A method for controlling autonomous vehicles and pedestrians at an intersection

Abstract

The present invention discloses a method for controlling an autonomous vehicle and pedestrians at an intersection, comprising the following steps: S1, using the Weibull probability distribution function to model the length of pedestrian queues, and estimating the maximum length of the queues of pedestrians waiting at the crosswalk; S2, calculating the maximum pressure of each entrance road and crosswalk at the intersection based on the maximum length of pedestrian queues; S3, combining the maximum pressure control method with the existing intersection autonomous vehicle trajectory planning algorithm, and performing constraint optimization on the trajectory of the autonomous vehicle. The method for controlling an autonomous vehicle and pedestrians at an intersection provided by the present invention can allow the simultaneous activation of crosswalks and non-conflicting autonomous vehicle movements, effectively reducing the delay time and queue length of autonomous vehicles and pedestrians at the intersection.

Description

Technical Field

[0001] The present invention relates to a driving control method, and in particular to an intersection automatic driving vehicle and pedestrian control method. Background Art

[0002] Intersections are key nodes and bottlenecks in urban road traffic networks. Pedestrians and vehicles share road space and compete for the right of way, which will cause a large number of queues and delays for pedestrians and vehicles. Therefore, improving the operation efficiency of intersections plays a vital role in improving traffic congestion and safety. At present, signal phases are usually used to separate multiple conflicting traffic flows at intersections. In order to further reduce delays and queues for pedestrians and vehicles, signal control methods have gradually shifted from fixed signal control to adaptive signal control, dynamically adjusting the time allocation of each signal phase according to traffic demand to achieve the optimal control effect. With the development of vehicle networking and autonomous driving technology, in the near future, autonomous vehicles will share intersection space with pedestrians. In a traffic environment with many pedestrians and unclear right of way, the existing autonomous vehicle control technology cannot effectively solve the problem of right of way allocation with pedestrians, and has to slow down or stop frequently, which seriously reduces the operation efficiency and stability of autonomous vehicle traffic flow. In addition to signal control, the movement of autonomous vehicles at intersections also needs to be controlled by motion trajectory planning methods. Therefore, it is necessary to reconstruct the control method of autonomous vehicles and pedestrians at intersections by integrating signal control and autonomous vehicle motion trajectory planning methods.

[0003] Although the current automatic intersection management (AIM) method can help self-driving cars with motion planning, the AIM control method optimizes control based on the position and speed information of the self-driving car at the intersection, and does not take pedestrians into consideration. Therefore, the optimized motion trajectory of the self-driving car is only applicable to situations where there are no pedestrians crossing the street. The existing method cannot help manage self-driving cars and pedestrians at intersections. In order to improve the delay of self-driving cars, the headway between vehicles after the AIM control method is optimized is usually very small, and pedestrians cannot find a safe gap to cross. At present, self-driving cars can only adopt conservative yield control strategies when facing pedestrians, which causes self-driving cars to stop frequently, thus failing to play its advantage of improving traffic flow efficiency.

[0004] In addition, based on the existing conservative decision-making method for the right of way of autonomous vehicles, autonomous vehicles only decide to stop or drive based on the results of obstacle detection. For example, when pedestrians are detected on the crosswalk, the autonomous vehicle immediately decides to stop or slow down to give pedestrians priority. Because the existing control method is too conservative and lacks unified control of autonomous vehicles and pedestrians, the right of way management of autonomous vehicles and pedestrians at intersections is chaotic. Autonomous vehicles can only take actions based on the presence or absence of pedestrians. Continuous traffic flow is frequently interrupted by pedestrians, and the operating efficiency of intersections is seriously reduced.

[0005] In summary, existing research focuses on the motion trajectory planning methods of autonomous vehicles under different optimization objectives, but ignores the existence of pedestrians at intersections. Summary of the invention

[0006] The technical problem to be solved by the present invention is to provide a method for controlling autonomous vehicles and pedestrians at an intersection, which can allow simultaneous activation of crosswalks and non-conflicting autonomous vehicle movements, effectively reducing the delay time and queue length of autonomous vehicles and pedestrians at the intersection.

[0007] The technical solution adopted by the present invention to solve the above-mentioned technical problems is to provide a method for controlling autonomous driving vehicles and pedestrians at an intersection, comprising the following steps: S1. Modeling the queue length of pedestrians using the Weibull probability distribution function to estimate the maximum queue length of pedestrians waiting at the crosswalk; S2. Calculating the maximum pressure of each entrance lane and crosswalk at the intersection based on the maximum queue length of pedestrians; S3. Combining the maximum pressure control method with the intersection autonomous driving vehicle trajectory planning algorithm to perform constrained optimization on the trajectory of the autonomous driving vehicle.

[0008] Furthermore, the step S1 comprises:

[0009] S11. For pedestrian crossing n, determine the pedestrian waiting time using formula (1);

[0010]

[0011] Where: represents the waiting time of pedestrians at crosswalk n at time t, represents the waiting time of pedestrians at crosswalk n at time t+1, x mn (t) represents the length of the queue of pedestrians entering the crosswalk n from other pedestrian roads (including roadside sidewalks and crosswalks) m at time t, is the set of entrance pedestrian roads connected to the pedestrian crossing n, Indicates the activation status of pedestrian crossing n at time t. When the pedestrian crossing signal is activated, the pedestrian crossing is activated. otherwise,

[0012] S12, using formula (2) to determine the distribution model of pedestrians arriving at the waiting area;

[0013]

[0014] Where: x is a random variable; a is a shape parameter that determines the shape of the model; b is a scale parameter that determines the mean and variance of the model;

[0015] S13. The maximum queue length of pedestrians in the waiting area is determined based on the pedestrian arrival rate, the size of the crosswalk, the waiting time and the pedestrian density in the waiting area.

[0016] Furthermore, the parameters a and b of formula (2) in step S12 are calculated according to formulas (3) and (4),

[0017] a=6.89-0.43w+0.044l-1.72ρ (3)

[0018] b=2.31-0.49w+0.089l-11.6ρ (4)

[0019] Where: l is the length of the crosswalk, w is the width of the crosswalk, and ρ is the pedestrian density in the waiting area;

[0020] The maximum queue length of pedestrians in the waiting area in step S13 is calculated according to formula (5):

[0021]

[0022] Where: is the waiting time of pedestrians on crosswalk n at time t, is the average arrival rate of pedestrians entering the crosswalk n from the roadside sidewalk m; the arrival rate of pedestrians in the area with the largest queue length is Where P max is the maximum value of the distribution frequency of pedestrians in the waiting area of ​​the crosswalk, calculated using formulas (2), (3) and (4).

[0023] Furthermore, step S2 includes: S21, calculating the queue length of autonomous driving vehicles in each import lane; S22, calculating the queue length of pedestrians at each crosswalk; S23, calculating the weight coefficient of a movement process of an autonomous driving vehicle or pedestrian based on the queue length; S24, calculating the pressure of each import lane and crosswalk.

