A mixed traffic group hierarchical cooperative control method for signal intersection area

Abstract

The application relates to a mixed traffic group hierarchical cooperative control method for a signal intersection area, and belongs to the mixed traffic organization and mixed vehicle control field. The method comprises the following steps: establishing a mixed traffic scene of the signal intersection area; constructing a sub-group division and sub-group reorganization strategy of the signal intersection area; establishing a leading networked automatic vehicle model and a traditional human-driven vehicle model; and establishing a hierarchical cooperative control strategy based on a distributed MPC based on the perspective of a traffic information physical system, wherein the hierarchical cooperative control strategy comprises an upper controller and a lower controller. The application can improve the overall traffic efficiency and safety of the signal intersection area, and can provide the advantages of a new perspective for solving new mixed traffic congestion and safety.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of mixed traffic organization and mixed vehicle control, and the field of cooperative control of networked automatic vehicles and traditional human-driven vehicles at signal intersection areas in mixed traffic, and particularly relates to a mixed traffic group hierarchical cooperative control method for signal intersection areas. BACKGROUND

[0002] In the development process of networked automatic driving vehicles and communication technology, mixed traffic composed of networked automatic vehicles and traditional human-driven vehicles will continue for a long time in the future. However, due to the uncontrollability and randomness of traditional human-driven vehicles, this will lead to frequent stop-and-go phenomenon. In this case, when the traffic flow is relatively large, traffic collisions are likely to occur, which further leads to decreased traffic efficiency and increased fuel emissions. At the same time, the above problems are more obvious at signal intersection areas, because vehicles are often constrained by traffic signals during driving.

[0003] In addition, networked automatic vehicles can obtain information of surrounding vehicles through vehicle-to-vehicle communication and vehicle-mounted sensors to automatically control their own state, while traditional human-driven vehicles are uncontrollable, and they can only track the state of the adjacent vehicle in front according to the information perceived by the driver. In this case, it can be considered that networked automatic vehicles randomly penetrate between traditional human-driven vehicles to indirectly guide the driving behavior of traditional human-driven vehicles, which can be regarded as a mixed traffic group (i.e., one networked automatic vehicle can indirectly lead the driving state of multiple traditional human-driven vehicles behind it). At the same time, at different green light phases at signal intersection areas, vehicles pass through the signal intersection area in groups after groups. Through review of relevant literature and patents, few documents study the hierarchical cooperative control problem of mixed traffic groups at signal intersection areas. Therefore, how to more effectively control mixed traffic groups to pass through signal intersection areas is a problem to be studied. SUMMARY

[0004] Therefore, the purpose of the present application is to provide a mixed traffic group hierarchical cooperative control method for signal intersection areas, which avoids the stop-and-go phenomenon of vehicles under the constraint of traffic signals at signal intersection areas, to ensure the maximum number of vehicles passing through signal intersection areas and the maximum fuel efficiency under mixed traffic conditions. The method can effectively ensure that mixed traffic groups pass through signal intersection areas consistently and stably under the constraint of traffic signals, thereby improving the overall traffic efficiency at signal intersection areas.

[0005] To achieve the above purpose, the present application provides the following technical solutions:

[0006] A mixed traffic group hierarchical cooperative control method for signal intersection areas, the method comprising the following steps:

[0007] S1, a signalized intersection area mixed traffic scenario is established;

[0008] S2, a signalized intersection area subgroup division and subgroup reorganization strategy is constructed;

[0009] S3, a leading connected automated vehicle model and a traditional human-driven vehicle model are established;

[0010] S4, a hierarchical collaborative control strategy based on distributed MPC is established based on the perspective of traffic information physical system, including an upper controller and a lower controller.

[0011] Further, in step S1, m connected automated vehicles and n traditional human-driven vehicles are included on a single lane at the signalized intersection area to form a mixed vehicle group, and m+n=N represents the total number of vehicles; the connected automated vehicles transmit information to each other through V2V communication, including position, speed, and acceleration information, and measure the speed and distance of the adjacent traditional human-driven vehicle in front through the vehicle-mounted sensor; the traditional human-driven vehicle obtains the state information of the adjacent vehicle in front through human perception; the connected automated vehicle obtains the signal phase information of the traffic signal through V2I communication, and the traditional human-driven vehicle obtains the signal phase information of the traffic signal through human perception, and the signal phase information includes signal phase, remaining red light time, and remaining green light time.

