CN107146412B - Expressway vehicle anti-collision early warning comprehensive variable construction method based on Internet of vehicles - Google Patents

Expressway vehicle anti-collision early warning comprehensive variable construction method based on Internet of vehicles Download PDF

Info

Publication number
CN107146412B
CN107146412B CN201710423241.0A CN201710423241A CN107146412B CN 107146412 B CN107146412 B CN 107146412B CN 201710423241 A CN201710423241 A CN 201710423241A CN 107146412 B CN107146412 B CN 107146412B
Authority
CN
China
Prior art keywords
vehicle
vehicles
early warning
mass
center
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN201710423241.0A
Other languages
Chinese (zh)
Other versions
CN107146412A (en
Inventor
熊晓夏
陈龙
梁军
蔡英凤
马世典
曹富贵
陈建锋
江晓明
陈小波
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Jiangsu University
Original Assignee
Jiangsu University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Jiangsu University filed Critical Jiangsu University
Priority to CN201710423241.0A priority Critical patent/CN107146412B/en
Publication of CN107146412A publication Critical patent/CN107146412A/en
Application granted granted Critical
Publication of CN107146412B publication Critical patent/CN107146412B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/16—Anti-collision systems

Landscapes

  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Chemical & Material Sciences (AREA)
  • Analytical Chemistry (AREA)
  • Traffic Control Systems (AREA)

Abstract

The invention provides a highway vehicle anti-collision early warning comprehensive variable construction method based on Internet of vehicles, which is characterized by comprising the following steps of: the method comprises the following steps: constructing an anti-collision early warning comprehensive variable; step two: determining an early warning threshold value of an early warning comprehensive variable; step three: acquiring real-time vehicle motion and position characteristic data; step four: and calculating an anti-collision early warning comprehensive variable and determining an anti-collision early warning strategy. The invention overcomes the defect that the traditional early warning variables do not sufficiently consider the motion vector characteristics, the relative positions of the vehicles and the vehicle size characteristics of the vehicles changing lanes or deviating lanes and the surrounding vehicles in real time, and can meet the vehicle anti-collision early warning requirements under different vehicle driving behavior characteristic conditions.

