A conflict recognition and warning method and system for merging area based on driving risk field

Abstract

A method and system for conflict identification and warning in a merging area based on a driving risk field, relating to the field of intelligent traffic management and control technology. In view of the problem that existing methods do not consider the impact of vehicle size and the conflict angle between mainline vehicles and ramp vehicles on merging risks, which in turn leads to traffic accidents, this application establishes a vehicle driving risk field to evaluate the conflict situation between mainline vehicles and ramp vehicles in the merging area, and uses vehicle-road collaborative technology to achieve real-time warning for drivers in the merging area, thereby avoiding the occurrence of traffic accidents, ensuring vehicle driving safety, reducing energy consumption, and improving the traffic efficiency of the entire merging area.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent traffic management and control, and in particular to a merging area conflict recognition and early warning method and system based on a driving risk field. Background Art

[0002] The merging area of ​​a highway includes ramps, acceleration lanes, and main line sections. It is a bottleneck section of the highway system and is prone to traffic congestion and traffic accidents, posing a great safety hazard. Research on vehicle conflict identification and early warning in merging areas of highways is of great significance for improving the level of road traffic safety in merging areas of highways.

[0003] Most of the current studies on highway merging area warning systems do not consider the impact of vehicle size and the conflict angle between mainline vehicles and ramp vehicles on merging risks, which in turn leads to traffic accidents. Summary of the invention

[0004] The purpose of the present invention is to propose a merging area conflict identification and warning method and system based on a driving risk field to address the problem that existing methods do not consider the impact of vehicle size and the conflict angle between mainline vehicles and ramp vehicles on merging risks, which in turn leads to traffic accidents.

[0005] The technical solution adopted by the present invention to solve the above technical problems is:

[0006] A method for identifying and warning conflicts in a merging area based on a driving risk field comprises the following steps:

[0007] Step 1: Collect vehicle information and environmental information of the merging area, wherein the vehicle information includes vehicle speed, acceleration, vehicle size and vehicle position information, and the environmental information is visibility;

[0008] Step 2: Using the vehicle position information, the movement trajectories of the mainline vehicles and ramp vehicles in the merging area are predicted, and the coordinates of the potential conflict point C of the mainline vehicles and ramp vehicles and the angle θ between the mainline vehicles and ramp vehicles and the potential conflict point C are obtained according to the prediction results;

[0009] Step 3: According to the vehicle speed, acceleration, vehicle size, visibility and angle θ, the main line vehicle driving risk field intensity E is obtained. ij and the risk field strength E' of the ramp vehicle driving ij , expressed as:

[0010]

[0011] Among them, (x j ,y j ) represents the center of mass coordinates of vehicle j, (x i ,yi ) represents the center of mass coordinates of vehicle i, r ij Represents a point (x i ,y i ) and point (x j ,y j ), Represents a point (x i ,y i ) and point (x j ,y j ) between the relative distance magnification vector, M i represents the equivalent mass of vehicle i, S i represents the projection area of ​​vehicle i onto the ground, V i represents the speed of vehicle i, α i Indicates V i With r ij The angle, Z i It means (x i ,y i ) road condition influencing factors, G, k 1 , k 2 is a constant term;

[0012] Step 4: Obtain all PET values ​​in the confluence area, and then obtain a PET cumulative frequency curve, and then divide the PET cumulative frequency into regions according to the first quantile value, the second quantile value, and the third quantile value;

[0013] Step 5: Calculate the distance d between the mainline vehicle and the potential conflict point C when t is the PET corresponding to the first quantile value 1 , t is the distance d between the mainline vehicle and the potential conflict point C when the PET corresponds to the second quantile value 2 And t is the distance d between the mainline vehicle and the potential conflict point C when the PET corresponding to the third quantile value 3 , let d = |r ij |, get d 1 d 2 and d 3 The corresponding main line vehicle driving risk field strength E 1 、E 2 and E 3 ;

[0014] Step 6: Calculate the distance d' between the ramp vehicle and the potential conflict point C when t is the PET corresponding to the first quantile value 1 , t is the distance d' between the ramp vehicle and the potential conflict point C when the PET corresponds to the second quantile value 2 And t is the distance d' between the ramp vehicle and the potential conflict point C when the PET corresponding to the third quantile value 3 , let d = |r ij |, get d'1 , d' 2 and d' 3 The corresponding ramp vehicle driving risk field strength E' 1 , E' 2 and E' 3 ;

[0015] Step 7: Using E ij , E' ij 、E 1 、E 2 、E 3 , E' 1 , E' 2 and E' 3 To identify conflict risks, the specific steps are as follows:

[0016] When E ij >E 1 or E' ij >E' 1 There are serious conflicts;

[0017] When E 2 <E ij <E 1 or E' 2 <E' ij <E' 1 When there is a general conflict;

[0018] When E 3 <E ij <E 2 or E' 3 <E' ij <E' 2 There was a slight conflict.

[0019] When E 3 >E ij or E' 3 >E' ij There is no conflict when

[0020] Step 8: Send a warning signal to the driver based on the conflict identification result in step 7.

[0021] Furthermore, in the step one, the vehicle information is collected by laser radar and video acquisition equipment, and the environmental information is collected by a digital camera visibility meter.

[0022] Furthermore, the first quantile value, the second quantile value and the third quantile value are 15%, 50% and 85% respectively.

