Adaptive hierarchical warning system based on V2V multi-scenario warning

Abstract

The present invention discloses an adaptive hierarchical warning system based on V2V multi-scenario warning, comprising: a calculation unit, multiple single-vehicle warning units, and a multi-vehicle warning unit, wherein the calculation unit obtains status information of a warning remote vehicle and a main vehicle of a triggering scenario, and a scenario warning signal triggered by the remote vehicle; an adaptive priority adjustment module of each single-vehicle warning unit receives status information of a triggering scenario and a main vehicle of the remote vehicle based on the scenario warning signal to determine the scenario threat degree, transmits the scene threat degree calculation result to a warning priority screening module to determine the priority of each scenario, packages the scene with the highest priority and its corresponding scene threat degree into single-vehicle warning information and transmits it to the multi-vehicle warning unit; the multi-vehicle warning unit screens the scene threat degrees of multiple single vehicles to determine the priority of the scene, and uses the scene with the highest priority among all the scenes as the final warning result, so as to facilitate the driver to make a more accurate judgment on the current vehicle condition and improve the driver's driving experience and driving safety.

Description

Technical Field

[0001] The present invention relates to the field of vehicle networking technology, and in particular to an adaptive hierarchical warning system based on V2V multi-scenario warning. Background Art

[0002] In practical V2V applications, vehicles package their own information, such as their latitude and longitude, heading angle, speed, and acceleration, into a BSM message set, which they periodically broadcast and periodically receive messages broadcast by other vehicles. By parsing the message set and acquiring and calculating vehicle information, the system can obtain information such as the relative position and speed of other vehicles without relying on its own sensors, such as radar and cameras. This eliminates obstacles that could affect the sensors and the driver's line of sight, and, based on the vehicle's driving intent, predicts dangerous situations and issues warnings for different scenarios.

[0003] Vehicles will face many unexpected situations during actual driving, such as the simultaneous triggering of multiple scenarios for a single vehicle and the simultaneous triggering of multiple scenarios for multiple vehicles. The simultaneous alarming of multiple warning messages will increase the difficulty of the driver's judgment, causing information confusion and distraction of the driver's attention. Therefore, it is necessary to arrange the warning priorities of different scenarios, that is, to ensure that at the same time, when multiple scenarios for a single vehicle are triggered simultaneously or when multiple scenarios for multiple vehicles are triggered simultaneously, the human-machine interface HMI can only issue a single warning or warnings in sequence. In current research, the most widely used method is to sort the priorities in the algorithm in advance, but sorting in advance will reduce the adaptability of the algorithm and make it impossible to set a safety threshold. As a result, when warnings for multiple scenarios are triggered simultaneously, the scene broadcast cannot be switched according to the actual driving situation of the vehicle, thereby threatening the safety of the driver. Summary of the Invention

[0004] Therefore, the technical problem to be solved by the present invention is to overcome the defects existing in the prior art, thereby providing an adaptive graded warning system based on V2V multi-scenario warning, which adjusts the warning priority in real time according to the change of the threat level of the triggering scene warning, and realizes the adaptive graded warning function of multi-scenario warning.

[0005] In order to achieve the above object, the present invention provides the following technical solutions:

[0006] An embodiment of the present invention provides an adaptive hierarchical warning system based on V2V multi-scenario warning, comprising: a computing unit, multiple single-vehicle warning units, and multiple-vehicle warning units, each of which includes: an adaptive priority adjustment module and a warning priority screening module, wherein:

[0007] a computing unit configured to obtain status information of a remote warning vehicle that triggers a scene, a scene warning signal triggered by the remote warning vehicle, and status information of the host vehicle;

[0008] An adaptive priority adjustment module is used to determine the scene threat level based on the scene warning signal triggered by the warning remote vehicle and the status information of the triggering warning remote vehicle and the host vehicle, and pass the scene threat level calculation result of each scene to the warning priority screening module;

[0009] The warning priority screening module is used to receive the scene threat level of each scene from the adaptive priority adjustment module and screen it, determine the priority of the scene, and package the highest priority scene and its corresponding scene threat level into a single-vehicle warning information and transmit it to the multi-vehicle warning unit;

[0010] The multi-vehicle warning unit is used to obtain the scene threat level of multiple single-vehicle warning units. The amount of information received corresponds to the number of remote vehicles that triggered the scene warning. Each vehicle that triggered the warning corresponds to a warning scene and a scene threat level. The scene threat levels of multiple single vehicles are screened to determine the priority of the scene. The scene with the highest priority among all the scenes triggered by all vehicles is output as the final warning result.