[0024] Furthermore, the step S2 comprises:

[0025] The step S21 uses formula (6) to construct the state equation of the evolution process of the queue length of the autonomous driving vehicles, so as to calculate the queue length of the autonomous driving vehicles on each import lane per second.

[0026]

[0027] Where: at time t, represents the queue length of the autonomous vehicles on vehicle road i; represents the number of autonomous vehicles that leave vehicle road i and enter vehicle road j; Demand for autonomous vehicles for imported vehicle roads; represents the set of all imported vehicle roads; is the set of imported vehicle roads connected to vehicle road i.

[0028] The step S22 uses formula (7) to construct the state equation of the evolution process of the pedestrian queue length,

[0029]

[0030] Where: at time t, represents the estimated maximum queue length; represents the estimated incoming flow; represents the estimated departure flow;

[0031] Use formula (5) to estimate the incoming flow Estimated pedestrian departure flow If the crosswalk is not activated, it is 0; then formula (7) is transformed into formula (8), and formula (8) is used to calculate the queue length of pedestrians in the waiting area of ​​each crosswalk per second,

[0032]

[0033] Where: represents the waiting time of pedestrians at crosswalk n at time t, Indicates the proportion of time that pedestrians are allowed to move; Q mn represents the capacity of a pedestrian's movement process (m,n), Q mn =min{Q m ,Q n}, Q m ,Q n are the capacities of pedestrian road m and pedestrian crossing n respectively; a pedestrian movement process (m, n) represents the pedestrian flow process from pedestrian road m to pedestrian crossing n. A pedestrian movement process (m, n) is composed of an entrance pedestrian road m and a corresponding exit pedestrian crossing n.

[0034] The step S23 calculates the weight coefficients of the movement process (i, j) and (m, n) of the autonomous driving car and the pedestrian based on the queue length calculation results of formulas (7) and (8), using formula (9).

[0035]

[0036] In the formula, a movement process (i, j) of the autonomous vehicle represents the vehicle flow process from vehicle road i to vehicle road j. The vehicle movement process (i, j) is composed of an import vehicle road i and a corresponding export vehicle road j. is the weight coefficient of the vehicle moving process (i, j); and Calculated using formula (7); is the weight coefficient of the pedestrian moving process (m,n); and Calculated using formula (8);

[0037] In step S24, each autonomous driving vehicle moving process or pedestrian moving process is represented by a corresponding vehicle signal phase. or pedestrian signal phase Control, signal phase refers to the state in which one or more traffic flows obtain the right of way at the same time within a signal cycle in the traffic system. Each signal phase periodically alternates to obtain the green light display, that is, obtains the right of way to pass through the intersection. Each transition of the signal phase is called a signal phase stage, and a signal cycle is composed of the sum of all pre-set phase time periods. The pressure of each moving process (i, j) or (m, n) is calculated using formula (10), and the signal corresponding to the moving process with the largest pressure will be activated in the next second;

[0038]

[0039] Where P ij (t) is the pressure of the vehicle moving process (i, j), M is the set of all autonomous driving vehicle moving processes; P mn (t) is the pressure of the pedestrian moving process (m,n), and W is the set of all pedestrian moving processes.

[0040] Furthermore, the step S3 includes: S31, determining the objective function of the intersection autonomous driving car and pedestrian control so as to maximize the total pressure of the intersection; S32, dividing the intersection into a number of conflict zones, and determining the constraints of the intersection autonomous driving car and pedestrian control; S33, establishing an intersection autonomous driving car and pedestrian control method by integrating vehicle dynamics, trajectory, pressure (calculated by formula (10)), vehicle-to-vehicle conflict, vehicle-to-pedestrian conflict, and pedestrian and road constraints.

[0041] Furthermore, the objective function of the intersection maximum pressure control method in S31 is formula (11):

[0042]

[0043] Where M is the set of all autonomous vehicle movement processes; W is the set of all pedestrian movement processes; is the weight coefficient of the vehicle moving process (i, j); is the weight coefficient of the pedestrian moving process (m,n); represents the number of autonomous vehicles that leave vehicle road i and enter vehicle road j; represents the number of pedestrians leaving pedestrian road m and entering pedestrian road n; Q ij represents the capacity of a vehicle moving process (i, j), Q ij =min{Q i ,Q j}, Q i ,Q j are the capacity of vehicle roads i and j respectively; Q mn represents the capacity of a pedestrian's movement process (m,n), Q mn =min{Q m ,Q n}, Q m ,Q n are the capacities of pedestrian roads m and n respectively.

[0044] The constraints in S32 are formulas (12), (13), (14), (15), (16) and (17);

[0045]

[0046] Where: represents the queue length of the autonomous vehicles on vehicle road i; represents the number of autonomous vehicles that leave vehicle road i and enter vehicle road j; It represents the capacity of a vehicle moving process (i, j), are the capacities of vehicle roads i and j respectively; It represents the proportion of time that pedestrians are allowed to move in the pedestrian movement process (m,n) at time t; represents the proportion of time that pedestrians are allowed to move in the pedestrian movement process (l',n) at time t; M and W represent the set of all vehicle movement processes and the set of all pedestrian movement processes, respectively; Indicates whether the vehicle's moving process (i, j) passes through the crosswalk n. If so, otherwise Indicates that the vehicle movement process (i, j) is any element of the set of all vehicle movement processes; Indicates that crosswalk n is any element of the set of all crosswalks; Indicates that pedestrian roads m and l' are the set of all incoming pedestrian roads connected to pedestrian crossing n Any two elements in; the set of all conflict zones is denoted as C, C ij represents the set of conflict zones containing the trajectory of the vehicle moving process (i, j); Indicates whether the vehicle movement process (i, j) passes through the conflict zone c, c∈C, c is any element in all conflict sets, Indicates that the trajectory of the vehicle moving process (i, j) passes through the conflict area c, otherwise Q c Represents the traffic capacity of conflict zone c.

[0047] At each moment in S33, the intersection phase setting S*(t) when the intersection pressure is maximum is obtained, as shown in formula (30),

[0048]

[0049] In the formula, at time t, if the phase of the vehicle moving process (i, j) is activated, Otherwise it is 0; if the phase of the pedestrian moving process (m,n) is activated, Otherwise, 0; is the weight coefficient of the vehicle moving process (i, j); is the weight coefficient of the pedestrian moving process (m,n); It represents the capacity of a vehicle moving process (i, j), are the capacities of vehicle roads i and j respectively; represents the capacity of a pedestrian movement process (m,n), are the capacities of pedestrian roads m and n respectively;

[0050] Furthermore, the step S32 also includes adding kinematic trajectory constraints of the autonomous driving vehicle at time t, specifically including: ensuring the motion state of the autonomous driving vehicle α; limiting the speed selection range of the autonomous driving vehicle α; ensuring that the vehicle α selects the acceleration that can achieve the best performance within the acceleration range to update its motion speed at the next moment; and controlling the rate of change of acceleration to ensure the smoothness of the autonomous driving vehicle's driving.