[0023] Further, the angle θ is expressed as:

[0024]

[0025] Among them, (x c ,y c ) represents the coordinates of the potential conflict point C, (x a ,y a ) represents the coordinates of the main line vehicle, (x b ,y b ) represents the coordinates of the ramp vehicle;

[0026] Said It is expressed as:

[0027]

[0028] a=V i ·ttcb

[0029] b = τ

[0030] Among them, a and b represent the longitudinal and lateral shape control factors of the vehicle driving risk field, respectively, ttcb represents the safety boundary threshold, and τ represents the radius of the vehicle safety circle;

[0031] The M i It is expressed as:

[0032] M i =M i (m i ,T i ,V i )=m i ·T i g(V i )

[0033] Among them, m i represents the vehicle mass, T i represents the type of vehicle i, g(V i ) represents the speed function of vehicle i;

[0034] The Z i It is expressed as:

[0035] Z i =ψ u ·ψ ρ ·ψ ξ ·ψ σ ·ψ δ

[0036] Among them, ψ u represents the road adhesion coefficient, ψ ρ represents the road curvature, ψ ξ represents the road slope, ψ σ represents the road width, ψ δ Indicates road visibility;

[0037] The distance between the vehicle and the potential conflict point C is:

[0038]

[0039] Where d represents the distance between the vehicle and the potential conflict point C, v represents the vehicle speed, a represents the acceleration, and t represents the straight-line driving time.

[0040] Furthermore, the sending of the warning signal in step eight is performed by a light alarm module and a voice alarm module arranged in the vehicle, wherein the light alarm module is implemented by an LED light, and the voice alarm module is implemented by a voice broadcast device;

[0041] The warning signal in step eight is divided into four levels according to the conflict identification result in step seven, specifically:

[0042] When there is a serious conflict, a first-level warning signal is sent;

[0043] When there is a general conflict, a secondary warning signal is sent;

[0044] When there is a minor conflict, a level 3 warning signal is sent;

[0045] When there is no conflict, no warning signal is sent.

[0046] A merging area conflict identification and warning system based on a driving risk field, the system comprising a data acquisition subsystem, an information processing subsystem and a vehicle-mounted warning subsystem;

[0047] The data collection subsystem is used to collect vehicle information and environmental information in the merging area, wherein the vehicle information includes vehicle speed, acceleration, vehicle size and vehicle position information, and the environmental information is visibility;

[0048] The information processing subsystem includes a motion trajectory prediction module, a vehicle driving risk field calculation module and a conflict judgment module;

[0049] The motion trajectory prediction module predicts the motion trajectories of the mainline vehicle and the ramp vehicle in the merging area according to the vehicle position information, and obtains the coordinates of the potential conflict point C of the mainline vehicle and the ramp vehicle and the angle θ between the mainline vehicle and the ramp vehicle and the potential conflict point C according to the prediction results;

[0050] The vehicle driving risk field calculation module obtains the main line vehicle driving risk field intensity E according to the vehicle driving speed, acceleration, vehicle size, visibility and angle θ ij and the risk field strength E' of the ramp vehicle driving ij , expressed as:

[0051]

[0052] Among them, (x j ,y j ) represents the center of mass coordinates of vehicle j, (x i ,y i ) represents the center of mass coordinates of vehicle i, r ij Represents a point (x i ,y i ) and point (x j ,y j ), Represents a point (x i ,y i ) and point (x j ,y j ) between the relative distance magnification vector, M i represents the equivalent mass of vehicle i, S i represents the projection area of ​​vehicle i onto the ground, V i represents the speed of vehicle i, α i Indicates V i With r ij The angle, Z i It means (x i ,y i ) road condition influencing factors, G, k 1 , k 2 is a constant term;

[0053] The conflict judgment module is used to obtain all PET values ​​in the merging area, and then obtain a PET cumulative frequency curve, and then divide the PET cumulative frequency into regions according to the first quantile value, the second quantile value and the third quantile value, and then respectively calculate the distance d1 between the mainline vehicle and the potential conflict point C when t is the PET corresponding to the first quantile value, the distance d2 between the mainline vehicle and the potential conflict point C when t is the PET corresponding to the second quantile value, and the distance d3 between the mainline vehicle and the potential conflict point C when t is the PET corresponding to the third quantile value, and let d=|r ij |, and obtain the main line vehicle driving risk field strength E corresponding to d1, d2 and d3 1 、E 2 and E 3 Then, the distance d' between the ramp vehicle and the potential conflict point C is calculated when t is the PET corresponding to the first quantile value. 1 , t is the distance d' between the ramp vehicle and the potential conflict point C when the PET corresponds to the second quantile value 2 And t is the distance d' between the ramp vehicle and the potential conflict point C when the PET corresponding to the third quantile value 3 , let d = |r ij |, get d' 1 , d' 2 and d'3 The corresponding ramp vehicle driving risk field strength E' 1 , E' 2 and E' 3 , and then use E ij , E' ij 、E 1 、E 2 、E 3 , E' 1 , E' 2 and E' 3 To identify conflict risks, the specific steps are as follows:

[0054] When E ij >E 1 or E' ij >E' 1 There are serious conflicts;

[0055] When E 2 <E ij <E 1 or E' 2 <E' ij <E' 1 When there is a general conflict;

[0056] When E 3 <E ij <E 2 or E' 3 <E' ij <E' 2 There was a slight conflict.

