coordinate information of the front vehicle and the autonomous driving vehicle according to the positions of the center of mass of the front vehicle and the autonomous driving vehicle on the basis of the coordinate axis established in step 21, and obtaining the distance information between the center of mass of the two vehicles through the sensor on the autonomous driving vehicle; The specific process of step 3 is as follows: Step 31, when the autonomous driving vehicle B is driving along a curve, according to the mechanical balance requirements of the two-dimensional space plane, the autonomous driving vehicle B is acted upon by traction and centripetal force, and its mechanical function expression is: F1=M B a B Where: F1 is the traction of the autonomous driving vehicle B; F2 is the centripetal force on the autonomous driving vehicle B at the road bend; M B is the gravity of the autonomous vehicle B; a B is the acceleration of the autonomous driving vehicle B along the driving direction; v B is the speed of the autonomous driving vehicle B along the driving direction; R is the turning radius at the curve of the road; Step 32, according to the magnitude of the traction force and the centripetal force on the autonomous driving vehicle B, the steering angle of the autonomous driving vehicle B along the curve is obtained as: Step 33, according to the steering angle of the autonomous driving vehicle B obtained by back-calculating the traction force and the centripetal force in step 32, the resultant force of the centripetal force and the traction force of the autonomous driving vehicle B at the road bend is obtained: Step 34, based on the resultant force magnitude of the autonomous driving vehicle B at the road bend obtained in step 33, the acceleration of the autonomous driving vehicle B in the direction of the resultant force at the bend is obtained as: Step 35: Similarly, the acceleration of the front vehicle A in the direction of the resultant force at the curve is obtained as: Where: F1′ is the traction of the front vehicle A; F2′′ is the centripetal force on the front vehicle A at the road bend; M A is the gravity of the vehicle A in front; a A is the acceleration of the vehicle A in front along the travel direction; v A is the speed of the front vehicle A along the driving direction; θ′ is the steering angle of the front vehicle A along the curve; F 合 ′ is the resultant force of the centripetal force and traction of the front vehicle A at the road curve; In step 4: The road potential field model is: Where: E S is the potential field strength of the road boundary; d S Aj is the distance vector from the front vehicle A to the road boundary line; d S Aj =(y s，j -y A );y A is the y-axis coordinate of the front vehicle A; s，j is the y-axis coordinate of the road line field j; η is the road boundary field coefficient; The vehicle potential field model is: Where: E V is the potential field strength of the autonomous driving vehicle B; λ, β1 are coefficients; M B is the mass of the autonomous vehicle B; a 合 is the acceleration of the autonomous driving vehicle B in the direction of the resultant force at the curve; is the angle between the driving direction of the autonomous driving vehicle B and the line between the center of mass of the autonomous driving vehicle B and the center of mass of the vehicle A in front; k′ is the pseudo distance; θ is the steering angle when the vehicle turns; (x * ,y * ) is the corresponding deflection value after the original coordinate (x, y) is turned; τ is the critical threshold of the safety distance; v B is the speed of the autonomous driving vehicle B along the driving direction; a is a parameter related to the speed; (x0, y0) is the coordinate of the center of mass of the autonomous driving vehicle B; The specific process of step 5 is as follows: Step 51, according to the safety potential field theory, the magnitude of the potential field force of the front vehicle A on the autonomous driving vehicle B is expressed as: Where: Δx is the distance between the center of mass of the autonomous driving vehicle B and the center of mass of the vehicle in front A; E V ′ is the potential field strength of the vehicle A in front; M A is the mass of the front vehicle A; β2 is the coefficient; Step 52: The autonomous driving vehicle B is driven by F AB The acceleration a generated by the action AB for: Step 53: The acceleration of the autonomous driving vehicle B and F AB The common acceleration a is generated under the action 场 for: Where: is the angle between the driving direction of the front vehicle A and the line connecting the center of mass of the autonomous driving vehicle B and the center of mass of the front vehicle A; d AB is the distance between the center of mass of the autonomous driving vehicle B and the center of mass of the vehicle A in front; μ is the reduction coefficient of the acceleration generated by the potential field force as the distance changes; Step 54, the following model of the autonomous driving vehicle B at a road curve based on the safety potential field theory: Where: a max is the maximum acceleration of the autonomous vehicle B; v0 (a) is the expected speed of the autonomous driving vehicle B under acceleration conditions at a certain point on the coordinate axis at a certain time; δ is the coefficient value of the difference between the current speed of the vehicle and the expected speed.

2. The method for establishing a vehicle following model on a road curve based on the safety potential field theory according to claim 1 is characterized in that: In step 1, the road geometry information includes: road curve turning radius, road surface width, where the road turning radius includes the inner road boundary radius and the outer road boundary radius; the front vehicle information includes the traction force of the front vehicle and the steering angle of the front vehicle; the autonomous driving vehicle information includes the traction force of the autonomous driving vehicle, the steering angle of the autonomous driving vehicle, and the relative distance between the center of mass of the autonomous driving vehicle and the center of mass of the front vehicle.

3. A device for establishing a vehicle following model at a road curve based on safety potential field theory, comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the steps of the method for establishing a vehicle following model at a road curve based on the safety potential field theory as described in any one of claims 1 to 2 are implemented.

4. 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 method for establishing a vehicle following model at a road curve based on the safety potential field theory as described in any one of claims 1 to 2 are implemented.

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