[0012] Further, in step S2, S21, a subgroup division strategy is constructed; and S22, a signalized intersection area subgroup reorganization strategy is established.

[0013] In step S21, under mixed traffic conditions, one connected automated vehicle is the leading vehicle, and the traditional human-driven vehicles follow one after another to automatically form a small group, which is called a subgroup; in this case, a mixed vehicle group upstream of the signalized intersection area is regarded as including multiple subgroups; if one connected automated vehicle is followed by one or more connected automated vehicles, in this case, the connected automated vehicle is regarded as a separate subgroup; the initial state of the subgroup is described as:

[0014]

[0015] In the formula, P l ,V l ,A l and L l represent the position, speed, acceleration, and length of the lth subgroup, represents the position of the leading connected automated vehicle in the lth subgroup, v l,i and a l,i represent the speed and acceleration of the ith vehicle in the lth subgroup, q represents the number of vehicles in the lth subgroup, p l,T represents the tail vehicle in the lth subgroup, and len represents the length of the vehicle.

[0016] Further, in step S22, specifically comprising the following steps:

[0017] Step S221: The traffic signal transmits the remaining red light time and green light time to the leading connected car through V2I communication, and the driver perceives the remaining red light time and green light time through the human eye, which are marked as T r and T g , respectively. The number of subgroups is determined according to the number of connected cars;

[0018] Step S222: Estimate the time when the lth subgroup can pass the near signal area; the heterogeneous vehicles in the lth subgroup estimate the time to pass the signal intersection area according to the current state at time t and the position p stop of the stop line in the signal intersection area, and the time to pass the signal intersection area is:

[0019]

[0020] Step S223: Subgroup reorganization; if the heterogeneous vehicles in the lth subgroup obtain the remaining red light time and green light time, and and , the lth subgroup and the (l-1)th subgroup are reorganized to form a new subgroup.

[0021] Further, in step S3, the leading connected car model in the subgroup is:

[0022] The state space of the connected car in the lth subgroup is represented as:

[0023]

[0024] In the formula, wherein represent the position, speed and acceleration of the leading connected car in the lth subgroup at time t, respectively; represents the speed of the tail car in the (l-1)th subgroup at time t, and

[0025]

[0026] represents the control input of the leading connected car in the lth subgroup at time t, which is represented as:

[0027]

[0028] In the formula, is the acceleration derivative of the leading connected car in the lth subgroup and serves as the control input of the connected car; k lj,p ,k lj,v and k lj,acontrol gain representing the position difference, velocity difference and acceleration difference between the leading connected automated vehicle in the lth subgroup and the leading connected automated vehicle in the jth subgroup; p #,0 (t), v #,0 (t), a #,0 (t) (# = j, l) represents the position, velocity and acceleration of the leading vehicle in the #th subgroup, represents the desired inter-vehicle distance of the connected automated vehicles;

[0029] If the preceding vehicle of the leading connected automated vehicle in the lth subgroup is a connected automated vehicle, the state space of the connected automated vehicle in the lth subgroup is rewritten as:

[0030]

[0031] Further, in step S3, the conventional human-driven vehicle model is:

[0032] The ith conventional human-driven vehicle model in the lth subgroup is represented as:

[0033]

[0034] wherein, and respectively represent the position and velocity of the ith conventional human-driven vehicle in the lth subgroup, represents the inter-vehicle distance between the ith conventional human-driven vehicle and its preceding adjacent vehicle i-1 in the lth subgroup, wherein * represents a conventional human-driven vehicle or a leading connected automated vehicle, and ξ1 and ξ2 represent the response parameters of the driver in the conventional human-driven vehicle; represents the velocity of the i-1th vehicle in the lth subgroup at time t;

[0035] The estimated inter-vehicle distance and velocity error of the ith conventional human-driven vehicle in the lth subgroup through human perception are represented as:

[0036]

[0037] wherein, and respectively represent the estimated inter-vehicle distance and velocity error of the ith conventional human-driven vehicle in the lth subgroup, and respectively represent the position and velocity of the ith conventional human-driven vehicle in the lth subgroup, and respectively represent the driver's estimation of the position and velocity of the preceding vehicle i-1 of the ith conventional human-driven vehicle in the lth subgroup;

[0038] The ith conventional human-driven vehicle model in the lth subgroup is then rewritten as:

[0039]

[0040] The estimated position and velocity can be calculated as:

[0041]

[0042] Further, in step S4, comprising:

[0043] S41: constructing a signal intersection area upper controller based on distributed MPC;

[0044] S42: establishing a lower controller between the signal intersection area subgroups.