Description

Expressway vehicle anti-collision early warning comprehensive variable construction method based on Internet of vehicles
Technical Field
The invention relates to the technical field of traffic safety evaluation and active safety of intelligent traffic systems, in particular to a highway vehicle anti-collision early warning comprehensive variable construction method based on Internet of vehicles.
Background
The increase of the quantity of the automobile and the rapid development of the road transportation industry bring increasingly serious pressure to the road traffic safety environment while flourishing economy and facilitating the life of people. The number of deaths caused by traffic accidents in China is the first in the world on average, and the road traffic accidents have become the field with the most deaths in the safety production in China. According to a plurality of research reports, if a driver can realize that the accident risk exists 0.5s in advance and take corresponding correct measures, 50% of accidents can be avoided; if the time is earlier than 1s, 90% of accidents can be avoided. Therefore, vehicle active safety technology and system development has become an important research area for intelligent vehicle development.
The vehicle anti-collision early warning system is used as a key component of an intelligent vehicle active safety system, reduces the burden of a driver and avoids the judgment error of the driver by organically combining related technologies such as perception, communication and control, can effectively reduce the casualty rate of road traffic accidents, and plays an important role in improving the road traffic safety. At present, the anti-collision early warning system of the vehicle mainly establishes different control strategies by calculating selected early warning variables in real time and comparing the early warning variables with preset threshold values. The existing early warning variables with wide application mainly comprise workshop time THW, collision time TTC, workshop distance and the like. However, these variables are mainly the risk criterion established for the same-lane rear-end collision accident, and the collision risk possibly occurring during the lane change (conscious) or lane departure (unconscious) of the vehicle is not considered, such as the motion vector characteristics and the relative position of the vehicle and the vehicle size characteristics of the vehicle changing in real time between the vehicle changing the lane or the lane departure and the surrounding vehicles are not considered. In fact, lane change is the most common driving behavior in the driving process and also has higher danger degree, and according to research data of the National Highway Traffic Safety Administration (NHTSA), the occupation ratio of traffic accidents caused by the lane change process in all statistical traffic accidents is up to 27%. In addition, collision accidents caused by lane departure due to inattention of the driver are also common. Therefore, it is necessary to research the comprehensive variables of the vehicle anti-collision warning that satisfy the characteristic conditions of different vehicle driving behaviors (vehicle-following behavior, vehicle lane-changing execution behavior, vehicle lane-departure behavior, and the like).
At present, advanced sensor technology, communication technology, data processing technology, network technology, automatic control technology, information publishing technology and the like are organically applied to the whole transportation management system by the rapidly developing internet of vehicles, real-time interaction of driving information between parties involved in the transportation operation process can be realized, and a technical basis is laid for accurately calculating and acquiring anti-collision early warning comprehensive variables meeting the conditions.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a highway vehicle anti-collision early warning comprehensive variable construction method based on the Internet of vehicles, which mainly achieves the technical purpose through the following technical means.
A highway vehicle anti-collision early warning comprehensive variable construction method based on Internet of vehicles is characterized by comprising the following steps:
the method comprises the following steps: constructing an anti-collision early warning comprehensive evaluation index variable I;
step two: determining an early warning threshold value of an anti-collision early warning comprehensive evaluation index variable I and an early warning threshold value Th of a near collision risk1And an emergency collision risk early warning threshold Th2；
Step three: acquiring real-time vehicle motion and position characteristic data;
step four: and calculating a real-time anti-collision early warning comprehensive variable I, and determining an anti-collision early warning strategy.
The implementation method for constructing the anti-collision early warning comprehensive evaluation index variable I in the first step is as follows:
step 1: establishing a coordinate system according to the right-hand rule of a Cartesian coordinate system, wherein the direction of a vehicle body is an x axis, the direction vertical to the vehicle body is a y axis, and the running speed V of the vehicle is decomposed into vehicle body component speeds V along the direction of the vehicle bodyxAnd a component velocity V in a direction perpendicular to the vehicle bodyyAnd obtaining the running direction H of the vehicle as follows:
H＝α±θ
wherein α is the heading angle of the vehicle, and theta is the vehicle body component velocity VxAngle to vehicle running speed V:
Figure GDA0002096805650000021
when the component velocity V is perpendicular to the vehicle bodyyH takes the positive sign when the value is more than 0, otherwise H takes the negative sign;
step 2: velocity vectors of the host vehicle and surrounding vehicles are respectively represented as V1And V2Then the relative velocity vector of the two vehicles can be represented as V12＝V1-V2V can be determined from the velocity vector map12The vector size of (d) is:
Figure GDA0002096805650000022
where ψ is a velocity vector V of the host vehicle and surrounding vehicles1And V2The included angle of (A):
ψ＝|H1-H2|
wherein H1And H2Respectively representing the running directions of the vehicle and the surrounding vehicles;
vehicle velocity vector V1And a surrounding vehicle speed vector V2Relative velocity vector direction ω1Comprises the following steps:
Figure GDA0002096805650000023
when H is present1-H2If the number is more than 0, taking a positive sign before the arccos item, otherwise, taking a negative sign after the arccos item;
and step 3: the centroids of the own vehicle and the surrounding vehicles are respectively represented as O1And O2Then the relative position vector of the two vehicles can be expressed as
Figure GDA0002096805650000035
Establishing an independent plane rectangular coordinate system by taking the position of the road section base station as an origin, the positive north direction of the geographic position as the positive direction of a longitudinal axis and the positive east direction as the positive direction of a transverse axis, and calculating the distance between the origin and the position of the road section base station1And O2The GPS longitude and latitude coordinates are converted and calculated through coordinate axes to obtain O in an independent plane rectangular coordinate system1And O2Coordinate value (x) of1,y1) And (x)2,y2) And then the vector magnitude of the relative position of the two vehicles is as follows:
Figure GDA0002096805650000031
direction omega of relative position vector of two vehicles2Comprises the following steps:
Figure GDA0002096805650000032
and 4, step 4: the vector size of the relative position of the two vehicles is corrected according to the specific size of the two vehicles,corrected vector size d of relative position of two vehiclesRCan be expressed as: dR＝O12-c1-c2，