[0011] In one embodiment, the number of the single-vehicle warning units is determined by the number of vehicles triggering the scene warning calculated by the calculation unit.

[0012] In one embodiment, the computing unit obtains status information of the warning remote vehicle and the host vehicle of the triggering scene, including: vehicle length, vehicle width, longitude and latitude information, heading angle, vehicle speed, and acceleration.

[0013] In one embodiment, the adaptive priority adjustment module uses the status information of the warning remote vehicle and the host vehicle to calculate the straight-line distance, longitudinal distance, and lateral distance between the host vehicle and the trigger scene warning remote vehicle to calculate the preset collision time, and brings the preset collision time into the adaptive threat level model to calculate the scene threat level.

[0014] In one embodiment, the straight-line distance D between the host vehicle and the remote vehicle that triggers the scene warning is calculated as follows:

[0015]

[0016] Among them, R is the radius of the earth, a=HV_lat-RV_lat, HV_lat and RV_lat are the latitudes of the main vehicle and the potential dangerous vehicle, and b=HV_lng-RV_lng, HV_lng and RV_lng are the longitudes of the main vehicle and the potential dangerous vehicle.

[0017] In one embodiment, the longitudinal distance between the host vehicle and the remote vehicle triggering the scene warning at non-intersections and intersections is calculated as follows:

[0018] Non-intersection:

[0019]

[0020] Intersection:

[0021]

[0022] Among them, HV length , RV length Respectively represent the length of the main vehicle and the warning remote vehicle, RV wide Represents the width of the remote vehicle that triggers the warning. tmp = Heading_HV - angle, where Heading_HV is the heading angle of the main vehicle and angle is the relative angle between the main vehicle and the remote vehicle that triggers the scene warning. The relative angle between the main vehicle and the remote vehicle that triggers the scene warning is calculated based on the relative longitude and latitude changes between the main vehicle and the remote vehicle that triggers the scene warning. The calculation formula is:

[0023] angle=arctan(cos(U1)*sin(-b),-sin(U1)*cos(U2)+cos(U1)*sin(U2)cos(-b))*180 / pi,

[0024]

[0025] sin(U1)=tan(U1)*cos(U1),

[0026] sin(U2)=tan(U2)*cos(U2),

[0027] Among them, U1 and U2 represent the naturalized latitudes of the main vehicle and the warning remote vehicle respectively. If tmp<0, then tmp=tmp+360; if tmp>360, tmp=tmp-360.

[0028] In one embodiment, the lateral distance between the host vehicle and the remote vehicle that triggers the scene warning at a non-intersection and an intersection is calculated as follows:

[0029] Non-intersection:

[0030]

[0031] Intersection:

[0032]

[0033] Among them, HV wide Indicates the width of the host vehicle.

[0034] In one embodiment, the acquired speed is the longitudinal speed of the vehicle, and the longitudinal relative speed v between the host vehicle and the trigger scene warning remote vehicle is x_rela The calculation formula is:

[0035] v x_rela =v x_hv -v x_rv

[0036] Among them, v x_hv , v x_rv are the longitudinal speeds of the host vehicle and the warning vehicle respectively;

[0037] The lateral relative speed v between the host vehicle and the remote vehicle that triggers the scene warning y_rela The calculation formula is:

[0038] v y_rela =v y_hv -v y_rv

[0039] Among them, v y_hv , v y_rv They are the lateral speed of the warning distant vehicle;

[0040] According to the vehicle lateral acceleration and yaw rate combined with the vehicle dynamics model, the lateral speed of the main vehicle and the remote vehicle that triggers the scene warning are calculated respectively. The calculation formula is:

[0041] v y =∫(a y -v x ω r )dt,

[0042] Among them, a y is the lateral acceleration of the car; w r is the yaw angular velocity.