[0057] When E 3 >E ij or E' 3 >E' ij There is no conflict when

[0058] The vehicle-mounted warning subsystem is used to send a warning signal to the driver according to the conflict identification result.

[0059] Furthermore, the vehicle information is collected by laser radar and video acquisition equipment, and the environmental information is collected by a digital camera visibility meter.

[0060] Furthermore, the first quantile value, the second quantile value and the third quantile value are 15%, 50% and 85% respectively.

[0061] Further, the angle θ is expressed as:

[0062]

[0063] Among them, (x c ,y c) represents the coordinates of the potential conflict point C, (x a ,y a ) represents the coordinates of the main line vehicle, (x b ,y b ) represents the coordinates of the ramp vehicle;

[0064] Said It is expressed as:

[0065]

[0066] a=V i ·ttcb

[0067] b = τ

[0068] Among them, a and b represent the longitudinal and lateral shape control factors of the vehicle driving risk field, respectively, ttcb represents the safety boundary threshold, and τ represents the radius of the vehicle safety circle;

[0069] The M i It is expressed as:

[0070] M i =M i (m i ,T i ,V i )=m i ·T i g(V i )

[0071] Among them, m i represents the vehicle mass, T i represents the type of vehicle i, g(V i ) represents the speed function of vehicle i;

[0072] The Z i It is expressed as:

[0073] Z i =ψ u ·ψ ρ ·ψ ξ ·ψ σ ·ψ δ

[0074] Among them, ψ u represents the road adhesion coefficient, ψ ρ represents the road curvature, ψ ξ represents the road slope, ψ σ represents the road width, ψ δ Indicates road visibility;

[0075] The distance between the vehicle and the potential conflict point C is:

[0076]

[0077] Where d represents the distance between the vehicle and the potential conflict point C, v represents the vehicle speed, a represents the acceleration, and t represents the straight-line driving time.

[0078] Furthermore, the sending of the warning signal is performed by a light alarm module and a voice alarm module arranged in the vehicle, wherein the light alarm module is implemented by an LED light, and the voice alarm module is implemented by a voice broadcast device;

[0079] The warning signal in step eight is divided into four levels according to the conflict identification result in step seven, specifically:

[0080] When there is a serious conflict, a first-level warning signal is sent;

[0081] When there is a general conflict, a secondary warning signal is sent;

[0082] When there is a minor conflict, a level 3 warning signal is sent;

[0083] When there is no conflict, no warning signal is sent.

[0084] The beneficial effects of the present invention are:

[0085] This application establishes a vehicle driving risk field to evaluate the conflict situation between mainline vehicles and ramp vehicles in the merging area, and uses vehicle-road collaborative technology to achieve real-time warning for drivers in the merging area, avoid traffic accidents, ensure vehicle driving safety, reduce energy consumption, and improve the traffic efficiency of the entire merging area. BRIEF DESCRIPTION OF THE DRAWINGS

[0086] Figure 1 This is a structural diagram of the merging area conflict identification and warning system based on the driving risk field of this application;

[0087] Figure 2 A schematic diagram of the layout location and predicted trajectory of the information collection equipment for this application;

[0088] Figure 3 This is a diagram of the working mechanism of the convolutional neural network of this application;

[0089] Figure 4 The vehicle driving risk field classification map for this application;

[0090] Figure 5 This is a schematic diagram of the 4-level conflict risk warning for this application;

[0091] Figure 6 This is a schematic diagram of the installation of the vehicle-mounted warning device of this application;

[0092] Figure 7 This is a rendering of a small car versus a small car conflict;

[0093] Figure 8 This is a rendering of a conflict between a large car and a small car. DETAILED DESCRIPTION

[0094] It should be particularly noted that, in the absence of conflict, the various embodiments disclosed in this application can be combined with each other.

[0095] Specific implementation method 1: refer to Figure 1 Specifically describing this embodiment, a method for identifying and warning a conflict in a merging area based on a driving risk field described in this embodiment includes the following steps:

[0096] Step 1: Collect vehicle information and environmental information of the merging area, wherein the vehicle information includes vehicle speed, acceleration, vehicle size and vehicle position information, and the environmental information is visibility;

[0097] Step 2: Using the vehicle position information, the movement trajectories of the mainline vehicles and ramp vehicles in the merging area are predicted, and the coordinates of the potential conflict point C of the mainline vehicles and ramp vehicles and the angle θ between the mainline vehicles and ramp vehicles and the potential conflict point C are obtained according to the prediction results;

[0098] Step 3: According to the vehicle speed, acceleration, vehicle size, visibility and angle θ, the main line vehicle driving risk field intensity E is obtained. ij and the risk field strength E' of the ramp vehicle driving ij , expressed as:

[0099]

[0100] Among them, (x j ,y j ) represents the center of mass coordinates of vehicle j, (x i ,y i ) represents the center of mass coordinates of vehicle i, r ij Represents a point (x i ,y i ) and point (x j ,y j ), Represents a point (x i ,y i ) and point (x j ,y j ) between the relative distance magnification vector, M i represents the equivalent mass of vehicle i, S i represents the projection area of ​​vehicle i onto the ground, V i represents the speed of vehicle i, α i Indicates V i With rij The angle, Z i It means (x i ,y i ) road condition influencing factors, G, k 1 , k 2 is a constant term;

[0101] Step 4: Obtain all PET values ​​in the confluence area, and then obtain a PET cumulative frequency curve, and then divide the PET cumulative frequency into regions according to the first quantile value, the second quantile value, and the third quantile value;