Wherein c is1Representing the center of mass O of the vehicle1According to the relative position vector O of two vehicles12Distance of direction to edge of vehicle body, c2Representing the center of mass O of the surrounding vehicle2According to the relative position vector O of two vehicles12Distance of direction to surrounding vehicle body edges; according to a planar geometric relationship, c1，c2The magnitude of (c) can be calculated as follows:
Figure GDA0002096805650000033
Figure GDA0002096805650000034
wherein delta1Indicating heading angle α of the host vehicle1Relative position vector O with two vehicles12Included angle between directions: delta1＝|ω2-α1|；δ2Indicating the heading angle α of the surrounding vehicle2Relative position vector O with two vehicles12Included angle between directions: delta2＝|ω2-α2|；
η f1 denotes the center of mass O of the vehicle11/2 of an included angle formed by the left end and the right end of the head of the vehicle:
Figure GDA0002096805650000041
ηf2representing the center of mass O of the surrounding vehicle21/2 of included angle formed by the left end and the right end of the head of the surrounding vehicle:
Figure GDA0002096805650000042
wherein w1Is the width of the vehicle bodyf1Is the center of mass O of the vehicle1The vertical distance to the edge of the head of the vehicle; w is a2For the width of the surrounding vehicle body, /)f2As the center of mass O of the surrounding vehicle2Vertical distance to the surrounding vehicle nose edge;
ηr1representing the center of mass O of the vehicle11/2 of included angle formed by the left end and the right end of the tail of the vehicle:
Figure GDA0002096805650000043
wherein lr1Is the center of mass O of the vehicle1Vertical distance to the rear edge of the vehicle ηr2Representing the center of mass O of the surrounding vehicle21/2 for an included angle formed by the left end and the right end of the tail of the surrounding vehicle:
Figure GDA0002096805650000044
wherein lr2As the center of mass O of the surrounding vehicle2Vertical distance to the trailing edge of a surrounding vehicle;
Rf1representing the center of mass O of the vehicle1Distance to the left end or the right end of the vehicle head:
Figure GDA0002096805650000045
Rf2representing the center of mass O of the surrounding vehicle2Distance to left end or right end of surrounding vehicle head:
Figure GDA0002096805650000046
Rr1representing the center of mass O of the vehicle1Distance to left end or right end of vehicle tail:
Figure GDA0002096805650000047
Rr2representing the center of mass O of the surrounding vehicle2Distance to left or right end of vehicle tail:
Figure GDA0002096805650000048
and 5: the relative velocity vector V of the two vehicles12At two relative position vectors O12The projection is defined as the relative speed of the two vehicles in the direction of the mass center, and the relative speed V of the two vehicles in the direction of the mass center can be determined by the step 2 and the step 3RThe size of (A) is as follows: vR＝cosω·V12Where ω represents the relative velocity vector V of the two vehicles12And two vehicles relative position vector O12The included angle of (A): ω ═ ω1-ω2|；
Step 6: correcting the collision time TTC of the traditional early warning variable, and correcting the corrected collision time TTCmFor the corrected vector size d of the relative position of the two vehiclesRRelative speed V between two vehicles in mass center directionRThe ratio of (A) to (B):
Figure GDA0002096805650000049
the speed vector V of the vehicle1At two relative position vectors O12The projection is defined as the center of mass projection speed of the vehicle, and the center of mass projection speed V of the vehicle can be determined by step 41RThe size is as follows: v1R＝COSω*·V1Where ω denotes the vehicle speed vector V1And two vehicles relative position vector O12The included angle of (A): ω ═ H1-ω2L, |; correcting the traditional early warning variable workshop time THW, and correcting the corrected workshop time THWmFor the corrected vector size d of the relative position of the two vehiclesRThe projection speed V of the vehicle to the mass center1RThe ratio of (A) to (B):
Figure GDA0002096805650000051
and 8: when the two vehicles center of mass is towards the relative speed VRVery little time, to TTCmAnd THWmWeighting and summing are carried out, and an anti-collision early warning comprehensive evaluation index variable I is established: i ═ f1·TTCm+f2·THWmWherein f is1And f2The weight coefficient is obtained by a coefficient of variation method:
Figure GDA0002096805650000052
i is 1,2, where σiIs the standard deviation of the i-th index,
Figure GDA0002096805650000053
is the average of the i index.
The method for determining the early warning threshold value of the early warning comprehensive variable in the second step comprises the following steps:
method for comprehensively evaluating index variable I for anti-collision early warning by using equal-step method in iterative methodThe early warning threshold value is subjected to value adjustment, and the calculation formula is as follows: thi＝ai+n·step n＝1,2,...,N；i＝1,2；
Wherein ThiThe early warning threshold value of I after n times of updating adjustment is divided into Th1And Th2(Th2＜Th2) Two-stage early warning threshold, Th1And Th2Respectively representing an imminent and an imminent risk of collision warning threshold: when I > Th1When the two vehicles are in a collision risk-free state, the driver does not need to take any treatment measures; when I < Th1When the two vehicles are in a near collision risk state, the driver should take a deceleration or steering avoidance measure; when I < Th2When the two vehicles are in an emergency collision risk state, the driver or the vehicle should take emergency braking or emergency steering avoiding measures; a is1,a2And respectively representing the initial values of the two-stage early warning threshold values, step representing the adjustment step length, and N representing the adjustment cycle number.
The method for acquiring the real-time vehicle motion and position characteristic data in the third step comprises the following steps:
step a, under the environment of the Internet of vehicles, the information acquisition equipment acquires the course angle α of the vehicle in real time1Vehicle body partial velocity VxComponent velocity V in the direction perpendicular to the vehicle bodyyThe center of mass O of the vehicle1The GPS coordinates of (a).
Step b: b, the information collected in the step a and the width w of the body of the vehicle prestored in the vehicle1Center of mass O of the vehicle1Vertical distance l to the edge of the head of the vehiclef1The center of mass O of the vehicle1Vertical distance l to the tail edge of the vehicler1And real-time sharing with surrounding vehicles is performed through a dedicated short-range wireless communication technology DSRC.
The method for calculating the real-time anti-collision early warning comprehensive variable I and determining the anti-collision early warning strategy in the fourth step comprises the following steps:
step A, repeating the step I, and obtaining the real-time course angle α of the vehicle in the step a1Vehicle body partial velocity VxComponent velocity V in the direction perpendicular to the vehicle bodyyThe center of mass O of the vehicle1GPS seatCalculating in the anti-collision early warning comprehensive evaluation index variable I constructed in the first step of marking-in to obtain the real-time anti-collision early warning comprehensive variable I*；
And B: will prevent collision early warning in real time and synthesize variable I*And imminent collision risk early warning threshold Th1And an emergency collision risk early warning threshold Th2Comparing;
and C: when I is*＞Th1In time, the two vehicles are in a collision risk-free state without issuing early warning information; when I is*＜Th1When the two vehicles are in a near collision risk state, a prompt early warning is issued; when I is*＜Th2And when the two vehicles are in an emergency collision risk state, emergency early warning is issued.
The information acquisition equipment is a gyroscope sensor and a GPS sensor.
The invention has the beneficial effects that:
