Risk control system and method considering lane changing behavior of special vehicle

Abstract

The invention relates to the technical field of special vehicle lane changing, and discloses a risk control system and method considering a special vehicle lane changing behavior. According to the method, the field domain theory is introduced into the research of the microscopic driving behavior of the intelligent network connection special vehicle, and an established special vehicle risk potential field model fuses the motion state of the intelligent network connection special vehicle and information obtained through sensing equipment. Risks faced by special vehicles in different motion states are reflected through the potential field intensity, and a foundation is laid for safe driving of intelligent network connection special vehicles. On the basis of a special vehicle risk potential field model, a special vehicle safe lane changing distance model is established, the model fuses parameters such as the speed, the acceleration, the mass and the steering angle of a special vehicle and the motion state and the space position of surrounding vehicles, the distance between the vehicles is corrected, and the vehicle safety lane changing distance is obtained. And the size of the safe distance required by lane changing of special vehicles in different motion states can be dynamically reflected.

Description

Technical Field

[0001] The present invention relates to the technical field of special vehicle lane changing, and in particular to a risk control system and method that takes into account the lane changing behavior of special vehicles. Background Art

[0002] Vehicle communication technology: Use vehicle-to-vehicle communication (V2V) and vehicle-to-infrastructure communication (V2I) technologies to enable special vehicles to communicate with surrounding vehicles and traffic infrastructure in real time. This helps share information such as location, speed, driving intention, etc. for effective lane change coordination. Real-time traffic flow data analysis: Use real-time traffic flow data analysis to identify congestion, traffic conditions and other factors that affect the passage of special vehicles on the road. This can help the system consider real-time traffic conditions in lane change decisions. Traffic behavior analysis: Considering the behavior of other road users is critical, traffic behavior analysis can be used to predict the behavior of other vehicles and pedestrians to better understand potential risks and take corresponding measures. Vehicle perception technology: Use advanced vehicle perception technology such as radar, cameras and lidar to detect surrounding vehicles, pedestrians and obstacles. This helps improve the system's perception of the surrounding environment and reduce potential collision risks. Autonomous driving technology: Combined with autonomous driving technology, special vehicles can perform lane change operations more accurately and reduce the risk of human error. Map and positioning services: Use high-precision maps and positioning services to support precise positioning and path planning of special vehicles. This helps the system better understand road topology and geographical environment. Emergency service collaboration: Work with emergency services to ensure that the system can obtain timely information about emergency situations and effectively collaborate with other emergency service vehicles. Human-machine interaction interface: Provide an intuitive and easy-to-understand human-machine interaction interface so that drivers and system operators can understand system status, decisions and risks in a timely manner so that they can make informed decisions;

[0003] In the prior art, the risk control system of special vehicle lane changing behavior fails to obtain the motion status of surrounding vehicles through communication means. The present invention, in an intelligent network environment, obtains the motion status of surrounding vehicles through a roadside sensing unit, and then calculates the minimum lane changing distance through an edge computing unit, so that special vehicles can successfully change lanes. Summary of the invention

[0004] 1. Technical issues to be solved

[0005] In view of the deficiencies in the prior art, the present invention provides a risk control system and method that takes into account the lane changing behavior of special vehicles, which has the advantages of safe lane changing of special vehicles and solves the above-mentioned technical problems.

[0006] (II) Technical solution

[0007] To achieve the above object, the present invention provides the following technical solution: a risk control method considering lane changing behavior of special vehicles, comprising the following steps:

[0008] S1. A roadside sensing module is provided on the road, and when a special vehicle running at a set speed needs to change lanes, a lane change communication signal is sent to the roadside sensing module;

[0009] S2: After receiving the lane change communication signal, the roadside perception module collects the distribution status of social vehicles on the lane and uploads the distribution status to the edge computing module;

[0010] S3, the edge computing module calculates the minimum lane-changing distance between the special vehicle and the preceding vehicle and the following vehicle in the target lane according to the position and speed of the special vehicle and the distribution of social vehicles on the lane, and sends it to the near-end server. The special vehicle changes lanes safely according to the minimum lane-changing distance.

[0011] S4. The near-end server sends the lane change information to the social vehicles in the lane to remind the rear social vehicles in the target lane to slow down;

[0012] The special vehicles include police cars, ambulances, fire trucks and sprinkler trucks.

[0013] As a preferred technical solution of the present invention, the edge computing module in step S3 constructs a risk potential field model for the special vehicle's own attributes and its motion state parameters. The specific expression is as follows:

[0014]

[0015] Among them, U SV represents the risk potential field of the special vehicle, μ, β, k are all undetermined coefficients that do not represent 0, θ0 represents the angle between any point around the special vehicle and the coordinates (x0, y0) of the center of mass of the special vehicle (the vehicle's forward direction is the X-axis, the direction of the roadside unit extending into the road is the Y-axis, and the origin of the coordinate is the intersection of the roadside unit vertically downward with the road edge), cosθ0 represents the cosine value of θ0, exp(β·v·cosθ0) represents the magnitude of the field force on any object, exp(*) represents the exponential function with the natural logarithm e as the base, v represents the current speed of the special vehicle, ε represents the influence factor of the weather conditions of the special vehicle at that time, R0 represents the influence factor of the road conditions where the special vehicle is located, b' represents the vector distance between the two points, and M j represents the equivalent mass of the special vehicle j, and |b'| represents the corrected distance.