[0051] Furthermore, step S32 also includes constraining the vehicle to avoid conflicts within the intersection, specifically including: ensuring that the two adjacent self-driving cars α and α' on the vehicle road i maintain a safe distance, where the safe distance is the sum of the vehicle body length, the minimum interval and the braking distance; ensuring that the self-driving car α on the vehicle road i and the conflicting vehicle road The vehicle β∈CAV i' (t) Keep a safe distance between the two vehicles, that is, the sum of the distances between the rear ends of the two vehicles and the collision point is always greater than the safe distance; represents the set of vehicle roads that have traffic conflicts with vehicle road i; CAV i' (t) represents the set of all autonomous vehicles on vehicle road i'; avoid two vehicles with possible conflict passing through the conflict area at the same time, that is, ensure that one vehicle has safely passed the conflict point before the other vehicle reaches the conflict point; ensure that vehicle α on vehicle road i maintains a safe distance from the pedestrian on the crosswalk where there is a conflict, that is, the sum of the distances from the rear of the vehicle and the pedestrian to the conflict point is always greater than the safe distance.

[0052] Furthermore, the step S32 adds the following constraints to the model to control the activation of the vehicle and pedestrian roads, and establishes the connection between the decision variables. The constraint formula (27) shows the mutual constraints between the activation state of the pedestrian crossing n and the vehicle movement process (i, j), and is a {0,1} variable, Indicates whether the vehicle moving process (i, j) and the pedestrian crossing n There is a conflict; if the pedestrian crossing n is activated, and the vehicle moving process (i, j) conflicts with the pedestrian crossing, the vehicle running on the moving process (i, j) cannot pass at the current moment. The constraint formula (28) restricts the movement states of the front and rear vehicles on the same lane to restrict each other, preventing the rear vehicle from forcibly overtaking the front vehicle and causing crushing.

[0053]

[0054] In the formula, at time t, if the phase of the vehicle moving process (i, j) is activated, Otherwise it is 0; if the phase of the pedestrian moving process (m,n) is activated, Otherwise, 0; Indicates whether the vehicle's moving process (i, j) passes through the crosswalk n. If so, otherwise CAV i (t) represents the set of all autonomous vehicles on vehicle road i; α and α' are the two autonomous vehicles in front and behind on vehicle road i; represents the phase activation state of vehicle α on vehicle road i; represents the phase activation state of vehicle α′ on vehicle road i; It means that the vehicle movement process (i, j) is any element in the set M of all vehicle movement processes; It means that the pedestrian movement process (m,n) is any element in the set W of all pedestrian movement processes.

[0055] Compared with the prior art, the present invention has the following beneficial effects: the method for controlling autonomous vehicles and pedestrians at intersections provided by the present invention utilizes the AIM control algorithm to coordinate the motion trajectory of autonomous vehicles, prevents collisions of autonomous vehicles, and optimizes the speed and acceleration of autonomous vehicles at each time step; and adopts the MPC method to optimize the queue lengths of pedestrians and autonomous vehicles, thereby improving the operating efficiency of mixed flows of autonomous vehicles and pedestrians, thereby allowing simultaneous activation of crosswalks and non-conflicting autonomous vehicle movements, effectively reducing the delay time and queue length of autonomous vehicles and pedestrians at intersections. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 A schematic diagram of the process of pedestrian queuing and dispersing at an intersection of the present invention;

[0057] Figure 2 Schematic diagram of applicable scenarios of the present invention;

[0058] Figure 3 Schematic diagram of the conflict area and conflict point of the intersection of the present invention. DETAILED DESCRIPTION

[0059] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0060] The present invention designs a control method for autonomous vehicles and pedestrians at intersections, solving the problem of serious reduction in intersection operation efficiency in an autonomous driving environment due to the rigid management of autonomous vehicles and pedestrians at intersections. First, based on the Weibull probability distribution function, a quantitative evaluation model is developed for the maximum queue length of pedestrians waiting at the crosswalk, solving the problem of accurate estimation of the maximum queue length of pedestrians waiting by the intersection controller in the future urban road environment; second, based on the pedestrian queue length estimation result, the automatic intersection control (AIM) method and the maximum pressure control algorithm (MPC), a control method for autonomous vehicles and pedestrians at intersections is established. This method can not only dynamically adjust the phase of traffic flow in all directions according to pedestrian demand, thereby reducing the queue length and delay of autonomous vehicles and pedestrians; but also can control the motion trajectory of autonomous vehicles to avoid collisions between conflicting vehicles and improve the operation efficiency of autonomous vehicles, which not only solves the problem of difficult management of autonomous vehicles and pedestrians, but also improves the queue length and delay of autonomous vehicles and pedestrians.

[0061] In order to achieve the above-mentioned purpose, the present invention takes the control of autonomous driving cars and pedestrians at intersections as an example to specifically illustrate the technical solution of the present invention. However, in specific applications, the method of controlling autonomous driving cars and pedestrians at intersections to be protected by the present invention is not limited to the road environment of uncontrolled intersections, but is also applicable to the management process of autonomous driving cars and pedestrians in road environments such as road sections, near shopping malls and around schools.

[0062] Step 1: Pedestrian queue length calculation

[0063] Intersections are nodes and bottlenecks in the urban road traffic system. For traffic managers, the most concerned issue in the autonomous driving environment is how to reduce the queues and delays of autonomous driving cars and pedestrians at intersections, thereby improving the operating efficiency of intersections. The invention is based on the maximum pressure control method and the automatic intersection management method. The queue length of autonomous driving cars and pedestrians is the basic parameter of the maximum pressure control method. Autonomous driving cars can use V2V and V2I technologies to achieve information sharing and communication between cars and roads, while pedestrians cannot communicate information, so it is difficult to estimate the queue length when pedestrians are waiting. The length of the pedestrian queue will be used for maximum pressure control and crosswalk activation. Therefore, the first core problem of the present invention is to estimate the maximum queue length of pedestrians in the waiting area of ​​the crosswalk. To this end, the present invention designs a pedestrian maximum queue length estimation method based on Weibull probability distribution, so as to estimate the queue length of pedestrians in the waiting area of ​​the crosswalk, and use the result as the basis for the control of autonomous driving cars and pedestrians.