1. the method overcomes the defect that the traditional early warning variables do not sufficiently consider the motion vector characteristics, the relative positions of the vehicles and the size characteristics of the vehicles, which change in real time between the vehicles changing lanes or deviating lanes and the surrounding vehicles, and can meet the vehicle anti-collision early warning requirements under the characteristic conditions of different vehicle driving behaviors (vehicle following behavior, vehicle lane changing execution behavior, vehicle lane deviating behavior and the like);
2. the method avoids the insufficient evaluation of the traditional early warning variable TTC on the condition of risk of reducing the distance between vehicles and the condition of risk of increasing the relative speed of two vehicles by THW, and provides a reliable comprehensive evaluation index for the anti-collision early warning of the vehicles;
3. parameters required by the anti-collision early warning comprehensive variable constructed by the method can be acquired through traditional information acquisition and transmission equipment under the condition of the Internet of vehicles, no additional special vehicle-mounted equipment is needed, and the cost is low;
4. the early warning comprehensive variable constructed by the method is convenient to calculate, and can completely meet the real-time requirement of anti-collision early warning.
Drawings
FIG. 1 is a schematic plan view of the present invention showing the relationship between the heading angle and the running speed of the vehicle.
FIG. 2 is a schematic plane geometry of the relative velocity vector and relative position vector of two vehicles according to the present invention.
Fig. 3 is a schematic diagram of a method for correcting the relative distance between two vehicles according to the present invention.
Detailed Description
The invention will be further described with reference to the following figures and specific examples, but the scope of the invention is not limited thereto.
A highway vehicle anti-collision early warning comprehensive variable construction method based on Internet of vehicles is characterized by comprising the following steps:
the method comprises the following steps: constructing an anti-collision early warning comprehensive evaluation index variable I;
step two: determining an early warning threshold value of an anti-collision early warning comprehensive evaluation index variable I and an early warning threshold value Th of a near collision risk1And an emergency collision risk early warning threshold Th2；
Step three: acquiring real-time vehicle motion and position characteristic data;
step four: and calculating a real-time anti-collision early warning comprehensive variable I, and determining an anti-collision early warning strategy.
The implementation method for constructing the anti-collision early warning comprehensive evaluation index variable I in the first step is as follows:
step 1: as shown in FIG. 1, a coordinate system is established according to the right-hand rule of a Cartesian coordinate system, the direction of a vehicle body is an x axis, the direction vertical to the vehicle body is a y axis, and the running speed V of the vehicle is decomposed into the component speed V of the vehicle body along the direction of the vehicle bodyxAnd a component velocity V in a direction perpendicular to the vehicle bodyyAnd obtaining the running direction H of the vehicle, namely an included angle from the geographical north direction to the running speed V direction of the vehicle according to the clockwise direction is as follows:
H＝α±θ
wherein α is the heading angle of the vehicle, i.e. the included angle from the north geographical direction to the positive direction of the vehicle x axis in the clockwise direction, the value range is [0,2 pi ], theta is the vehicle body component velocity VxAngle to vehicle running speed V:
Figure GDA0002096805650000071
when the component velocity V is perpendicular to the vehicle bodyyH takes the negative sign when the value is more than 0, otherwise H takes the positive sign;
step 2: as shown in fig. 2, velocity vectors of the host vehicle and surrounding vehicles are represented as V1And V2Then the relative velocity vector of the two vehicles can be represented as V12＝V1-V2V can be determined from the velocity vector map12The vector size of (d) is:
Figure GDA0002096805650000072
where ψ is a velocity vector V of the host vehicle and surrounding vehicles1And V2The included angle of (A):
ψ＝|H1-H2|
wherein H1And H2Respectively representing the running directions of the vehicle and the surrounding vehicles;
vehicle velocity vector V1And a surrounding vehicle speed vector V2Relative velocity vector direction ω1I.e. clockwise from the north geographical direction to the relative velocity vector V12The included angle of the direction is as follows:
Figure GDA0002096805650000073
when H is present1-H2When the value is more than 0, the front part of arccos term takes a positive sign, otherwise, arccos takes a negative sign, and the purpose of introducing a +/-2 pi term into the formula is to ensure omega1Value in the range of [0,2 π), relative velocity vector direction ω1Keeping consistent with the definition of the heading angle;
and step 3: the centroids of the own vehicle and the surrounding vehicles are respectively represented as O1And O2Then the relative position vector of the two vehicles can be expressed as
Figure GDA0002096805650000081
The position of a road section base station is taken as an origin, the positive north direction of the geographic position is the positive direction of a vertical axis, and the positive east direction is the positive direction of a horizontal axisEstablishing an independent plane rectangular coordinate system of the direction and calculating the distance between the two points1And O2The GPS longitude and latitude coordinates are converted and calculated through coordinate axes to obtain O in an independent plane rectangular coordinate system1And O2Coordinate value (x) of1,y1) And (x)2,y2) And then the vector magnitude of the relative position of the two vehicles is as follows:
Figure GDA0002096805650000082
direction omega of relative position vector of two vehicles2I.e. clockwise from the north of geography to the relative position vector O12The included angle of the direction is as follows:
Figure GDA0002096805650000083
and 4, step 4: as shown in fig. 3, the magnitude of the relative position vector of the two vehicles is corrected according to the specific size of the two vehicles, and the corrected magnitude d of the relative position vector of the two vehiclesRCan be expressed as: dR＝O12-c1-c2，
Wherein c is1Representing the center of mass O of the vehicle1According to the relative position vector O of two vehicles12Distance of direction to edge of vehicle body, c2Representing the center of mass O of the surrounding vehicle2According to the relative position vector O of two vehicles12Distance of direction to surrounding vehicle body edges; according to a planar geometric relationship, c1，c2The magnitude of (c) can be calculated as follows:
Figure GDA0002096805650000084
Figure GDA0002096805650000091
wherein delta1Indicating heading angle α of the host vehicle1Relative position vector O with two vehicles12Included angle between directions: delta1＝|ω2-α1|；δ2To representHeading angle α of surrounding vehicle2Relative position vector O with two vehicles12Included angle between directions: delta2＝|ω2-α2|；
ηf1Representing the center of mass O of the vehicle11/2 of an included angle formed by the left end and the right end of the head of the vehicle:
Figure GDA0002096805650000092
ηf2representing the center of mass O of the surrounding vehicle21/2 of included angle formed by the left end and the right end of the head of the surrounding vehicle:
Figure GDA0002096805650000093
wherein w1Is the width of the vehicle bodyf1Is the center of mass O of the vehicle1The vertical distance to the edge of the head of the vehicle; w is a2For the width of the surrounding vehicle body, /)f2As the center of mass O of the surrounding vehicle2Vertical distance to the surrounding vehicle nose edge;
ηr1representing the center of mass O of the vehicle11/2 of included angle formed by the left end and the right end of the tail of the vehicle:
Figure GDA0002096805650000094