[0102] Step 5: Calculate the distance d between the mainline vehicle and the potential conflict point C when t is the PET corresponding to the first quantile value 1 , t is the distance d between the mainline vehicle and the potential conflict point C when the PET corresponds to the second quantile value 2 And t is the distance d between the mainline vehicle and the potential conflict point C when the PET corresponding to the third quantile value 3 , let d = |r ij |, get d 1 d 2 and d 3 The corresponding main line vehicle driving risk field strength E 1 、E 2 and E 3 ;

[0103] Step 6: Calculate the distance d' between the ramp vehicle and the potential conflict point C when t is the PET corresponding to the first quantile value 1 , t is the distance d' between the ramp vehicle and the potential conflict point C when the PET corresponds to the second quantile value 2 And t is the distance d' between the ramp vehicle and the potential conflict point C when the PET corresponding to the third quantile value 3 , let d = |r ij |, get d' 1 , d' 2 and d' 3 The corresponding ramp vehicle driving risk field strength E' 1 , E' 2 and E' 3 ;

[0104] Step 7: Using E ij , E' ij 、E 1 、E 2 、E 3 , E' 1 , E' 2 and E' 3 To identify conflict risks, the specific steps are as follows:

[0105] When E ij>E 1 or E' ij >E' 1 There are serious conflicts;

[0106] When E 2 <E ij <E 1 or E' 2 <E' ij <E' 1 When there is a general conflict;

[0107] When E 3 <E ij <E 2 or E' 3 <E' ij <E' 2 There was a slight conflict.

[0108] When E 3 >E ij or E' 3 >E' ij There is no conflict when

[0109] Step 8: Send a warning signal to the driver based on the conflict identification result in step 7.

[0110] Step 1: Collect vehicle information and environmental information in the merging area:

[0111] Vehicle information and environmental information are collected through information collection equipment deployed in the merging area. The information collection equipment includes laser radar, video collection equipment and digital camera visibility meter. The collected vehicle information includes vehicle speed, acceleration, vehicle type, vehicle size, and vehicle location information. The collected environmental information is visibility.

[0112] Step 2: Predict vehicle motion trajectory:

[0113] According to the collected historical vehicle motion trajectory data, the convolutional neural network is used to predict the motion trajectories of the mainline vehicle A and the ramp vehicle B, and the intersection C of the motion trajectories of the mainline vehicle A and the ramp vehicle B is obtained. The intersection C is the potential conflict point between the mainline vehicle A and the ramp vehicle B.

[0114] The convolutional neural network consists of three modules: convolutional layer, pooling layer and fully connected layer. The convolution kernel in the convolutional layer extracts short-term features of the input vehicle position information; the pooling layer aggregates and reduces the combination dimension of the sequence features extracted from the convolutional layer, retains important local features, and reduces the complexity of the algorithm; the fully connected layer uses a nonlinear activation function to process the extracted feature maps and output the vehicle's trajectory prediction data:

[0115]

[0116] Where: (x, y) represents the position coordinates of the vehicle, m represents different vehicles, t n Represents a point in time.

[0117] Through the trajectory prediction data, the coordinates of the potential conflict point C (x c ,y c ), according to the coordinates of the mainline vehicle A (x a ,y a ) and the coordinates of ramp vehicle B (x b ,y b ), the angle θ between the two vehicles and the potential conflict point C can be calculated:

[0118]

[0119] Step 3: Calculate the vehicle driving risk field strength:

[0120] The size of the driving risk field generated by vehicle i around it is related to the vehicle's mass, size, running speed, and the distance from the point to the vehicle's center of mass. The driving risk field function formula generated by vehicle i around it can be expressed as:

[0121]

[0122] Where: vector E ij is located at the point (x i ,y i ) The driving risk field generated by vehicle i is at point (x j ,y j ) field strength vector. The greater the field strength, the higher the driving risk. (x i ,y i ) is the center of mass coordinate of vehicle i, r ij For point (x i ,y i ) and point (x j ,y j ) between the relative distance vector, the field strength direction and r ij The same direction, For point (x i ,y i ) and point (x j ,y j ) between the relative distance magnification vector, M i is the equivalent mass of vehicle i, S i is the projection area of ​​vehicle i onto the ground, V i is the speed of vehicle i, α i V i With r ij The angle between i ,yi ) road condition influencing factors, G, k 1 is a constant term.

[0123] Relative distance magnification vector The calculation formula is:

[0124]

[0125] Where: a and b are the longitudinal and transverse shape control factors of the vehicle driving risk field, respectively, which are used to change the influence size and range of the vehicle driving risk field in the longitudinal and transverse directions, where the longitudinal direction is the vehicle forward direction and the transverse direction is the direction perpendicular to it. Then the values ​​of a and b can be determined as:

[0126] a=V i ·ttcb,b=τ (5)

[0127] Where: ttcb is the safety boundary threshold, τ is the radius of the vehicle safety circle.

[0128] The equivalent mass M of vehicle i i The calculation formula is:

[0129] M i =M i (m i ,T i ,V i )=m i ·T i g(V i ) (6)

[0130] Where: M i is the vehicle equivalent mass, m i is the vehicle mass, T i is the type of vehicle i, g(V i ) is the speed function of vehicle i.