[0064] The present invention firstly integrates factors such as the size of the pedestrian crossing, pedestrian demand and road traffic environment to establish a pedestrian queue length estimation method, and then integrates the maximum pressure control method and the automatic intersection management method to construct a control method for autonomous driving vehicles and pedestrians at the intersection. In order to improve the ability of the control method to reduce the length of the pedestrian queue, the present invention establishes a pedestrian queue length estimation method under different traffic conditions based on the Weibull probability distribution. The specific process of the estimation method is as follows:

[0065] (1) In order to estimate the maximum length of pedestrian queues, it is necessary to record the pedestrian waiting time, which can be determined using formula (1). For pedestrian crossing n, if the pedestrian signal was not activated in the previous second and there were pedestrians, the waiting time increases; if the pedestrian signal was activated in the previous second or there were no pedestrians, the pedestrian waiting time is 0;

[0066]

[0067] Where: represents the waiting time of pedestrians at crosswalk n at time t, represents the waiting time of pedestrians at crosswalk n at time t+1, x mn(t) represents the length of the queue of pedestrians entering the crosswalk n from other pedestrian roads (including roadside sidewalks and crosswalks) m at time t, is the set of entrance pedestrian roads connected to the pedestrian crossing n, Indicates the activation status of pedestrian crossing n at time t. When the pedestrian crossing signal is activated, the pedestrian crossing is activated. otherwise,

[0068] (2) In queue theory, queue length is equal to the product of arrival rate and waiting time. However, considering the actual distribution of pedestrians in the waiting area, the distribution of pedestrians in the waiting area of ​​the crosswalk is not uniform. Pedestrians tend to wait in the middle of the waiting area, and the longest queue of pedestrians is also in the middle of the waiting area. Using formula (2), we can determine the distribution model of pedestrians arriving at the waiting area.

[0069]

[0070] Where: x is a random variable; a is a shape parameter that determines the shape of the model; b is a scale parameter that determines the mean and variance of the model;

[0071] (3) The parameters a and b in formula (2) can be calculated according to formulas (3) and (4),

[0072] a=6.89-0.43w+0.044l-1.72ρ (3)

[0073] b=2.31-0.49w+0.089l-11.6ρ (4)

[0074] Where: l is the length of the crosswalk, w is the width of the crosswalk, and ρ is the pedestrian density in the waiting area.

[0075] (4) The maximum queue length of pedestrians in the waiting area is determined by the pedestrian arrival rate, the size of the crosswalk, the waiting time, and the distribution of pedestrian arrivals. The maximum queue appears at the location with the highest pedestrian appearance frequency, e.g. Figure 1 According to the pedestrian arrival distribution model determined by formulas (2), (3) and (4), the pedestrian queue length in the maximum queue length area is calculated, as shown in formula (5):

[0076]

[0077] Where: is the waiting time of pedestrians on crosswalk n at time t, is the average arrival rate of pedestrians entering the crosswalk n from the roadside sidewalk m; the arrival rate of pedestrians in the area with the largest queue length is Where P maxis the maximum value of the distribution frequency of pedestrians in the waiting area of ​​the crosswalk, calculated using formulas (2), (3) and (4).

[0078] Step 2: Calculate the pressure of each entrance and crosswalk at the intersection

[0079] First, the relevant symbols of the present invention are explained. The present invention is applicable to intersections where pedestrians and self-driving cars coexist, such as Figure 2 As shown. The intersection contains vehicle roads and pedestrian roads. The vehicle road set is represented by G veh (N veh ,L veh ), the pedestrian road set is represented as G ped (N ped ,L ped ). Among them, N ped ∪N veh represents the intersection node set, L ped ∪L veh Represents the road set of the intersection. The road set contains two subsets L entry and L exit , respectively represent the set of import roads and the set of export roads. Pedestrians or self-driving cars enter from the import roads and exit from the export roads. and denotes the set of entry and exit roads of vehicle or pedestrian road i, respectively. For example, if road i is connected to pedestrian crossing j, it can be represented as or

[0080] Vehicle road set G veh (N veh ,L veh ), a vehicle movement process can be represented by a pair of road combinations (i, j), indicating that the vehicle enters road j from road i. The set of all vehicle movement processes in the scene is recorded as M When the capacity of road i is Q i When the moving process (i, j) represents the moving process capacity, the minimum value of the capacity of roads i and j is Q ij =min{Q i ,Q j There may be trajectory conflicts between vehicles in different directions at the intersection, so the present invention divides the intersection into four conflict zones, such as Figure 3 As shown, the set of all conflict regions is denoted as C, for example, C ij Represents the set of conflict zones that contain the trajectory of the moving process (i, j). Indicates whether the moving process (i, j) passes through the conflict zone c , c∈C. Indicates that the moving process (i, j) trajectory passes through the conflict zone, otherwise

[0081] Pedestrian Road Set G ped (N ped ,L ped ) contains a node set N ped and the road set L ped Pedestrian roads include roadside sidewalks and crosswalks. Figure 1 As shown in the figure, roads a, b, c, d, e, f, g, h are roadside sidewalks. There are four crosswalks i, j, k, m at the intersection, and pedestrians need to wait for the crosswalks to be activated before they can cross. A pedestrian's movement process is also represented by a pair of adjacent road combinations. For example, (b, c) means that a pedestrian enters the roadside crosswalk from the roadside sidewalk b. c If two roadside sidewalks are connected, pedestrians can complete the road change without waiting. For example, pedestrians can directly enter the roadside sidewalk from roadside sidewalk b. c Without waiting; pedestrians on the roadside sidewalk b need to enter the roadside sidewalk e , you need to activate the pedestrian crossing k, then cross the pedestrian crossing k and enter the sidewalk e .

[0082] The maximum pressure control method is to control the maximum pressure of all entrance roads at each moment, and the pressure calculation needs to use the queue length on each road. The specific process of the pressure calculation method for each entrance road and pedestrian crossing at the intersection is as follows:

[0083] (1) Calculation of the length of the queue of autonomous vehicles at each entrance. The state equation of the evolution of the length of the queue of autonomous vehicles is constructed using formula (6), which is used to calculate the length of the queue of autonomous vehicles at each entrance per second.

[0084]

[0085] Where: at time t, represents the queue length of the autonomous vehicles on vehicle road i; represents the number of autonomous vehicles that leave vehicle road i and enter vehicle road j; Demand for autonomous vehicles for imported vehicle roads; represents the set of all imported vehicle roads; is the set of imported vehicle roads connected to vehicle road i.

[0086] (2) Calculation of the length of pedestrian queues at each crosswalk. Formula (7) constructs the state equation of the evolution process of the length of pedestrian queues.