wherein lr1Is the center of mass O of the vehicle1Vertical distance to the rear edge of the vehicle ηr2Representing the center of mass O of the surrounding vehicle21/2 for an included angle formed by the left end and the right end of the tail of the surrounding vehicle:
Figure GDA0002096805650000095
wherein lr2As the center of mass O of the surrounding vehicle2Vertical distance to the trailing edge of a surrounding vehicle;
Rf1representing the center of mass O of the vehicle1Distance to the left end or the right end of the vehicle head:
Figure GDA0002096805650000096
Rf2representing the center of mass O of the surrounding vehicle2Distance to left end or right end of surrounding vehicle head:
Figure GDA0002096805650000097
Rr1representing the center of mass O of the vehicle1Distance to left end or right end of vehicle tail:
Figure GDA0002096805650000098
Rr2representing the center of mass O of the surrounding vehicle2Distance to left or right end of vehicle tail:
Figure GDA0002096805650000099
and 5: as shown in fig. 2, the relative velocity vector V of two vehicles is set12At two relative position vectors O12The projection is defined as the relative speed of the two vehicles in the direction of the mass center, and the relative speed V of the two vehicles in the direction of the mass center can be determined by the step 2 and the step 3RThe size of (A) is as follows: vR＝cosω·V12Where ω represents the relative velocity vector V of the two vehicles12And two vehicles relative position vector O12The included angle of (A): ω ═ ω1-ω2|；
Note that when ω is at a value
Figure GDA0002096805650000101
When the value is internal, cos omega takes a negative value, and the corresponding VRThe value is a negative value, and the situation shows that the two vehicles are driving away from each other reversely at the moment;
step 6: correcting the collision time TTC of the traditional early warning variable, and correcting the corrected collision time TTCmFor the corrected vector size d of the relative position of the two vehiclesRRelative speed V between two vehicles in mass center directionRThe ratio of (A) to (B):
Figure GDA0002096805650000102
the speed vector V of the vehicle1At two relative position vectors O12The projection is defined as the center of mass projection speed of the vehicle, and the center of mass projection speed V of the vehicle can be determined by step 41RThe size is as follows: v1R＝COSω*·V1Where ω denotes the vehicle speed vector V1And two vehicles relative position vector O12The included angle of (A): omega*＝|H1-ω2L, |; correcting the traditional early warning variable workshop time THW, and correcting the corrected workshop time THWmFor the corrected vector size d of the relative position of the two vehiclesRThe projection speed V of the vehicle to the mass center1RThe ratio of (A) to (B):
Figure GDA0002096805650000103
and 8: considering the relative speed V when the two vehicles are in the mass center directionRVery little time, TTCmThe risk of collision that may be caused by the decrease in the inter-vehicle distance, THW, cannot be correctly evaluatedmThe risk change caused by the relative speed change of the two vehicles cannot be correctly evaluated, so that the early warning defect possibly caused by the defects of the two indexes is avoided, and when the mass center of the two vehicles moves to the relative speed VRVery little time, to TTCmAnd THWmWeighting and summing are carried out, and an anti-collision early warning comprehensive evaluation index variable I is established:
I＝f1·TTCm+f2·THWm，
wherein f is1And f2The weight coefficient is obtained by a coefficient of variation method:
Figure GDA0002096805650000104
i is 1,2, where σiIs the standard deviation of the i-th index,
Figure GDA0002096805650000105
the average of the i-th index shows that the index with larger value difference can reflect the change of the collision risk between vehicles, so that the index needs to be given a higher weight coefficient.
The method for determining the early warning threshold value of the early warning comprehensive variable in the second step comprises the following steps:
the method comprises the following steps of utilizing an equal step method in an iterative method to carry out value adjustment on an early warning threshold value of an anti-collision early warning comprehensive evaluation index variable I, wherein a calculation formula is as follows: thi＝ai+n·step n＝1,2,...,N；i＝1,2；
Wherein ThiIndicating that the adjustment is done through n updatesEarly warning threshold of later I, divided into Th1And Th2(Th2＜Th2) Two-stage early warning threshold, Th1And Th2Respectively representing an imminent and an imminent risk of collision warning threshold: when I > Th1When the two vehicles are in a collision risk-free state, the driver does not need to take any treatment measures; when I < Th1When the two vehicles are in a near collision risk state, the driver should take a deceleration or steering avoidance measure; when I < Th2When the two vehicles are in an emergency collision risk state, the driver or the vehicle should take emergency braking or emergency steering avoiding measures; a is1,a2And respectively representing the initial values of the two-stage early warning threshold values, step representing the adjustment step length, and N representing the adjustment cycle number. Above ThiThe final value of the parameter is determined by the behavior characteristic of a driver and the acceptance characteristic of a user and can be finally determined through a same-lane double-car following experiment.
The method for acquiring the real-time vehicle motion and position characteristic data in the third step comprises the following steps:
step a, under the environment of the Internet of vehicles, the information acquisition equipment comprises a gyroscope sensor and a GPS sensor, and the course angle α of the vehicle is acquired in real time1Vehicle body partial velocity VxComponent velocity V in the direction perpendicular to the vehicle bodyyThe center of mass O of the vehicle1The GPS coordinates of (a).
Step b: b, the information collected in the step a and the width w of the body of the vehicle prestored in the vehicle1Center of mass O of the vehicle1Vertical distance l to the edge of the head of the vehiclef1The center of mass O of the vehicle1Vertical distance l to the tail edge of the vehicler1The own vehicle shares with the surrounding vehicles in real time by the dedicated short range wireless communication technology DSRC, and accordingly, the own vehicle acquires all the shared motion and position information of the surrounding vehicles in real time by the dedicated short range wireless communication technology DSRC.
Calculating real-time anti-collision early warning comprehensive variable I in the fourth step*The method for determining the anti-collision early warning strategy comprises the following steps:
step A, repeating the step I, and obtaining the real-time course angle α of the vehicle in the step a1Vehicle and its driving methodSpeed of identity VxComponent velocity V in the direction perpendicular to the vehicle bodyyThe center of mass O of the vehicle1The GPS coordinates are substituted into the constructed anti-collision early warning comprehensive evaluation index variable I in the step one for calculation, and the real-time anti-collision early warning comprehensive variable I is obtained*；
And B: will prevent collision early warning in real time and synthesize variable I*And imminent collision risk early warning threshold Th1And an emergency collision risk early warning threshold Th2Comparing;
and C: when I is*＞Th1In time, the two vehicles are in a collision risk-free state without issuing early warning information; when I is*＜Th1When the two vehicles are in a state close to collision risk, warning early warning is issued, such as warning sound reminding; when I is*＜Th2When the two vehicles are in an emergency collision risk state, emergency early warning is issued, such as alarm sound and steering wheel or seat vibration.
The present invention is not limited to the above-described embodiments, and any obvious improvements, substitutions or modifications can be made by those skilled in the art without departing from the spirit of the present invention.