[0045] Further, in step S41, comprising the following steps:

[0046] Step S411: inputting the input sequence into the prediction model in the prediction time domain, and giving the prediction output response at time t; to ensure the overall optimization of the driving state of all member vehicles in each subgroup in the current time and the future prediction time domain, the objective function of each subgroup is represented as:

[0047]

[0048] In the formula, τ represents the prediction step, N p represents the prediction time domain; represents the inter-vehicle distance tracking error, speed tracking error, acceleration tracking error and control increment of the ith vehicle in the lth subgroup, wherein * represents a connected automatic vehicle or a traditional human-driven vehicle; ψ is the weight coefficient of each item;

[0049] Step S412: solving the optimal control input sequence according to the defined objective function, and taking the first value of the input sequence as the control input to control the current vehicle system; the optimal control solving problem is represented as:

[0050]

[0051] Before solving the problem, the terminal time The terminal time is represented as:

[0052]

[0053] The desired speed is defined as the maximum speed of the vehicle passing through the near signal area, and the desired acceleration when all vehicles reach the steady state is determined as 0 m / s 2 ;

[0054] Step S413: At the next time, the model predictive controller corrects the model according to the deviation between the prediction result of the previous step and the actual output result of the system, and solves the optimal control input sequence again by using the objective function and the constraint condition, and takes the first value of the input control sequence as the control input.

[0055] Further, in step S42, the following steps are included:

[0056] Step S421: The lower-level controller of the subgroup is designed as:

[0057]

[0058] where u l (t) represents the control input of the lth subgroup, K l,P , K l,V , K l,A represents the control gain of the lth subgroup, t hd represents the desired headway between subgroups; K l,P , K l,V , K l,A The control gain of the lth subgroup is approximately equal to the control gain of the leading connected automated vehicle.

[0059] Step S422: The position and speed of the lth subgroup at the next time are calculated as:

[0060]

[0061] where Δt is the sampling period;

[0062] The position and speed of the traditional human-driven vehicle in the lth subgroup are updated as:

[0063]

[0064] Further, the driving behavior of heterogeneous vehicles in the signal intersection area is described in the physical space, and the current vehicle adjusts its driving state by obtaining information of other vehicles and traffic signal phase information from the information space; the mapping relationship between vehicles and traffic signal lights in the physical space, and the topological changes between vehicles and traffic lights through V2V communication, V2I communication, human perception and vehicle-mounted sensors in different time periods are described in the information space; the upper-level controller is used to ensure that the driving state of all member vehicles in each subgroup is optimal as a whole in the current time and the future prediction time domain under the mutual mapping of the information space and the physical space, and the lower-level controller is used to cooperatively control the subgroup that does not pass through the near signal area in the mixed vehicle group of the information space and the physical space.

[0065] The physical space is used for describing the driving behavior of heterogeneous vehicles in the signal intersection area, and the current vehicle adjusts its driving state by obtaining the information of other vehicles and the phase information of the traffic signal lamp from the information space; the information space is used to describe the mapping relationship of the vehicles and the traffic signal lamp in the physical space, and the topology change of the information obtained by V2V communication, V2I communication, human perception and vehicle-mounted sensor between the vehicle and the traffic light in different time periods.

[0066] The beneficial effects of the present application are:

[0067] The present application uses communication technology, automatic driving technology and car-road cooperation technology to obtain the state information of heterogeneous vehicles and the phase information of signal lights as control input, and designs a mixed traffic group hierarchical cooperative control method for signal intersection area, which can improve the overall traffic efficiency and safety of signal intersection area, and can provide the advantages of new perspective for solving new type of mixed traffic congestion and safety.

[0068] Other advantages, objects and features of the present application will be set forth in part in the following specification taken in conjunction with the accompanying drawings, and in part will become apparent to those skilled in the art from a consideration of the following specification and from the practice of the present application. The objects and other advantages of the present application can be realized and attained by the below description. BRIEF DESCRIPTION OF DRAWINGS

[0069] In order to make the objects, technical solutions and advantages of the present application clearer, the preferred detailed description of the present application will be combined with the drawings as follows, wherein:

[0070] Fig. 1 It is a mixed traffic scene schematic diagram under the signal intersection area of the present application;

[0071] Fig. 2 It is a mixed traffic group division multiple subgroups schematic diagram of the present application:

[0072] Fig. 3 It is a mixed traffic group hierarchical cooperative control method schematic diagram of the present application for signal intersection area based on MPC. DETAILED DESCRIPTION

[0073] The present application is further illustrated by the following specific examples that will enable practitioners in the art to make and use the application. Numerous modifications and adaptations will be apparent to those skilled in the art, without departing from the spirit and scope of the application. The examples provided below are merely illustrative of the basic ideas of the present application, and the following examples and features in the examples can be combined with each other without conflict, if necessary.