[0087]

[0088] Where: at time t, represents the estimated maximum queue length; represents the estimated incoming flow; represents the estimated departure flow;

[0089] Use formula (5) to estimate the incoming flow Estimated pedestrian departure flow If the crosswalk is not activated, it is 0; then formula (7) is transformed into formula (8), and formula (8) is used to calculate the queue length of pedestrians in the waiting area of ​​each crosswalk per second,

[0090]

[0091] Where: represents the waiting time of pedestrians at crosswalk n at time t, Indicates the proportion of time that pedestrians are allowed to move; Q mn represents the capacity of a pedestrian's movement process (m,n), Q mn =min{Q m ,Q n}, Q m ,Q n are the capacities of pedestrian road m and pedestrian crossing n respectively; a pedestrian movement process (m, n) represents the pedestrian flow process from pedestrian road m to pedestrian crossing n. A pedestrian movement process (m, n) is composed of an entrance pedestrian road m and a corresponding exit pedestrian crossing n.

[0092] (3) Calculation of weight coefficients. Real-time and accurate acquisition of the queue lengths of autonomous vehicles and pedestrians at intersections is crucial for MaxPressure Control (MPC). Its core idea is to maximize the "pressure" of the intersection. The calculation of "pressure" requires the determination of weight coefficients first. Based on the queue length calculation results of formulas (7) and (8), the present invention uses formula (9) to calculate the weight coefficients of a movement process (i, j) and (m, n) of autonomous vehicles and pedestrians.

[0093]

[0094] In the formula, a movement process (i, j) of the autonomous vehicle represents the vehicle flow process from vehicle road i to vehicle road j. The vehicle movement process (i, j) is composed of an import vehicle road i and a corresponding export vehicle road j. is the weight coefficient of the vehicle moving process (i, j); and Calculated using formula (7); is the weight coefficient of the pedestrian moving process (m,n); and Calculated using formula (8);

[0095] (4) Pressure calculation for each entrance and crosswalk. Each autonomous vehicle movement process or pedestrian movement process is represented by a corresponding vehicle signal phase. or pedestrian signal phase Control, signal phase refers to the state in which one or more traffic flows obtain the right of way at the same time within a signal cycle in the traffic system. Each signal phase periodically alternates to obtain the green light display, that is, obtains the right of way to pass through the intersection. Each transition of the signal phase is called a signal phase stage, and a signal cycle is composed of the sum of all pre-set phase time periods. The pressure of each moving process (i, j) or (m, n) is calculated using formula (10), and the signal corresponding to the moving process with the largest pressure will be activated in the next second;

[0096]

[0097] Where P ij (t) is the pressure of the vehicle moving process (i, j), M is the set of all autonomous driving vehicle moving processes; P mn (t) is the pressure of the pedestrian moving process (m,n), and W is the set of all pedestrian moving processes.

[0098] Step 3: Determine the control plan for autonomous vehicles and pedestrians at the intersection. The specific process is as follows:

[0099] (1) The objective function of intersection control for autonomous vehicles and pedestrians is determined. The maximum pressure control target is to maximize the total pressure at the intersection. The objective function of the intersection maximum pressure control method is formula (11):

[0100]

[0101] Where M is the set of all autonomous vehicle movement processes; W is the set of all pedestrian movement processes; is the weight coefficient of the vehicle moving process (i, j); is the weight coefficient of the pedestrian moving process (m,n); represents the number of autonomous vehicles that leave vehicle road i and enter vehicle road j; represents the number of pedestrians leaving pedestrian road m and entering pedestrian road n; Q ij represents the capacity of a vehicle moving process (i, j), Q ij =min{Q i ,Q j}, Qi ,Q j are the capacity of vehicle roads i and j respectively; Q mn represents the capacity of a pedestrian's movement process (m,n), Q mn =min{Q m ,Q n}, Q m ,Q n are the capacities of pedestrian roads m and n respectively.

[0102] (2) Determination of constraints for autonomous vehicles and pedestrian control at intersections. In order to resolve conflicts between autonomous vehicles and pedestrians at intersections, the intersection is divided into several conflict zones. Figure 3 Indicates the four conflict zones A, B, C, and D of an intersection and the conflict points of each traffic flow. In a vehicle movement (i, j), the trajectory of the autonomous vehicle will pass through one or more conflict zones. The set of all conflict zones is C ij For example, the left turn movement of the north entrance passes through conflict zones A, C, and D. The capacity of conflict zone C It restricts the actual number of vehicles passing through the conflict zone. Therefore, the constraints are formulas (12), (13), (14), (15), (16) and (17).

[0103]

[0104] Where: represents the queue length of the autonomous vehicles on vehicle road i; represents the number of autonomous vehicles that leave vehicle road i and enter vehicle road j; It represents the capacity of a vehicle moving process (i, j), are the capacities of vehicle roads i and j respectively; It represents the proportion of time that pedestrians are allowed to move in the pedestrian movement process (m,n) at time t; represents the proportion of time that pedestrians are allowed to move in the pedestrian movement process (l',n) at time t; M and W represent the set of all vehicle movement processes and the set of all pedestrian movement processes, respectively; Indicates whether the vehicle's moving process (i, j) passes through the crosswalk n. If so, otherwise Indicates that the vehicle movement process (i, j) is any element of the set of all vehicle movement processes; Indicates that crosswalk n is any element of the set of all crosswalks; Indicates that pedestrian roads m and l' are the set of all incoming pedestrian roads connected to pedestrian crossing n Any two elements in; the set of all conflicting regions is denoted as C, for example, Cij represents the set of conflict zones containing the trajectory of the vehicle moving process (i, j); Indicates whether the vehicle movement process (i, j) passes through the conflict zone c, c∈C, c is any element in all conflict sets, Indicates that the trajectory of the vehicle moving process (i, j) passes through the conflict area c, otherwise Q c Represents the traffic capacity of conflict zone c.

[0105] The above conditions represent the constraints of the traffic capacity of the conflict zone, and do not take into account the conflict avoidance and movement trajectory planning of vehicles inside the intersection. Therefore, in order to coordinate the conflict avoidance and movement selection behaviors of vehicles inside the intersection at each moment, Figure 2 In the research scenario shown in the figure, the present invention adds the kinematic trajectory constraint conditions of the autonomous driving vehicle at time t, as shown in formula (18) to formula (23):

[0106]

[0107] Where: at time t, CAV i (t) represents the set of autonomous vehicles on lane i; d iα (t) represents the distance α between the autonomous vehicle and the point on lane i The distance, such as Figure 2 As shown; Δt represents 1 second; v iα (t) represents the position of the self-driving car α in lane i t The speed of the moment; v max Indicates the vehicle's recommended maximum speed; a iα (t) represents the acceleration of the autonomous vehicle α in lane i; a min and a max Respectively represent the recommended maximum acceleration and minimum acceleration; u iα (t) represents the rate of change of the acceleration α of the autonomous vehicle in lane i; u min and u max Represent the minimum and maximum rates of change of acceleration respectively.