Claims (5)

1. A highway vehicle anti-collision early warning comprehensive variable construction method based on Internet of vehicles is characterized by comprising the following steps:
the method comprises the following steps: constructing an anti-collision early warning comprehensive evaluation index variable I;
step two: determining an early warning threshold value of an anti-collision early warning comprehensive evaluation index variable I and an early warning threshold value Th of a near collision risk1And an emergency collision risk early warning threshold Th2；
Step three: acquiring real-time vehicle motion and position characteristic data;
step four: substituting the real-time vehicle motion and position characteristic data acquired in the step three into the constructed anti-collision early warning comprehensive evaluation index variable I for calculation to acquire the real-time anti-collision early warning comprehensive variable I*Determining an anti-collision early warning strategy;
the implementation method for constructing the anti-collision early warning comprehensive evaluation index variable I in the first step is as follows:
step 1: establishing a coordinate system according to the right-hand rule of a Cartesian coordinate system, wherein the direction of a vehicle body is an x axis, the direction vertical to the vehicle body is a y axis, and the running speed V of the vehicle is decomposed into vehicle body component speeds V along the direction of the vehicle bodyxAnd a component velocity V in a direction perpendicular to the vehicle bodyyAnd obtaining the running direction H of the vehicle as follows:
H＝α±θ
wherein α is the heading angle of the vehicle, and theta is the vehicle body component velocity VxAngle to vehicle running speed V:
Figure FDA0002245756870000011
when the component velocity V is perpendicular to the vehicle bodyyH takes the positive sign when the value is more than 0, otherwise H takes the negative sign;
step 2: velocity vectors of the host vehicle and surrounding vehicles are respectively represented as V1And V2Then the relative velocity vector of the two vehicles can be represented as V12＝V1-V2V can be determined from the velocity vector map12The vector size of (d) is:
Figure FDA0002245756870000012
where ψ is a velocity vector V of the host vehicle and surrounding vehicles1And V2The included angle of (A):
ψ＝|H1-H2|
wherein H1And H2Respectively representing the running directions of the vehicle and the surrounding vehicles;
vehicle velocity vector V1And a surrounding vehicle speed vector V2Relative velocity vector direction ω1Comprises the following steps:
Figure FDA0002245756870000013
when H is present1-H2If the number is more than 0, taking a positive sign before the arccos item, otherwise, taking a negative sign after the arccos item;
and step 3: the centroids of the own vehicle and the surrounding vehicles are respectively represented as O1And O2Then the relative position vector of the two vehicles can be expressed as
Figure FDA0002245756870000021
Establishing an independent plane rectangular coordinate system by taking the position of the road section base station as an origin, the positive north direction of the geographic position as the positive direction of a longitudinal axis and the positive east direction as the positive direction of a transverse axis, and calculating the distance between the origin and the position of the road section base station1And O2The GPS longitude and latitude coordinates are converted and calculated through coordinate axes to obtain O in an independent plane rectangular coordinate system1And O2Coordinate value (x) of1,y1) And (x)2,y2) And then the vector magnitude of the relative position of the two vehicles is as follows:
Figure FDA0002245756870000022
direction omega of relative position vector of two vehicles2Comprises the following steps:
Figure FDA0002245756870000023
and 4, step 4: correcting the relative position vector of the two vehicles according to the specific sizes of the two vehicles, and correcting the relative position vector d of the two vehiclesRCan be expressed as: dR＝O12-c1-c2，
Wherein c is1Representing the center of mass O of the vehicle1According to the relative position vector O of two vehicles12Distance of direction to edge of vehicle body, c2Representing the center of mass O of the surrounding vehicle2According to the relative position vector O of two vehicles12Distance of direction to surrounding vehicle body edges; according to a planar geometric relationship, c1，c2The magnitude of (c) can be calculated as follows:
Figure FDA0002245756870000024
Figure FDA0002245756870000025
wherein delta1Indicating heading angle α of the host vehicle1Relative position vector O with two vehicles12Included angle between directions: delta1＝|ω2-α1|；δ2Indicating the heading angle α of the surrounding vehicle2Relative position vector O with two vehicles12Included angle between directions: delta2＝|ω2-α2|；
ηf1Representing the center of mass O of the vehicle11/2 of an included angle formed by the left end and the right end of the head of the vehicle:
Figure FDA0002245756870000031
ηf2representing the center of mass O of the surrounding vehicle21/2 of included angle formed by the left end and the right end of the head of the surrounding vehicle:
Figure FDA0002245756870000032
wherein w1Is the width of the vehicle bodyf1Is the center of mass O of the vehicle1The vertical distance to the edge of the head of the vehicle; w is a2For the width of the surrounding vehicle body, /)f2As the center of mass O of the surrounding vehicle2Vertical distance to the surrounding vehicle nose edge;
ηr1representing the center of mass O of the vehicle11/2 of included angle formed by the left end and the right end of the tail of the vehicle:
Figure FDA0002245756870000033
wherein lr1Is the center of mass O of the vehicle1Vertical distance to the rear edge of the vehicle ηr2Representing the center of mass O of the surrounding vehicle21/2 for an included angle formed by the left end and the right end of the tail of the surrounding vehicle:
Figure FDA0002245756870000034
wherein lr2As the center of mass O of the surrounding vehicle2Vertical distance to the trailing edge of a surrounding vehicle;
Rf1representing the center of mass O of the vehicle1Distance to the left end or the right end of the vehicle head:
Figure FDA0002245756870000035
Rf2representing the center of mass O of the surrounding vehicle2Distance to left end or right end of surrounding vehicle head:
Figure FDA0002245756870000036
Rr1representing the center of mass O of the vehicle1Distance to left end or right end of vehicle tail:
Figure FDA0002245756870000037
Rr2representing the center of mass O of the surrounding vehicle2Distance to left or right end of vehicle tail:
Figure FDA0002245756870000038
