Intersection-oriented anti-interruption formation driving method

Abstract

The invention provides an intersection-oriented anti-interruption formation driving method, which comprises the following steps of: enabling a plurality of following vehicles to follow a leading vehicle to form a motorcade; acquiring and storing vehicle running states in upstream and downstream ranges of the road intersection, running states of vehicles in the formation and state information of the inductive traffic lights; establishing a coordinate system, and performing state variable substitution under spatial domain modeling; solving an optimal control instruction of each controlled vehicle in the formation in a spatial domain through a controlled system according to the historical state information, and transmitting the optimal control instruction to each controlled vehicle; and executing the optimal control instruction. According to the invention, by establishing the controlled system comprising the formation integrated control system and the vehicle crossing passing probability system in the spatial domain, the green light passing probability of each vehicle in the automatic driving formation is optimized in real time, the integrity of the formation when passing through the crossing is ensured, the driving stability, continuity and safety are improved, and the driving safety is improved. The method can be widely applied to intelligent network connection motorcades of any scale.

Description

Technical Field

[0001] The present invention relates to the field of intelligent connected vehicle-road cooperative platoon driving control technology, and in particular to an anti-interruption platoon driving method for intersections. Background Art

[0002] Modern communication technology, computer science and network technology provide support for key capabilities of intelligent connected vehicles such as vehicle-to-vehicle communication, vehicle-to-road communication and environmental perception, thereby promoting the development of autonomous driving and vehicle-to-road cooperative technology. With the support of vehicle-to-road cooperative technology, several intelligent connected vehicles can form a fleet, driving with smaller vehicle-to-vehicle spacing and more coordinated cruising speeds, thereby improving road capacity and reducing energy consumption and emissions. Intelligent connected vehicle platoon driving has become a hot spot in the development of intelligent transportation.

[0003] In applications of continuous traffic flows such as highways and expressways, intelligent connected platoon driving has achieved remarkable results and has been widely put into practical operation. However, in intermittent traffic flow environments such as urban roads, especially when passing through intersections, intelligent connected platoon driving faces significant challenges. Specifically, vehicles in the formation are easily interrupted by the change of traffic lights, resulting in some vehicles passing while other vehicles cannot pass in time, causing the disintegration of the formation and potential safety hazards. To meet this challenge, existing technologies usually rely on historical data to predict traffic signal timing and control platoon driving on this basis. However, this method has several obvious limitations in practical applications:

[0004] The existing formation control strategy is mainly aimed at fixed signal timing intersections, but cannot adapt to the complex changes of induction signal timing intersections. Induction signal timing intersections are widely used in Chinese cities, and this method has limited applicability.

[0005] Most existing methods rely solely on historical data to predict signal timing, but fail to take the current dynamic traffic status into consideration. The prediction results fail to reflect the real-time and uncertainty of signal timing.

[0006] Existing platoon control strategies usually focus on the green light passing probability of the first vehicle in the platoon, ignoring the coordinated optimization of the entire platoon and failing to effectively optimize the green light passing probability of each vehicle in the platoon, resulting in some vehicles in the platoon being unable to pass the intersection synchronously, thus affecting the integrity and safety of the platoon. Summary of the invention

[0007] The purpose of the present invention is to overcome the above-mentioned defects and provide a method that can optimize the green light passing probability of each vehicle in the entire formation in real time to ensure the integrity and safety of the formation.

[0008] To achieve the above object, the present invention proposes an anti-interruption platoon driving method for intersections, comprising the following steps:

[0009] S1: Several following cars follow a leading car to form a convoy;

[0010] S2: Obtain the vehicle operation status information within the upstream and downstream range of the road intersection, the operation status information of each vehicle in the formation and the status information of the induction traffic signal light through the on-board unit of the pilot vehicle, and save them in the database;

[0011] S3: Establish a coordinate system, calculate the position information of each vehicle in the formation and other social vehicles in the coordinate system, and perform state variable substitution under spatial domain modeling;

[0012] S4: Based on the historical status information of each vehicle in the formation, other social vehicles and traffic lights, the on-board central controller of the pilot vehicle solves the optimal control instructions of each controlled vehicle in the formation in the spatial domain through the controlled system, and transmits the optimal control instructions to the power system and braking system of each controlled vehicle;

[0013] S5: Execute the optimal control instruction.