[0108] Constraints (18), (19) and (20) can ensure the motion state of the autonomous driving vehicle α. Constraint (21) limits the speed selection range of the autonomous driving vehicle α. Constraint (22) ensures that the vehicle α selects the acceleration that can achieve the best performance within the acceleration range to update its motion speed at the next moment. In addition to controlling the value range of the acceleration, the present invention sets constraint (23) to control the rate of change of the acceleration to avoid excessive fluctuations in the instantaneous acceleration of the vehicle, thereby ensuring the stability of the autonomous driving vehicle.

[0109] In addition, constraint (24) ensures that the two adjacent vehicles α and α' on lane i maintain a safe distance, which is the sum of the vehicle length, minimum interval and braking distance. Constraint (25) ensures that the autonomous driving car α on lane i and the conflicting lane Autonomous vehicle β∈CAV on j' (t) maintain a safe distance between the two vehicles, that is, the sum of the distances between the rear ends of the two vehicles and the conflict point is always greater than the safe distance. The distribution of conflict points at the intersection is as follows: Figure 3 Constraint (25) prevents two vehicles that may conflict from passing through the conflict area at the same time, that is, it ensures that one vehicle has safely passed the conflict point before the other vehicle reaches the conflict point. Here, the safety distance is represented by the sum of the length of the two vehicles and the minimum interval, and the conflict area is represented by the vehicle length plus the minimum safety distance.

[0110]

[0111] Where: D v represents the length of the autonomous vehicle; d C represents the minimum safe distance between autonomous vehicles; τ v is the vehicle's reaction time; F represents the set of lanes that conflict with the traffic flow on vehicle road i; ij' Represents the conflict point between the traffic flow on vehicle road i and vehicle road j' to the starting point of vehicle road i The distance, such as Figure 2 shown.

[0112] In addition to the above dynamic constraints to ensure the normal motion of the autonomous vehicle, constraint (26) ensures that the autonomous vehicle α on the vehicle road i and the pedestrian α on the conflicting crosswalk w' ped Keep a safe distance between them, that is, the sum of the distance between the rear of the vehicle and the distance between the pedestrian and the conflict point is always greater than the safe distance, to avoid vehicles and pedestrians with possible conflicts passing through the conflict area at the same time, that is, to ensure that one party has safely passed the conflict point before the other party reaches the conflict point. Here, the safe distance is the sum of the length of the vehicle and the minimum radius of the pedestrian to ensure his own comfort, and the conflict area is represented by the length of the vehicle plus the minimum safety radius. Therefore, the present invention adds the constraint of pedestrians, and the constraint condition is as shown in formula (26):

[0113]

[0114] Where: represents the set of pedestrian crossings that conflict with the traffic flow on vehicle road i; P w' (t) is the set of pedestrians on the crosswalk w'; α ped For pedestrians on the crosswalk w'; F iw'Indicates the conflict point between the traffic in lane i and the pedestrian in the crosswalk w' to the starting point of lane i distance; It indicates the minimum space radius required by pedestrians to ensure their comfort and safety.

[0115] In order to combine the maximum pressure control algorithm with the trajectory planning method, the present invention adds the following constraints to the model to control the activation of the vehicle and pedestrian roads and establish the connection between the decision variables. Constraint (27) indicates the mutual restriction between the activation state of the pedestrian crossing n and the vehicle movement process (i, j), and is a {0,1} variable, Indicates whether there is a conflict between the vehicle moving process (i, j) and the crosswalk n; if the crosswalk n is activated and the vehicle moving process (i, j) conflicts with the crosswalk, the vehicle running on the moving process (i, j) cannot pass at the current moment. The constraint formula (28) restricts the movement states of the front and rear vehicles on the same lane to restrict each other, preventing the rear vehicle from forcibly overtaking the front vehicle and causing a crushing phenomenon.

[0116]

[0117] In the formula, at time t, if the phase of the vehicle moving process (i, j) is activated, Otherwise it is 0; if the phase of the pedestrian moving process (m,n) is activated, Otherwise, 0; Indicates whether the vehicle's moving process (i, j) passes through the crosswalk n. If so, otherwise CAV i (t) represents the set of all autonomous vehicles on vehicle road i; α and α' are the two autonomous vehicles in front and behind on vehicle road i; represents the phase activation state of vehicle α on vehicle road i; represents the phase activation state of vehicle α′ on vehicle road i; It means that the vehicle movement process (i, j) is any element in the set M of all vehicle movement processes; It means that the pedestrian movement process (m,n) is any element in the set W of all pedestrian movement processes.

[0118] (3) Solving the optimal control scheme for autonomous vehicles and pedestrians at intersections. In the mixed traffic flow control method for pedestrians and autonomous vehicles proposed in the present invention, the present invention independently controls each autonomous vehicle, and comprehensively considers the constraints of relevant factors such as vehicle dynamics, trajectory, pressure, vehicle-to-vehicle conflict, pedestrian-to-vehicle conflict, pedestrians and roads, i.e., formula (12)-formula (29), and solves the maximum pressure control method and the vehicle trajectory planning method to determine the objective function of the autonomous vehicle pedestrian control at the intersection, i.e., formula (11), to achieve the optimal control solution for the intersection. At each moment, the maximum pressure control algorithm can obtain the intersection phase plan when the intersection pressure is maximum, i.e., the control plan (phase setting) S*(t), as shown in formula (30),

[0119]

[0120] In the formula, at time t, if the phase of the vehicle moving process (i, j) is activated, Otherwise it is 0; if the phase of the pedestrian moving process (m,n) is activated, Otherwise, 0; is the weight coefficient of the vehicle moving process (i, j); is the weight coefficient of the pedestrian moving process (m,n); It represents the capacity of a vehicle moving process (i, j), are the capacities of vehicle roads i and j respectively; represents the capacity of a pedestrian movement process (m,n), are the capacities of pedestrian roads m and n respectively;

[0121] The intersection autonomous driving car and pedestrian control method of the present invention greatly improves the operating stability and efficiency of the mixed traffic flow of autonomous driving cars and pedestrians, reduces the queue length and delay of autonomous driving cars and pedestrians, and is of great significance for improving the mixed traffic flow management level and traffic flow efficiency at intersections in an autonomous driving environment.

[0122] (1) Taking into account the impact of pedestrian arrival rate, crosswalk size, waiting time, and pedestrian arrival distribution on pedestrians, the Weibull probability distribution function is used to model the length of pedestrian queues. This method comprehensively integrates the impact of the external environment and pedestrian demand and comprehensively estimates the maximum length of the queue of pedestrians waiting at the crosswalk.