[0016] As a preferred technical solution of the present invention, the equivalent mass M of the special vehicle j is j The expression is as follows:

[0017]

[0018] Among them, M j represents the equivalent mass of the special vehicle j, m j represents the actual mass of a particular vehicle j, T j represents the type parameter of the special vehicle j, Represents the current speed of the particular vehicle j raised to the power of 6.687.

[0019] As a preferred technical solution of the present invention, the expression of the corrected distance |b'| is as follows:

[0020]

[0021] Among them, α1 and α2 represent the correlation coefficients of the length and width of the special vehicle, σ represents the coefficient related to the current driving speed v of the special vehicle, the center of mass coordinates of the special vehicle are represented by (x0, y0), exp(*) represents the exponential function with the natural logarithm e as the base, and the coordinates of any point in space are (x, y).

[0022] As a preferred technical solution of the present invention, in the lane change between the special vehicle SV and the preceding vehicle FV in step S3, the expression for the lane change success of the special vehicle SV is as follows:

[0023]

[0024] Where D0 represents the headway distance between the special vehicle SV and the preceding vehicle FV before lane change, D1 represents the headway distance between the special vehicle SV and the preceding vehicle FV when lane change is performed, and S S It represents the distance traveled by the special vehicle SV during the lane change preparation phase, S F Indicates the distance traveled by the preceding vehicle FV during the lane change preparation phase, L SV and L FV represents the body length of vehicles SV and FV, θ represents the angle between the speed direction of the special vehicle SV and the X-axis direction, S FV represents the range of the risk potential field in the rear direction of the front vehicle FV, S SV Indicates the range of the risk potential field in the front direction of the special vehicle SV;

[0025] In the lane change between the special vehicle SV and the preceding vehicle FV in step S3, the minimum safe lane change distance between the special vehicle SV and the preceding vehicle FV is the headway distance D0 between the special vehicle SV and the preceding vehicle FV before the lane change, which satisfies the following conditions:

[0026]

[0027] Among them, μ, β, k are all undetermined coefficients that do not represent 0, ε represents the weather condition influencing factor of the special vehicle at that time, exp(*) represents the exponential function with the natural logarithm e as the base, α1 represents the correlation coefficient of the special vehicle length, U SV represents the special vehicle risk potential field, S S represents the distance traveled by the special vehicle SV during the lane change preparation phase, v SV represents the current speed of the special vehicle SV, τ represents the coefficient related to acceleration, a represents the acceleration of the special vehicle SV, R0 represents the influencing factor of the road condition where the special vehicle is located, θ represents the angle between the speed direction of the special vehicle SV and the X-axis direction, S F Indicates the distance traveled by the preceding vehicle FV during the lane change preparation phase, L SV and L FV Indicates the body length of vehicles SV and FV.

[0028] As a preferred technical solution of the present invention, the risk potential field S of the front vehicle FV in the rear direction FV The expression is as follows:

[0029]

[0030] Among them, R0 represents the influencing factor of the road conditions where the special vehicle is located. represents the kth root, M SV represents the equivalent mass of the special vehicle SV, μ, β, k are all undetermined coefficients that do not represent 0, exp(*) represents an exponential function with the natural logarithm e as the base, α1 represents the correlation coefficient of the special vehicle length, ε represents the influence factor of the weather conditions of the special vehicle at that time, R0 represents the influence factor of the road conditions where the special vehicle is located, U FV 、v FV They represent the risk potential field of the front vehicle FV and the current speed of the front vehicle FV, U FV Calculation method and U SV same;

[0031] The risk potential field U of the special vehicle SV in the front direction SV The expression is as follows:

[0032]

[0033] Among them, S FV Indicates the range of the risk potential field in the rear direction of the front vehicle FV, represents the kth root, M SV represents the equivalent mass of the special vehicle SV, μ, β, k are all undetermined coefficients that do not represent 0, exp(*) represents an exponential function with the natural logarithm e as the base, α1 represents the special vehicle length correlation coefficient, U SVrepresents the risk potential field of a special vehicle, v SV represents the current speed of the special vehicle SV, τ represents the coefficient related to acceleration, a represents the acceleration of the special vehicle SV, and R0 represents the influencing factor of the road condition where the special vehicle is located.

[0034] As a preferred technical solution of the present invention, in the lane change between the special vehicle SV and the vehicle RV behind the target lane, the conditions for the special vehicle SV to successfully change lanes are:

[0035]

[0036] Where D2 is the headway between the special vehicle SV and the rear vehicle RV in the target lane before lane change, S R is the distance traveled by the vehicle RV in the target lane at the end of the lane change, S S is the longitudinal distance traveled by the special vehicle SV after the lane change is completed, D3 is the headway between the rear vehicle RV in the target lane and the special vehicle SV when the lane change is completed, and L RV and L SV are the body lengths of the rear vehicle RV and the special vehicle SV in the target lane, S RV is the range of the risk potential field in the direction of the front of the vehicle RV behind the target lane, S SV-R is the range of the risk potential field in the rear direction of the special vehicle SV;

[0037] In the lane change between the special vehicle SV and the rear vehicle RV in the target lane in step S3, the minimum safe lane change distance between the special vehicle SV and the rear vehicle RV in the target lane is the head distance D2 between the special vehicle SV and the rear vehicle RV in the target lane before the lane change, which satisfies the following conditions:

