Vehicle collision prediction method and device based on V2V

Abstract

The invention discloses a V2V-based vehicle collision prediction method. The method comprises the steps of obtaining basic safety information of a vehicle and basic safety information of a remote vehicle; respectively calculating the positions of the vehicle and the remote vehicle in the first coordinate system; respectively calculating the position of the vehicle in the second coordinate system at the current moment and the position of the remote vehicle in the second coordinate system at the current moment; according to the position of the vehicle in the second coordinate system at the current moment and the position of the remote vehicle in the second coordinate system at the current moment, a vehicle driving track and a remote vehicle driving track are predicted respectively; and according to the driving track of the vehicle and the driving track of the remote vehicle, judging whether the vehicle and the remote vehicle have a collision risk or not. According to the method, real-time traffic information sharing, including vehicle position, speed, acceleration, direction and the like, can be realized based on V2V communication of the vehicle and the remote vehicle, and travel track prediction can be carried out timely and accurately according to the information, so that possible collision situations can be predicted, and a user can be reminded to take effective measures in time to avoid vehicle collision accidents.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technology, and in particular to a vehicle collision prediction method and device based on V2V. Background Art

[0002] With the rapid development of society and the improvement of people's living standards, there are more and more vehicles on the road. The increase in vehicles has also brought many traffic accidents. Vehicle-to-vehicle collision detection is to improve road traffic safety and reduce the occurrence of traffic accidents. When vehicles are driving on the road, they may lose control, exceed speed, run red lights and other violations due to various reasons. These behaviors may cause collisions between vehicles and cause major traffic accidents. Through vehicle-to-vehicle collision detection technology, the system can monitor the driving conditions of vehicles in real time, detect abnormal conditions in time and issue early warnings, so that drivers can take timely measures to avoid collisions, reduce the incidence of traffic accidents, and thus protect the lives and property of drivers and passengers.

[0003] Existing vehicle collision risk detection usually uses a vehicle collision detection method based on sensor data, which determines whether the vehicle has collided by the threshold of the three-axis acceleration. Another method is to detect whether there is a collision by visual detection. However, the above methods all have the problem of poor detection accuracy and are easily affected by the environment.

[0004] Based on this, a new solution is needed. Summary of the invention

[0005] The main purpose of the present invention is to provide a vehicle collision prediction method and device based on V2V in order to solve the problem that the existing vehicle-to-vehicle collision detection has poor detection accuracy and is easily affected by the environment.

[0006] To achieve the above object, the present invention provides a vehicle collision prediction method based on V2V, comprising the following steps:

[0007] Obtain basic safety information of the vehicle and remote vehicles;

[0008] According to the basic safety information of the host vehicle and the basic safety information of the remote vehicle, respectively calculate the positions of the host vehicle and the remote vehicle in a first coordinate system, wherein the first coordinate system is a world coordinate system with the position of the host vehicle at a previous moment as the coordinate origin;

[0009] Using the position of the vehicle in the first coordinate system and the position of the remote vehicle in the first coordinate system, respectively calculate the current position of the vehicle in the second coordinate system and the current position of the remote vehicle in the second coordinate system, wherein the second coordinate system is a body coordinate system based on the vehicle established according to the position of the vehicle at a previous moment;

[0010] According to the current position of the host vehicle in the second coordinate system and the current position of the remote vehicle in the second coordinate system, respectively predict the driving trajectory of the host vehicle and the driving trajectory of the remote vehicle; and

[0011] Based on the driving trajectory of the own vehicle and the driving trajectory of the distant vehicle, determine whether there is a risk of collision between the own vehicle and the distant vehicle.