[0123] (2) A control method for autonomous vehicles and pedestrians at intersections based on the estimated maximum queue length of pedestrians is proposed. Based on the estimated length of pedestrian queues, a control method for the length of autonomous vehicles and pedestrian queues at intersections is constructed based on the maximum pressure control method. Then, in order to help autonomous vehicles inside intersections avoid collisions and obtain the best motion trajectory, the maximum pressure control method is combined with the existing intersection autonomous vehicle trajectory planning algorithm. On the basis of managing the queue length of autonomous vehicles and pedestrians at intersections, the trajectory optimization of autonomous vehicles is controlled at the same time. The addition of this autonomous vehicle and pedestrian control method can not only realize the planning and control of the motion trajectory of autonomous vehicles, but also quickly stabilize and reduce their delays and queue lengths, and improve the operation efficiency of intersections.

[0124] Although the present invention has been disclosed as above with preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art may make some modifications and improvements without departing from the spirit and scope of the present invention. Therefore, the scope of protection of the present invention shall be based on the definition of the claims.

Claims

1. A method for controlling an autonomous vehicle and pedestrians at an intersection, characterized in that: The steps include: S1. Use the Weibull probability distribution function to model the length of pedestrian queues and estimate the maximum length of the queue of pedestrians waiting on the sidewalk; S2. Calculate the maximum pressure of each entrance lane and pedestrian crossing at the intersection based on the maximum queue length of pedestrians; S3, combining the maximum pressure control method with the autonomous vehicle trajectory planning algorithm to perform constrained optimization on the autonomous vehicle trajectory at the intersection; The step S1 comprises: S11. For pedestrian crossing n, determine the pedestrian waiting time using formula (1); Where: represents the waiting time of pedestrians at crosswalk n at time t, represents the waiting time of pedestrians at crosswalk n at time t+1, x mn (t) represents the length of the queue of pedestrians entering the crosswalk n from other pedestrian roads m at time t, is the set of entrance pedestrian roads connected to the pedestrian crossing n, Indicates the activation status of pedestrian crossing n at time t. When the pedestrian crossing signal is activated, the pedestrian crossing is activated. otherwise, S12. Use formula (2) to determine the distribution model of pedestrians arriving at the waiting area: Where: x is a random variable; a is a shape parameter that determines the shape of the model; b is a scale parameter that determines the mean and variance of the model; S13. Determine the maximum queue length of pedestrians in the waiting area based on the pedestrian arrival rate, the size of the crosswalk, the waiting time and the pedestrian density in the waiting area; The step S2 comprises: S21. Calculate the length of the queue of autonomous vehicles in each import lane; S22, calculating the length of the pedestrian queue at each crosswalk; S23. Calculate a weight coefficient of a movement process of the autonomous driving vehicle or pedestrian according to the queue length; S24. Calculate the pressure of each entrance lane and pedestrian crossing; The step S3 comprises: S31, determining the objective function of the intersection autonomous driving vehicle and pedestrian control so as to maximize the total pressure of the intersection; S32, dividing the intersection into several conflict zones and determining the constraints for controlling the autonomous vehicles and pedestrians at the intersection; S33. A control method for autonomous vehicles and pedestrians at intersections is established by integrating vehicle dynamics, trajectory, pressure, vehicle-to-vehicle conflict, vehicle-to-pedestrian conflict, and pedestrian and road constraints.

2. The method for controlling an autonomous vehicle and pedestrians at an intersection as claimed in claim 1, characterized in that: The parameters a and b of formula (2) in step S12 are calculated according to formulas (3) and (4). a=6.89-0.43w+0.044l-1.72ρ (3) b=2.31-0.49w+0.089l-11.6ρ (4) Where: l is the length of the crosswalk, w is the width of the crosswalk, and ρ is the pedestrian density in the waiting area; The maximum queue length of pedestrians in the waiting area in step S13 is calculated according to formula (5): Where: is the waiting time of pedestrians on crosswalk n at time t, is the average arrival rate of pedestrians entering the crosswalk n from the roadside sidewalk m; the arrival rate of pedestrians in the area with the largest queue length is Where P max is the maximum value of the distribution frequency of pedestrians in the waiting area of ​​the crosswalk, calculated using formulas (2), (3) and (4).

3. The method for controlling an autonomous vehicle and pedestrians at an intersection as claimed in claim 1, characterized in that: The step S2 comprises: The step S21 uses formula (6) to construct the state equation of the evolution process of the queue length of the autonomous driving vehicles, so as to calculate the queue length of the autonomous driving vehicles on each import lane per second. Where: at time t, represents the queue length of the autonomous vehicles on vehicle road i; represents the number of autonomous vehicles that leave vehicle road i and enter vehicle road j; Demand for autonomous vehicles for imported vehicle roads; represents the set of all imported vehicle roads; is the set of imported vehicle roads connected to vehicle road i; The step S22 uses formula (7) to construct the state equation of the evolution process of the pedestrian queue length, Where: at time t, represents the estimated maximum queue length; represents the estimated incoming flow; represents the estimated departure flow; Use formula (5) to estimate the incoming flow Estimated pedestrian departure flow If the crosswalk is not activated, it is 0; then formula (7) is transformed into formula (8), and formula (8) is used to calculate the queue length of pedestrians in the waiting area of ​​each crosswalk per second, Where: represents the waiting time of pedestrians at crosswalk n at time t, Indicates the proportion of time that pedestrians are allowed to move; Q mn represents the capacity of a pedestrian's movement process (m,n), Q mn =min{Q m ,Q n }, Q m ,Q n are the capacity of pedestrian road m and pedestrian crossing n respectively; a pedestrian movement process (m, n) represents the pedestrian flow process from pedestrian road m to pedestrian crossing n, and the pedestrian flow process is composed of an entry pedestrian road m and a corresponding exit pedestrian crossing n; The step S23 calculates the weight coefficients of a movement process (i, j) of the autonomous driving vehicle and a movement process (m, n) of the pedestrian based on the queue length calculation results of formula (7) and formula (8), using formula (9). In the formula, a movement process (i, j) of the autonomous vehicle represents the vehicle flow process from vehicle road i to vehicle road j. The vehicle flow process is composed of an import vehicle road i and a corresponding export vehicle road j. is the weight coefficient of a moving process (i, j) of the autonomous vehicle; and Calculated using formula (6); is the weight coefficient of a pedestrian's movement process (m,n); and Calculated using formula (8); In step S24, each autonomous driving vehicle moving process or pedestrian moving process is represented by a corresponding vehicle signal phase. or pedestrian signal phase Control, signal phase refers to the state in which one or more traffic flows obtain the right of way at the same time within a signal cycle in the traffic system; each signal phase periodically alternates to obtain the green light display, that is, obtains the right of way to pass through the intersection; each conversion of the signal phase is called a signal phase stage, and a signal cycle is composed of the sum of all phase time periods set in advance; the pressure of each moving process (i, j) of an autonomous vehicle or a moving process (m, n) of a pedestrian is calculated using formula (10), and the signal corresponding to the moving process with the largest pressure will be activated in the next second; Where P ij (t) is the pressure of a moving process (i, j) of the autonomous vehicle, and M is the set of all moving processes of the autonomous vehicle; is the weight coefficient of a moving process (i, j) of the autonomous vehicle; represents the number of autonomous vehicles that leave vehicle road i and enter vehicle road j; P mn (t) is the pressure of a pedestrian's movement process (m,n), W is the set of all pedestrian movement processes, is the weight coefficient of a pedestrian's movement process (m,n); Indicates the number of pedestrians leaving pedestrian road m and entering pedestrian road n.