[0014] Furthermore, in step S1, both the pilot vehicle and the following vehicle are controlled autonomous driving vehicles.

[0015] Furthermore, in step S2, a roadside traffic detector is used to collect vehicle operation status information within the upstream and downstream range of the intersection through a ring coil, and transmit it to the pilot vehicle onboard unit through vehicle-road communication. The ring coil data is the state of coil j at time k, and the expression is:

[0016]

[0017] Furthermore, in step S2, the onboard unit of the pilot vehicle obtains the operating status information of each vehicle in the formation through vehicle-to-vehicle communication, and obtains the inductive traffic light status information from the traffic signal controller through vehicle-to-road communication; the inductive traffic light status is the status of the light i at time k, which is expressed as:

[0018]

[0019] Further, in step S3, the method for establishing the coordinate system is as follows: the pilot vehicle onboard unit takes the intersection stop line as the longitudinal control origin of the vehicle to establish a reference coordinate system; the state variable substitution under spatial domain modeling is to convert the state variable with time as the independent variable under time domain modeling into the state variable with spatial position as the independent variable under spatial domain modeling, and the conversion formula is:

[0020]

[0021] Among them, t is the time variable, s is the position variable, ω is the vehicle slowness under spatial domain modeling, v is the vehicle speed under time domain modeling, σ is a very small normal quantity, b is the vehicle slowness under spatial domain modeling, and a is the vehicle acceleration under time domain modeling.

[0022] Furthermore, the controlled system includes a formation integrated control system in the space domain and a vehicle crossing intersection probability system.

[0023] The system dynamic equation of the formation integrated control system in the space domain is:

[0024] ξ(k+1)=A k ξ(k)+B k u(k);

[0025] In the formula, B k is a time-varying matrix whose value changes with the current slowness of the platoon vehicles; ξ(k+1) is the state of the system at discrete time k+1, A k is the system matrix, which describes the relationship between the state of the system and itself, ξ(k) is the state of the system at discrete time k, and u(k) is the control input vector at discrete time k.

[0026] When there are i (i>2) following vehicles in the formation (i.e., there are i+1 vehicles in the formation including the lead vehicle), ξ(k+1), A k ,ξ(k),u(k),B k The values ​​are as follows:

[0027]

[0028] in, O is a zero matrix;

[0029] ξ(k) is the system state value sampled at discrete time instant k;

[0030] u(k)=[a L a 1 a 2 … a i ] T

[0031]

[0032] The vehicle passing through the intersection probability system expression is:

[0033]

[0034] Where δ=(δ 1 ,δ 2 ,…,δT ) is the historical traffic status information, is the time when the controlled vehicle i is expected to pass the stop line of the intersection, is the final state value of the passing time state quantity in the planning interval in the formation integrated control system in the spatial domain, and f is the time convolutional neural network.

[0035] Furthermore, the formation integrated control system in the spatial domain adopts a dynamic planning interval, and the planning interval ranges from the current position of the vehicle to the stop line of the intersection.

[0036] Furthermore, ξ is the state vector and u is the control input;

[0037] When the number of vehicles in the formation is 3, ξ is:

[0038]

[0039] Among them, t L ,t 1 ,t 2 is the time of the formation leader car, the first following car, and the second following car, ω L ,ω 1 ,ω 2 The speeds of the leading car, the first following car, and the second following car in the formation. is the expected headway between the first following car and the leading car, h 01 is the actual headway between the first following car and the leading car, is the expected headway between the second following car and the leading car, h 02 is the actual headway between the second following car and the leading car, is the expected headway between the second following car and the first following car;

[0040] The control input u is:

[0041] u=[a L a 1 a 2 ] T

[0042] Among them, a L 、a 1 、a 2 is the expected acceleration of the formation leader car, the first following car, and the second following car;

[0043] The desired state vector of the central controller is

[0044] ξ ref =[0 0 0 0 0 0 0 0 0 0 0 0] T

[0045] Among them, the 1st, 2nd, 5th, 6th, 11th and 12th formation time and slowness state quantities have no expected values, and their error weights are set to zero.

[0046] Furthermore, in the system dynamic equation of the formation integrated control system in the spatial domain, A k The values ​​of are as follows:

[0047]

[0048] B k The values ​​of are as follows:

[0049]

[0050] B k It is a time-varying matrix, and its value changes with the current slowness of the vehicles in the platoon.