[0038]

[0039] Among them, μ, β, k are all undetermined coefficients that do not represent 0, ε represents the weather condition influencing factor of the special vehicle at that time, exp(*) represents the exponential function with the natural logarithm e as the base, α1 represents the correlation coefficient of the special vehicle length, U SV represents the special vehicle risk potential field, U RV represents the risk potential field of the vehicle RV behind the target lane, U RV Calculation method and U SV Same. S represents the distance traveled by the special vehicle SV during the lane change preparation phase, v SV represents the current speed of the special vehicle SV, τ represents the coefficient related to acceleration, a represents the acceleration of the special vehicle SV, R0 represents the influencing factor of the road condition where the special vehicle is located, and L RV and L SVare the body lengths of the rear vehicle RV and the special vehicle SV in the target lane, and θ represents the angle between the velocity direction of the special vehicle SV and the X-axis direction.

[0040] As a preferred technical solution of the present invention, the range S of the risk potential field in the rear direction of the special vehicle SV is SV-R The expression is as follows:

[0041]

[0042] Among them, μ, β, k are all undetermined coefficients that do not represent 0, ε represents the weather condition influencing factor of the special vehicle at that time, exp(*) represents the exponential function with the natural logarithm e as the base, R0 represents the road condition influencing factor where the special vehicle is located, α1 represents the length correlation coefficient of the special vehicle, U SV represents the risk potential field of a special vehicle, σ represents the coefficient related to speed, M SV represents the equivalent mass of the special vehicle SV;

[0043] The range S of the risk potential field in the direction of the vehicle head of the rear vehicle RV in the target lane RV The expression is as follows:

[0044]

[0045] Among them, μ, β, k are all undetermined coefficients that do not represent 0, ε represents the weather condition influencing factor of the special vehicle at that time, exp(*) represents the exponential function with the natural logarithm e as the base, R0 represents the road condition influencing factor where the special vehicle is located, α1 represents the length correlation coefficient of the special vehicle, U RV represents the risk potential field of the vehicle RV behind the target lane, σ represents the coefficient related to the speed, M RV Represents the equivalent mass of the vehicle RV behind the target lane.

[0046] A risk control system considering lane-changing behavior of special vehicles, based on the above-mentioned risk control method considering lane-changing behavior of special vehicles, comprising a roadside perception module, an edge computing module and a proximal server;

[0047] The roadside sensing module is used to receive the lane change communication signal of the special vehicle, collect the distribution status of social vehicles on the lane, and upload the distribution status to the edge computing module;

[0048] The edge computing module calculates the minimum lane-changing distance between the special vehicle and the preceding vehicle and the following vehicle in the target lane according to the position and speed of the special vehicle and the distribution pattern of social vehicles on the lane, and sends it to the proximal server;

[0049] The near-end server is used to send lane change information to social vehicles on the lane, and remind the rear social vehicles in the target lane to slow down.

[0050] Compared with the prior art, the present invention provides a risk control system and method that takes into account special vehicle lane-changing behaviors, which has the following beneficial effects:

[0051] The present invention introduces field theory into the study of the micro-driving behavior of intelligent networked special vehicles, and establishes a special vehicle risk potential field model that integrates the motion state of the intelligent networked special vehicle and the information obtained through the sensing device, and reflects the risk faced by special vehicles in different motion states through the potential field intensity, laying the foundation for the safe driving of intelligent networked special vehicles. Based on the special vehicle risk potential field model, a special vehicle safe lane change distance model is established. The model integrates parameters such as the speed, acceleration, mass, steering angle of the special vehicle and the motion state and spatial position of the surrounding vehicles, corrects the distance between vehicles, and can dynamically reflect the size of the safe distance required for special vehicles in different motion states to change lanes. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 It is a schematic diagram of the process of the present invention;

[0053] Figure 2 A schematic diagram of a vehicle in an intelligent network environment of the present invention;

[0054] Figure 3 A schematic diagram of a lane changing scenario when lanes merge in the present invention;

[0055] Figure 4 A schematic diagram of lane changing when there is an obstacle vehicle in front of the lane of the present invention;

[0056] Figure 5 A schematic diagram of a lane changing scenario in an interweaving area of ​​the present invention;

[0057] Figure 6 It is a schematic diagram of a lane changing scenario when a special vehicle changes lanes freely according to the present invention. DETAILED DESCRIPTION

[0058] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0059] See also Figure 1-Figure 6 , a risk control method considering special vehicle lane changing behavior, comprising the following steps:

[0060] S1. A roadside sensing module is arranged on the road. When a special vehicle running at a set speed needs to change lanes, a lane-changing communication signal is sent to the roadside sensing module. The special vehicle uses the on-board unit OBU to send lane-changing request information to the roadside unit RSU. The OBU and RSU communicate by wireless communication to transmit information.

[0061] S2: After receiving the lane change communication signal, the roadside perception module collects the distribution status of social vehicles on the lane and uploads the distribution status to the edge computing module;

[0062] S3, the edge computing module calculates the minimum lane-changing distance between the special vehicle and the preceding vehicle and the following vehicle in the target lane according to the position and speed of the special vehicle and the distribution of social vehicles on the lane, and sends it to the near-end server. The special vehicle changes lanes safely according to the minimum lane-changing distance.