[0131] Road condition influencing factor Z i The calculation formula is:

[0132] Z i =ψ u ·ψ ρ ·ψ ξ ·ψ σ ·ψ δ (7)

[0133] Where: u is the road adhesion coefficient, ψ ρ is the road curvature, ψ ξ is the road slope, ψ σ is the road width, ψ δ For road visibility.

[0134] For the merging area, the merging conditions of ramp vehicles will affect the traffic conditions of the entire merging area. Through the prediction of vehicle motion trajectories in step 2, the angle between the mainline vehicle A, the ramp vehicle B and the potential conflict point C is θ. When the angle θ is larger, the loss caused by the conflict between the mainline vehicle and the ramp vehicle is greater, that is, the greater the risk of the vehicle. If the risk field of the mainline vehicle remains unchanged, the driving risk field generated by the ramp vehicle around it will increase in strength. Therefore, the driving risk field of the ramp vehicle needs to be corrected. The corrected driving risk field function formula is:

[0135]

[0136] Where: k 2 is a constant term, and θ is the angle between the mainline vehicle A, the ramp vehicle B and the potential conflict point C.

[0137] Step 4: PET value division:

[0138] Based on the statistical data of the confluence area, the PET cumulative frequency curve was obtained, and the PET cumulative frequency was divided according to the 15%, 50% and 85% quantiles. 15％ When PET 15％ <PET<PET 50％ When PET 50％ <PET<PET 85％ It is a minor conflict, when PET>PET 85％ Invalid conflict.

[0139] Step 5: Calculate the critical value of the main line vehicle driving risk field strength:

[0140] When the vehicle speed is v, the acceleration is a, and the vehicle reaches the potential conflict point C after traveling in a straight line for t time, the distance d between the vehicle and the potential conflict point C is:

[0141]

[0142] According to the classification of PET values, the formula (9) can be used to calculate the values ​​when t is PET. 15％ , PET 50％ and PET 85％ The distance d between the vehicle and the potential conflict point C is 15％ d 50％ d 85％ , the critical value of the driving risk field strength of the main line vehicle at the corresponding distance from the centroid can be calculated by formula (3), which is denoted as E 1 、E 2 、E 3 and E' 1 , E'2 , E' 3 .

[0143] Step 6: Calculate the critical value of the ramp vehicle driving risk field strength:

[0144] According to the classification of PET values, the formula (9) can be used to calculate the values ​​when t is PET. 15％ , PET 50％ and PET 85％ The distance d between the vehicle and the potential conflict point C is 15％ d 50％ d 85％ , the critical values ​​of the driving risk field strength of the mainline vehicles and ramp vehicles at the corresponding distance from the centroid can be calculated by equations (3) and (8), which are denoted as E 1 、E 2 、E 3 and E' 1 , E' 2 , E′ 3 .

[0145] Step 7: Using E ij , E' ij 、E 1 、E 2 、E 3 , E' 1 , E' 2 and E' 3 To perform conflict identification:

[0146] (1) When the vehicle is on the main line or ramp, the driving risk field strength E ij or E' ij Satisfaction: E ij >E 1 or E' ij >E' 1 , that is, the vehicle is in area I of the main line vehicle or ramp vehicle, and there is a serious conflict at this time;

[0147] (2) When the vehicle is in the driving risk field of the main line vehicle or ramp vehicle, the field strength E ij or E' ij Satisfaction: E 2 <E ij <E 1 or E' 2 <E' ij <E' 1 , that is, the vehicle is located in the II area of ​​the main line vehicle or ramp vehicle, and there is a general conflict at this time;

[0148] (3) When the vehicle is in the driving risk field strength E of the main line vehicle or ramp vehicle ij or E' ij Satisfaction: E3 <E ij <E 2 or E' 3 <E' ij <E' 2 , that is, the vehicle is in the III zone of the main line vehicle or ramp vehicle, and there is a slight conflict at this time;

[0149] (4) When the vehicle is in the driving risk field strength E of the main line vehicle or ramp vehicle ij or E' ij Satisfaction: E 3 >E ij or E' 3 >E' ij , that is, the vehicle is located in area IV of the main line vehicle or ramp vehicle, which is an invalid conflict.

[0150] Step 8: Send warning information to the driver:

[0151] Drivers mainly obtain information through vision and hearing. In order to allow drivers to better receive warning information, light alarm modules and voice alarm modules are set up in the car for conflict warning. The light alarm module is implemented through LED lights, and the voice alarm module is implemented through voice broadcast equipment.

[0152] The warning signals are divided into four levels according to the conflict identification results in step seven, specifically:

[0153] (1) When there is a serious conflict, a Level 1 warning signal is sent;

[0154] (2) When there is a general conflict, a secondary warning signal is sent;

[0155] (3) When there is a minor conflict, a Level 3 warning signal is sent;

[0156] (4) When there is no conflict, no warning signal is sent;

[0157] The warning information corresponding to the warning signals at all levels is:

[0158]

[0159] This application can also be implemented in a system form, as follows:

[0160] A merging area conflict identification and warning system based on a driving risk field, the system comprising a data acquisition subsystem, an information processing subsystem and a vehicle-mounted warning subsystem;

[0161] The data collection subsystem is used to collect vehicle information and environmental information in the merging area, wherein the vehicle information includes vehicle speed, acceleration, vehicle size and vehicle position information, and the environmental information is visibility;

[0162] The information processing subsystem includes a motion trajectory prediction module, a vehicle driving risk field calculation module and a conflict judgment module;

[0163] The motion trajectory prediction module predicts the motion trajectories of the mainline vehicle and the ramp vehicle in the merging area according to the vehicle position information, and obtains the coordinates of the potential conflict point C of the mainline vehicle and the ramp vehicle and the angle θ between the mainline vehicle and the ramp vehicle and the potential conflict point C according to the prediction results;

[0164] The vehicle driving risk field calculation module obtains the main line vehicle driving risk field intensity E according to the vehicle driving speed, acceleration, vehicle size, visibility and angle θ ij and the risk field strength E' of the ramp vehicle driving ij , expressed as:

[0165]

[0166] Among them, (x j ,y j ) represents the center of mass coordinates of vehicle j, (x i ,y i ) represents the center of mass coordinates of vehicle i, r ij Represents a point (x i ,y i ) and point (x j ,y j ), Represents a point (x i ,y i ) and point (x j ,y j ) between the relative distance magnification vector, M i represents the equivalent mass of vehicle i, S i represents the projection area of ​​vehicle i onto the ground, V i represents the speed of vehicle i, α i Indicates V i With r ij The angle, Z i It means (x i ,y i ) road condition influencing factors, G, k 1 , k 2 is a constant term;

[0167] The conflict judgment module is used to obtain all PET values ​​in the merging area, and then obtain a PET cumulative frequency curve, and then divide the PET cumulative frequency into regions according to the first quantile value, the second quantile value and the third quantile value, and then respectively calculate the distance d1 between the mainline vehicle and the potential conflict point C when t is the PET corresponding to the first quantile value, the distance d2 between the mainline vehicle and the potential conflict point C when t is the PET corresponding to the second quantile value, and the distance d3 between the mainline vehicle and the potential conflict point C when t is the PET corresponding to the third quantile value, and let d=|r ij |, and obtain the main line vehicle driving risk field strength E corresponding to d1, d2 and d3 1 、E 2 and E 3 Then, the distance d' between the ramp vehicle and the potential conflict point C is calculated when t is the PET corresponding to the first quantile value. 1 , t is the distance d' between the ramp vehicle and the potential conflict point C when the PET corresponds to the second quantile value 2 And t is the distance d' between the ramp vehicle and the potential conflict point C when the PET corresponding to the third quantile value 3 , let d = |r ij |, get d' 1 , d' 2 and d' 3 The corresponding ramp vehicle driving risk field strength E' 1 , E' 2 and E' 3 , and then use E ij , E' ij 、E 1 、E 2 、E 3 , E' 1 , E' 2 and E' 3 To identify conflict risks, the specific steps are as follows:

[0168] When E ij >E 1 or E' ij >E' 1 There are serious conflicts;

[0169] When E 2 <E ij <E 1 or E' 2 <E' ij <E' 1 When there is a general conflict;

[0170] When E 3 <E ij <E 2 or E' 3 <E' ij<E' 2 There was a slight conflict.

[0171] When E 3 >E ij or E' 3 >E' ij There is no conflict when

[0172] The vehicle-mounted warning subsystem is used to send a warning signal to the driver according to the conflict identification result.

[0173] Example:

[0174] The following combination Figure 7 and Figure 8 The embodiments of the present invention are described in detail.

[0175] The main line of a highway merge area is a two-way four-lane road with a speed limit of 120km / h, and the ramp is a single lane with a speed limit of 60km / h. According to the PET cumulative frequency curve of the merge area, the PET 15％ , PET 50％ and PET 85％ The values ​​are 1.56s, 3.39s, and 5.80s respectively. By using formula (9), we can calculate the values ​​when t is PET 15％ , PET 50％ and PET 85％ The distance d between the vehicle and the potential conflict point C is 15％ d 50％ d 85％ .

[0176] like Figure 7 As shown in the figure, the main line vehicle A and the ramp vehicle B are both small cars. The distance between the two vehicles and the center of mass is d, which can be calculated by equations (3) and (8). 15％ d 50％ d 85％ The critical value of driving risk field strength E 1 、E 2 、E 3 and E' 1 , E' 2 , E' 3 According to the interaction of the two vehicles’ driving risk field, the mainline vehicle A is located in the II area of ​​the ramp vehicle B. At this time, the warning information of the ramp vehicle B is: the LED light is flashing orange, and the voice prompt is "there is a general conflict, please pay attention"; the ramp vehicle B is located in the III area of ​​the mainline vehicle A, so the warning information of the mainline vehicle is: the LED light is flashing yellow, and the voice prompt is "there is a minor conflict, please pay attention". Figure 8As shown, the mainline vehicle A is a large vehicle, and the ramp vehicle B is a small vehicle. Due to its large body mass and size, the mainline vehicle A generates a greater driving risk field strength in the surrounding area. At this time, the warning information of the mainline vehicle A is: the LED light status is flashing yellow, and the voice prompt is "There is a minor conflict, please pay attention"; the warning information of the ramp vehicle B is: the LED light status is flashing orange, and the voice prompt is "There is a general conflict, please pay attention".

[0177] It should be noted that the specific implementation is only an explanation and description of the technical solution of the present invention, and cannot be used to limit the scope of protection of the rights. Any partial changes made according to the claims and description of the present invention should still fall within the scope of protection of the present invention.