[0074] The accompanying drawings are included to provide a further understanding of the application, and are incorporated in and constitute apart of this specification. The drawings illustrate embodiments of the present application and, together with the description, serve to explain the principles of the present application. In the drawings:

[0075] The same or similar reference numerals in the drawings of the embodiments of the present application correspond to the same or similar components; in the description of the present application, it should be understood that the orientation or position relationship indicated by the terms "upper", "lower", "left", "right", "front", "back", etc. is based on the orientation or position relationship shown in the drawings, and is only for the convenience of describing the present application and simplifying the description, and does not indicate or imply that the device or element referred to must have a particular orientation, be constructed and operated in a particular orientation, therefore the terms describing the position relationship in the drawings are only for illustrative purposes, and cannot be understood as a limitation of the present application, for those skilled in the art, the specific meaning of the above terms can be understood according to the specific circumstances.

[0076] Please refer to Figs. 1-3 , a mixed traffic group hierarchical cooperative control method for signal intersection area.

[0077] The mixed traffic group hierarchical cooperative control method for signal intersection area of the present application comprises the following steps:

[0078] S1: setting a mixed traffic scene of signal intersection area;

[0079] S2: in order to depict the formation mode and internal mechanism of mixed traffic group, a sub-group division and sub-group reorganization method of signal intersection area is constructed;

[0080] S3: in order to distinguish the difference between the network-connected automatic vehicle and the traditional human-driven vehicle in the sub-group, a leading network-connected automatic vehicle model and a traditional human-driven vehicle model considering the human factors such as the driver's insensitivity to the distance and speed of the front vehicle are established;

[0081] S4: To ensure the maximum number of vehicles passing through the signalized intersection area and the shortest travel time under the constraint of traffic signal, a hierarchical cooperative control method based on distributed MPC is established from the perspective of cyber-physical systems.

[0082] Further, in step S1, a mixed traffic group is formed on a single lane at the signalized intersection area, which contains m connected and automated vehicles and n traditional human-driven vehicles, and m+n=N represents the total number of vehicles, as shown in Fig. 1 Under such a scenario, connected and automated vehicles can exchange information such as position, speed, and acceleration through V2V communication, and measure the speed and distance of the adjacent traditional human-driven vehicle in front through vehicle-mounted sensors (e.g., laser radar, millimeter wave radar). Traditional human-driven vehicles can only obtain the state information of the vehicle in front through human perception (eyes). At the same time, connected and automated vehicles can obtain phase information such as signal phase, remaining red light time, and remaining green light time of the traffic signal through V2I communication, and traditional human-driven vehicles can obtain information such as signal phase of the traffic signal through human perception.

[0083] Further, in step S2, the following steps are included:

[0084] S21: Constructing a sub-group division method;

[0085] S22: Establishing a signalized intersection area sub-group reorganization method.

[0086] Further, in step S21, under mixed traffic conditions, due to the advantages of connected and automated vehicles, a small group is automatically formed with a connected and automated vehicle as the leading vehicle and traditional human-driven vehicles following one after another, which is called a sub-group. In this case, a mixed traffic group upstream of the signalized intersection area can be regarded as being composed of multiple sub-groups, as shown in Fig. 2 If one connected and automated vehicle is followed by one or more connected and automated vehicles, in this case, the connected and automated vehicle is regarded as a separate sub-group. In order to conveniently describe and analyze the driving behavior of the sub-group, the initial state of the sub-group is described as:

[0087]

[0088] In the formula, P l , V l , A l , and L l represent the position, speed, acceleration, and length of the lth sub-group, represents the position of the leading connected and automated vehicle in the lth sub-group, v l,i and a l,i represent the speed and acceleration of the ith vehicle in the lth sub-group, q represents the number of vehicles in the lth sub-group, p l,TTail vehicle in the lth subgroup, len represents the length of the vehicle.