[0043] In one embodiment, the calculation formula for the preset collision time TTC between the host vehicle and the remote vehicle that triggers the scene warning is:

[0044] Preset collision time TTC for non-intersections:

[0045]

[0046] The calculation formula for the longitudinal preset collision time TTC_x is:

[0047]

[0048] The calculation formula for the preset lateral collision time TTC_y is:

[0049]

[0050] The calculation formula for the preset collision time TTC at the intersection is:

[0051]

[0052] In one embodiment, the formula of the adaptive threat level model is:

[0053]

[0054] Where W_result is the calculation result of the adaptive threat level model; the smaller the result, the greater the threat level; w1 is the scene importance weight, whose value is customized based on the understanding and requirements of each scene and ranges from 0 to 1; w2 is the scaling factor to prevent the larger number from eating the smaller number, whose value can be determined through simulation experiments; e is an irrational number; H is the maximum distance between two vehicles in adjacent lanes;

[0055] If the same vehicle triggers different warning scenarios in the same area, the importance of different warning scenarios is weighted by w1. The more important the warning scenario, the greater the weight, so that the smaller W_result is, the higher the warning priority is.

[0056] If multiple vehicles trigger the same scene warning in the same area, TTC is used for judgment. The closer the warning vehicle is to the vehicle, the smaller the TTC is, which makes W_result smaller.

[0057] If multiple vehicles trigger different warning scenarios in the same area, TTC is used for judgment. The closer the warning vehicle is to the vehicle, the smaller the TTC is, which makes W_result smaller.

[0058] If multiple vehicles trigger the same scene warning in different areas, a preliminary judgment is made through TTC. When the TTC gap is not obvious, it is adjusted through w2(H-D_y). Since only the warning distance vehicles in the lane where the vehicle is traveling and the lanes on both sides of the vehicle are concerned, the vehicle is traveling in the lane, and the warning distance vehicles are in the lanes on both sides of the vehicle. When the vehicle is closer to the vehicle laterally, the smaller the lateral distance D_y between the two vehicles, the larger the w2(H-D_y) value is, which will eventually make W_result smaller and the warning priority higher; when the alarm vehicle is in front of the vehicle, the lateral distance D_y between the two vehicles is 0, and w2(H-D_y) is the largest. Assuming that only the vehicles on both sides are warned and the w2(H-D_y) values ​​are the same, use the lateral acceleration a of the vehicle. y Adjust, w2 is the weight to prevent w2(H-D_y) from being inconsistent with a y The values ​​are too close, so the exponential form of e is used to prevent the divisor from being zero. When the car tends to drive to a certain side, the lateral acceleration a of the car is used. y Adjust W_result.

[0059] Multiple vehicles trigger different scenario warnings in different areas, and all the above parameters are used to adjust W_result.

[0060] The technical solution of the present invention has the following advantages:

[0061] The present invention provides an adaptive hierarchical warning system based on V2V multi-scenario warning. When a single vehicle triggers a multi-scenario warning, the system uses a threat degree model to determine the threat level posed to the host vehicle based on information from the host vehicle and the warning remote vehicle. The threat degree calculation results are then filtered to determine the priority of the scenarios, and a single-vehicle scenario warning signal is output. Based on the number of vehicles triggering the scenario warning, the system inputs multiple single-vehicle warning results into a multi-vehicle warning unit, performs threat degree calculation and filtering, determines the priority of each single-vehicle scenario warning, and outputs the final scenario warning result. When multiple scenarios are triggered simultaneously, the adaptive hierarchical warning system can prioritize the scenario warning information that poses the greatest risk to the driver, thereby improving the driver's memory and making it easier for the driver to make a more accurate judgment of the current vehicle condition, thereby improving the driver's driving experience and driving safety. Furthermore, when multiple dangerous scenarios are continuously triggered simultaneously, the adaptive hierarchical warning system can adjust the priority of the warning scenarios in real time based on the changes in the driving status of the warning vehicles and the changes in the threat degree, preventing a scenario that threatens the vehicle's safety from being overwritten by other scenario warnings, thereby ensuring vehicle safety. The system provided by the present invention is not only applicable to single-vehicle multi-scenario warning, but also to multi-vehicle multi-scenario warning in complex situations. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0063] Figure 1 This is a framework diagram of an adaptive hierarchical warning system based on V2V multi-scenario warning in an embodiment of the present invention. DETAILED DESCRIPTION

[0064] The technical solution of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0065] In addition, the technical features involved in the different embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0066] Example