Claims

1. A conflict recognition and warning method for merging areas based on a driving risk field, characterized in that The following steps are involved: Step 1: Collect vehicle information and environmental information of the merging area, wherein the vehicle information includes vehicle speed, acceleration, vehicle size and vehicle position information, and the environmental information is visibility; Step 2: Using the vehicle position information, the movement trajectories of the mainline vehicles and ramp vehicles in the merging area are predicted, and the coordinates of the potential conflict point C of the mainline vehicles and ramp vehicles and the angle θ between the mainline vehicles and ramp vehicles and the potential conflict point C are obtained according to the prediction results; Step 3: According to the vehicle speed, acceleration, vehicle size, visibility and angle θ, the main line vehicle driving risk field intensity E is obtained. ij and the risk field strength E' of the ramp vehicle driving ij , expressed as: Among them, (x j ,y j ) represents the center of mass coordinates of vehicle j, (x i ,y i ) represents the center of mass coordinates of vehicle i, r ij Represents a point (x i ,y i ) and point (x j ,y j ), Represents a point (x i ,y i ) and point (x j ,y j ) between the relative distance magnification vector, M i represents the equivalent mass of vehicle i, S i represents the projection area of ​​vehicle i onto the ground, V i represents the speed of vehicle i, α i Indicates V i With r ij The angle, Z i It means (x i ,y i ) is the road condition influencing factor, G, k1, k2 are constant terms; Step 4: Obtain all PET values ​​in the confluence area, and then obtain a PET cumulative frequency curve, and then divide the PET cumulative frequency into regions according to the first quantile value, the second quantile value, and the third quantile value; Step 5: Calculate the distance d1 between the mainline vehicle and the potential conflict point C when t is the first quantile PET, the distance d2 between the mainline vehicle and the potential conflict point C when t is the second quantile PET, and the distance d3 between the mainline vehicle and the potential conflict point C when t is the third quantile PET, respectively. Let d = |r ij |, and obtain the main line vehicle driving risk field strength E1, E2 and E3 corresponding to d1, d2 and d3; Step 6: Calculate the distance d'1 between the ramp vehicle and the potential conflict point C when t is the first quantile PET, the distance d'2 between the ramp vehicle and the potential conflict point C when t is the second quantile PET, and the distance d'3 between the ramp vehicle and the potential conflict point C when t is the third quantile PET, and let d = |r ij |, and obtain the ramp vehicle driving risk field strength E'1, E'2 and E'3 corresponding to d'1, d'2 and d'3; Step 7: Using E ij , E' ij , E1, E2, E3, E'1, E'2 and E'3 to identify the conflict risks. The specific steps are: When E ij >E1 or E' ij When >E'1, there is a serious conflict; When E2 < E ij < E1 or E'2 < E' ij < E'1, there is a general conflict; When E3 < E ij < E2 or E'3 < E' ij < E'2, there is a slight conflict; When E3>E ij or E'3>E' ij There is no conflict when Step 8: Send a warning signal to the driver based on the conflict identification result in step 7.

2. The method for identifying and warning a conflict in a merging area based on a driving risk field according to claim 1 is characterized in that In the step 1, the vehicle information is collected by laser radar and video acquisition equipment, and the environmental information is collected by a digital camera visibility meter.

3. The method for identifying and warning a conflict in a merging area based on a driving risk field according to claim 1 is characterized in that The first quantile value, the second quantile value and the third quantile value are 15%, 50% and 85% respectively.

4. The method for identifying and warning a conflict in a merging area based on a driving risk field according to claim 1, characterized in that The angle θ is expressed as: Among them, (x c ,y c ) represents the coordinates of the potential conflict point C, (x a ,y a ) represents the coordinates of the main line vehicle, (x b ,y b ) represents the coordinates of the ramp vehicle; Said It is expressed as: a=V i ·ttcb b = τ Among them, a and b represent the longitudinal and lateral shape control factors of the vehicle driving risk field, respectively, ttcb represents the safety boundary threshold, and τ represents the radius of the vehicle safety circle; The M i It is expressed as: M i =M i (m i ,T i ,V i )=m i ·T i ·g(V i ) Among them, m i represents the vehicle mass, T i represents the type of vehicle i, g(V i ) represents the speed function of vehicle i; The Z i It is expressed as: Z i =ψ u ·ψ ρ ·ψ ξ ·ψ σ ·ψ δ Among them, ψ u represents the road adhesion coefficient, ψ ρ represents the road curvature, ψ ξ represents the road slope, ψ σ represents the road width, ψ δ Indicates road visibility; The distance between the vehicle and the potential conflict point C is: Where d represents the distance between the vehicle and the potential conflict point C, v represents the vehicle speed, a represents the acceleration, and t represents the straight-line driving time.

5. The method for identifying and warning a conflict in a merging area based on a driving risk field according to claim 1, characterized in that The sending of the warning signal in step eight is performed by a light alarm module and a voice alarm module arranged in the vehicle, wherein the light alarm module is implemented by an LED light, and the voice alarm module is implemented by a voice broadcast device; The warning signal in step eight is divided into four levels according to the conflict identification result in step seven, specifically: When there is a serious conflict, a first-level warning signal is sent; When there is a general conflict, a secondary warning signal is sent; When there is a minor conflict, a level 3 warning signal is sent; When there is no conflict, no warning signal is sent.