[0063] S4. The near-end server sends the lane change information to the social vehicles in the lane to remind the rear social vehicles in the target lane to slow down;

[0064] Special vehicles include police cars, ambulances, fire trucks, and water trucks;

[0065] In step S3, the edge computing module constructs a risk potential field model for the special vehicle's own attributes and its motion state parameters. The specific expression is as follows:

[0066]

[0067] Among them, U SV represents the risk potential field of the special vehicle, μ, β, k are all undetermined coefficients that do not represent 0, θ0 represents the angle between any point around the special vehicle and the coordinates (x0, y0) of the center of mass of the special vehicle (the vehicle's forward direction is the X-axis, the direction of the roadside unit extending into the road is the Y-axis, and the origin of the coordinate is the intersection of the roadside unit vertically downward with the road edge), cosθ0 represents the cosine value of θ0, exp(β·ν·cosθ0) represents the magnitude of the field force on any object, exp(*) represents the exponential function with the natural logarithm e as the base, v represents the current speed of the special vehicle, ε represents the influence factor of the weather conditions of the special vehicle at that time, R0 represents the influence factor of the road conditions where the special vehicle is located, and b' represents the vector distance between the two points. Indicates the use of a power function to describe the distance change from any object to a special vehicle, M j represents the equivalent mass of the special vehicle j, |b'| represents the corrected distance;

[0068] The equivalent mass M of a particular vehicle j j The expression is as follows:

[0069]

[0070] Among them, M j represents the equivalent mass of the special vehicle j, m j represents the actual mass of a particular vehicle j, T j represents the type parameter of the special vehicle j, represents the current speed of the special vehicle j raised to the power of 6.687;

[0071] The corrected distance |b'| is expressed as follows:

[0072]

[0073] Wherein, α1 and α2 represent the correlation coefficients of the length and width of the special vehicle, σ represents the coefficient related to the speed v, the coordinates of the center of mass of the special vehicle are represented by (x0, y0), exp(*) represents the exponential function with the natural logarithm e as the base, and the coordinates of any point in space are (x, y);

[0074] In the lane change between the special vehicle SV and the preceding vehicle FV in step S3, the expression for the lane change success of the special vehicle SV is as follows:

[0075]

[0076] Where D0 represents the headway distance between the special vehicle SV and the preceding vehicle FV before lane change, D1 represents the headway distance between the special vehicle SV and the preceding vehicle FV when lane change is performed, and S S It represents the distance traveled by the special vehicle SV during the lane change preparation phase, S F Indicates the distance traveled by the preceding vehicle FV during the lane change preparation phase, L SV and L FV represents the body length of vehicles SV and FV, θ represents the angle between the speed direction of the special vehicle SV and the X-axis direction, S FV represents the range of the risk potential field in the rear direction of the front vehicle FV, S SV Indicates the range of the risk potential field in the front direction of the special vehicle SV;

[0077] Risk potential field S of the front vehicle FV in the rear direction FV The expression is as follows:

[0078]

[0079] Among them, R0 represents the influencing factor of the road conditions where the special vehicle is located. represents the kth root, M SVrepresents the equivalent mass of the special vehicle SV, μ, β, k are all undetermined coefficients that do not represent 0, exp(*) represents an exponential function with the natural logarithm e as the base, α1 represents the correlation coefficient of the special vehicle length, ε represents the influence factor of the weather conditions of the special vehicle at that time, R0 represents the influence factor of the road conditions where the special vehicle is located, U FV 、v FV They represent the risk potential field of the front vehicle FV and the current speed of the front vehicle FV, U FV Calculation method and U SV same;

[0080] Special vehicle SV head direction risk potential field S SV The expression is as follows:

[0081]

[0082] Among them, S FV Indicates the range of the risk potential field in the rear direction of the front vehicle FV, represents the kth root, M SV represents the equivalent mass of the special vehicle SV, μ, β, k are all undetermined coefficients that do not represent 0, exp(*) represents an exponential function with the natural logarithm e as the base, α1 represents the special vehicle length correlation coefficient, U SV represents the special vehicle risk potential field, ν SV represents the current speed of the special vehicle SV, τ represents the coefficient related to acceleration, a represents the acceleration of the special vehicle SV, and R0 represents the influencing factor of the road condition where the special vehicle is located;

[0083] In the lane change between the special vehicle SV and the preceding vehicle FV in step S3, the minimum safe lane change distance between the special vehicle SV and the preceding vehicle FV is the headway distance D0 between the special vehicle SV and the preceding vehicle FV before the lane change, which satisfies the following conditions:

[0084]

[0085] Among them, μ, β, k are all undetermined coefficients that do not represent 0, ε represents the weather condition influencing factor of the special vehicle at that time, exp(*) represents the exponential function with the natural logarithm e as the base, α1 represents the correlation coefficient of the special vehicle length, U SV represents the special vehicle risk potential field, S S represents the distance traveled by the special vehicle SV during the lane change preparation phase, v SV represents the current speed of the special vehicle SV, τ represents the coefficient related to acceleration, a represents the acceleration of the special vehicle SV, R0 represents the influencing factor of the road condition where the special vehicle is located, θ represents the angle between the speed direction of the special vehicle SV and the X-axis direction, S FIndicates the distance traveled by the preceding vehicle FV during the lane change preparation phase, L SV and L FV Indicates the body length of vehicles SV and FV;