[0051] Furthermore, the cost function of the formation integrated control system in the spatial domain is:

[0052]

[0053] in, δ is the traffic status at the sampling time and the historical traffic status information, x 0 is the system state at the sampling time, is the control input vector to be optimized. The first term of the cost function is the control target of the formation passing through the intersection without interruption, the second term is the formation state control error, the third term is the ecological target, M is the weight coefficient, Q and R are weight matrices.

[0054] The system dynamic equation describes the evolution of the formation integrated control system and defines the behavior of the formation integrated control system at each moment, that is, how to determine the next state of the system from the current state and input. The cost function formula is used to quantify the performance of the formation integrated control system in the spatial domain under a certain control strategy.

[0055] Compared with the prior art, the advantages of the present invention are:

[0056] 1. The present invention uses the on-board unit of the pilot vehicle to model the vehicle state changes in the spatial domain, and uses the on-board central controller of the pilot vehicle to establish a controlled system including a formation integrated control system in the spatial domain and a vehicle intersection probability system, so as to optimize the green light passing probability of each vehicle in the automatic driving formation in real time, enhance the ability of the formation driving to cope with the uncertain signal timing of the inductive signal controlled intersection, prevent the formation from being interrupted by the signal timing switching, effectively avoid the formation from being interrupted when passing the inductive signal controlled intersection, ensure the integrity of the formation when passing the intersection, and improve the driving stability, continuity and safety.

[0057] 2. The present invention has universal applicability and can be widely used in intelligent connected vehicle fleets of any size. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 The present invention is a flowchart of an anti-interruption platoon driving method for intersections according to an embodiment of the present invention. DETAILED DESCRIPTION

[0059] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be further described below.

[0060] This embodiment proposes an anti-interruption platoon driving method for intersections, such as Figure 1 As shown, the method comprises the following steps:

[0061] S1: Two following cars follow a pilot car to form a convoy of three cars. Both the pilot car and the following car are controlled autonomous vehicles. The pilot car is equipped with an on-board unit and a central controller, and the following car is equipped with an on-board unit. The on-board unit of the following car sends the operating status information of the vehicle to the on-board unit of the pilot car through inter-vehicle communication.

[0062] S2: The vehicle operation status information within the upstream and downstream range of the road intersection, the operation status information of each vehicle in the formation and the status information of the inductive traffic light are obtained through the on-board unit of the pilot vehicle, and saved in the database. The implementation method is as follows:

[0063] 1) Use roadside traffic detectors to collect vehicle operation status information within the upstream and downstream range of the intersection through a circular coil, and transmit it to the pilot vehicle's onboard unit through vehicle-road communication;

[0064] The toroidal coil data is the state of coil j at time k, expressed as:

[0065]

[0066] 2) The on-board unit of the pilot vehicle obtains the operating status information of each vehicle in the formation through vehicle-to-vehicle communication;

[0067] 3) Obtaining the inductive traffic light status information from the traffic signal controller through vehicle-road communication; wherein the inductive traffic light status is the status of the light i at time k, expressed as:

[0068]

[0069] 4) The pilot vehicle onboard unit saves the above-collected historical information on the operating status of each vehicle in the formation and other social vehicles, and the historical information on the status of the inductive traffic lights into a database.

[0070] S3: The pilot vehicle onboard unit establishes a reference coordinate system with the intersection stop line as the longitudinal control origin of the vehicle, calculates the position information of each vehicle in the formation and other social vehicles in the selected coordinate system, and performs state variable substitution under spatial domain modeling, converting the state variable with time as the independent variable under time domain modeling into the state variable with spatial position as the independent variable under spatial domain modeling; the conversion formula is:

[0071]

[0072]

[0073] Among them, t is the time variable, s is the position variable, ω is the vehicle slowness under spatial domain modeling, v is the vehicle speed under time domain modeling, σ is a very small normal quantity, b is the vehicle slowness under spatial domain modeling, and a is the vehicle acceleration under time domain modeling.

[0074] S4: Based on the historical status information of each vehicle in the formation, other social vehicles and traffic lights, the on-board central controller of the pilot vehicle solves the optimal control instructions of each controlled vehicle in the formation in the spatial domain through the controlled system, and transmits the optimal control instructions to the power system and braking system of each controlled vehicle.

[0075] Among them, the controlled system includes the formation integrated control system in the spatial domain and the vehicle intersection probability system.