[0012] In addition, to achieve the above-mentioned purpose, the present invention also provides a vehicle collision prediction device based on V2V, comprising:

[0013] An information acquisition module is used to obtain basic safety information of the vehicle and the remote vehicle;

[0014] A first calculation module, used for calculating the positions of the host vehicle and the remote vehicle in a first coordinate system according to the basic safety information of the host vehicle and the basic safety information of the remote vehicle, respectively, wherein the first coordinate system is a world coordinate system with the position of the host vehicle at a previous moment as the coordinate origin;

[0015] a second calculation module, for calculating the current position of the host vehicle in a second coordinate system and the current position of the remote vehicle in a second coordinate system respectively by using the position of the host vehicle in the first coordinate system and the position of the remote vehicle in the first coordinate system, wherein the second coordinate system is a body coordinate system based on the host vehicle established according to the position of the host vehicle at a previous moment;

[0016] A vehicle trajectory prediction module, used to predict the vehicle trajectory of the vehicle and the remote vehicle respectively according to the current position of the vehicle in the second coordinate system and the current position of the remote vehicle in the second coordinate system; and

[0017] The judgment module is used to judge whether there is a collision risk between the own vehicle and the distant vehicle according to the driving trajectory of the own vehicle and the driving trajectory of the distant vehicle.

[0018] In addition, to achieve the above-mentioned purpose, the present invention also provides a vehicle collision prediction device based on V2V, comprising:

[0019] Memory for storing computer programs;

[0020] A processor is used to implement the steps of the V2V-based vehicle collision prediction method as described above when executing the computer program.

[0021] In addition, to achieve the above-mentioned purpose, the present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the vehicle collision prediction method based on V2V as described above are implemented.

[0022] In the present invention, basic safety information of the vehicle and the remote vehicle is obtained, and the positions of the two vehicles in a world coordinate system with the position of the vehicle at the previous moment as the coordinate origin are calculated based on the information; then, the positions of the two vehicles in the world coordinate system are combined to calculate the current positions of the vehicle and the remote vehicle in a body coordinate system based on the vehicle established according to the position of the vehicle at the previous moment; then, the positions of the two vehicles in the body coordinate system are used to predict the driving trajectory of the vehicle and the remote vehicle to determine whether there is a risk of collision between the vehicle and the remote vehicle; this method can realize real-time traffic information sharing based on V2V communication between the vehicle and the remote vehicle, including vehicle position, speed, acceleration, direction, etc., and can instantly and accurately predict the travel trajectory based on this information, so as to predict possible collision situations and promptly remind users to take effective measures to avoid vehicle collision accidents. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art are briefly introduced below. Obviously, the drawings in the following description are only embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on the provided drawings without creative work:

[0024] Figure 1 Schematic diagram of the flow of a vehicle collision prediction method based on V2V provided in Embodiment 1 of the present invention. DETAILED DESCRIPTION

[0025] In order to facilitate understanding of the present invention, the present invention will be described more fully below with reference to the relevant drawings. Typical embodiments of the present invention are given in the drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. On the contrary, the purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive.

[0026] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used in the specification of the present invention herein are only for the purpose of describing specific embodiments and are not intended to limit the present invention.

[0027] The general idea of ​​the present invention is: in view of the problems that the vehicle-to-vehicle collision risk detection in the prior art has poor detection accuracy and is easily affected by the environment, a vehicle collision prediction method and device based on V2V is provided, by obtaining basic safety information of the own vehicle and the remote vehicle, and calculating the positions of the two vehicles in the world coordinate system with the position of the own vehicle at the previous moment as the coordinate origin based on this information; then, combining the positions of the two in the world coordinate system, the positions of the own vehicle and the remote vehicle at the current moment in the body coordinate system based on the own vehicle established according to the position of the own vehicle at the previous moment are calculated; then, the positions of the two in the body coordinate system are used to predict the driving trajectory of the own vehicle and the remote vehicle, so as to determine whether there is a collision risk between the own vehicle and the remote vehicle; the method can realize real-time traffic information sharing based on V2V communication between the own vehicle and the remote vehicle, including vehicle position, speed, acceleration, direction, etc., and predict the travel trajectory instantly and accurately based on this information, so as to predict the possible collision situation and promptly remind the user to take effective measures to avoid the occurrence of vehicle collision accidents.

[0028] In order to better understand the above-mentioned technical scheme, the above-mentioned technical scheme will be described in detail below in conjunction with the accompanying drawings and specific implementation methods. It should be understood that the embodiments of the present invention and the specific features in the embodiments are detailed descriptions of the technical scheme of the present application, rather than limitations on the technical scheme of the present application. In the absence of conflict, the embodiments of the present invention and the technical features in the embodiments can be combined with each other.