[0089] Further, in step S22, in order to make each subgroup pass through the signal intersection area quickly without stopping, recombination is needed between subgroups to make them pass through the signal intersection area quickly in the next green light phase. The specific steps are as follows:

[0090] Step S221: The traffic signal transmits the remaining red light time and green light time to the leading connected automatic vehicle through V2I communication, and the driver perceives the remaining red light time and green light time through the human eye. The remaining red light time and green light time are marked as T r and T g , respectively. The number of subgroups is determined by the number of connected automatic vehicles.

[0091] Step S222: Estimate the time for the lth subgroup to pass through the near signal area. The heterogeneous vehicles in the lth subgroup estimate the time to pass through the signal intersection area according to the current state at time t and the position p stop of the stop line in the signal intersection area.

[0092]

[0093] Step S223: Subgroup recombination. The heterogeneous vehicles in the lth subgroup recombine the lth subgroup and the l-1th subgroup to form a new subgroup according to the obtained remaining red light time and green light time, if and , and use the proposed control strategy to make the new subgroup continuously pass through the signal intersection area without stopping in the next phase cycle.

[0094] Further, in step S3, the following steps are included:

[0095] S31: Establish a model of the leading connected automatic vehicle in the subgroup;

[0096] S32: Establish a model of the traditional human-driven vehicle in the subgroup considering human factors.

[0097] Further, in step S31, the connected automatic vehicle in the lth subgroup obtains the information of the connected automatic vehicles in other subgroups through V2V communication, and measures the speed and distance of the adjacent traditional human-driven vehicle in front through the vehicle-mounted sensor. Then, the state space of the connected automatic vehicle in the lth subgroup can be represented as:

[0098]

[0099] In the formula, where x v

[0100]

[0101] u

[0102]

[0103] where is the acceleration derivative of the lead CAV in the lth platoon, also known as jerk, and is the control input of the lead CAV in the lth platoon. lj,p ,k lj,v and k lj,a are the control gains of the position difference, velocity difference, and acceleration difference between the lead CAV in the lth platoon and the lead CAV in the jth platoon. #,0 (t), v #,0 (t), a #,0 (t) (# = j, l) are the position, velocity, and acceleration of the lead vehicle in the #th platoon, is the desired inter-vehicle distance of the CAVs. If the front vehicle of the lead CAV in the lth platoon is a CAV, then the state space of the CAV in the lth platoon is rewritten as:

[0104]

[0105] Further, in step S32, the conventional human-driven vehicle in the lth platoon can only obtain the state information of the adjacent vehicle in front of it through the eyes of the driver, and the conventional human-driven vehicle is uncontrollable. Therefore, the model of the ith conventional human-driven vehicle in the lth platoon can be represented as:

[0106]

[0107] where and are the position and velocity of the ith conventional human-driven vehicle in the lth platoon, respectively, is the inter-vehicle distance between the ith conventional human-driven vehicle and the adjacent vehicle i-1 in front of it in the lth platoon, where * represents the conventional human-driven vehicle or the lead CAV, and ξ1 and ξ2 represent the response parameters of the driver in the conventional human-driven vehicle. is the velocity of the i-1th vehicle in the lth platoon at time t.

[0108] The speed and position of the preceding vehicle obtained by the driver in the traditional human-driven vehicle can be regarded as the state estimated by human perception. Therefore, the estimated inter-vehicle distance and speed error of the ith traditional human-driven vehicle in the first subgroup can be expressed as:

[0109]

[0110] wherein, and respectively represent the estimated inter-vehicle distance and speed error of the ith traditional human-driven vehicle in the first subgroup, and respectively represent the position and speed of the ith traditional human-driven vehicle in the first subgroup, and respectively represent the position and speed estimated by the driver of the ith traditional human-driven vehicle in the first subgroup for the preceding vehicle i-1. Then, the model of the ith traditional human-driven vehicle in the first subgroup can be rewritten as:

[0111]

[0112] The estimated position and speed can be calculated as:

[0113]

[0114] Further, in step S4, specifically comprising:

[0115] S41: constructing a top-level controller based on distributed MPC for the signal intersection area

[0116] S42: establishing a lower-level controller between subgroups in the signal intersection area

[0117] Further, in step S41, in order to ensure that the driving state of all member vehicles in each subgroup is optimal as a whole in the current time and the future prediction time domain, a top-level controller based on distributed MPC is constructed for the signal intersection area, and the specific steps are:

[0118] Step S411: input the input sequence into the prediction model in the prediction time domain, and give the prediction output response at time t. In order to ensure the overall optimization of the driving state of all member vehicles in each subgroup in the current time and the future prediction time domain, the objective function of each subgroup can be expressed as:

[0119]

[0120] wherein, τ represents the prediction step, N p represents the prediction time domain. denotes the inter-vehicle distance tracking error, speed tracking error, acceleration tracking error and control increment of the i-th vehicle in the l-th subgroup, where * denotes the connected automated vehicle or the traditional human-driven vehicle. ψ is the weight coefficient of each term.

