A vehicle cooperative control method considering overall risk level of intersection

Abstract

The application provides a vehicle cooperative control method considering overall risk level of an intersection, the method obtains motion state data of all vehicles in the intersection except the vehicle through a vehicle-mounted device and a network-connected device, discretizes the intersection in a grid manner, calculates risks caused by the vehicles in the intersection to each grid center point based on a risk field theory, establishes an intersection overall risk level evaluation index by comprehensively considering the risk values of each grid center point, obtains a vehicle passing trajectory in the intersection by using a vehicle unified path model based on a position relationship of an intersection entry road and an exit road, establishes an intersection vehicle cooperative control model by taking the intersection overall risk level and vehicle passing efficiency as optimization targets, and obtains accelerations of all vehicles in the intersection along the passing trajectory, so that the cooperative control of the vehicles is realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of intelligent driving and vehicle motion cooperative control, and particularly relates to a vehicle cooperative control method considering overall risk level of intersection BACKGROUND

[0002] In recent years, with the increase of the number of vehicles, traffic accidents occur more frequently, causing serious loss to people's life and property safety. As an important node of urban road network, intersections have a large number of vehicle conflict behaviors and frequent traffic accidents. Therefore, how to establish intersection risk assessment indicators and realize the safety control of vehicles is the top priority to reduce the incidence of road accidents and improve road safety level.

[0003] At present, domestic scholars have carried out a lot of work on intersection safety evaluation and vehicle motion control. In terms of intersection safety evaluation, the Highway Safety Manual, HSM, published by the American National Highway and Transportation Association, details the Safety Performance Function, SPF, of the plane intersection and the Crash Modification Factors, CMF, of the plane intersection accident prediction model, and points out the accident modification factors of intersection type, traffic control measures, access management measures and various intersection design parameters. In addition, scholars have proposed a variety of traffic conflict indicators to quantify the risk level on the road, such as Time to Collision, TTC, Time to Accident, TA, and Post Encroachment Time, PET. In terms of vehicle cooperative control, Wenjuan Ruo takes the unsignalized intersection as the research object, based on the vehicle-road cooperative thought, proposes a conflict detection method and resolution strategy, which makes the vehicle-vehicle conflict be resolved before it turns into an accident, and eliminates the traffic accidents caused by conflicts, but only studies the conflict relationship between the head vehicles in different lanes and the resolution strategy, which has certain deviation from the actual traffic conditions; Lee et al. convert the coordination problem of vehicles into a nonlinear constrained optimization problem with vehicle acceleration as the variable, vehicles in the conflict direction will not appear in the conflict area of the intersection at the same time, so only the accurate control of vehicle acceleration is needed. Fangwu Ma et al. constructed a multi-vehicle cooperative formation control system based on vehicle-to-vehicle communication to improve the stability, safety and comfort of vehicle formation driving. In summary, although the current scholars consider the safety factor when conducting cooperative control of intersection vehicles, they often only take reducing vehicle-vehicle conflicts as the goal, and do not control the vehicles from the perspective of reducing the overall risk level of the intersection.

[0004] Therefore, in view of the deficiencies of the existing intersection vehicle cooperative control algorithm, the intersection is discretized, the overall risk level evaluation index of the intersection is established based on the risk field theory, the traffic path of the vehicle at the intersection is generated based on the positional relationship of the entrance and exit of the intersection, and the vehicle motion control model is established to reduce the overall risk level of the intersection and improve the vehicle traffic efficiency, so as to realize the cooperative control of the vehicles at the intersection. SUMMARY

[0005] In view of the deficiencies of the existing vehicle cooperative control method, the present application aims to provide a vehicle cooperative control method capable of reducing the overall risk level of the intersection, which specifically comprises the following steps:

[0006] Step 1: Obtain the motion state data of all vehicles inside the intersection based on laser radar, millimeter wave radar, GPS, combined inertial navigation and networked equipment;

[0007] Step 2: Discretize the intersection in the form of a grid, calculate the risk caused by each vehicle inside the intersection to each grid center point, and superimpose by taking the maximum value;

[0008] Step 3: Establish an overall risk level evaluation index of the intersection based on the risk of all grid center points;

[0009] Step 4: Establish the traffic path of the vehicle at the intersection based on the positional relationship of the entrance and exit of the intersection;

[0010] Step 5: Establish a vehicle motion control model with the lowest overall risk level of the intersection as the target based on the overall risk level evaluation index of the intersection established in step 3 and the vehicle motion constraint.