and 5: the relative velocity vector V of the two vehicles12At two relative position vectors O12The projection is defined as the relative speed of the two vehicles in the direction of the mass center, and the relative speed V of the two vehicles in the direction of the mass center can be determined by the step 2 and the step 3RThe size of (A) is as follows: vR＝cosω·V12Where ω represents the relative velocity vector V of the two vehicles12And two vehicles relative position vector O12The included angle of (A): ω ═ ω1-ω2|；
Step 6: correcting the collision time TTC of the traditional early warning variable, and correcting the corrected collision time TTCmFor the corrected vector size d of the relative position of the two vehiclesRRelative speed V between two vehicles in mass center directionRThe ratio of (A) to (B):
Figure FDA0002245756870000039
the speed vector V of the vehicle1At two relative position vectors O12The projection is defined as the center of mass projection speed of the vehicle, and the step 4 can determineCentroid projection speed V of local vehicle1RThe size is as follows: v1R＝COSω*·V1Wherein ω is*Indicates the vehicle velocity vector V1And two vehicles relative position vector O12The included angle of (A): omega*＝|H1-ω2L, |; correcting the traditional early warning variable workshop time THW, and correcting the corrected workshop time THWmFor the corrected vector size d of the relative position of the two vehiclesRThe projection speed V of the vehicle to the mass center1RThe ratio of (A) to (B):
Figure FDA0002245756870000041
and 8: when the two vehicles center of mass is towards the relative speed VRVery little time, to TTCmAnd THWmWeighting and summing are carried out, and an anti-collision early warning comprehensive evaluation index variable I is established: i ═ f1·TTCm+f2·THWmWherein f is1And f2The weight coefficient is obtained by a coefficient of variation method:
Figure FDA0002245756870000042
i is 1,2, where σiIs the standard deviation of the i-th index,
Figure FDA0002245756870000043
is the average of the i index.
2. The method for constructing the anti-collision early warning comprehensive variables of the vehicles on the expressway based on the internet of vehicles as claimed in claim 1, wherein the method for determining the early warning threshold values of the early warning comprehensive variables in the second step is as follows:
the method comprises the following steps of utilizing an equal step method in an iterative method to carry out value adjustment on an early warning threshold value of an anti-collision early warning comprehensive evaluation index variable I, wherein a calculation formula is as follows: thi＝ai+n·step n＝1,2,...,N；i＝1,2；
Wherein ThiThe early warning threshold value of I after n times of updating adjustment is divided into Th1And Th2The two-stage pre-warning threshold value,Th2＜Th1，Th1and Th2Respectively representing an imminent and an imminent risk of collision warning threshold: when I > Th1When the two vehicles are in a collision risk-free state, the driver does not need to take any treatment measures; when I < Th1When the two vehicles are in a near collision risk state, the driver should take a deceleration or steering avoidance measure; when I < Th2When the two vehicles are in an emergency collision risk state, the driver or the vehicle should take emergency braking or emergency steering avoiding measures; a is1,a2And respectively representing the initial values of the two-stage early warning threshold values, step representing the adjustment step length, and N representing the adjustment cycle number.
3. The method for constructing the anti-collision early warning comprehensive variables of the vehicles on the expressway based on the Internet of vehicles as claimed in claim 1, wherein the method for acquiring the real-time vehicle motion and position characteristic data in the third step comprises the following steps:
step a, under the environment of the Internet of vehicles, the information acquisition equipment acquires the course angle α of the vehicle in real time1Vehicle body partial velocity VxComponent velocity V in the direction perpendicular to the vehicle bodyyThe center of mass O of the vehicle1(ii) GPS coordinates;
step b: b, the information collected in the step a and the width w of the body of the vehicle prestored in the vehicle1Center of mass O of the vehicle1Vertical distance l to the edge of the head of the vehiclef1The center of mass O of the vehicle1Vertical distance l to the tail edge of the vehicler1And real-time sharing with surrounding vehicles is performed through a dedicated short-range wireless communication technology DSRC.
4. The method for constructing the comprehensive variable for anti-collision early warning of the vehicles on the expressway based on the Internet of vehicles as claimed in claim 3, wherein the real-time comprehensive variable I for anti-collision early warning is calculated in the fourth step*The method for determining the anti-collision early warning strategy comprises the following steps:
step A, repeating the step I, and obtaining the real-time course angle α of the vehicle in the step a1Vehicle body partial velocity VxHang downComponent velocity V in the direction perpendicular to the bodyyThe center of mass O of the vehicle1The GPS coordinates are substituted into the constructed anti-collision early warning comprehensive evaluation index variable I in the step one for calculation, and the real-time anti-collision early warning comprehensive variable I is obtained*；
And B: will prevent collision early warning in real time and synthesize variable I*And imminent collision risk early warning threshold Th1And an emergency collision risk early warning threshold Th2Comparing;
and C: when I is*＞Th1In time, the two vehicles are in a collision risk-free state without issuing early warning information; when I is*＜Th1When the two vehicles are in a near collision risk state, a prompt early warning is issued; when I is*＜Th2And when the two vehicles are in an emergency collision risk state, emergency early warning is issued.
5. The method for constructing the comprehensive variable for the anti-collision early warning of the vehicles on the expressway based on the Internet of vehicles as claimed in claim 3, wherein the information acquisition equipment is a gyroscope sensor and a GPS sensor.
CN201710423241.0A 2017-06-07 2017-06-07 Expressway vehicle anti-collision early warning comprehensive variable construction method based on Internet of vehicles Active CN107146412B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201710423241.0A CN107146412B (en) 2017-06-07 2017-06-07 Expressway vehicle anti-collision early warning comprehensive variable construction method based on Internet of vehicles