[0086] When the special vehicle SV changes lanes with the vehicle RV behind the target lane, the conditions for the special vehicle SV to successfully change lanes are:

[0087]

[0088] Where D2 is the headway between the special vehicle SV and the rear vehicle RV in the target lane before lane change, S R is the distance traveled by the vehicle RV in the target lane at the end of the lane change, S S is the longitudinal distance traveled by the special vehicle SV after the lane change is completed, D3 is the headway between the rear vehicle RV in the target lane and the special vehicle SV when the lane change is completed, and L RV and L SV are the body lengths of the rear vehicle RV and the special vehicle SV in the target lane, S RV is the range of the risk potential field in the direction of the front of the vehicle RV behind the target lane, S SV-R is the range of the risk potential field in the rear direction of the special vehicle SV;

[0089] The range S of the risk potential field in the rear direction of the special vehicle SV SV-R The expression is as follows:

[0090]

[0091] Among them, μ, β, k are all undetermined coefficients that do not represent 0, ε represents the weather condition influencing factor of the special vehicle at that time, exp(*) represents the exponential function with the natural logarithm e as the base, R0 represents the road condition influencing factor where the special vehicle is located, α1 represents the length correlation coefficient of the special vehicle, U SV represents the risk potential field of a special vehicle, σ represents the coefficient related to speed, M SV represents the equivalent mass of the special vehicle SV;

[0092] The range S of the risk potential field in the direction of the vehicle head of the rear vehicle RV in the target lane RV The expression is as follows:

[0093]

[0094] Among them, μ, β, k are all undetermined coefficients that do not represent 0, ε represents the weather condition influencing factor of the special vehicle at that time, exp(*) represents the exponential function with the natural logarithm e as the base, R0 represents the road condition influencing factor where the special vehicle is located, α1 represents the length correlation coefficient of the special vehicle, U RV represents the risk potential field of the vehicle RV behind the target lane, URV with U SV The calculation method is the same, σ represents the coefficient related to speed, M RV Represents the equivalent mass of the vehicle RV behind the target lane.

[0095] In the lane change between the special vehicle SV and the rear vehicle RV in the target lane in step S3, the minimum safe lane change distance between the special vehicle SV and the rear vehicle RV in the target lane is the head distance D2 between the special vehicle SV and the rear vehicle RV in the target lane before the lane change, which satisfies the following conditions:

[0096]

[0097] Among them, μ, β, k are all undetermined coefficients that do not represent 0, ε represents the weather condition influencing factor of the special vehicle at that time, exp(*) represents the exponential function with the natural logarithm e as the base, α1 represents the correlation coefficient of the special vehicle length, U SV represents the special vehicle risk potential field, U RV represents the risk potential field of the vehicle RV behind the target lane, S S represents the distance traveled by the special vehicle SV during the lane change preparation phase, v SV represents the current speed of the special vehicle SV, τ represents the coefficient related to acceleration, a represents the acceleration of the special vehicle SV, R0 represents the influencing factor of the road condition where the special vehicle is located, and L RV and L SV are the body lengths of the rear vehicle RV and the special vehicle SV in the target lane, and θ represents the angle between the velocity direction of the special vehicle SV and the X-axis direction.

[0098] A risk control system considering lane-changing behavior of special vehicles, based on the above-mentioned risk control method considering lane-changing behavior of special vehicles, comprising a roadside perception module, an edge computing module and a proximal server;

[0099] The roadside perception module is used to receive special vehicle lane change communication signals, collect the distribution status of social vehicles on the lane, and upload the distribution status to the edge computing module;

[0100] The edge computing module calculates the minimum lane-changing distance between the front vehicle of the special vehicle and the rear vehicle of the target lane based on the position and speed of the special vehicle and the distribution of social vehicles on the lane, and sends it to the near-end server;

[0101] The near-end server is used to send lane change information to other vehicles in the lane, reminding the rear vehicles in the target lane to slow down.

[0102] When a special vehicle is dissatisfied with the current driving environment, it uses the on-board unit to send a lane change request. After receiving the lane change information, the roadside perception module collects vehicle information and distribution in the lane and the target lane, calculates the minimum safe lane change distance using the edge computing module, and sends instructions to the server for the special vehicle to change lanes.

[0103] In mathematics, field refers to the mapping of a vector to another vector or number. In physics, field refers to a certain spatial region in which an object with a certain property can exert a force on a similar object that is not in contact with it, and the magnitude of the force changes with the change of the relative position between the objects. There is an interaction force between two objects, so there is a potential energy field affected by the relative position. The potential energy field has measurability (the size of the field is determined by its corresponding properties and the state of the object, so the field strength can be measured by correctly quantifying the properties and motion state of the object), variability (the field strength will change with the change of time and space, so when the driving environment changes, the field strength will also change), universality (various fields are ubiquitous on the earth, as long as the properties of the object exist, the field determined by it exists, so the impact of traffic factors on driving safety is ubiquitous in the entire traffic system), objectivity (the existence of the field is an objective fact, and will not change with the will of people, so the driving risks generated during the driving of vehicles are objective). From the perspective of traffic flow theory, the micro-driving behavior of vehicles has similarities with the risk potential field theory. Micro-driving behavior includes following behavior and lane-changing behavior. Taking lane-changing behavior as an example, a special vehicle has the intention to change lanes and changes lanes. During the lane-changing process, the special vehicle has no physical contact with other social vehicles, but the change in the motion state of the surrounding social vehicles will cause the motion state of the special vehicle to change. According to Newton's first law, "any object must maintain uniform linear motion or a state of rest until an external force forces it to change motion", it means that there is a virtual external force between the special vehicle and the surrounding vehicles, which causes the vehicle's motion state to change. The force is generated by the surrounding vehicles, and its size is determined by the spatial position and motion state. Therefore, this force can be called a field force, and the risk potential field can be regarded as a physical field that reflects traffic safety. Therefore, the determining factor of whether a special vehicle can successfully change lanes does not lie in itself, but is determined by its relative position with the surrounding vehicles and their motion state;

[0104] Every element of the road traffic system, including people, vehicles, roads, and environment, will have an impact on the vehicles in the driving risk field. In the intelligent network environment, only the risk field generated by the vehicle and the road environment is considered. The type, mass, and motion state of special vehicles affect the size of the range of the risk potential field generated by themselves. As the speed of special vehicles increases, the equivalent mass will increase, that is, the greater the speed of special vehicles of the same type and mass, the greater the equivalent mass, and the greater the risk level faced during driving. Within the range of the vehicle risk potential field, the potential field strength at any point is related to the distance to the special vehicle. The different motion states of special vehicles will have different impacts on the risk potential field. The current driving speed, acceleration, and distance from other vehicles of special vehicles are the main factors affecting the risk potential field. During the driving process of the vehicle, no velocity component is generated in the vertical direction in the forward direction. Other social vehicles approach the special vehicle at different angles, but as long as the approaching distance is the same, the distance from any point in the space to the special vehicle during the lane change process of the special vehicle is equal. However, the actual situation is not the case. The risk level generated by other vehicles approaching the special vehicle from different directions is different. Therefore, the actual distance between the two points is corrected. At the same time, adding the consideration of acceleration can more accurately reflect the impact of the vehicle's motion state on the vehicle risk field. By correcting the acceleration, the dynamic change trend of the vehicle risk field under the influence of acceleration can be described. The influence of the steering angle is not considered here. The acceleration of the vehicle is the acceleration and deceleration of the vehicle in the longitudinal direction of the lane;

[0105] Lane changing behavior is traditionally divided into forced lane changing and free lane changing according to the driver's motivation for changing lanes. Forced lane changing may occur in emergency situations, sudden road conditions or other situations of improper vehicle behavior, and the driver is forced to change lanes, usually due to obstacles or dangerous situations ahead. At this time, the vehicle has a clear target lane and must change lanes within a certain range of roads. Free lane changing refers to the driver's lane changing behavior based on judging the surrounding traffic conditions in order to overtake, avoid congestion, prepare for the next turn or enter a specific lane, in order to obtain a faster driving speed, a more comfortable driving environment, etc. The driver subjectively judges whether he wants to change lanes. The present invention studies the lane changing behavior of special vehicles in an intelligent network environment due to road condition restrictions, or changes in the road environment and the driver's wishes.

[0106] The purpose of constructing the vehicle risk potential field is to allow special vehicles in the intelligent network environment to collect corresponding information based on the different risk potential fields they are exposed to, and calculate the safe distance from other vehicles through the edge computing module to ensure driving safety. Only when the distance between other vehicles and the special vehicle is within a certain range will they be affected by the potential field.

[0107] The vehicle risk potential field model can quantify the potential field distribution of the vehicle in different states and reflect the risk situation of its surrounding area.

[0108] When the speed and acceleration of the special vehicle are both 0, the special vehicle is at rest and its risk potential field distribution is similar to a circle. Under the same distance, other vehicles face basically the same risk level no matter from which angle they approach the special vehicle.

[0109] When a special vehicle moves at a constant speed along the X-axis direction (the vehicle's forward direction is defined as the X-axis, the roadside unit's position extending toward the road is defined as the Y-axis, and the origin of the coordinates is the intersection of the roadside unit vertically downward with the road sideline), the potential field strength in the X-axis direction increases with the increase in speed, and the risk field range generated by the special vehicle to other vehicles will expand. Since the velocity component in the Y-axis direction is 0, the potential field strength in the Y-axis direction is the same as that in the circular direction. When the special vehicle travels at a constant speed, the distribution of its risk potential field is similar to an ellipse.

[0110] When a special vehicle is traveling at a certain speed and the acceleration is positive, compared with traveling at a constant speed, the special vehicle is in an accelerated state and the risk level in front of it is greater than that behind the special vehicle.

[0111] When a special vehicle travels at a certain speed and the acceleration is negative, compared with traveling at a constant speed, the special vehicle is in a deceleration state, and the risk level behind it is greater than that in front of the special vehicle.

[0112] Special vehicles have specific road conditions and speed requirements when driving. If the current driving environment does not meet the above requirements, the special vehicle will have a lane change intention. The generation of the lane change intention is related to the expected speed, the change of road conditions and road environment, and the relative position of the special vehicle and the vehicle in front.