[0011] Further, the discretization of the intersection in the form of a grid in step 2, the calculation of the risk caused by each vehicle inside the intersection to each grid center point, and the superposition by taking the maximum value comprise the following steps:

[0012] Step 21: Obtain the coordinates of all grid center points in the world coordinate system after discretization, represented as (x i ,y i );

[0013] Step 22: Establish a coordinate system with the heading direction of each vehicle in the intersection as the x-axis, the lateral direction as the y-axis, and the vehicle center as the origin, convert the world coordinates of the grid center points to the vehicle coordinate system based on the coordinate conversion formula, and the coordinate conversion formula is as follows:

[0014]

[0015] Step 23, calculate the risk of all vehicles in the intersection to each grid center point, the specific risk calculation formula is as follows:

[0016] The longitudinal risk function corresponding to the motor vehicle is:

[0017]

[0018]

[0019] The lateral risk function corresponding to the motor vehicle is:

[0020]

[0021]

[0022] The risk function corresponding to the motor vehicle is:

[0023]

[0024]

[0025] Wherein, α k,x , β k,x , α k,y and β k,y respectively determine the influence degree of the kth motor vehicle around the risk value with distance and speed change, the four factors are mainly related to the type of motor vehicle, for example, compared with a small car, at the same speed, a large truck will maintain a larger relative distance.v k,x (t) and v k,y (t) are the longitudinal speed and lateral speed of the kth motor vehicle at time t, L k and W k are the length and width of the kth motor vehicle, δ x,k (x,y,t) is the longitudinal risk attenuation coefficient of the motor vehicle, δ y,k (x,y,t) is the lateral risk attenuation coefficient of the motor vehicle, and δ k (x,y,t) is the risk attenuation coefficient of the motor vehicle.

[0026] Step 24, for each grid point, superimpose the risk of all vehicles to the grid point in the form of taking the maximum value.

[0027] Further, the risk based on all grid center points in step 3 is used to establish the intersection overall risk level evaluation index, which can be described by the following expression:

[0028]

[0029] wherein, n1 is the number of grids after intersection discretization, r i Risk is the risk value of the ith grid point, w0 and w1 determine the influence degree of the risk value on the safety level, and Risk is the overall risk level evaluation index of the intersection.

[0030] Further, the step 4 establishes the vehicle passing path at the intersection based on the positional relationship between the intersection entrance and exit, including the following steps:

[0031] Step 41, taking the intersection entrance as the reference, the midpoint of the intersection entrance stop line as the origin of the coordinate system, the straight line where the stop line is located as the X-axis of the coordinate system, and the perpendicular of the stop line as the Y-axis of the coordinate system, a coordinate system is established.

[0032] Step 42, a vehicle passing path based on a third-order Bezier curve is established, which is controlled by control points P o , P d , P1 and P2, wherein P o represents the starting point of the vehicle path, with coordinates (0, 0); P d represents the end point of the vehicle path, with coordinates (w, h); P1 controls the shape of the path after the vehicle enters the intersection, and is located on the extension line of the intersection entrance center, with coordinates (0, L1); P2 controls the shape of the path when the vehicle is about to leave the intersection, and is located on the extension line of the intersection exit center, with coordinates (w-L2sinα, h-L2cosα); L1 represents the distance between the control points P o and P1, and L2 represents the distance between the control points P d and P2; for each set of intersection entrance and exit, the optimal control parameters L1 and L2 of the vehicle path can be obtained through the actual trajectory data of the vehicle. Wherein, w and h represent the transverse and longitudinal distances between the midpoint of the entrance exit and the midpoint of the exit entrance, respectively, and α represents the supplementary angle of the included angle between the straight lines where the entrance and exit are located, i.e. the included angle between the straight line where the exit is located and the Y-axis.