[0121] Step S412: Solve the optimal control input sequence according to the defined objective function, and take the first value of the input sequence as the control input to control the current vehicle system. The optimal control solving problem is to obtain the optimal control sequence In MPC, the first parameter value of the optimal control sequence is input as the control quantity to the controlled vehicle system. This optimal control solving problem can be expressed as:

[0122]

[0123] Before solving the problem, the terminal time The terminal time can be expressed as:

[0124]

[0125] In addition, the expected speed and expected acceleration of the vehicle passing through the signal intersection area also need to be determined. In order to ensure that all vehicles in the mixed vehicle group can pass through the signal intersection area without stopping, and to minimize fuel consumption and travel time, the expected speed is defined as the maximum speed of the vehicle passing through the signal area, and the expected acceleration of all vehicles reaching the steady state is determined as 0 m / s 2 .

[0126] Step S413: At the next time, the model predictive controller feedbacks and corrects the model according to the deviation between the prediction result of the last step and the actual output result of the system, and solves the optimal control input sequence again using the objective function (5.22) and the constraint condition, and takes the first value of the input control sequence as the control input.

[0127] Further, in step S42, the lower-level controller between the subgroups in the signal intersection area is established, and the specific steps are as follows:

[0128] Step S421: The lower-level control mainly solves the cooperative control problem between subgroups before the mixed vehicle group passes through the signal intersection area, so the controller of the subgroup can be designed as:

[0129]

[0130] In the formula, u l (t) represents the control input of the l-th subgroup, K l,P , K l,V , K l,A represents the control gain of the l-th subgroup, t hdThis represents the expected headway between subgroups. Since the leading connected autonomous vehicle within each subgroup needs to synchronize with the state of each subgroup to ensure that the driving state of traditional human-driven vehicles within each subgroup can quickly track the state of the leading connected autonomous vehicle, K... l,P K l,V K l,A The control gain is approximately equal to the control gain of the leading connected autonomous vehicle.

[0131] Step S422: Calculate the position and velocity of the l-th subgroup at the next time step:

[0132]

[0133] In the formula, Δt is the sampling period.

[0134] To ensure that the l-th subgroup can pass through the signalized intersection area during the green light phase, traditional pedestrians and vehicles within the l-th subgroup need to follow the driving status of the vehicle in front of them. Therefore, the position and speed of traditional pedestrians and vehicles within the l-th subgroup are updated as follows:

[0135]

[0136] Fig. 3 A schematic diagram of a hierarchical cooperative control method for mixed-traffic groups based on MPC is shown for signalized intersection areas. Fig. 3 In this context, the physical space primarily depicts the driving behavior of heterogeneous vehicles in the signalized intersection area. Each vehicle adjusts its driving state by acquiring information from other vehicles and traffic light phase information from the information space to pass through the signalized intersection area without stopping. Fig. 3 In this context, the information space depicts the mapping relationship between vehicles and traffic lights in the physical space, as well as the topological changes between vehicles and traffic lights at different time periods through V2V communication, V2I communication, human perception, and information acquired by onboard sensors. The upper-level controller ensures that the overall driving state of all member vehicles in each subgroup is optimal in the current and future prediction time domains, based on the mutual mapping between the information and physical spaces. The lower-level controller primarily addresses the cooperative control problem between subgroups in the mixed traffic flow between the information and physical spaces before they pass the near-signal zone.

[0137] This invention utilizes communication technology, autonomous driving technology, and vehicle-road cooperative technology to obtain the state information of heterogeneous vehicles and the phase information of traffic lights as control inputs. It designs a hierarchical cooperative control method for mixed traffic groups in signalized intersection areas. This method can improve the overall traffic efficiency and safety in signalized intersection areas and provides a new perspective for solving new types of mixed traffic congestion and safety.

[0138] Finally, it is to be explained that the above embodiments are only used to illustrate the technical solutions of the present application but not to limit the present application. Although the present application is described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or equivalently replaced without departing from the purpose and scope of the technical solutions, and all should be covered in the scope of the claims of the present application.