[0076] 1. Formation integrated control system in space domain

[0077] In this embodiment, when the number of vehicles in the formation is 3, the state vector of the formation integrated control system in the spatial domain is:

[0078]

[0079] Among them, t L ,t 1 ,t 2 is the time of the formation leader car, the first following car, and the second following car, ω L ,ω 1 ,ω 2 The speeds of the leading car, the first following car, and the second following car in the formation. is the expected headway between the first following car and the leading car, h 01 is the actual headway between the first following car and the leading car, is the expected headway between the second following car and the leading car, h 02 is the actual headway between the second following car and the leading car, is the expected headway between the second following car and the first following car;

[0080] The control input u of the formation integrated control system in the space domain is:

[0081] u=[a L a 1 a 2 ] T

[0082] Among them, a L 、a 1 、a 2 is the expected acceleration of the formation leader car, the first following car, and the second following car;

[0083] The desired state vector of the central controller is

[0084] ξ ref =[0 0 0 0 0 0 0 0 0 0 0 0] T

[0085] Among them, the 1st, 2nd, 5th, 6th, 11th and 12th formation time and slowness state quantities have no expected values, and their error weights are set to zero.

[0086] The system dynamic equation of the formation integrated control system in the space domain is:

[0087] ξ(k+1)=A k ξ(k)+B k u(k)

[0088] Among them, A k The values ​​of are as follows:

[0089]

[0090] B k The values ​​of are as follows:

[0091]

[0092] B k It is a time-varying matrix, and its value changes with the current slowness of the vehicles in the platoon.

[0093] The formation integrated control system in the spatial domain adopts a dynamic planning interval, and the planning interval ranges from the current position of the vehicle to the stop line of the intersection.

[0094] The cost function of the formation integrated control system in the spatial domain is:

[0095]

[0096] in, δ is the traffic status at the sampling time and the historical traffic status information, x 0 is the system state at the sampling time, is the control input vector to be optimized. The first term of the cost function is the control target of the formation passing through the intersection without interruption, the second term is the formation state control error, and the third term is the ecological target. M is the weight coefficient, Q and R are weight matrices.

[0097] 2. Vehicle crossing intersection probability system

[0098] The expression formula of the vehicle passing through the intersection probability system is:

[0099]

[0100] Where δ=(δ 1 ,δ 2 ,…,δ T ) is the historical traffic status information, is the time when the controlled vehicle i is expected to pass the stop line of the intersection, is the final state value of the passing time state quantity in the planning interval in the formation integrated control system in the spatial domain, and f is the time convolutional neural network.

[0101] In this embodiment, the central controller adjusts the input to the controlled system to achieve the output of the expected value, so as to solve the optimal control instructions of each controlled vehicle in the formation in the spatial domain. The solution method is as follows: construct an optimal control problem, take the dynamic equation of the formation integrated control system as the constraint condition of the control problem, take the cost function formula as the control target of the formation integrated control system in the spatial domain, and solve the control strategy that minimizes the cost function through the gradient descent method, that is, solve J(θ,x 0 ,u * ) to achieve the optimal control strategy u * .

[0102] S5: Execute the above optimal control instructions through the power system and braking system of each controlled vehicle in the formation.

[0103] The above is only a preferred embodiment of the present invention and does not limit the present invention in any way. Any technician in the relevant technical field, without departing from the scope of the technical solution of the present invention, makes any form of equivalent replacement or modification to the technical solution and technical content disclosed in the present invention, which does not depart from the content of the technical solution of the present invention and still falls within the protection scope of the present invention.

Claims

1. A method for anti-interruption platoon driving at intersections, characterized in that: The following steps are involved: S1: Several following cars follow a leading car to form a convoy; S2: Obtain the vehicle operation status information within the upstream and downstream range of the road intersection, the operation status information of each vehicle in the formation and the status information of the induction traffic signal light through the on-board unit of the pilot vehicle, and save them in the database; S3: Establish a coordinate system, calculate the position information of each vehicle in the formation and other social vehicles in the coordinate system, and perform state variable substitution under spatial domain modeling; S4: Based on the historical status information of each vehicle in the formation, other social vehicles and traffic lights, the on-board central controller of the pilot vehicle solves the optimal control instructions of each controlled vehicle in the formation in the spatial domain through the controlled system, and transmits the optimal control instructions to the power system and braking system of each controlled vehicle; S5: Execute the optimal control instruction.