[0113] Special vehicles in the intelligent network environment can collect the driving status of each vehicle in the current traffic environment in real time through sensing equipment and obtain it through the on-board unit. By establishing a vehicle risk field model to represent the impact of the driving status of other vehicles on special vehicles during lane changing, in the established lane changing scenario, the main risk of special vehicles in lane changing comes from the front vehicle FV in the same lane and the rear vehicle RV in the target lane. In order to ensure the safety of the lane changing process, special vehicles need to maintain a certain safe distance from other surrounding vehicles to successfully change lanes. Based on the risk potential field model, special vehicles can adjust their own speed, acceleration, etc. to change the size of the risk potential field. Different motion states will produce different risk potential fields. The risk potential field of special vehicles is combined with the lane changing scenario, and the lane changing model is established by replacing the risk potential field of special vehicles with an ellipse.

[0114] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A risk control method considering special vehicle lane-changing behavior, Features: The following steps are involved: S1. A roadside sensing module is provided on the road, and when a special vehicle running at a set speed needs to change lanes, a lane change communication signal is sent to the roadside sensing module; S2: After receiving the lane change communication signal, the roadside perception module collects the distribution status of social vehicles on the lane and uploads the distribution status to the edge computing module; S3, the edge computing module calculates the minimum lane-changing distance between the special vehicle and the preceding vehicle and the following vehicle in the target lane according to the position and speed of the special vehicle and the distribution of social vehicles on the lane, and sends it to the near-end server. The special vehicle changes lanes safely according to the minimum lane-changing distance. S4. The near-end server sends the lane change information to the social vehicles in the lane to remind the rear social vehicles in the target lane to slow down; The special vehicles include police cars, ambulances, fire trucks and sprinkler trucks.

2. A risk control method considering special vehicle lane-changing behavior according to claim 1, Features: In step S3, the edge computing module constructs a risk potential field model for the special vehicle's own attributes and its motion state parameters. The specific expression is as follows: Among them, U SV represents the risk potential field of special vehicles, μ, β, k are all undetermined coefficients that do not represent 0, θ 0 Represents the coordinates (x 0 ,y 0 ), the vehicle's forward direction is the X axis, the direction of the roadside unit extending into the road is the Y axis, and the origin of the coordinate is the angle between the roadside unit and the road edge line vertically downward, cosθ 0 Represents θ 0 The cosine value, exp(β·v·cosθ 0 ) represents the magnitude of the field force on any object, exp(*) represents the exponential function with the natural logarithm e as the base, v represents the current speed of the special vehicle, ε represents the weather condition factor affecting the special vehicle at that time, and R 0 represents the influencing factor of the road condition where the special vehicle is located, b' represents the vector distance between two points, M j represents the equivalent mass of the special vehicle j, and |b'| represents the corrected distance.

3. A risk control method considering special vehicle lane-changing behavior according to claim 2, Features: The equivalent mass M of the special vehicle j j The expression is as follows: Among them, M j represents the equivalent mass of the special vehicle j, m j represents the actual mass of a particular vehicle j, T j represents the type parameter of the special vehicle j, Represents the current speed of the particular vehicle j raised to the power of 6.

687.

4. A risk control method considering special vehicle lane-changing behavior according to claim 2, Features: The corrected distance |b'| is expressed as follows: Among them, α 1 and α 2 represents the correlation coefficient between the length and width of the special vehicle, σ represents the coefficient related to the current speed v of the special vehicle, and the center of mass coordinate of the special vehicle represents (x 0 ,y 0 ), exp(*) represents an exponential function with the natural logarithm e as the base, and the coordinates of any point in space are (x, y).

5. A risk control method considering special vehicle lane-changing behavior according to claim 2, Features: In the lane change between the special vehicle SV and the preceding vehicle FV in step S3, the expression for the lane change success of the special vehicle SV is as follows: Among them, D 0 Indicates the headway distance between the special vehicle SV and the preceding vehicle FV before the lane change, D 1 It indicates the headway between the special vehicle SV and the preceding vehicle FV when changing lanes, S S It represents the distance traveled by the special vehicle SV during the lane change preparation phase, S F Indicates the distance traveled by the preceding vehicle FV during the lane change preparation phase, L SV and L FV represents the body length of vehicles SV and FV, θ represents the angle between the speed direction of the special vehicle SV and the X-axis direction, S FV represents the range of the risk potential field in the rear direction of the front vehicle FV, S SV Indicates the range of the risk potential field in the front direction of the special vehicle SV; In the lane change between the special vehicle SV and the preceding vehicle FV in step S3, the minimum safe lane change distance between the special vehicle SV and the preceding vehicle FV is the headway distance D between the special vehicle SV and the preceding vehicle FV before the lane change. 0 The following conditions are met: Among them, μ, β, and k are all undetermined coefficients that do not represent 0, ε represents the influencing factor of the weather conditions of the special vehicle at that time, exp(*) represents the exponential function with the natural logarithm e as the base, and α 1 represents the special vehicle length correlation coefficient, U SV represents the special vehicle risk potential field, S S represents the distance traveled by the special vehicle SV during the lane change preparation phase, v SV represents the current speed of the special vehicle SV, τ represents the coefficient related to acceleration, a represents the acceleration of the special vehicle SV, R 0 represents the influencing factor of the road condition where the special vehicle is located, θ represents the angle between the speed direction of the special vehicle SV and the X-axis direction, S F Indicates the distance traveled by the preceding vehicle FV during the lane change preparation phase, L SV and L FV Indicates the body length of vehicles SV and FV.