[0067] The embodiment of the present invention provides an adaptive hierarchical warning system based on V2V multi-scenario warning, such as Figure 1 As shown, it includes: a calculation unit, multiple single-vehicle warning units, and multiple-vehicle warning units. Each single-vehicle warning unit includes: an adaptive priority adjustment module and a warning priority screening module, wherein:

[0068] The computing unit is used to obtain the status information of the remote warning vehicle that triggers the scene, the scene warning signal triggered by the remote warning vehicle, and the status information of the main vehicle, specifically including: vehicle length, vehicle width, longitude and latitude information, heading angle, vehicle speed, and acceleration. In the subsequent multi-scene warning process, the specific data used are the vehicle length, vehicle width, longitude and latitude information of the remote warning vehicle and the main vehicle, the heading angle information of the main vehicle, the longitudinal speed of the main vehicle and the remote warning vehicle, and the lateral acceleration and yaw angular velocity of the remote warning vehicle and the main vehicle.

[0069] The adaptive priority adjustment module is used to determine the scene threat level based on the scene warning signal triggered by the remote warning vehicle and the status information of the triggering remote warning vehicle and the host vehicle, and pass the scene threat level calculation results for each scene to the warning priority screening module. In this embodiment of the present invention, the adaptive priority adjustment module uses the status information of the remote warning vehicle and the host vehicle to calculate the linear distance, longitudinal distance, and lateral distance between the host vehicle and the triggering remote warning vehicle to calculate the preset collision time, and then uses this information to calculate the scene threat level in the adaptive threat level model. The specific calculation process is as follows:

[0070] 1. The formula for calculating the straight-line distance D between the host vehicle and the remote vehicle that triggers the scene warning is:

[0071]

[0072] Among them, R is the radius of the earth, a=HV_lat-RV_lat, HV_lat and RV_lat are the latitudes of the main vehicle and the potential dangerous vehicle, and b=HV_lng-RV_lng, HV_lng and RV_lng are the longitudes of the main vehicle and the potential dangerous vehicle.

[0073] 2. The calculation formula for the longitudinal distance between the host vehicle and the remote vehicle that triggers the scene warning at non-intersections and intersections is:

[0074] Non-intersection:

[0075]

[0076] Intersection:

[0077]

[0078] Among them, HV length , RV length Respectively represent the length of the main vehicle and the warning remote vehicle, RV wideRepresents the width of the remote vehicle that triggers the warning. tmp = Heading_HV - angle, where Heading_HV is the heading angle of the main vehicle and angle is the relative angle between the main vehicle and the remote vehicle that triggers the scene warning. The relative angle between the main vehicle and the remote vehicle that triggers the scene warning is calculated based on the relative longitude and latitude changes between the main vehicle and the remote vehicle that triggers the scene warning. The calculation formula is:

[0079] angle=arctan(cos(U1)*sin(-b),-sin(U1)*cos(U2)+cos(U1)*sin(U2)cos(-b))*180 / pi,

[0080] sin(U1)=tan(U1)*cos(U1),

[0081] sin(U2)=tan(U2)*cos(U2),

[0082] Among them, U1 and U2 represent the naturalized latitudes of the main vehicle and the warning remote vehicle respectively. If tmp<0, then tmp=tmp+360; if tmp>360, tmp=tmp-360.

[0083] 3. The formula for calculating the lateral distance between the host vehicle and the remote vehicle that triggers the scene warning at non-intersections and intersections is:

[0084] Non-intersection:

[0085]

[0086] Intersection:

[0087]

[0088] Among them, HV wide Indicates the width of the host vehicle.

[0089] 4. The acquired speed is the longitudinal speed of the vehicle, the longitudinal relative speed v between the main vehicle and the remote vehicle that triggers the scene warning x_rela The calculation formula is:

[0090] v x_rela =v x_hv -v x_rv

[0091] Among them, v x_hv , v x_rv are the longitudinal speeds of the host vehicle and the warning vehicle respectively;

[0092] The lateral relative speed v between the host vehicle and the remote vehicle that triggers the scene warning y_relaThe calculation formula is:

[0093] v y_rela =v y_hv -v y_rv

[0094] Among them, v y_hv , v y_rv They are the lateral speed of the warning distant vehicle;

[0095] Since the lateral velocity cannot be measured directly, the embodiment of the present invention calculates the lateral velocity of the host vehicle and the triggering scene warning remote vehicle based on the vehicle lateral acceleration and yaw rate combined with the vehicle dynamics model. The calculation formula is:

[0096] v y =∫(a y -v x ω r )dt,

[0097] Among them, a y is the lateral acceleration of the car; w r is the yaw angular velocity.