[0033] Further, the vehicle motion control model based on the overall risk level evaluation index of the intersection and the vehicle motion constraint, with the lowest overall risk level of the intersection as the target, can be described by the following expression:

[0034]

[0035]

[0036] i = 1, 2…n1, j = 1, 2…n2, k = 1, 2…N p

[0037] wherein, r iRisk value of the ith grid point, a j Acceleration of the jth vehicle at a certain moment, v j Speed of the jth vehicle at a certain moment, x j Position of the jth vehicle at a certain moment, n1 is the number of grids after discretization of the intersection, n2 is the number of vehicles inside the intersection, N p Predicted step number, Δt is the predicted step length, a j,min Lower bound of the acceleration value, a j,max Upper bound of the acceleration value, a des Desired acceleration input of each vehicle. BRIEF DESCRIPTION OF DRAWINGS

[0038] Figure 1 Discretization diagram of the intersection

[0039] Figure 2 Diagram of the vehicle coordinate system

[0040] Figure 3 Diagram of the vehicle passing path DETAILED DESCRIPTION

[0041] The present application will be described in detail below in conjunction with the drawings and embodiments. It should be understood that the examples are only used to illustrate the present application and are not used to limit the scope of the present application. The vehicle cooperative control method provided by the present application considers the overall risk level of the intersection, and the specific implementation method includes the following steps:

[0042] Step 1, based on laser radar, millimeter wave radar, GPS, combined inertial navigation and networked equipment, obtain the motion state data of all vehicles inside the intersection;

[0043] Step 2, discretize the intersection in the form of a grid, respectively calculate the risk caused by each vehicle inside the intersection to each grid center point, and superimpose by taking the maximum value, the specific steps are as follows:

[0044] Step 21, discretize the intersection as shown in Figure 1 at a step length of 0.1 m, obtain the coordinates of all grid center points in the world coordinate system after discretization of the intersection, represented as (x i ,y i );

[0045] Step 22, respectively take the heading direction of each vehicle inside the intersection as the x-axis, the lateral direction as the y-axis, and the vehicle center as the origin to establish a coordinate system, as shown in Figure 2 , based on the coordinate transformation formula, respectively transform the world coordinates of the grid center points to the vehicle coordinate system, and the coordinate transformation formula is as follows:

[0046]

[0047] Step 23, calculate the risk of all vehicles in the intersection to each grid center point, the specific risk calculation formula is as follows:

[0048] The longitudinal risk function corresponding to the motor vehicle is:

[0049]

[0050]

[0051] The lateral risk function corresponding to the motor vehicle is:

[0052]

[0053]

[0054] The risk function corresponding to the motor vehicle is:

[0055]

[0056]

[0057] Wherein, α k,x , β k,x , α k,y and β k,y respectively determine the influence degree of the kth motor vehicle around the risk value with distance and speed change, the four factors are mainly related to the type of motor vehicle, for example, compared with a small car, at the same speed, a large truck will maintain a larger relative distance.v k,x (t) and v k,y (t) are the longitudinal speed and lateral speed of the kth motor vehicle at time t, L k and W k are the length and width of the kth motor vehicle, δ x,k (x,y,t) is the longitudinal risk attenuation coefficient of the motor vehicle, δ y,k (x,y,t) is the lateral risk attenuation coefficient of the motor vehicle, δ k (x,y,t) is the risk attenuation coefficient of the motor vehicle.

[0058] Step 24, for each grid point, superimpose the risk of all vehicles to the grid point in the form of taking the maximum value.

[0059] Step 3, based on the risk of all grid center points, establish the intersection overall risk level evaluation index, which can be described by the following expression:

[0060]

[0061] wherein n1 is the number of grids after the intersection is discretized, r i is the risk value of the i-th grid point, w0 and w1 determine the influence degree of the risk value on the safety level, Risk is the overall risk level evaluation index of the intersection, w0 is 1, and w1 is 0.5.

[0062] Step 4, based on the positional relationship between the entrance and exit of the intersection, the passing path of the vehicle at the intersection is established, as shown in Figure 3 .

[0063] Step 5, based on the overall risk level evaluation index of the intersection established in step 3 and the vehicle motion constraint, a vehicle motion control model with the lowest overall risk level of the intersection as the target is established. It can be described by the following expression:

[0064]

[0065]

[0066] i = 1, 2…n1, j = 1, 2…n2, k = 1, 2…N p

[0067] wherein r i is the risk value of the i-th grid point, a j is the acceleration of the j-th vehicle at a certain moment, v j is the speed of the j-th vehicle at a certain moment, x j is the position of the j-th vehicle at a certain moment, n1 is the number of grids after the intersection is discretized, n2 is the number of vehicles inside the intersection, N p is the number of steps for prediction, Δt is the step length for prediction, a j,min is the lower limit of the acceleration value, a j,max is the upper limit of the acceleration value, a des is the expected acceleration input of each vehicle, w2 is 1, and N p is 5, and a solver is used for solving.

[0068] The above steps describe the implementation process of the present application in detail, but the present application is not limited to the specific details in the above embodiments. Any within the concept of the present application should not be excluded from the protection scope of the present application.

Claims

1. A vehicle cooperative control method considering an overall risk level of an intersection, characterized by, Comprise the following steps: Step 1, based on laser radar, millimeter wave radar, GPS, combined inertial navigation and networked equipment to obtain the motion state data of all vehicles inside the intersection; Step 2, the intersection is discretized in the form of grid, the risk caused by each vehicle in the intersection to each grid center point is calculated respectively, and the maximum value is taken to superimpose; Step 3, based on the risk of all grid center points, the intersection overall risk level evaluation index is established, which is described by the following expression: wherein n1 is the number of discrete grids after the intersection, r i Risk is the risk value of the ith grid point, w0and w1determine the influence degree of the risk value on the safety level, and Risk is the overall risk level evaluation index of the intersection. Step 4, based on the position relationship of the entrance and exit of the intersection, the passing path of the vehicle in the intersection is established; Step 5, based on the intersection overall risk level evaluation index established in step 3 and the vehicle motion constraint, a vehicle motion control model with the lowest intersection overall risk level as the target is established, which is described by the following expression: i = 1,2...ni, j = 1,2...n2, k = 1,2...N p where r i is the risk value of the ith grid point, a j is the acceleration of the jth vehicle at a certain time, v j is the speed of the jth vehicle at a certain time, x j is the position of the jth vehicle at a certain time, n1 is the number of grids after discretization of the intersection, n2 is the number of vehicles inside the intersection, N p is the number of steps of prediction, Δt is the step length of prediction, a j,min is the lower bound of the acceleration value, a j,max is the upper bound of the acceleration value, a des is the expected acceleration input of each vehicle. 2.The vehicle cooperative control method of claim 1, wherein, Step 2 is discretized in the form of grid, the risk caused by each vehicle in the intersection to each grid center point is calculated respectively, and the maximum value is taken to superimpose, comprising the following steps: Step 21, get the coordinates of all grid center points in the world coordinate system after the intersection is discretized, denoted as (x i ,y i ) Step 22, respectively, the heading direction of each vehicle in the intersection is taken as the x-axis, the transverse direction is taken as the y-axis, and the vehicle center is taken as the origin to establish the coordinate system, and the world coordinates of the grid center points are converted to the vehicle coordinate system based on the coordinate conversion formula, and the coordinate conversion formula is as follows: Wherein, x, y are the coordinates of the grid center point in the vehicle coordinate system; Step 23, calculate the risk caused by all vehicles in the intersection to each grid center point, and the specific risk calculation formula is as follows: The longitudinal risk function corresponding to the motor vehicle is: The transverse risk function corresponding to the motor vehicle is: The risk function corresponding to the motor vehicle is: wherein a k,x , β k,x , a k,y and β k,y respectively determine the degree of influence of the distance and speed on the risk value of the kth motor vehicle, v k,x (t) and v k,y (t) are the longitudinal and lateral speed of the kth motor vehicle at time t, L k and W k are the length and width of the kth motor vehicle, δ x,k (x, y, t) is the longitudinal risk attenuation coefficient of the motor vehicle, δ y,k (x, y, t) is the lateral risk attenuation coefficient of the motor vehicle, and δ k (x, y, t) is the risk attenuation coefficient of the motor vehicle. Step 24, for each grid point, the risk generated by all vehicles to the grid point is superimposed in the form of taking the maximum value.

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