2. The anti-interruption platoon driving method for intersections according to claim 1, characterized in that: In step S1, both the pilot vehicle and the following vehicle are controlled automatic driving vehicles.

3. The anti-interruption platoon driving method for intersections according to claim 1, characterized in that: In step S2, a traffic detector on the road side is used to collect vehicle operation status information within the upstream and downstream range of the intersection through a circular coil, and transmit it to the on-board unit of the pilot vehicle through vehicle-road communication; The toroidal coil data is the state of coil j at time k, expressed as:

4. The anti-interruption platoon driving method for intersections according to claim 1, characterized in that: In step S2, the onboard unit of the pilot vehicle obtains the operating status information of each vehicle in the formation through vehicle-to-vehicle communication, and obtains the inductive traffic light status information from the traffic signal controller through vehicle-to-road communication; the inductive traffic light status is the status of the light i at time k, which is expressed as:

5. The anti-interruption platoon driving method for intersections according to claim 1, characterized in that: In step S3, the method for establishing the coordinate system is as follows: the pilot vehicle onboard unit takes the intersection stop line as the longitudinal control origin of the vehicle to establish a reference coordinate system; the state variable substitution under spatial domain modeling is to convert the state variable with time as the independent variable under time domain modeling into the state variable with spatial position as the independent variable under spatial domain modeling, and the conversion formula is: Among them, t is the time variable, s is the position variable, ω is the vehicle slowness under spatial domain modeling, v is the vehicle speed under time domain modeling, σ is a very small normal quantity, b is the vehicle slowness under spatial domain modeling, and a is the vehicle acceleration under time domain modeling.

6. The anti-interruption platoon driving method for intersections according to claim 1, characterized in that: The controlled system includes a formation integrated control system in the space domain and a vehicle crossing probability system. The system dynamic equation of the formation integrated control system in the space domain is: ξ(k+1)=A k ξ(k)+B k u(k); The vehicle passing through the intersection probability system expression is: Where δ=(δ1,δ2,…,δ T ) is the historical traffic status information, is the time when the controlled vehicle i is expected to pass the stop line of the intersection, is the final state value of the passing time state quantity in the planning interval in the formation integrated control system in the spatial domain, and f is the time convolutional neural network.

7. The anti-interruption platoon driving method for intersections according to claim 6, characterized in that: The formation integrated control system in the spatial domain adopts a dynamic planning interval, and the planning interval ranges from the current position of the vehicle to the stop line of the intersection.

8. The anti-interruption platoon driving method for intersections according to claim 6, characterized in that: ξ is the state vector, u is the control input; When the number of vehicles in the formation is 3, ξ is: Among them, t L , t1, t2 are the times of the formation leader car, the first following car, and the second following car, ω L , ω1, ω2 are the slowness of the formation leader car, the first following car, and the second following car, is the expected headway between the first following car and the leading car, h 01 is the actual headway between the first following car and the leading car, is the expected headway between the second following car and the leading car, h 02 is the actual headway between the second following car and the leading car, is the expected headway between the second following car and the first following car; The control input u is: <h2 style=";text-align:left;direction:ltr">u=[a<h2 style=";text-align:left;direction:ltr"> L <h2 style=";text-align:left;direction:ltr"> a1 a2]<h2 style=";text-align:left;direction:ltr"> T Among them, a L , a1, a2 are the expected accelerations of the formation leader car, the first following car, and the second following car; The desired state vector of the central controller is x ref =[0 0 0 0 0 0 0 0 0 0 0 0] T Among them, the 1st, 2nd, 5th, 6th, 11th and 12th formation time and slowness state quantities have no expected values, and their error weights are set to zero.

9. The anti-interruption platoon driving method for intersections according to claim 1, characterized in that: In the system dynamic equation of the formation integrated control system in the spatial domain, A k The values ​​of are as follows: B k The values ​​are as follows B k It is a time-varying matrix, and its value changes with the current slowness of the vehicles in the platoon.

10. The anti-interruption platoon driving method for intersections according to claim 1, characterized in that: The cost function of the formation integrated control system in the spatial domain is: in, δ is the traffic status and historical traffic status information at the sampling time, x0 is the system status at the sampling time, is the control input vector to be optimized. The first term of the cost function is the control target of the formation passing through the intersection without interruption, the second term is the formation state control error, the third term is the ecological target, M is the weight coefficient, Q and R are weight matrices.

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