[0098] 5. The calculation formula for the preset collision time TTC between the host vehicle and the remote vehicle that triggers the scene warning is:

[0099] Preset collision time TTC for non-intersections:

[0100]

[0101] The calculation formula for the longitudinal preset collision time TTC_x is:

[0102]

[0103] The calculation formula for the preset lateral collision time TTC_y is:

[0104]

[0105] The calculation formula for the preset collision time TTC at the intersection is:

[0106]

[0107] 6. The formula of the adaptive threat model is:

[0108]

[0109] Among them, W_result is the calculation result of the adaptive threat degree model, and the smaller the result, the greater the threat degree; w1 is the scene importance weight, and its value is customized based on the understanding and requirements of each scene, and its value range is 0 to 1; w2 is the proportional factor that prevents large numbers from eating small numbers, and its value can be determined through simulation experiments, and e is an irrational number; H is the maximum distance between two vehicles in adjacent lanes. my country's standard specifications for lane width take into account factors such as "design speed, vehicle type, intersection, and renovation and expansion conditions." The width value is generally 2.8 to 3.75 meters. In the present invention, H is taken as 5 meters, which is only used as an example and is not limited to this.

[0110] If the same vehicle triggers different warning scenarios in the same area, the importance of different warning scenarios is weighted by w1. The more important the warning scenario, the greater the weight, so that the smaller W_result is, the higher the warning priority is.

[0111] If multiple vehicles trigger the same scene warning in the same area, TTC is used for judgment. The closer the warning vehicle is to the vehicle, the smaller the TTC is, which makes W_result smaller.

[0112] If multiple vehicles trigger different warning scenarios in the same area, TTC is used for judgment. The closer the warning vehicle is to the vehicle, the smaller the TTC is, which makes W_result smaller.

[0113] If multiple vehicles trigger the same scene warning in different areas, a preliminary judgment is made through TTC. When the TTC gap is not obvious, it is adjusted through w2(5-D_y). Since only the warning distance vehicles in the lane where the vehicle is traveling and the lanes on both sides of the vehicle are concerned, the vehicle is traveling in the lane, and the warning distance vehicles are in the lanes on both sides of the vehicle. When the vehicle is closer to the vehicle laterally, the smaller the lateral distance D_y between the two vehicles, the larger the w2(5-D_y) value is, which will eventually make W_result smaller and the warning priority higher; when the alarm vehicle is in front of the vehicle, the lateral distance D_y between the two vehicles is 0, and w2(5-D_y) is the largest. Assuming that only the vehicles on both sides are warned and the w2(5-D_y) value is the same, use the lateral acceleration a of the vehicle. y Adjust, w2 is the weight to prevent w2(5-D_y) from being y The values ​​are too close, so the exponential form of e is used to prevent the divisor from being zero. When the car tends to drive to a certain side, the lateral acceleration a of the car is used. y Adjust W_result. Multiple vehicles trigger different scenario warnings in different areas. Use all the above parameters to adjust W_result.

[0114] The warning priority screening module is used to receive the scene threat level of each scene from the adaptive priority adjustment module and screen it, determine the priority of the scene, and package the scene with the highest priority and its corresponding scene threat level into a single-vehicle warning information and transmit it to the multi-vehicle warning unit; the calculation formula for its warning priority screening is: J max =min(W_result1,W_result 2,W_result 3,…)where, J max It is the scene warning with the highest warning priority; W_result1, W_result2, and W_result3 are the scene threat degrees calculated by multiple scene warnings triggered simultaneously by the same vehicle, and each scene warning corresponds to one value.

[0115] The multi-vehicle warning unit is used to obtain the scenario threat levels of multiple single-vehicle warning units. The amount of information received corresponds to the number of vehicles triggering the scenario warning. Each vehicle triggering the warning corresponds to a warning scenario and a scenario threat level. The scenario threat levels of multiple single-vehicle warning units are screened, the scenario priority is determined, and the highest-priority scenario among all scenarios triggered by all vehicles is output as the final warning result. In this embodiment of the present invention, the final warning result is sent to the human-machine interface (HMI), facilitating the driver's more accurate assessment of the current vehicle condition and improving the driver's driving experience and safety.