6. A conflict recognition and warning system for merging areas based on a traffic risk field, characterized in that The system includes a data acquisition subsystem, an information processing subsystem and a vehicle-mounted warning subsystem; The data collection subsystem is used to collect vehicle information and environmental information in the merging area, wherein the vehicle information includes vehicle speed, acceleration, vehicle size and vehicle position information, and the environmental information is visibility; The information processing subsystem includes a motion trajectory prediction module, a vehicle driving risk field calculation module and a conflict judgment module; The motion trajectory prediction module predicts the motion trajectories of the mainline vehicle and the ramp vehicle in the merging area according to the vehicle position information, and obtains the coordinates of the potential conflict point C of the mainline vehicle and the ramp vehicle and the angle θ between the mainline vehicle and the ramp vehicle and the potential conflict point C according to the prediction results; The vehicle driving risk field calculation module obtains the main line vehicle driving risk field intensity E according to the vehicle driving speed, acceleration, vehicle size, visibility and angle θ ij and the risk field strength E' of the ramp vehicle driving ij , expressed as: Among them, (x j ,y j ) represents the center of mass coordinates of vehicle j, (x i ,y i ) represents the center of mass coordinates of vehicle i, r ij Represents a point (x i ,y i ) and point (x j ,y j ), Represents a point (x i ,y i ) and point (x j ,y j ) between the relative distance magnification vector, M i represents the equivalent mass of vehicle i, S i represents the projection area of ​​vehicle i onto the ground, V i represents the speed of vehicle i, α i Indicates V i With r ij The angle, Z i It means (x i ,y i ) is the road condition influencing factor, G, k1, k2 are constant terms; The conflict judgment module is used to obtain all PET values ​​in the merging area, and then obtain a PET cumulative frequency curve, and then divide the PET cumulative frequency into regions according to the first quantile value, the second quantile value and the third quantile value, and then respectively calculate the distance d1 between the mainline vehicle and the potential conflict point C when t is the PET corresponding to the first quantile value, the distance d2 between the mainline vehicle and the potential conflict point C when t is the PET corresponding to the second quantile value, and the distance d3 between the mainline vehicle and the potential conflict point C when t is the PET corresponding to the third quantile value, and let d=|r ij |, get the main line vehicle driving risk field strength E1, E2 and E3 corresponding to d1, d2 and d3, then calculate the distance d'1 between the ramp vehicle and the potential conflict point C when t is the first quantile value corresponding to the PET, the distance d'2 between the ramp vehicle and the potential conflict point C when t is the second quantile value corresponding to the PET, and the distance d'3 between the ramp vehicle and the potential conflict point C when t is the third quantile value corresponding to the PET, let d = |r ij |, and obtain the ramp vehicle driving risk field strength E'1, E'2 and E'3 corresponding to d'1, d'2 and d'3, and then use E ij , E' ij , E1, E2, E3, E'1, E'2 and E'3 to identify the conflict risks. The specific steps are: When E ij >E1 or E' ij When >E'1, there is a serious conflict; When E2 < E ij < E1 or E'2 < E' ij < E'1, there is a general conflict; When E3 < E ij < E2 or E'3 < E' ij < E'2, there is a slight conflict; When E3>E ij or E'3>E' ij There is no conflict when The vehicle-mounted warning subsystem is used to send a warning signal to the driver according to the conflict identification result.

7. The merging area conflict recognition and warning system based on the driving risk field according to claim 6 is characterized in that The vehicle information is collected through laser radar and video acquisition equipment, and the environmental information is collected through a digital camera visibility meter.

8. The merging area conflict recognition and warning system based on the driving risk field according to claim 6 is characterized in that The first quantile value, the second quantile value and the third quantile value are 15%, 50% and 85% respectively.

9. The merging area conflict recognition and warning system based on the driving risk field according to claim 6 is characterized in that The angle θ is expressed as: Among them, (x c ,y c ) represents the coordinates of the potential conflict point C, (x a ,y a ) represents the coordinates of the main line vehicle, (x b ,y b ) represents the coordinates of the ramp vehicle; Said It is expressed as: a=V i ·ttcb b = τ Among them, a and b represent the longitudinal and lateral shape control factors of the vehicle driving risk field, respectively, ttcb represents the safety boundary threshold, and τ represents the radius of the vehicle safety circle; The M i It is expressed as: M i =M i (m i ,T i ,V i )=m i ·T i ·g(V i ) Among them, m i represents the vehicle mass, T i represents the type of vehicle i, g(V i ) represents the speed function of vehicle i; The Z i It is expressed as: Z i =ψ u ·ψ ρ ·ψ ξ ·ψ σ ·ψ δ Among them, ψ u represents the road adhesion coefficient, ψ ρ represents the road curvature, ψ ξ represents the road slope, ψ σ represents the road width, ψ δ Indicates road visibility; The distance between the vehicle and the potential conflict point C is: Where d represents the distance between the vehicle and the potential conflict point C, v represents the vehicle speed, a represents the acceleration, and t represents the straight-line driving time.

10. The merging area conflict recognition and warning system based on the driving risk field according to claim 6 is characterized in that The sending of the warning signal is carried out by a light alarm module and a voice alarm module arranged in the vehicle, wherein the light alarm module is implemented by an LED light, and the voice alarm module is implemented by a voice broadcast device; The warning signal in step eight is divided into four levels according to the conflict identification result in step seven, specifically: When there is a serious conflict, a first-level warning signal is sent; When there is a general conflict, a secondary warning signal is sent; When there is a minor conflict, a level 3 warning signal is sent; When there is no conflict, no warning signal is sent.

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