4. The method for controlling an autonomous vehicle and pedestrians at an intersection as claimed in claim 1, characterized in that: The objective function of the intersection maximum pressure control method in step S31 is formula (11): Where M is the set of all autonomous vehicle movement processes; W is the set of all pedestrian movement processes; is the weight coefficient of a moving process (i, j) of the autonomous vehicle; is the weight coefficient of a pedestrian's movement process (m,n); represents the number of autonomous vehicles that leave vehicle road i and enter vehicle road j; represents the number of pedestrians leaving pedestrian road m and entering pedestrian road n; Q ij represents the capacity of an autonomous vehicle riding a moving process (i, j), Q ij =min{Q i ,Q j }, Q i ,Q j are the capacity of vehicle roads i and j respectively; Q mn represents the capacity of a pedestrian's movement process (m,n), Q mn =min{Q m ,Q n }, Q m ,Q n are the capacities of pedestrian roads m and n respectively; The constraints in step S32 are formulas (12), (13), (14), (15), (16) and (17); Where: represents the queue length of the autonomous vehicles on vehicle road i; represents the number of autonomous vehicles that leave vehicle road i and enter vehicle road j; represents the capacity of a moving process (i, j) of an autonomous vehicle, are the capacities of vehicle roads i and j respectively; It represents the proportion of time that pedestrians are allowed to move in the pedestrian movement process (m,n) at time t; represents the proportion of time that pedestrians are allowed to move in the pedestrian movement process (l',n) at time t; M and W represent the set of all vehicle movement processes and the set of all pedestrian movement processes, respectively; Indicates whether the vehicle's moving process (i, j) passes through the crosswalk n. If so, otherwise Indicates that the vehicle movement process (i, j) is any element of the set of all vehicle movement processes; Indicates that crosswalk n is any element of the set of all crosswalks; Indicates that pedestrian roads m and l' are the set of all incoming pedestrian roads connected to pedestrian crossing n Any two elements in; the set of all conflict zones is denoted as C, C ij represents the set of conflict zones containing the trajectory of the vehicle moving process (i, j); Indicates whether the vehicle movement process (i, j) passes through the conflict zone c, c∈C, c is any element in all conflict sets, Indicates that the trajectory of the vehicle moving process (i, j) passes through the conflict area c, otherwise Q c represents the traffic capacity of the conflict zone c; At each moment in S33, the intersection phase setting S*(t) when the intersection pressure is maximum is obtained, as shown in formula (30), In the formula, at time t, if the phase of a self-driving car moving (i, j) is activated, Otherwise it is 0; if the phase of a pedestrian's movement process (m,n) is activated, Otherwise, 0; is the weight coefficient of a moving process (i, j) of the autonomous vehicle; is the weight coefficient of a pedestrian's movement process (m,n); represents the capacity of a moving process (i, j) of an autonomous vehicle, are the capacities of vehicle roads i and j respectively; represents the capacity of a pedestrian movement process (m,n), are the capacities of pedestrian roads m and n respectively.

5. The method for controlling an autonomous vehicle and pedestrians at an intersection as claimed in claim 4, characterized in that: The step S32 also includes adding the kinematic trajectory constraints of the autonomous driving vehicle at time t, specifically including: Ensure the motion state of the autonomous driving car α; Limit the speed range of the autonomous vehicle α; Ensure that the autonomous vehicle α selects the acceleration that can achieve the best performance within the acceleration range to update its movement speed at the next moment; Control the rate of change of acceleration to ensure the smooth driving of the autonomous vehicle.

6. The method for controlling an autonomous vehicle and pedestrians at an intersection as claimed in claim 4, characterized in that: The step S32 also includes constraining the vehicle to avoid conflicts within the intersection, specifically including: Ensure that the two adjacent self-driving cars α and α' on vehicle road i maintain a safe distance, where the safe distance is the sum of the vehicle length, the minimum interval and the braking distance; Ensure that the autonomous vehicle α on vehicle path i is in conflict with the vehicle path The vehicle β∈CAV i' (t) Keep a safe distance between the two vehicles, that is, the sum of the distances between the rear ends of the two vehicles and the collision point is always greater than the safe distance; represents the set of vehicle roads that have traffic conflicts with vehicle road i; CAV i' (t) represents the set of all autonomous vehicles on vehicle road i'; Avoid two vehicles that may conflict with each other from passing through the conflict area at the same time, that is, ensure that one vehicle has safely passed the conflict point before the other vehicle reaches the conflict point; Ensure that a safe distance is maintained between vehicle α on vehicle road i and the pedestrian on the crosswalk where there is a conflict, that is, the sum of the distances between the rear of the vehicle and the pedestrian to the conflict point is always greater than the safe distance.

7. The method for controlling an autonomous vehicle and pedestrians at an intersection as claimed in claim 1, characterized in that: The step S32 adds the following constraints to the model to control the activation of vehicle and pedestrian roads and establish the connection between decision variables; The constraint formula (27) shows the mutual constraints between the pedestrian crossing n and the activation state of the autonomous vehicle in a moving process (i, j). and is a {0,1} variable, Indicates whether there is a conflict between the autonomous vehicle's movement process (i, j) and the crosswalk n; If the pedestrian crossing n is activated, and there is a conflict between the movement process (i, j) of the autonomous vehicle and the pedestrian crossing, the vehicles running on the movement process (i, j) of the autonomous vehicle cannot pass at the current moment. The constraint formula (28) restricts the movement states of the front and rear vehicles on the same lane to restrict each other, preventing the rear vehicle from forcibly overtaking the front vehicle and causing a crushing phenomenon. The constraint formula is as follows: In the formula, at time t, if the phase (i, j) of a moving process of the autonomous vehicle is activated, Otherwise it is 0; if the phase of a pedestrian's movement process (m,n) is activated, Otherwise, 0; Indicates whether the autonomous vehicle passes through the crosswalk n during a movement process (i, j). If so, otherwise CAV i (t) represents the set of all autonomous vehicles on vehicle road i; α and α' are the two autonomous vehicles in front and behind on vehicle road i; represents the phase activation state of vehicle α on vehicle road i; represents the phase activation state of vehicle α′ on vehicle road i; It means that the vehicle movement process (i, j) is any element in the set M of all vehicle movement processes; It means that a pedestrian movement process (m,n) is any element in the set W of all pedestrian movement processes.

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