[0116] The calculation formula for the multi-vehicle warning unit to screen the warning priority in the embodiment of the present invention is:

[0117] J' max =min(W_result1,W_result2,W_result3,…)

[0118] Among them, J' max It is the scene warning with the highest priority among multiple vehicle warnings; W_result1, W_result2, and W_result3 are the scene threat degrees calculated by the multi-scene warnings triggered by multiple vehicles at the same time. One value is given for each scene warning for each vehicle.

[0119] Obviously, the above embodiments are merely examples for clarity of explanation and are not intended to limit the implementation methods. Those skilled in the art will appreciate that other variations or modifications can be made based on the above description. It is not necessary and impossible to enumerate all implementation methods here. Obvious variations or modifications arising therefrom remain within the scope of protection of the present invention.

Claims

1. Adaptive hierarchical warning system based on V2V multi-scenario warning, characterized by: include: The calculation unit, multiple single-vehicle warning units, and multiple-vehicle warning units each include: an adaptive priority adjustment module and a warning priority screening module, wherein: a computing unit configured to obtain status information of a remote warning vehicle that triggers a scene, a scene warning signal triggered by the remote warning vehicle, and status information of the host vehicle; an adaptive priority adjustment module for receiving, based on a scene warning signal triggered by a remote warning vehicle, status information of the remote warning vehicle and the host vehicle of the triggering scene, determining a scene threat level, and transmitting the scene threat level calculation result for each scene to the warning priority screening module. The adaptive priority adjustment module uses the status information of the remote warning vehicle and the host vehicle to calculate the straight-line distance, longitudinal distance, and lateral distance between the host vehicle and the triggering scene remote warning vehicle to calculate a preset collision time, and then uses the preset collision time to calculate the scene threat level using an adaptive threat level model; The warning priority screening module is used to receive the scene threat level of each scene from the adaptive priority adjustment module and screen it, determine the priority of the scene, and package the highest priority scene and its corresponding scene threat level into a single-vehicle warning information and transmit it to the multi-vehicle warning unit; The multi-vehicle warning unit is used to obtain the scene threat level of multiple single-vehicle warning units. The amount of information received corresponds to the number of remote vehicles that triggered the scene warning. Each vehicle that triggered the warning corresponds to a warning scene and a scene threat level. The scene threat levels of multiple single vehicles are screened to determine the priority of the scene. The scene with the highest priority among all the scenes triggered by all vehicles is output as the final warning result.

2. The adaptive hierarchical warning system based on V2V multi-scenario warning according to claim 1 is characterized in that: The number of the single-vehicle warning units is determined by the number of vehicles that trigger the scene warning calculated by the calculation unit.

3. The adaptive hierarchical warning system based on V2V multi-scenario warning according to claim 2 is characterized in that: The calculation unit obtains status information of the warning remote vehicle and the host vehicle of the triggering scene, including: vehicle length, vehicle width, longitude and latitude information, heading angle, vehicle speed, and acceleration.

4. The adaptive hierarchical warning system based on V2V multi-scenario warning according to claim 1 is characterized in that: The formula for calculating the straight-line distance D between the main vehicle and the remote vehicle that triggers the scene warning is: Where R is the radius of the earth, a=HV_lat-RV_lat, HV_lat and RV_lat are the latitudes of the main vehicle and the potential dangerous vehicle, and b=HV_lng-RV_lng, HV_lng and RV_lng are the longitudes of the main vehicle and the potential dangerous vehicle.

5. The adaptive hierarchical warning system based on V2V multi-scenario warning according to claim 1 is characterized in that: The calculation formula for the longitudinal distance between the host vehicle and the remote vehicle that triggers the scene warning at non-intersections and intersections is: Non-intersection: Intersection: Among them, HV length , RV length Respectively represent the length of the main vehicle and the warning remote vehicle, RV wide Represents the width of the remote vehicle that triggers the warning. tmp = Heading_HV - angle, where Heading_HV is the heading angle of the main vehicle and angle is the relative angle between the main vehicle and the remote vehicle that triggers the warning. The relative angle between the main vehicle and the remote vehicle that triggers the warning is calculated based on the relative longitude and latitude changes between the main vehicle and the remote vehicle that triggers the warning. The calculation formula is: , , , ， ， ， ， Among them, U1 and U2 represent the naturalized latitudes of the main vehicle and the warning remote vehicle respectively. If tmp<0, then tmp = tmp + 360; if tmp>360, tmp = tmp -360.