6. A risk control method considering special vehicle lane-changing behavior according to claim 5, Features: The risk potential field S of the front vehicle FV in the rear direction FV The expression is as follows: Among them, R 0 Indicates the influencing factor of the road conditions where the special vehicle is located, represents the kth root, M SV represents the equivalent mass of the special vehicle SV, μ, β, and k are all undetermined coefficients that do not represent 0, exp(*) represents an exponential function with the natural logarithm e as the base, and α 1 represents the special vehicle length correlation coefficient, ε represents the weather condition influencing factor of the special vehicle at that time, R 0 Indicates the impact factor of the road conditions where the special vehicle is located, U FV 、v FV They represent the risk potential field of the front vehicle FV and the current speed of the front vehicle FV, U FV with U SV The calculation method is the same; The risk potential field U of the special vehicle SV in the front direction SV The expression is as follows: Among them, S FV Indicates the range of the risk potential field in the rear direction of the front vehicle FV, represents the kth root, M SV represents the equivalent mass of the special vehicle SV, μ, β, and k are all undetermined coefficients that do not represent 0, exp(*) represents an exponential function with the natural logarithm e as the base, and α 1 represents the special vehicle length correlation coefficient, U SV represents the risk potential field of a special vehicle, v SV represents the current speed of the special vehicle SV, τ represents the coefficient related to acceleration, a represents the acceleration of the special vehicle SV, R 0 Indicates the influencing factor of the road conditions where the special vehicle is located.

7. A risk control method considering special vehicle lane-changing behavior according to claim 5, Features: In the lane change between the special vehicle SV and the rear vehicle RV in the target lane, the conditions for the special vehicle SV to successfully change lanes are: Among them, D 2 is the headway between the special vehicle SV and the rear vehicle RV in the target lane before lane change, S R is the distance traveled by the vehicle RV in the target lane at the end of the lane change, S S D is the longitudinal distance traveled by the special vehicle SV after the lane change is completed. 3 is the headway distance between the rear vehicle RV and the special vehicle SV in the target lane when the lane change is completed, L RV and L SV are the body lengths of the rear vehicle RV and the special vehicle SV in the target lane, S RV is the range of the risk potential field in the direction of the front of the vehicle RV behind the target lane, S SV-R is the range of the risk potential field in the rear direction of the special vehicle SV; In the lane change between the special vehicle SV and the rear vehicle RV in the target lane in step S3, the minimum safe lane change distance between the special vehicle SV and the rear vehicle RV in the target lane is the head distance D between the special vehicle SV and the rear vehicle RV in the target lane before the lane change. 2 The following conditions are met: Among them, μ, β, and k are all undetermined coefficients that do not represent 0, ε represents the influencing factor of the weather conditions of the special vehicle at that time, exp(*) represents the exponential function with the natural logarithm e as the base, and α 1 represents the special vehicle length correlation coefficient, U SV represents the special vehicle risk potential field, U RV represents the risk potential field of the vehicle RV behind the target lane, U RV with U SV The calculation method is the same, S S represents the distance traveled by the special vehicle SV during the lane change preparation phase, v SV represents the current speed of the special vehicle SV, τ represents the coefficient related to acceleration, a represents the acceleration of the special vehicle SV, R 0 Indicates the impact factor of the road conditions where the special vehicle is located, L RV and L SV are the body lengths of the rear vehicle RV and the special vehicle SV in the target lane, and θ represents the angle between the velocity direction of the special vehicle SV and the X-axis direction.

8. A risk control method considering special vehicle lane-changing behavior according to claim 7, Features: The range S of the risk potential field in the rear direction of the special vehicle SV SV-R The expression is as follows: Among them, μ, β, and k are all undetermined coefficients that do not represent 0, ε represents the influencing factor of the weather conditions of the special vehicle at that time, exp(*) represents the exponential function with the natural logarithm e as the base, and R 0 Indicates the impact factor of the road conditions where the special vehicle is located, α 1 represents the special vehicle length correlation coefficient, U SV represents the risk potential field of a special vehicle, σ represents the coefficient related to speed, M SV represents the equivalent mass of the special vehicle SV; The range S of the risk potential field in the direction of the vehicle head of the rear vehicle RV in the target lane RV The expression is as follows: Among them, μ, β, and k are all undetermined coefficients that do not represent 0, ε represents the weather condition factor affecting the special vehicle at that time, exp(*) represents an exponential function with the natural logarithm e as the base, and R 0 Indicates the impact factor of the road conditions where the special vehicle is located, α 1 represents the special vehicle length correlation coefficient, U RV represents the risk potential field of the vehicle RV behind the target lane, σ represents the coefficient related to the speed, M RV Represents the equivalent mass of the vehicle RV behind the target lane.

9. A risk control system considering lane-changing behavior of special vehicles, based on a risk control method considering lane-changing behavior of special vehicles as claimed in any one of claims 1 to 8, Features: Includes roadside perception module, edge computing module and proximal server; The roadside sensing module is used to receive the lane change communication signal of the special vehicle, collect the distribution status of social vehicles on the lane, and upload the distribution status to the edge computing module; The edge computing module calculates the minimum lane-changing distance between the special vehicle and the preceding vehicle and the following vehicle in the target lane according to the position and speed of the special vehicle and the distribution pattern of social vehicles on the lane, and sends it to the proximal server; The near-end server is used to send lane change information to social vehicles on the lane, and remind the rear social vehicles in the target lane to slow down.

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