Claims

1. A hierarchical cooperative control method for mixed-traffic groups in signalized intersection areas, characterized in that: The method includes the following steps: S1. Establish a mixed traffic scenario in the signalized intersection area; S2. Construct a strategy for subgrouping and reorganizing signalized intersection areas; S3. Establish leading connected autonomous vehicle models and traditional human-driven vehicle models; S4. Establish a hierarchical collaborative control strategy based on distributed MPC from the perspective of traffic cyber-physical systems, including upper-level controllers and lower-level controllers; Step S2 includes: S21, constructing a subgroup partitioning strategy; S22, establishing a subgroup reorganization strategy for the signalized intersection area; In step S21, under mixed traffic conditions, a small group automatically forms with a connected autonomous vehicle as the lead vehicle and traditional human-driven vehicles following one after another, which is called a subgroup. In this case, a mixed traffic group upstream of the signalized intersection area is considered to include multiple subgroups. If a connected autonomous vehicle is followed by one or more connected autonomous vehicles, in this case, the connected autonomous vehicle is considered to be a single subgroup. Step S22 specifically includes the following steps: Step S221: The traffic light transmits the remaining red and green light times to the leading connected autonomous vehicle via V2I communication. The driver perceives the remaining red and green light times visually, and these times are marked as T. r and T g The number of subgroups is determined based on the number of connected autonomous vehicles; Step S222: Estimate the time it takes for the l-th subgroup to pass through the near-signal zone; heterogeneous vehicles within the l-th subgroup are assessed based on their current state at time t and the position p of the stop line at the signalized intersection. stop The estimated time to travel through the signalized intersection area is: Among them, P l (t),V l (t) represents the position and velocity of the l-th subgroup at time t, respectively, and p l,T (t), v l,T (t) represents the position and velocity of the last car in the l-th subgroup at time t, respectively; p l,0 (t), v l,0 (t) represents the position and velocity of the lead car in the l-th subgroup at time t; Step S223: Subgroup Reorganization; Heterogeneous vehicles in the l-th subgroup, based on the obtained remaining red and green light times, if... and Then the l-th subgroup and the (l-1)-th subgroup are recombined to form a new subgroup; Step S4 includes: S41: Construct an upper-layer controller for the signalized intersection area based on distributed MPC; S42: Establish lower-level controllers between subgroups of signalized intersection areas; The driving behavior of heterogeneous vehicles in the signalized intersection area is characterized by physical space, and the current vehicle adjusts its own driving state by obtaining information from other vehicles and traffic light phase information from the information space. The information space is used to depict the mapping relationship between vehicles and traffic lights in the physical space, as well as the topological changes between vehicles and traffic lights at different time periods through V2V communication, V2I communication, human perception and on-board sensors. The upper-level controller is used to ensure that the overall driving status of all member vehicles in each subgroup is optimal in the current time and future prediction time domain under the mutual mapping between information space and physical space. The lower-level controller is used to perform coordinated control on the subgroups that have not passed the near signal zone in the mixed traffic group of information space and physical space.

2. The hierarchical cooperative control method for mixed-traffic groups in signalized intersection areas according to claim 1, characterized in that: In step S1, a mixed traffic group consisting of m connected autonomous vehicles and n traditional human-driven vehicles is formed on a single lane in the signalized intersection area, where m+n=N represents the total number of vehicles. The connected autonomous vehicles exchange information with each other via V2V communication, including position, speed, and acceleration information, and measure the speed and distance of adjacent traditional human-driven vehicles in front of them through onboard sensors. The traditional human-driven vehicles obtain the status information of the adjacent vehicles in front of them through human perception. The connected autonomous vehicles obtain the signal phase information of the traffic lights through V2I communication, and the traditional human-driven vehicles obtain the signal phase information of the traffic lights through human perception. The signal phase information includes the signal phase, the remaining red light time, and the remaining green light time.