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201710423241.0A CN107146412B (en) 2017-06-07 2017-06-07 Expressway vehicle anti-collision early warning comprehensive variable construction method based on Internet of vehicles

Publications (2)

Publication Number Publication Date
CN107146412A CN107146412A (en) 2017-09-08
CN107146412B true CN107146412B (en) 2020-06-09

Family

ID=59780504

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201710423241.0A Active CN107146412B (en) 2017-06-07 2017-06-07 Expressway vehicle anti-collision early warning comprehensive variable construction method based on Internet of vehicles

Country Status (1)

Country Link
CN (1) CN107146412B (en)

Families Citing this family (14)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108062600B (en) * 2017-12-18 2021-06-18 北京星云互联科技有限公司 Vehicle collision early warning method and device based on rectangular modeling
CN108597251A (en) * 2018-04-02 2018-09-28 昆明理工大学 A kind of traffic intersection distribution vehicle collision prewarning method based on car networking
CN109147394A (en) * 2018-11-12 2019-01-04 安徽江淮汽车集团股份有限公司 The vehicle collision prewarning method of intersection
CN110148312B (en) * 2019-04-30 2021-04-16 惠州市德赛西威智能交通技术研究院有限公司 Collision early warning method and device based on V2X system and storage medium
CN110379203B (en) * 2019-06-21 2021-08-03 江苏大学 Driving steering collision early warning method
CN111477003A (en) * 2020-04-13 2020-07-31 联陆智能交通科技(上海)有限公司 Calculation method and system for acquiring time of collision with weak traffic and medium
CN113689735B (en) * 2020-05-19 2023-03-21 广州汽车集团股份有限公司 Vehicle lane change collision early warning method and device
CN113744561B (en) * 2020-05-28 2022-11-11 广州汽车集团股份有限公司 Vehicle early warning method and device
CN111966108A (en) * 2020-09-02 2020-11-20 成都信息工程大学 Extreme weather unmanned control system based on navigation system
CN112965489B (en) * 2021-02-05 2022-04-08 北京理工大学 Intelligent vehicle high-speed lane change planning method based on collision detection
CN113936453B (en) * 2021-09-09 2022-08-19 上海宝康电子控制工程有限公司 Information identification method and system based on headway
CN114093187B (en) * 2021-11-26 2023-02-24 交通运输部公路科学研究所 Risk early warning control method for automatic driving vehicle and intelligent network system
CN115223396B (en) * 2021-12-15 2023-10-20 广州汽车集团股份有限公司 Rear collision early warning method, device, controller, vehicle and storage medium
CN114419929B (en) * 2022-03-04 2022-12-27 季华实验室 Collision risk assessment method and device, electronic equipment and storage medium

Family Cites Families (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2004086714A (en) * 2002-08-28 2004-03-18 Nissan Motor Co Ltd Vehicle deceleration control device
CN101407199A (en) * 2008-10-28 2009-04-15 湖北汽车工业学院 Vehicle collision prewarning method and apparatus
CN101488290B (en) * 2009-02-27 2011-01-05 清华大学 Pre-alarm method and system for preventing rear-end collision by following car
CN102320280B (en) * 2011-06-30 2013-02-13 清华大学 Automatic alarm method for preventing front crash of vehicles at turning
CN103531042B (en) * 2013-10-25 2015-08-19 吉林大学 Based on the vehicle rear-end collision method for early warning of driver's type
CN104134371A (en) * 2014-07-09 2014-11-05 奇瑞汽车股份有限公司 Method and device for early warning of vehicle collision

Also Published As

Publication number Publication date
CN107146412A (en) 2017-09-08

Similar Documents

Publication Publication Date Title
CN107146412B (en) Expressway vehicle anti-collision early warning comprehensive variable construction method based on Internet of vehicles
CN110379203B (en) Driving steering collision early warning method
CN113593273B (en) No-signal control road intersection collision early warning method based on V2I communication
US8315756B2 (en) Systems and methods of vehicular path prediction for cooperative driving applications through digital map and dynamic vehicle model fusion
DE102019102944B4 (en) Systems and methods for a low feed forward vehicle control strategy
CN106056972A (en) Security anti-collision early-warning method based on vehicle driving speed and position information fusion
WO2021103511A1 (en) Operational design domain (odd) determination method and apparatus and related device
DE102019102942A1 (en) Systems and methods for the correction of vehicle trailers
CN106708057A (en) Intelligent vehicle formation driving method
US20140257686A1 (en) Vehicle lane determination
WO2016024315A1 (en) Travel control device and method for vehicle
CN109871010B (en) Method and system based on reinforcement learning
CN111361564A (en) Lane change system considering benefit maximization and comprehensive decision method
WO2016024314A1 (en) Travel control device and method for vehicle
CN111653113A (en) Method, device, terminal and storage medium for determining local path of vehicle
CN110979339B (en) Front road form reconstruction method based on V2V
CN113682305B (en) Vehicle-road cooperative self-adaptive cruise control method and device
US20210046951A1 (en) Apparatus for generating acceleration profile and method for autonomous driving on curved road using the same
CN111599215B (en) Non-signalized intersection mobile block vehicle guiding system and method based on Internet of vehicles
Ding et al. Cooperative adaptive cruise control in vehicle platoon under environment of i-VICS
CN113602265A (en) Processing cooperative lane changing method and system based on vehicle-to-vehicle communication
JP2016038838A (en) Travel control device and travel control method
CN114187760A (en) Vehicle-road cooperative system-based method for classifying relative positions of vehicles at curves
US20240034365A1 (en) Processing method, processing system, storage medium storing processing program, and processing device
Shadrin Affordable and efficient autonomous driving in all weather conditions

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant