Method for calculating mutual interference intensity between vehicles in crowd sensing environment and early warning system

Abstract

The invention discloses a method for calculating the mutual interference intensity between vehicles in a crowd intelligence perception environment, and the method comprises the steps: building a vehicle group coordinate system, building a Cartesian coordinate system with a leftmost lower vehicle in an intelligent network connection vehicle group as a coordinate center, and converting all vehicles in the intelligent network connection vehicle group into the coordinate system; calculating a driving influence area, and calculating a corresponding driving influence area based on the behavior data of each vehicle; calculating the mutual interference intensity of adjacent vehicles, calculating the overlapping area and potential energy ratio of the driving influence areas of the adjacent vehicles, and obtaining the mutual interference intensity value between the vehicles by taking the average value of the two ratios; a multi-vehicle interaction directed network is constructed, and mutual interference behaviors among intelligent network connection vehicle groups are represented through a network theory; and risk grading and early warning: carrying out grading on the interaction risk between the vehicles based on the mutual interference intensity value between the adjacent vehicles, and carrying out early warning aiming at the occurring risk. According to the method, support is provided for vehicle running state monitoring through calculation of the mutual interference intensity between the vehicles in the crowd sensing environment and risk grade division.

Description

Technical Field

[0001] The present invention relates to the field of risk warning for intelligent connected vehicles, and in particular to a method for calculating the intensity of mutual interference between vehicles in a crowd-sensing environment and a warning system. Background Art

[0002] In an intelligent connected environment, quantifying the degree of mutual interference between vehicles is an important support for ensuring driving safety and traffic efficiency. With the development of crowd sensing technology, vehicles can not only perceive the surrounding environment in real time through their own sensors, but also share information and make collaborative decisions with surrounding vehicles, infrastructure equipment, etc. However, in a crowd sensing environment, the problem of mutual interference between vehicles becomes more complicated. Behaviors such as speed, lane changes, and emergency braking may cause mutual interference between vehicles, leading to safety hazards and reduced traffic efficiency. Although existing technologies can capture vehicle behavior to a certain extent, how to accurately calculate the intensity of mutual interference between vehicles in a dynamic traffic environment and prevent and control risks through an effective early warning mechanism has become one of the hot topics in current intelligent transportation research.

[0003] The traditional way of calculating the risk of interaction between vehicles is limited by the horizontal and vertical divisions, and it is difficult to meet the needs of risk quantification between vehicles with different position relationships in a group of intelligent connected vehicles, such as adjacent vehicles involving both horizontal and vertical interactions.

[0004] In order to deal with these problems, it is urgent to develop a method that can dynamically calculate the intensity of mutual interference between vehicles in complex road environments. Combining the advantages of crowd sensing technology, making full use of multi-source sensing data to improve calculation accuracy and real-time performance, and preventing possible safety accidents in advance through early warning systems to ensure efficient operation of traffic flow. Summary of the invention

[0005] Purpose of the invention: In view of the defects of insufficient calculation accuracy and real-time performance in the prior art, the present invention provides a method for calculating the intensity of mutual interference between vehicles in a group intelligence perception environment and a warning system.

[0006] Technical solution: The method for calculating the mutual interference intensity between vehicles in a population intelligent perception environment of the present invention comprises the following steps:

[0007] (1) Constructing the vehicle group coordinate system: Establishing a Cartesian coordinate system with the lower left vehicle in the intelligent connected vehicle group as the coordinate center, and converting all vehicles in the intelligent connected vehicle group into this coordinate system;

[0008] (2) Calculation of driving influence area: Calculate the corresponding driving influence area based on the behavior data of each vehicle;

[0009] (3) Calculation of the mutual interference intensity between adjacent vehicles: Calculate the overlapping area and potential energy ratio of the driving influence zones of adjacent vehicles, and take the average of the two ratios to obtain the mutual interference intensity value between vehicles;

[0010] (4) Construction of a multi-vehicle interactive directed network, characterizing the mutual interference behavior among intelligent connected vehicles through network theory;

[0011] (5) Risk level classification and early warning: The interaction risks between vehicles are classified into different levels based on the mutual interference intensity values ​​between adjacent vehicles, and early warnings are issued for any risks that arise.

[0012] Furthermore, the method of step (1) is as follows: determining the size of the intelligent connected vehicle group, selecting the central vehicle within the spatial range as the center, constructing a Cartesian coordinate system with the vehicle's driving direction as the positive direction of the x-axis and the 90-degree counterclockwise direction of the x-axis as the positive direction of the y-axis, and integrating all vehicles in the intelligent connected vehicle group into the Cartesian coordinate system according to their relative position relationship with the central vehicle, so that each vehicle has a unique coordinate value in the coordinate system, and storing the information of all vehicles in the intelligent connected vehicle group.

[0013] Furthermore, the method of step (2) is as follows: for each vehicle in the intelligent connected vehicle group, the lateral and longitudinal possible affected ranges of the vehicle are calculated to form the vehicle's driving impact area.

[0014] Furthermore, for the distance in front of the vehicle, the safe stopping distance of the vehicle is calculated as the boundary distance in the longitudinal front of the influence zone; for the distance behind the vehicle, the safety field distribution of the vehicle is first calculated, and the potential energy value at the influence distance in front of the vehicle is determined, and the longitudinal rear influence distance is taken as the position where the safety field behind the vehicle is equal to the potential energy; for the lateral influence distance, if the lateral speed of the vehicle is zero, the lateral influence distance of the vehicle is equal, and the value is equal to half the width of the driving lane; if the lateral speed of the vehicle is not zero, the lateral safe stopping distance in the direction where the speed is not zero is the corresponding influence distance, and the other direction is half the width of the driving lane, thereby forming the vehicle's driving influence zone. If the vehicle's driving road is a straight line, the shape of the driving influence zone is a rectangle; if the vehicle's driving road is a curve, the shape of the driving influence zone is consistent with the shape of the driving road.

[0015] Furthermore, the method is as follows:

[0016] S1: Calculate the potential energy field distribution around the vehicle based on the vehicle's lateral / longitudinal speed and acceleration;

[0017] First, calculate the potential energy value of any point around the vehicle and the center point of the vehicle, and then get the potential energy distribution around the vehicle. Assuming that the coordinates of any point around the vehicle are (x, y), the potential energy value of the point is calculated as follows:

[0018]

[0019] Among them, mi is the actual mass of the vehicle, (x0, y0) is the coordinate of the current vehicle position, (x, y) is the coordinate of any position around the vehicle where the field strength needs to be calculated, α and τ are parameters to be determined related to speed, α = 0.029, τ = 2.717, λ and β0 are coefficients to be determined, λ = 0.056, β0 = -0.169, θ is the clockwise angle between any point in the surrounding area and its center of mass and its speed, E(x, y) is the field strength at the position with coordinates (x, y);

[0020] S2: For the longitudinal direction, based on the longitudinal speed and acceleration of the vehicle, calculate the affected distance in front of the vehicle;

[0021] The possible affected distance (d front ), calculated as:

[0022]

[0023] Among them, v x and a x The longitudinal velocity and longitudinal acceleration of the vehicle are in m / s and m / s respectively. 2 ; τ lon is the driver's longitudinal reaction time, which is 1.2s; a xmax is the maximum deceleration that the vehicle can achieve, take 8.45m / s 2 ; L veh is the length of the current vehicle;

[0024] S3: For the longitudinal direction, determine the potential energy value at the impact distance in front of the vehicle, determine the impact distance behind the vehicle through the equipotential energy line, determine the equipotential line corresponding to the potential energy at the impact distance in front of the vehicle, move toward the rear of the vehicle along the equipotential line, and stop when the equipotential line moves to the rear of the vehicle. This point is the distance behind the vehicle's driving impact area;

[0025] S4: For the lateral direction, if the lateral speed of the vehicle is 0, the affected distances on the left and right sides of the vehicle are determined to be half the width of the lane. When the lateral speed of the vehicle is 0, it means that the vehicle has no lateral movement. At this time, the distances on both sides of the vehicle's impact area are half the width of the lane in which the vehicle is traveling.

[0026] S5: For the lateral direction, for the side where the lateral speed of the vehicle is not 0, the lateral safe stopping distance of the vehicle is calculated as the affected distance. The affected distance on the other side is half the width of the lane. When the lateral speed of the vehicle is not 0, it means that the vehicle is moving laterally. The affected distance in the lateral moving direction should be larger. Assuming that the vehicle moves to the left, the affected distance on the left side (d left ) is calculated by the following formula:

[0027]

[0028] Among them, v y and a y The lateral velocity and lateral acceleration of the vehicle are in m / s and m / s respectively. 2 , τ lat is the driver's lateral reaction time, take 1.7s, a ymax is the maximum lateral deceleration of the vehicle, taken as 0.68m / s 2 , w lane The width of a single lane is 3.75m.

[0029] Right impact distance (d right ) is half the lane width:

[0030]

[0031] When the vehicle moves to the right, the calculation methods for the left and right sides should be interchanged.

[0032] Furthermore, the method of step (3) is as follows: for adjacent vehicle pairs, the overlapping part of the driving influence areas of the two vehicles is calculated, and the mutual interference intensity is calculated from two aspects: the degree of proximity of the conflict space and the severity of the conflict. For the degree of proximity of the conflict space, the area of ​​the overlapping part of the driving influence area is calculated, and the area ratio of the influence area is calculated with the smaller area of ​​the two vehicle driving influence areas as the denominator; for the severity of the conflict, the sum of the potential energy of the overlapping part of the driving influence area is calculated, and the potential energy ratio is calculated with the sum of the potential energy in the driving influence area of ​​the vehicle with the smaller area as the denominator; the average of the two calculated ratios is taken to obtain the mutual interference intensity value between vehicles. Further, the mutual interference intensity value is calculated for all adjacent vehicle pairs in the intelligent connected vehicle group, and the minimum value of the calculated mutual interference intensity between vehicles is 0, and the maximum value is 1.

[0033] Furthermore, the specific method is:

[0034] S1: For the driving influence zones of adjacent vehicles, determine the overlapping area of ​​the driving influence zones of the two vehicles.

[0035] Specifically, in the same coordinate system, the influence area of ​​the adjacent vehicle is determined according to the vertices of the influence areas of the two vehicles. If the overlapping area of ​​the influence areas of the adjacent vehicles is 0, the mutual interference intensity between the adjacent vehicles is judged to be 0, that is, there is no mutual interference behavior;

[0036] S2: If the overlapping area is not 0, calculate the size of the overlapping area, and further calculate the ratio of the overlapping area to the smaller driving influence area of ​​the two vehicles.

[0037] Specifically, the calculation method is as follows:

[0038]

[0039] Among them, A i and A j Represent the influence area of ​​vehicles i and j, ROL ij is the ratio of the overlapping area of ​​the influence zone to the minimum area of ​​the influence zone of vehicle i and vehicle j,

[0040] S21: Calculate the potential energy ratio of the overlapping parts of the influence zones of adjacent vehicles. The calculation formula is as follows:

[0041] S3: Calculate the proportion of the potential energy in the overlapping area to the potential energy of the smaller driving influence area of ​​the two vehicles by integration

[0042] Specifically, the calculation method is as follows:

[0043]

[0044] A i and A j denote the influence areas of vehicles i and j respectively, E i (x, y) represents the potential energy calculation formula when the coordinate position of vehicle i is (x, y),

[0045] S4: The mutual interference intensity value of adjacent vehicles is obtained by taking the average of the area ratio and potential energy ratio of the overlapping parts of the driving influence zone.

[0046] Specifically, calculate the mutual interference intensity value (IS ij ), as follows:

[0047] IS ij =ROL i，j ×(RE i +RE j ) / 2

[0048] Through the above steps, the mutual interference strength values ​​of all directly adjacent vehicle pairs in the vehicle group are calculated.

[0049] Furthermore, the method of step (4) is as follows: for all vehicles in the intelligent connected vehicle group, a multi-vehicle interactive directed network is constructed, with nodes representing vehicles, edges representing the mutual interference relationship between vehicles, the weight value on the edge is equal to the mutual interference intensity value, and the direction is from the front vehicle to the rear vehicle. If adjacent vehicles are traveling completely in parallel, the two nodes are connected by a bidirectional arrow.

[0050] Furthermore, the specific method is as follows:

[0051] S1: Create a directed network graph, where the number of nodes is equal to the number of vehicles in the intelligent connected vehicle group, and the position coordinates of the nodes are the actual position coordinates of the vehicles. Specifically, after performing coordinate conversion on the perception data of the intelligent connected vehicle group, the intelligent connected vehicle group is converted into a multi-vehicle interactive directed network, where each node represents a vehicle, and the position coordinates of the node are the same as the actual position of the vehicle;

[0052] S2: For each pair of adjacent vehicles, if the calculated mutual interference strength value between vehicles is greater than 0, the nodes representing the two vehicles are connected by edges, with the direction from the front vehicle to the rear vehicle. If the two vehicles are parallel, they are connected by double arrows. For the actual directly adjacent intelligent connected vehicles, the calculated mutual interference strength value between vehicles is extracted. If the mutual interference strength value is equal to 0, it means that there is no edge connection between the nodes of the two vehicles.

[0053] S3: For nodes connected by edges, the weight value on the edge is equal to the mutual interference strength value of the two vehicles. Specifically, for edges connecting nodes, the weight value on the edge is the mutual interference strength value between the connected vehicles.

[0054] S4: Classification of vehicle-to-vehicle interaction risk levels; if the mutual interference intensity value belongs to (0, 0.39], it is judged as low risk; if the mutual interference intensity value belongs to (0.39, 0.62], it is judged as medium risk; if the mutual interference intensity value belongs to (0.62, 1], it is judged as high risk,

[0055] Specifically, the mutual interference risk level between vehicles is judged based on the size of the weight value on the network edge. When the mutual interference intensity value is in the range of (0, 0.39], the system judges that the mutual influence between vehicles is small, and the relative distance, speed and other factors of the vehicles indicate that the current state is relatively safe. In this case, the system will not trigger an early warning, and the vehicle can continue to drive normally without the driver having to perform any additional operations. When the mutual interference intensity value is in the range of (0.39, 0.62], the system judges it as a medium risk, indicating that the mutual influence between vehicles begins to increase and there may be potential dangers. When the mutual interference intensity value is in the range of (0.62, 1], the system judges that there is a high degree of mutual interference between vehicles, the potential risk of accidents is high, and serious traffic accidents may be triggered.

[0056] S5: For medium- and high-risk edges, warn the vehicles corresponding to the nodes on the edges. Specifically, if the two vehicles are in a front-to-back relationship, warn that the front vehicle may be rear-ended by the rear vehicle, and warn that the rear vehicle may rear-end the front vehicle. If the two vehicles are in a parallel relationship, warn that the two vehicles may have a side-hanging accident with the vehicle on the left or right.

[0057] Specifically, for the detected medium and high-risk vehicles, the system will issue an early warning to the vehicles related to the edge. If the relationship between the two vehicles is detected as a front-to-back relationship, that is, the driving trajectories of the two vehicles are arranged longitudinally, and there is a high relative speed or a small distance between vehicles, the system will issue an early warning to the relevant vehicles. Specifically, the vehicle in front will receive an early warning signal, indicating that it may face the risk of rear-end collision with the vehicle behind; at the same time, the vehicle behind will also receive a corresponding early warning, indicating that it is at risk of rear-end collision with the vehicle in front, and recommending that it adjust its speed or maintain a safe distance.

[0058] If the system detects that the two vehicles are in a parallel relationship, that is, they are traveling side by side in the same direction, and their lateral distance is at a critical value or risk value, the system will send warnings to both vehicles at the same time. At this time, the warning information received by the two vehicles will remind them that they are at risk of side collision with the adjacent vehicle, and suggest that the driver avoid potential danger by adjusting the speed or changing lanes. The sending of warning information can be achieved through the in-vehicle display, sound prompts or intelligent driving assistance system, so as to help the driver respond in time and reduce the probability of accidents.

[0059] Furthermore, the method of step (5) is as follows: for the constructed multi-vehicle interactive directed network, the mutual interference risk level is judged based on the weight values ​​on the connecting edges of adjacent vehicles; for the risk level classification of vehicles, different levels of mutual interference risk level classification are performed through a threshold classification method, and the classification thresholds include: 0-0.39 for low risk, 0.39-0.62 for medium risk, and 0.62-1 for high risk; if the mutual interference intensity between the vehicles is greater than different thresholds, an early warning is issued, and the early warning direction is the vehicle at the tail of the connecting edge arrow pointing to the vehicle indicated by the arrow.

[0060] A warning system, when the instructions of the warning system are executed by a processor, executes the method for calculating and warning the intensity of mutual interference between vehicles in a crowd sensing environment according to any one of claims 1 to 5.

[0061] Compared with the prior art, the advantages of the present invention are:

[0062] In the intelligent connected vehicle group perception environment, the present invention simultaneously considers the lateral and longitudinal behaviors of vehicles, invents vehicle driving impact areas, adjacent vehicle mutual interference intensity, multi-vehicle interactive directed networks, and vehicle mutual interference risk warnings, which can provide technical support for risk quantification between vehicles in the intelligent connected environment, and further provide effective support for improving road traffic safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 A schematic diagram of a vehicle position coordinate system in a crowd-sensing environment provided by an embodiment of the present invention;

[0064] Figure 2 A flow chart of vehicle driving influence zone calculation provided by an embodiment of the present invention;

[0065] Figure 3 A flowchart for calculating the mutual interference intensity of adjacent vehicles provided in an embodiment of the present invention;

[0066] Figure 4 A flowchart for constructing a multi-vehicle interactive directed network provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0067] The present invention is described in detail below in conjunction with specific embodiments. The following embodiments will help those skilled in the art to further understand the present invention, but are not intended to limit the present invention in any form. It should be noted that, for those of ordinary skill in the art, several changes and improvements can also be made without departing from the concept of the present invention. These all belong to the protection scope of the present invention.

[0068] Figure 1 A schematic diagram of a vehicle position coordinate system in a crowd sensing environment provided by an embodiment of the present invention, such as Figure 1 As shown, the coordinate origin is the center position of the lower left corner of all vehicles in the group, the driving direction of all vehicles is consistent with the longitudinal direction, and the lateral direction is perpendicular to the driving direction.

[0069] In this embodiment, a vehicle-side detector of an intelligent networked vehicle is used as a way to obtain information about surrounding vehicles, and the vehicle's own information is obtained through the vehicle's own sensors. The intensity of mutual interference between vehicles is calculated based on the perception data of the group of intelligent vehicles. The perception information of the vehicle itself and surrounding vehicles includes the lateral and longitudinal speeds, lateral and longitudinal accelerations, and vehicle size information of the vehicle. Although the present invention uses the perception data of a group of intelligent networked vehicles as an example to explain the technology, the method for calculating the intensity of mutual interference between vehicles and the early warning system of the present invention are also applicable to the full sample vehicle behavior and size data collected by the roadside radar vision integrated machine or other equipment.

[0070] Figure 2 The vehicle driving influence zone calculation flow chart provided by the embodiment of the present invention is as follows: Figure 2 As shown, the vehicle driving influence area calculation provided by the embodiment of the present invention includes the following steps:

[0071] S1: Calculate the potential energy field distribution around the vehicle based on the vehicle's lateral / longitudinal speed and acceleration;

[0072] Specifically, the safety field around the vehicle is calculated based on the lateral / longitudinal speed, acceleration and size information of each vehicle. First, the potential energy value of any point around the vehicle and the center point of the vehicle is calculated, and then the potential energy distribution around the vehicle is obtained. Assuming that the coordinates of any point around the vehicle are (x, y), the potential energy value of the point is calculated as follows:

[0073]

[0074] Among them, m i is the actual mass of the vehicle, (x0, y0) is the coordinate of the current vehicle position, (x, y) is the coordinate of any position around the vehicle where the field strength needs to be calculated, α and τ are parameters to be determined related to speed, α=0.029, τ=2.717, λ and β0 are coefficients to be determined, λ=0.056, β0=-0.169, θ is the clockwise angle between any point in the surrounding area and the line connecting its center of mass and its speed, and E(x, y) is the field strength at the position with coordinates (x, y).

[0075] S2: For the longitudinal direction, based on the longitudinal speed and acceleration of the vehicle, calculate the affected distance in front of the vehicle.

[0076] Specifically, the possible affected distance (d front ). The calculation method is:

[0077]

[0078] Among them, v x and a x The longitudinal velocity and longitudinal acceleration of the vehicle are in m / s and m / s respectively. 2 ; τ lon is the driver's longitudinal reaction time, which is 1.2s; a xmax is the maximum deceleration that the vehicle can achieve, take 8.45m / s 2 ; L veh is the length of the current vehicle.

[0079] S3: For the longitudinal direction, determine the potential energy value at the impact distance in front of the vehicle, and determine the impact distance behind the vehicle through the equal potential energy lines.

[0080] Specifically, determine the equipotential line corresponding to the potential energy at the influence distance in front of the vehicle, move along the equipotential line toward the rear of the vehicle, and stop when the equipotential line moves to the rear of the vehicle. This point is the distance behind the vehicle's driving influence area.

[0081] S4: For the lateral direction, if the lateral speed of the vehicle is 0, the affected distances on the left and right sides of the vehicle are determined to be half the width of the driving lane.

[0082] Specifically, when the lateral speed of the vehicle is 0, it means that the vehicle has no lateral movement. At this time, the distances on both sides of the vehicle's impact zone are half the width of the lane in which the vehicle is traveling.

[0083] S5: For the lateral direction, for the side where the lateral speed of the vehicle is not 0, the lateral safe stopping distance of the vehicle is calculated as the affected distance, and the affected distance on the other side is half the width of the lane.

[0084] Specifically, when the lateral velocity of the vehicle is not 0, it means that the vehicle is moving laterally, and the impact distance in the lateral direction should be larger. Assuming that the vehicle moves to the left, the impact distance on the left (d left ) is calculated by the following formula:

[0085]

[0086] Among them, v y and a y The lateral velocity and lateral acceleration of the vehicle are in m / s and m / s respectively. 2 , τ lat is the driver's lateral reaction time, take 1.7s, a ymax is the maximum lateral deceleration of the vehicle, taken as 0.68m / s 2 , w lane The width of a single lane is 3.75m.

[0087] Right impact distance (d right ) is half the lane width:

[0088]

[0089] When the vehicle moves to the right, the calculations for the left and right sides should be swapped.

[0090] Figure 3 The flow chart of calculating the mutual interference intensity of adjacent vehicles provided by the embodiment of the present invention is as follows: Figure 3 As shown, the calculation of the mutual interference intensity of adjacent vehicles provided in the embodiment of the present invention includes the following steps:

[0091] S1: For the driving influence zones of adjacent vehicles, determine the overlapping area of ​​the driving influence zones of the two vehicles

[0092] Specifically, in the same coordinate system, the influence area of ​​the adjacent vehicle is determined according to the vertices of the influence areas of the two vehicles. If the overlapping area of ​​the influence areas of the adjacent vehicles is 0, the mutual interference intensity between the adjacent vehicles is judged to be 0, that is, there is no mutual interference behavior.

[0093] S2: If the overlapping area is not 0, calculate the size of the overlapping area, and further calculate the proportion of the overlapping area to the smaller driving influence area of ​​the two vehicles.

[0094] Specifically, the calculation method is as follows:

[0095]

[0096] Among them, A i and A j Represent the influence areas of vehicles i and j respectively. ij is the ratio of the overlapping area of ​​the influence zone to the minimum area of ​​the influence zone of vehicle i and vehicle j.

[0097] S21: Calculate the potential energy ratio of the overlapping parts of the influence zones of adjacent vehicles. The calculation formula is as follows:

[0098] S3: Calculate the proportion of the potential energy in the overlapping area to the potential energy of the smaller driving influence area of ​​the two vehicles by integration

[0099] Specifically, the calculation method is as follows:

[0100]

[0101] A i and A j denote the influence areas of vehicles i and j respectively, E i (x, y) represents the potential energy calculation formula when the coordinate position of the vehicle i is (x, y).

[0102] S4: The mutual interference intensity value of adjacent vehicles is obtained by taking the average of the area ratio and potential energy ratio of the overlapping parts of the driving influence zone.

[0103] Specifically, calculate the mutual interference intensity value (IS ij ), as follows:

[0104] IS ij =ROL i，j ×(RE i +RE j ) / 2

[0105] Through the above steps, the mutual interference strength values ​​of all directly adjacent vehicle pairs in the vehicle group are calculated.

[0106] Figure 4 A flowchart of constructing a multi-vehicle interactive directed network provided by an embodiment of the present invention is shown in FIG. Figure 3 As shown, the multi-vehicle interactive directed network construction provided by the embodiment of the present invention includes the following steps:

[0107] S1: Create a directed network graph, where the number of nodes is equal to the number of vehicles in the intelligent connected vehicle group, and the location coordinates of the nodes are the actual location coordinates of the vehicles.