3. The hierarchical cooperative control method for mixed-traffic groups in signalized intersection areas according to claim 2, characterized in that: In step S3, the leading connected autonomous vehicle model within the subgroup is: The state space representation of the connected autonomous vehicles in the l-th subgroup is as follows: In the formula, in Let represent the position, velocity, and acceleration of the leading connected autonomous vehicle in the l-th subgroup at time t, respectively; Let represent the velocity of the last car in the (l-1)th subgroup at time t, and The control input of the leading connected automated vehicle in the l-th subgroup at time t is expressed as: In the formula, Let k be the acceleration derivative of the leading connected autonomous vehicle in the l-th subgroup, and serve as the control input for the connected autonomous vehicle; lj,p ,k lj,v and k lj,a p represents the control gain representing the position, velocity, and acceleration differences between the leading connected autonomous vehicle in the l-th subgroup and the leading connected autonomous vehicle in the j-th subgroup; #,0 (t), v #,0 (t), a #,0 (t) represents the position, velocity, and acceleration of the lead car in the #th subgroup, where # = j, l; This indicates the desired vehicle spacing for connected autonomous vehicles. If the vehicle preceding the leading connected autonomous vehicle in the l-th subgroup is also a connected autonomous vehicle, then the state space of the connected autonomous vehicles in the l-th subgroup is rewritten as follows:

4. The hierarchical cooperative control method for mixed-traffic groups in signalized intersection areas according to claim 3, characterized in that: In step S3, the traditional human-driven car model is as follows: The i-th traditional human-driven vehicle model in the l-th subgroup is represented as: In the formula, and Let i represent the position and speed of the i-th traditional human-driven vehicle in the l-th subgroup, respectively. This represents the distance between the i-th traditional human-driven vehicle and the i-1 adjacent vehicles in front of it in the l-th subgroup, obtained through human eyes, where * represents traditional human-driven vehicle or leading connected autonomous vehicle, and ξ1 and ξ2 represent the driver's response parameters in traditional human-driven vehicle. This represents the speed of the (i-1)th vehicle in the l-th subgroup at time t; The errors in the distance and speed estimated by human perception for the i-th traditional human-driven vehicle in the l-th subgroup are expressed as follows: In the formula, and Let represent the estimated distance between vehicles and the speed error of the i-th traditional human-driven vehicle within the l-th subgroup, respectively. and Let these represent the position and speed of the i-th traditional human-driven vehicle within the l-th subgroup. and Let represent the driver's estimation of the position and speed of the preceding vehicle i-1 in the i-th traditional human-driven vehicle within the l-th subgroup; Then the i-th traditional human-driven vehicle model in the l-th subgroup is rewritten as: in, This represents the traditional driving expectation of vehicle spacing; The estimated position and velocity can be calculated as follows: in, Let represent the acceleration of the i-th conventional human-driven vehicle in the l-th subgroup.

5. A hierarchical cooperative control method for mixed-traffic groups in signalized intersection areas according to claim 4, characterized in that: Step S41 includes the following steps: Step S411: Input the input sequence into the prediction model in the prediction time domain and provide the predicted output response at time t; with the goal of ensuring the overall optimization of the driving state of all member vehicles in each subgroup in the current time and future prediction time domain, the objective function of each subgroup is expressed as: In the formula, τ represents the prediction step size, and N p Indicates the prediction time domain; Let represent the vehicle spacing tracking error, speed tracking error, acceleration tracking error, and control increment of the i-th vehicle in the l-th subgroup, where * indicates connected autonomous vehicle or traditional human-driven vehicle; ψ1, ψ2, ψ3, ψ4, ψ5, ψ6, and ψ7 are the weight coefficients for each item; Step S412: Solve for the optimal control input sequence according to the defined objective function, and use the first value of the input sequence as the control input to control the current vehicle system; the optimal control problem is expressed as: Before solving the problem, the terminal time needs to be calculated. Terminal time is expressed as: a l,i (t) represents the acceleration of the i-th vehicle in the l-th subgroup at time t. The desired speed is defined as the maximum speed of the vehicle when passing through the near-signal zone, and the desired acceleration when all vehicles reach steady state is determined to be 0 m / s². 2 ; Step S413: At the next moment, the model predictive controller performs feedback correction on the model based on the deviation between the prediction result of the previous step and the actual output result of the system, and solves the optimal control input sequence again using the objective function and constraints, and takes the first value of the input control sequence as the control input. Step S42 includes the following steps: Step S421: The lower-level controller of the subgroup is designed as follows: In the formula, A j (t), A l (t) represents the acceleration of the j-th subgroup and the l-th subgroup, respectively, u l (t) represents the control input of the l-th subgroup, K l,P K l,V K l,A t represents the control gain of the l-th subgroup. hd K represents the expected headway between subgroups; l,P K l,V K l,A The control gain is approximately equal to the control gain of the leading connected autonomous vehicle. Step S422: Calculate the position and velocity of the l-th subgroup at the next time step: In the formula, Δt is the sampling period; The position and speed of traditional human drivers within the l-th subgroup are updated as follows:

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