6. The adaptive hierarchical warning system based on V2V multi-scenario warning according to claim 5 is characterized in that: The formula for calculating the lateral distance between the host vehicle and the remote vehicle that triggers the scene warning at non-intersections and intersections is: Non-intersection: Intersection: in, HV wide Indicates the width of the host vehicle.

7. The adaptive hierarchical warning system based on V2V multi-scenario warning according to claim 6 is characterized in that: The speed obtained is the longitudinal speed of the vehicle, the longitudinal relative speed between the main vehicle and the remote vehicle that triggers the scene warning v x_rela The calculation formula is: in, vx_hv , vx_rv are the longitudinal speeds of the host vehicle and the warning vehicle respectively; The lateral relative speed between the host vehicle and the remote vehicle that triggers the scene warning v y_rela The calculation formula is: in, vy_hv , vy_rv They are the lateral speed of the warning distant vehicle; According to the vehicle lateral acceleration and yaw rate combined with the vehicle dynamics model, the lateral speed of the main vehicle and the remote vehicle that triggers the scene warning are calculated respectively. The calculation formula is: ， in, ay is the lateral acceleration of the car; w r is the yaw angular velocity.

8. The adaptive hierarchical warning system based on V2V multi-scenario warning according to claim 7 is characterized in that: The calculation formula for the preset collision time TTC between the host vehicle and the remote vehicle that triggers the scene warning is: Preset collision time TTC for non-intersections: The calculation formula for the longitudinal preset collision time TTC_x is: The calculation formula for the preset lateral collision time TTC_y is: The calculation formula for the preset collision time TTC at the intersection is: 。 9. The adaptive hierarchical warning system based on V2V multi-scenario warning according to claim 8 is characterized in that: The formula of the adaptive threat degree model is: Where W_result is the calculation result of the adaptive threat level model; the smaller the result, the greater the threat level; w1 is the scene importance weight, whose value is customized based on the understanding and requirements of each scene and ranges from 0 to 1; w2 is the scaling factor to prevent the larger number from eating the smaller number, whose value can be determined through simulation experiments; e is an irrational number; H is the maximum distance between two vehicles in adjacent lanes; If the same vehicle triggers different warning scenarios in the same area, the importance of different warning scenarios is weighted by w1. The more important the warning scenario, the greater the weight, so that the smaller W_result is, the higher the warning priority is. If multiple vehicles trigger the same scene warning in the same area, TTC is used for judgment. The closer the warning vehicle is to the vehicle, the smaller the TTC is, which makes W_result smaller. If multiple vehicles trigger different warning scenarios in the same area, TTC is used for judgment. The closer the warning vehicle is to the vehicle, the smaller the TTC is, which makes W_result smaller. If multiple vehicles trigger the same scene warning in different areas, TTC will make a preliminary judgment. TTC When the gap is not obvious, w2 ( H-D_y ) to adjust, because only the warning distance vehicles in the lane where the vehicle is traveling and the lanes on both sides of the vehicle are concerned, the vehicle is traveling in the lane where the warning distance vehicles are in the lanes on both sides of the vehicle. When the vehicle is closer to the vehicle laterally, the lateral distance between the two vehicles is D_y The smaller, w2 ( H-D_y ) value is larger, it will eventually make W_result The smaller the value, the higher the warning priority. When the alarm vehicle is in front of the vehicle, the lateral distance between the two vehicles is D_y is 0, w2 ( H-D_y ) is the largest, assuming that there are only warnings for vehicles on both sides, and w2 ( H-D_y ) values ​​are the same, using the lateral acceleration of the vehicle ay Make adjustments, w2 To prevent weight w2 ( H-D_y )and ay The values ​​are too close, so e The exponential form prevents division by zero. When the car tends to drive to a certain side, the lateral acceleration of the car is used. ay adjust W _ result; Multiple vehicles trigger different scenario warnings in different areas, and all the above parameters are used to adjust W_result.

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