[0108] Specifically, after the coordinate transformation of the perception data of the intelligent networked vehicle group, the intelligent networked vehicle group is transformed into a multi-vehicle interactive directed network. Each node represents a vehicle, and the position coordinates of the node are the same as the actual position of the vehicle.

[0109] S2: For each pair of adjacent vehicles, if the calculated mutual interference intensity value between the vehicles is greater than 0, the nodes representing the two vehicles are connected by edges, with the direction pointing from the front vehicle to the rear vehicle, and double arrows are used to connect the two vehicles if they are parallel.

[0110] Specifically, for the actual directly adjacent intelligent connected vehicles, the calculated mutual interference strength value between vehicles is extracted. If the mutual interference strength value is less than 0, it means that there is no edge connection between the nodes of the two vehicles. If the mutual interference strength value is greater than 0, an edge is added to connect the two nodes, and the direction is from the front vehicle to the rear vehicle, that is, the vehicle with a larger longitudinal coordinate value points to the vehicle with a smaller longitudinal coordinate value. If the longitudinal coordinate values ​​of the two adjacent vehicles are equal, a double arrow is used to connect the two vehicles.

[0111] S3: For nodes connected by edges, the weight value on the edge is equal to the mutual interference intensity value of the two vehicles.

[0112] Specifically, for the edge connecting the nodes, the weight value on the edge is taken as the mutual interference intensity value between the connected vehicles.

[0113] S4: Classification of vehicle-to-vehicle interaction risk levels; if the mutual interference intensity value belongs to (0, 0.39], it is judged as low risk; if the mutual interference intensity value belongs to (0.39, 0.62], it is judged as medium risk; if the mutual interference intensity value belongs to (0.62, 1], it is judged as high risk.

[0114] Specifically, the risk level of mutual interference between vehicles is determined based on the size of the weight value on the network edge. When the mutual interference intensity value is in the range of (0,0.39], the system determines that the mutual influence between vehicles is small, and factors such as the relative distance and speed of the vehicles indicate that the current state is relatively safe. In this case, the system will not trigger an early warning, the vehicle can continue to drive normally, and the driver does not need to perform any additional operations. When the mutual interference intensity value is in the range of (0.39,0.62], the system determines that it is a medium risk, indicating that the mutual influence between vehicles begins to increase and there may be potential dangers. When the mutual interference intensity value is in the range of (0.62,1], the system determines that there is a high degree of mutual interference between vehicles, the potential risk of accidents is greatly increased, and more serious consequences may be triggered.

[0115] S5: For edges with medium and high risks, early warning is issued for vehicles corresponding to the connected nodes on the edge; specifically, if the two vehicles are in a front-to-back relationship, early warning is issued that the front vehicle may be rear-ended by the rear vehicle, and early warning is issued that the rear vehicle may rear-end the front vehicle; if the two vehicles are in a parallel relationship, early warning is issued that the two vehicles may have a side-hanging accident with the vehicle on the left or right.

[0116] Specifically, for the detected medium and high-risk vehicles, the system will issue an early warning to the vehicles related to the edge. If the relationship between the two vehicles is detected as a front-to-back relationship, that is, the driving trajectories of the two vehicles are arranged longitudinally, and there is a high relative speed or a small distance between vehicles, the system will issue an early warning to the relevant vehicles. Specifically, the vehicle in front will receive an early warning signal, indicating that it may face the risk of rear-end collision with the vehicle behind; at the same time, the vehicle behind will also receive a corresponding early warning, indicating that it is at risk of rear-end collision with the vehicle in front, and recommending that it adjust its speed or maintain a safe distance.

[0117] If the system detects that the relationship between the two vehicles is parallel, that is, the two vehicles are traveling side by side in the same direction, and their lateral distance is at a critical value or risk value, the system will send a warning to both vehicles at the same time. At this time, the warning information received by the two vehicles will remind them that they are at risk of side-mounted collision with the adjacent vehicle, and suggest that the driver avoid potential danger by adjusting the speed or changing lanes. The sending of warning information can be achieved through the in-vehicle display, sound prompts or intelligent driving assistance system, so as to help the driver respond in time and reduce the probability of accidents.

[0118] Compared with the prior art, in an intelligent network environment, the present invention realizes risk quantification and early warning between vehicles with different position relationships by calculating the mutual interference intensity between adjacent vehicles, providing support for driving safety in an intelligent network environment.

[0119] The above is only a preferred embodiment of the present invention and does not limit the present invention in any way. Any technician in the relevant technical field, without departing from the scope of the technical solution of the present invention, makes any form of equivalent replacement or modification to the technical solution and technical content disclosed in the present invention, which does not depart from the content of the technical solution of the present invention and still falls within the protection scope of the present invention.

Claims

1. A method for calculating the mutual interference intensity between vehicles in a crowd-sensing environment, characterized in that: The following steps are involved: (1) Constructing the vehicle group coordinate system: Establishing a Cartesian coordinate system with the lower left vehicle in the intelligent connected vehicle group as the coordinate center, and converting all vehicles in the intelligent connected vehicle group into this coordinate system; (2) Calculation of driving influence area: Calculate the corresponding driving influence area based on the behavior data of each vehicle; (3) Calculation of the mutual interference intensity between adjacent vehicles: Calculate the overlapping area and potential energy ratio of the driving influence zones of adjacent vehicles, and take the average of the two ratios to obtain the mutual interference intensity value between vehicles; (4) Construction of a multi-vehicle interactive directed network, characterizing the mutual interference behavior among intelligent connected vehicles through network theory; (5) Risk level classification and early warning: The interaction risks between vehicles are classified into different levels based on the mutual interference intensity values ​​between adjacent vehicles, and early warnings are issued for any risks that arise.

2. The method for calculating the mutual interference intensity between vehicles in a crowd-sensing environment according to claim 1 is characterized in that: The method of step (1) is as follows: determine the size of the intelligent connected vehicle group, select the central vehicle within the spatial range as the center, construct a Cartesian coordinate system with the vehicle's driving direction as the positive direction of the x-axis and the x-axis 90 degrees counterclockwise as the positive direction of the y-axis, and integrate all vehicles in the intelligent connected vehicle group into the Cartesian coordinate system according to their relative position relationship with the central vehicle, so that each vehicle has a unique coordinate value in the coordinate system, and store the information of all vehicles in the intelligent connected vehicle group.

3. The method for calculating the mutual interference intensity between vehicles in a crowd-sensing environment according to claim 2 is characterized in that: The method of step (2) is as follows: for each vehicle in the intelligent connected vehicle group, the lateral and longitudinal possible affected ranges of the vehicle are calculated to form the vehicle's driving impact area.

4. The method for calculating the mutual interference intensity between vehicles in a crowd-sensing environment according to claim 3 is characterized in that: For the distance in front of the vehicle, the safe stopping distance of the vehicle is calculated as the boundary distance in the longitudinal front of the impact zone; for the distance behind the vehicle, the safety field distribution of the vehicle is first calculated, and the potential energy value at the impact distance in front of the vehicle is determined, and the longitudinal rear impact distance is taken as the position where the safety field behind the vehicle is equal to the potential energy; for the lateral impact distance, if the lateral speed of the vehicle is zero, the lateral impact distance of the vehicle is equal, and the value is equal to half the width of the driving lane; if the lateral speed of the vehicle is not zero, the lateral safe stopping distance in the direction where the speed is not zero is the corresponding impact distance, and half the width of the driving lane in the other direction, thereby forming the vehicle's driving impact zone. If the vehicle's driving road is a straight line, the shape of the driving impact zone is a rectangle; if the vehicle's driving road is a curve, the shape of the driving impact zone is consistent with the shape of the driving road.

5. The method for calculating the mutual interference intensity between vehicles in a crowd-sensing environment according to claim 4 is characterized in that: The specific method is as follows: S1: Calculate the potential energy field distribution around the vehicle based on the vehicle's lateral / longitudinal speed and acceleration; First, calculate the potential energy value of any point around the vehicle and the center point of the vehicle, and then get the potential energy distribution around the vehicle. Assuming that the coordinates of any point around the vehicle are (x, y), the potential energy value of the point is calculated as follows: Among them, m i is the actual mass of the vehicle, (x0, y0) is the coordinate of the current vehicle position, (x, y) is the coordinate of any position around the vehicle where the field strength needs to be calculated, α and τ are parameters to be determined related to speed, α = 0.029, τ = 2.717, λ and β0 are coefficients to be determined, λ = 0.056, β0 = -0.169, θ is the clockwise angle between any point in the surrounding area and its center of mass and its speed, E(x, y) is the field strength at the position with coordinates (x, y); S2: For the longitudinal direction, based on the longitudinal speed and acceleration of the vehicle, calculate the affected distance in front of the vehicle; The possible affected distance (d front ), calculated as: Among them, v x and a x The longitudinal velocity and longitudinal acceleration of the vehicle are in m / s and m / s respectively. 2 ; τ lon is the driver's longitudinal reaction time, which is 1.2s; a xmax is the maximum deceleration that the vehicle can achieve, take 8.45m / s 2 ; L veh is the length of the current vehicle; S3: For the longitudinal direction, determine the potential energy value at the impact distance in front of the vehicle, determine the impact distance behind the vehicle through the equipotential energy line, determine the equipotential line corresponding to the potential energy at the impact distance in front of the vehicle, move toward the rear of the vehicle along the equipotential line, and stop when the equipotential line moves to the rear of the vehicle. This point is the distance behind the vehicle's driving impact area; S4: For the lateral direction, if the lateral speed of the vehicle is 0, the affected distances on the left and right sides of the vehicle are determined to be half the width of the lane. When the lateral speed of the vehicle is 0, it means that the vehicle has no lateral movement. At this time, the distances on both sides of the vehicle's impact area are half the width of the lane in which the vehicle is traveling. S5: For the lateral direction, for the side where the lateral speed of the vehicle is not 0, the lateral safe stopping distance of the vehicle is calculated as the affected distance. The affected distance on the other side is half the width of the lane. When the lateral speed of the vehicle is not 0, it means that the vehicle is moving laterally. The affected distance in the lateral moving direction should be larger. Assuming that the vehicle moves to the left, the affected distance on the left side (d left ) is calculated by the following formula: Among them, v y and a y The lateral velocity and lateral acceleration of the vehicle are in m / s and m / s respectively. 2 , τ lat is the driver's lateral reaction time, take 1.7s, a ymax is the maximum lateral deceleration of the vehicle, taken as 0.68m / s 2 , w lane The width of a single lane is 3.75m. Right impact distance (d right ) is half the lane width: When the vehicle moves to the right, the calculation methods for the left and right sides should be interchanged.

6. The method for calculating the mutual interference intensity between vehicles in a crowd-sensing environment according to claim 5 is characterized in that: The method of step (3) is as follows: for adjacent vehicle pairs, the overlapping part of the driving influence zones of the two vehicles is calculated, the mutual interference intensity is calculated from two aspects: the degree of spatial proximity of the conflict and the degree of severity of the conflict, the area of ​​the overlapping part of the driving influence zones is calculated based on the degree of spatial proximity of the conflict, and the area ratio of the influence zones is calculated with the smaller area of ​​the driving influence zones of the two vehicles as the denominator; According to the severity of the conflict, the total potential energy of the overlapping parts of the driving influence area is calculated, and the potential energy ratio is calculated with the sum of the potential energy in the driving influence area of ​​the vehicle with a smaller area as the denominator; the average of the two calculated ratios is taken to obtain the mutual interference intensity value between vehicles. Furthermore, the mutual interference intensity value is calculated for all adjacent vehicle pairs in the intelligent connected vehicle group. The minimum value of the calculated mutual interference intensity between vehicles is 0, and the maximum value is 1.

7. The method for calculating the mutual interference intensity between vehicles in a crowd-sensing environment according to claim 6 is characterized in that: The specific method is: S1: For the driving influence zones of adjacent vehicles, determine the overlapping area of ​​the driving influence zones of the two vehicles. Specifically, in the same coordinate system, the influence area of ​​the adjacent vehicle is determined according to the vertices of the influence areas of the two vehicles. If the overlapping area of ​​the influence areas of the adjacent vehicles is 0, the mutual interference intensity between the adjacent vehicles is judged to be 0, that is, there is no mutual interference behavior; S2: If the overlapping area is not 0, calculate the size of the overlapping area, and further calculate the ratio of the overlapping area to the smaller driving influence area of ​​the two vehicles. Specifically, the calculation method is as follows: Among them, A i and A j Represent the influence area of ​​vehicles i and j, ROL ij is the ratio of the overlapping area of ​​the influence zone to the minimum area of ​​the influence zone of vehicle i and vehicle j, S21: Calculate the potential energy ratio of the overlapping parts of the influence zones of adjacent vehicles. The calculation formula is as follows: S3: Calculate the proportion of the potential energy in the overlapping area to the potential energy of the smaller driving influence area of ​​the two vehicles by integration. Specifically, the calculation method is as follows: A i and A j denote the influence areas of vehicles i and j respectively, E i (x, y) represents the potential energy calculation formula when the coordinate position of vehicle i is (x, y), S4: The mutual interference intensity value of adjacent vehicles is obtained by taking the average of the area ratio and potential energy ratio of the overlapping parts of the driving influence zone. Specifically, calculate the mutual interference intensity value (IS ij ), as follows: IS ij =ROL i，j ×(RE i +RE j ) / 2 Through the above steps, the mutual interference strength values ​​of all directly adjacent vehicle pairs in the vehicle group are calculated.

8. The method for calculating the mutual interference intensity between vehicles in a crowd-sensing environment according to claim 1 is characterized in that: The method of step (4) is as follows: for all vehicles in the intelligent connected vehicle group, a multi-vehicle interactive directed network is constructed, with nodes representing vehicles and edges representing the mutual interference relationship between vehicles. The weight value on the edge is equal to the mutual interference intensity value, and the direction is from the front vehicle to the rear vehicle. If adjacent vehicles are traveling completely in parallel, the two nodes are connected by a bidirectional arrow.

9. The method for calculating the mutual interference intensity between vehicles in a crowd-sensing environment according to claim 8 is characterized in that: The method of step (5) is as follows: for the constructed multi-vehicle interactive directed network, judging the mutual interference risk level based on the weight values ​​of the connecting edges of adjacent vehicles; For the risk level classification of vehicles, the threshold classification method is used to classify the mutual interference risk levels of different levels. The classification thresholds include: 0-0.39 for low risk, 0.39-0.62 for medium risk, and 0.62-1 for high risk; If the mutual interference intensity between the vehicles is greater than different thresholds, an early warning is issued, and the direction of the early warning is from the vehicle at the tail of the connecting edge arrow to the vehicle indicated by the arrow.

10. An early warning system, characterized in that: When the instructions of the warning system are executed by the processor, the method for calculating the intensity of mutual interference between vehicles in a crowd sensing environment as described in any one of claims 1 to 9 is executed.

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