[0029] Embodiment 1

[0030] Reference Figure 1 , Figure 1 FIG. 1 is a flow chart of an embodiment of a vehicle collision prediction method based on V2V of the present invention. Figure 1 As shown, the method includes:

[0031] Step S10, obtaining basic safety information of the host vehicle and basic safety information of the remote vehicle.

[0032] Specifically, in one embodiment of the present invention, the host vehicle and the remote vehicle are equipped with vehicle-mounted terminal units, and the host vehicle and the remote vehicle communicate with each other through the vehicle-mounted terminal units. The vehicle-mounted terminal unit of the host vehicle collects and stores basic safety information such as the speed, longitude and latitude, and heading angle of the host vehicle, and at the same time, the host vehicle receives basic safety information package data of the remote vehicle, and extracts information such as the speed, longitude and latitude, and heading angle of the remote vehicle from the basic safety information of the remote vehicle.

[0033] Step S20, calculating the positions of the host vehicle and the remote vehicle in a first coordinate system respectively according to the host vehicle basic safety information and the remote vehicle basic safety information, wherein the first coordinate system is a world coordinate system with the host vehicle's previous position as the coordinate origin.

[0034] Specifically, after obtaining the basic safety information of the vehicle and the remote vehicle, a world coordinate system is established with the position of the vehicle at the previous moment as the coordinate origin, the east direction as the X-axis, and the north direction as the Y-axis, and then the positions of the vehicle and the remote vehicle in the coordinate system are calculated respectively.

[0035] Furthermore, the coordinates of the vehicle at the current moment in the first coordinate system are calculated by the following formula.

[0036] xEastHV=0+arc*cos(zero_lat*rad)*(hv_data.position1.lng-zero_lng);

[0037] yNorthHV=0+arc*(hv_data.position1.lat-zero_lat);

[0038] Among them, M_PI = 3.14159265358979323846, rad = M_PI / 180; arc = 6378398.0*rad; zero_lat is the latitude of the vehicle at the previous moment; zero_lng is the longitude of the vehicle at the previous moment; hv_data.position1.lat is the latitude of the vehicle at the current moment; hv_data.position1.lng is the longitude of the vehicle at the current moment.

[0039] Furthermore, the heading angle of the vehicle at the current moment is converted in the following way, so as to subsequently convert the coordinates of the vehicle at the current moment in the world coordinate system to the vehicle body coordinate system at the previous moment. If hv_data.heading>M_PI / rad, then hv_data.heading=hv_data.heading-2*M_PI / rad; if hv_data.heading<-1*M_PI / rad, then hv_data.heading=hv_data.heading+2*M_PI / rad; where M_PI=3.14159265358979323846, rad=M_PI / 180, and hv_data.heading is the heading angle of the vehicle at the current moment.

[0040] Furthermore, the coordinates of the remote vehicle at the current moment in the first coordinate system are calculated by the following formula.

[0041] xEastRV=xEastHV+arc*cos(hv_data.position1.lat*rad)*(rv_data.position1.ln g-hv_data.position1.lng);

[0042] yNorthRV=yNorthHV+arc*(rv_data.position1.lat-hv_data.position1.lat);

[0043] Among them, M_PI = 3.14159265358979323846, rad = M_PI / 180; arc = 6378398.0*rad; hv_data.position1.lat is the latitude of the vehicle at the current moment; hv_data.position1.lng is the longitude of the vehicle at the current moment, rv_data.position1.lat is the latitude of the remote vehicle at the current moment; rv_data.position1.lng is the longitude of the remote vehicle at the current moment.

[0044] Furthermore, the heading angle of the remote vehicle at the current moment is converted in the following way, so as to facilitate the subsequent conversion of the coordinates of the remote vehicle at the current moment in the world coordinate system to the body coordinate system of the vehicle at the previous moment: if rv_data.heading>M_PI / rad, then rv_data.heading=rv_data.heading-2*M_PI / rad; if rv_data.heading<-1*M_PI / rad, then rv_data.heading=rv_data.heading+2*M_PI / rad; wherein, M_PI=3.14159265358979323846, rad=M_PI / 180; rv_data.heading is the heading angle of the remote vehicle.

[0045] Step S30, using the position of the host vehicle in the first coordinate system and the position of the remote vehicle in the first coordinate system, respectively calculate the current position of the host vehicle in the second coordinate system and the current position of the remote vehicle in the second coordinate system, wherein the second coordinate system is a body coordinate system based on the host vehicle established according to the position of the host vehicle at a previous moment;

[0046] Specifically, in one embodiment of the present invention, a body coordinate system based on the vehicle is established as the second coordinate system according to the position of the vehicle at the previous moment. In order to describe the position of the vehicle at the current moment i in the second coordinate system, the following coordinate conversion formula is used:

[0047]

[0048] in, and are the horizontal coordinate, vertical coordinate and heading angle of the vehicle at time i in the second coordinate system, are the horizontal coordinate, vertical coordinate and heading angle of the vehicle at time i in the first coordinate system, are respectively the horizontal coordinate, vertical coordinate and heading angle of the vehicle in the first coordinate system at time i-1.

[0049] Then, solve the state equation:

[0050]

[0051] Specifically, in one embodiment of the present invention, in order to describe the position of the remote vehicle at the current time i in the second coordinate system, the following coordinate conversion formula is used:

[0052]

[0053] in, are the horizontal coordinate, vertical coordinate and heading angle of the vehicle at time i-1 in the first coordinate system, and are the horizontal coordinate, vertical coordinate and heading angle of the remote vehicle at time i in the second coordinate system, are respectively the horizontal coordinate, vertical coordinate and heading angle of the remote vehicle at time i in the first coordinate system.

[0054] Then, solve the state equation:

[0055]

[0056] Transforming the position in the world coordinate system to the position in the body coordinate system can more intuitively represent the movement of the vehicle. Through the body coordinate system, we can easily study the various motion characteristics of the vehicle, such as steering, acceleration, deceleration, etc., and at the same time better understand the movement trajectory of the vehicle in space. In practical applications, if the position in the world coordinate system is used directly, it will be more difficult to judge and predict the vehicle state, because the position of the vehicle in the world coordinate system is always changing at different times, and the impact of environmental factors on movement also needs to be considered. Therefore, the use of the body coordinate system can simplify data calculation and processing, and improve the accuracy and reliability of traffic safety prediction.

[0057] Step S40, predicting the driving trajectory of the vehicle and the driving trajectory of the remote vehicle respectively according to the current position of the vehicle in the second coordinate system and the current position of the remote vehicle in the second coordinate system; and

[0058] Specifically, in one embodiment of the present invention, predicting the driving trajectory of the vehicle according to the current position of the vehicle in the second coordinate system includes:

[0059] Step S41: The vehicle state equation is expressed as follows: Predict the next position of the vehicle in, is the current speed of the vehicle in the second coordinate system, is the yaw angle of the vehicle at the current moment in the second coordinate system;

[0060]

[0061]

[0062] w k ∈N(0,0.1)

[0063] Step S42, calculating the covariance matrix of the vehicle state equation;

[0064] The state vector of the a priori state and the a priori covariance matrix can be predicted from previous data.

[0065]

[0066]

[0067] in, is the state estimate of the previous step, and is the covariance matrix from the previous step.

[0068] Step S43: Calculate the Kalman gain according to the covariance matrix, where R is the measurement noise of the covariance

[0069]

[0070] Step S44: Correct the prediction result according to the Kalman gain.

[0071]

[0072]

[0073]

[0074] v k ∈N(0,0.25)

[0075] Specifically, in one embodiment of the present invention, predicting the driving trajectory of the remote vehicle according to the current position of the remote vehicle in the second coordinate system includes:

[0076] Step S45: The remote vehicle state equation is expressed as follows: Predict the next position of a distant vehicle in, is the current speed of the remote vehicle in the second coordinate system, is the yaw angle of the remote vehicle in the second coordinate system at the current moment;

[0077]

[0078]

[0079] w k ∈N(0,0.1)

[0080] Step S46, calculating the covariance matrix of the remote vehicle state equation;

[0081] The state vector of the a priori state and the a priori covariance matrix can be predicted from previous data.

[0082]

[0083]

[0084]

[0085] Step S47, calculating the Kalman gain according to the covariance matrix;

[0086]

[0087] Step S48: Correct the prediction result according to the Kalman gain

[0088]

[0089]

[0090]

[0091] v k ∈N(0,0.25)

[0092] Step S50: judging whether there is a collision risk between the host vehicle and the remote vehicle according to the driving trajectory of the host vehicle and the driving trajectory of the remote vehicle.

[0093] Specifically, in this embodiment, through trajectory prediction, the n-second trajectory of the vehicle and the n-second trajectory of the distant vehicle are first predicted. Then the distance between the trajectory point of the vehicle and the trajectory point of the distant vehicle is calculated, for example, the trajectory point of the vehicle at 0.1 second and the trajectory point of the distant vehicle at 0.1 second, the trajectory of the vehicle at 0.2 second and the trajectory of the distant vehicle at 0.2 second, and so on, after calculating the distance of all trajectories. Then the trajectory points whose distance is less than the sum of the vehicle lengths of the vehicle and the distant vehicle are extracted. If there is a trajectory point whose distance between the vehicle and the distant vehicle is less than the sum of the vehicle lengths of the vehicle and the distant vehicle, it means that the vehicle and the distant vehicle are traveling at a very close distance, and there may be a risk of collision.

[0094] Embodiment 2

[0095] The present invention also provides a vehicle collision prediction device based on V2V, comprising:

[0096] An information acquisition module is used to obtain basic safety information of the vehicle and the remote vehicle;

[0097] A first calculation module, used for calculating the positions of the host vehicle and the remote vehicle in a first coordinate system according to the basic safety information of the host vehicle and the basic safety information of the remote vehicle, respectively, wherein the first coordinate system is a world coordinate system with the position of the host vehicle at a previous moment as the coordinate origin;

[0098] a second calculation module, for calculating the current position of the host vehicle in a second coordinate system and the current position of the remote vehicle in a second coordinate system respectively by using the position of the host vehicle in the first coordinate system and the position of the remote vehicle in the first coordinate system, wherein the second coordinate system is a body coordinate system based on the host vehicle established according to the position of the host vehicle at a previous moment;

[0099] A vehicle trajectory prediction module, used to predict the vehicle trajectory of the vehicle and the remote vehicle respectively according to the current position of the vehicle in the second coordinate system and the current position of the remote vehicle in the second coordinate system; and

[0100] The judgment module is used to judge whether there is a collision risk between the own vehicle and the distant vehicle according to the driving trajectory of the own vehicle and the driving trajectory of the distant vehicle.

[0101] Specifically, in one embodiment of the present invention, the second calculation module calculates the current position of the host vehicle in the second coordinate system and the current position of the remote vehicle in the second coordinate system by the following formulas:

[0102]

[0103] in, and are the horizontal coordinate, vertical coordinate and heading angle of the vehicle at time i in the second coordinate system, are the horizontal coordinate, vertical coordinate and heading angle of the vehicle at time i in the first coordinate system, are the horizontal coordinate, vertical coordinate and heading angle of the vehicle at time i-1 in the first coordinate system, and are the horizontal coordinate, vertical coordinate and heading angle of the remote vehicle at time i in the second coordinate system, are respectively the horizontal coordinate, vertical coordinate and heading angle of the remote vehicle at time i in the first coordinate system.

[0104] Specifically, in one embodiment of the present invention, the travel trajectory prediction module includes a vehicle travel trajectory prediction unit, and the vehicle travel trajectory prediction unit is used to:

[0105] According to the vehicle state equation shown in the following formula, the vehicle's current position in the second coordinate system is Predict the next position of the vehicle in, is the current speed of the vehicle in the second coordinate system, is the yaw angle of the vehicle at the current moment in the second coordinate system;

[0106]

[0107]

[0108] w k ∈N(0,0.1)

[0109] Calculating the covariance matrix of the vehicle state equation;

[0110] According to the covariance matrix, calculate the Kalman gain;

[0111] The prediction results are corrected according to the Kalman gain.

[0112] Specifically, in one embodiment of the present invention, the travel trajectory prediction module includes a remote vehicle travel trajectory prediction unit, and the remote vehicle travel trajectory prediction unit is used to:

[0113] According to the remote vehicle state equation shown in the following formula, the current position of the remote vehicle in the second coordinate system Predict the next position of a distant vehicle in, is the current speed of the remote vehicle in the second coordinate system, is the yaw angle of the remote vehicle in the second coordinate system at the current moment;

[0114]

[0115]

[0116] w k ∈N(0,0.1)

[0117] Calculating the covariance matrix of the remote vehicle state equation;

[0118] According to the covariance matrix, calculate the Kalman gain;

[0119] The prediction results are corrected according to the Kalman gain.

[0120] Those skilled in the art will appreciate that the above is an embodiment of a V2V-based vehicle collision prediction device provided in an embodiment of the present invention, and the system and device belong to the same inventive concept as the above-mentioned V2V-based vehicle collision prediction method. For details not fully described in the embodiment of the V2V-based vehicle collision prediction device, reference may be made to the above-mentioned V2V-based vehicle collision prediction method embodiment.

[0121] The embodiment of the present invention further provides a vehicle collision prediction device based on V2V, which may include:

[0122] Memory for storing computer programs;

[0123] The processor can implement the following steps when executing the computer program stored in the above memory:

[0124] Obtain basic safety information of the vehicle and basic safety information of the remote vehicle; calculate the positions of the vehicle and the remote vehicle in a first coordinate system respectively according to the basic safety information of the vehicle and the basic safety information of the remote vehicle, wherein the first coordinate system is a world coordinate system with the position of the vehicle at a previous moment as the coordinate origin; calculate the current position of the vehicle and the current position of the remote vehicle in a second coordinate system respectively using the position of the vehicle in the first coordinate system and the position of the remote vehicle in the first coordinate system, wherein the second coordinate system is a body coordinate system based on the vehicle established according to the position of the vehicle at a previous moment; predict the driving trajectory of the vehicle and the driving trajectory of the remote vehicle respectively according to the current position of the vehicle in the second coordinate system and the current position of the remote vehicle in the second coordinate system; and determine whether there is a collision risk between the vehicle and the remote vehicle according to the driving trajectory of the vehicle and the driving trajectory of the remote vehicle.

[0125] The embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the following steps can be implemented;

[0126] Obtain basic safety information of the vehicle and basic safety information of the remote vehicle; calculate the positions of the vehicle and the remote vehicle in a first coordinate system respectively according to the basic safety information of the vehicle and the basic safety information of the remote vehicle, wherein the first coordinate system is a world coordinate system with the position of the vehicle at a previous moment as the coordinate origin; calculate the current position of the vehicle and the current position of the remote vehicle in a second coordinate system respectively using the position of the vehicle in the first coordinate system and the position of the remote vehicle in the first coordinate system, wherein the second coordinate system is a body coordinate system based on the vehicle established according to the position of the vehicle at a previous moment; predict the driving trajectory of the vehicle and the driving trajectory of the remote vehicle respectively according to the current position of the vehicle in the second coordinate system and the current position of the remote vehicle in the second coordinate system; and determine whether there is a collision risk between the vehicle and the remote vehicle according to the driving trajectory of the vehicle and the driving trajectory of the remote vehicle.

[0127] The computer-readable storage medium may include: a USB flash drive, a mobile hard disk, a read-only memory (ROM)> a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program codes.

[0128] In the description provided herein, a large number of specific details are described. However, it is understood that embodiments of the present invention can be practiced without these specific details. In some instances, well-known methods, structures and techniques are not shown in detail so as not to obscure the understanding of this description.

[0129] Similarly, it should be understood that in order to streamline the present disclosure and aid in understanding one or more of the various inventive aspects, in the above description of exemplary embodiments of the present invention, various features of the present invention are sometimes grouped together into a single embodiment, figure, or description thereof. However, this disclosed method should not be interpreted as reflecting the intention that the claimed invention requires more features than those explicitly recited in each claim. More specifically, as reflected in the claims below, inventive aspects lie in less than all the features of the individual embodiments disclosed above. Therefore, the claims that follow the specific embodiment are hereby expressly incorporated into the specific embodiment, with each claim itself serving as a separate embodiment of the present invention.

[0130] Those skilled in the art will appreciate that the modules in the devices in the embodiments can be adaptively changed and arranged in one or more devices different from the embodiments. The modules or units or components in the embodiments can be combined into one module or unit or component, and in addition they can be divided into multiple submodules or subunits or subassemblies. Except that at least some of such features and / or processes or units are mutually exclusive, all features disclosed in this specification and all processes or units of any method or device disclosed in this manner can be combined in any combination. Unless otherwise clearly stated, each feature disclosed in this specification can be replaced by an alternative feature providing the same, equivalent or similar purpose.

[0131] In addition, those skilled in the art will appreciate that, although some embodiments described herein include certain features included in other embodiments but not other features, the combination of features of different embodiments is meant to be within the scope of the present invention and form different embodiments. For example, any one of the claimed embodiments may be used in any combination.

[0132] The various component embodiments of the present invention can be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. It should be understood by those skilled in the art that a microprocessor or digital signal processor (DSP) can be used in practice to implement some or all of the functions of some or all of the components in the embodiments of the present invention. The present invention can also be implemented as a device or apparatus program (e.g., computer program and computer program product) for executing part or all of the methods described herein. Such a program implementing the present invention can be stored on a computer-readable medium, or can have the form of one or more signals. Such a signal can be downloaded from an Internet website, or provided on a carrier signal, or provided in any other form.

[0133] It should be noted that the above embodiments illustrate the present invention rather than limit it, and that those skilled in the art may devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference symbol between brackets should not be constructed as a limitation to the claims. The word "one" or "an" preceding an element does not exclude the presence of a plurality of such elements. The present invention may be implemented by means of hardware including a number of different elements and by means of a suitably programmed computer. Several of these devices may be embodied by the same hardware item. The use of the words first, second, and third, etc. does not indicate any order. These words may be interpreted as names.

Claims

1. A vehicle collision prediction method based on V2V, characterized in that: The following steps are involved: Obtain basic safety information of the vehicle and remote vehicles; According to the basic safety information of the host vehicle and the basic safety information of the remote vehicle, respectively calculate the positions of the host vehicle and the remote vehicle in a first coordinate system, wherein the first coordinate system is a world coordinate system with the position of the host vehicle at a previous moment as the coordinate origin; Using the position of the vehicle in the first coordinate system and the position of the remote vehicle in the first coordinate system, respectively calculate the current position of the vehicle in the second coordinate system and the current position of the remote vehicle in the second coordinate system, wherein the second coordinate system is a body coordinate system based on the vehicle established according to the position of the vehicle at a previous moment; According to the current position of the host vehicle in the second coordinate system and the current position of the remote vehicle in the second coordinate system, respectively predict the driving trajectory of the host vehicle and the driving trajectory of the remote vehicle; and Based on the driving trajectory of the own vehicle and the driving trajectory of the distant vehicle, determine whether there is a risk of collision between the own vehicle and the distant vehicle.

2. The V2V-based vehicle collision prediction method according to claim 1, characterized in that: The current position of the host vehicle in the second coordinate system and the current position of the remote vehicle in the second coordinate system are calculated by the following formulas (1) and (2), respectively: in, and are the horizontal coordinate, vertical coordinate and heading angle of the vehicle at time i in the second coordinate system, are the horizontal coordinate, vertical coordinate and heading angle of the vehicle at time i in the first coordinate system, are the horizontal coordinate, vertical coordinate and heading angle of the vehicle at time i-1 in the first coordinate system, and are the horizontal coordinate, vertical coordinate and heading angle of the remote vehicle at time i in the second coordinate system, are respectively the horizontal coordinate, vertical coordinate and heading angle of the remote vehicle at time i in the first coordinate system.

3. The V2V-based vehicle collision prediction method according to claim 2, characterized in that: According to the current position of the vehicle in the second coordinate system, the steps of predicting the vehicle's driving trajectory include: According to the vehicle state equation shown in the following formula, the vehicle's current position in the second coordinate system is Predict the next position of the vehicle in, is the current speed of the vehicle in the second coordinate system, is the yaw angle of the vehicle at the current moment in the second coordinate system; In k ∈N(0,0.1) Calculating the covariance matrix of the vehicle state equation; According to the covariance matrix, calculate the Kalman gain; The prediction results are corrected according to the Kalman gain.

4. The V2V-based vehicle collision prediction method according to claim 2, characterized in that: According to the current position of the remote vehicle in the second coordinate system, the steps of predicting the driving trajectory of the remote vehicle include: According to the remote vehicle state equation shown in the following formula, the current position of the remote vehicle in the second coordinate system Predict the next position of a distant vehicle in, is the current speed of the remote vehicle in the second coordinate system, is the yaw angle of the remote vehicle in the second coordinate system at the current moment; In k ∈N(0,0.1) Calculating the covariance matrix of the remote vehicle state equation; According to the covariance matrix, calculate the Kalman gain; The prediction results are corrected according to the Kalman gain.

5. A vehicle collision prediction device based on V2V, characterized in that: include: An information acquisition module is used to obtain basic safety information of the vehicle and the remote vehicle; A first calculation module, used for calculating the positions of the host vehicle and the remote vehicle in a first coordinate system according to the basic safety information of the host vehicle and the basic safety information of the remote vehicle, respectively, wherein the first coordinate system is a world coordinate system with the position of the host vehicle at a previous moment as the coordinate origin; a second calculation module, for calculating the current position of the host vehicle in a second coordinate system and the current position of the remote vehicle in a second coordinate system respectively by using the position of the host vehicle in the first coordinate system and the position of the remote vehicle in the first coordinate system, wherein the second coordinate system is a body coordinate system based on the host vehicle established according to the position of the host vehicle at a previous moment; A vehicle trajectory prediction module, used to predict the vehicle trajectory of the vehicle and the remote vehicle respectively according to the current position of the vehicle in the second coordinate system and the current position of the remote vehicle in the second coordinate system; and The judgment module is used to judge whether there is a collision risk between the own vehicle and the distant vehicle according to the driving trajectory of the own vehicle and the driving trajectory of the distant vehicle.

6. The V2V-based vehicle collision prediction device according to claim 5, characterized in that: The second calculation module calculates the current position of the vehicle and the current position of the remote vehicle in the second coordinate system by the following formulas (1) and (2), respectively: in, and are the horizontal coordinate, vertical coordinate and heading angle of the vehicle at time i in the second coordinate system, are the horizontal coordinate, vertical coordinate and heading angle of the vehicle at time i in the first coordinate system, are the horizontal coordinate, vertical coordinate and heading angle of the vehicle at time i-1 in the first coordinate system, and are the horizontal coordinate, vertical coordinate and heading angle of the remote vehicle at time i in the second coordinate system, are respectively the horizontal coordinate, vertical coordinate and heading angle of the remote vehicle at time i in the first coordinate system.

7. The V2V-based vehicle collision prediction device according to claim 6, characterized in that: The travel trajectory prediction module includes a vehicle travel trajectory prediction unit, and the vehicle travel trajectory prediction unit is used to: According to the vehicle state equation shown in the following formula, the vehicle's current position in the second coordinate system is Predict the next position of the vehicle in, is the current speed of the vehicle in the second coordinate system, is the yaw angle of the vehicle at the current moment in the second coordinate system; In k ∈N(0,0.1) Calculating the covariance matrix of the vehicle state equation; According to the covariance matrix, calculate the Kalman gain; The prediction results are corrected according to the Kalman gain.

8. The V2V-based vehicle collision prediction device according to claim 6, characterized in that: The travel trajectory prediction module includes a remote vehicle travel trajectory prediction unit, and the remote vehicle travel trajectory prediction unit is used to: According to the remote vehicle state equation shown in the following formula, the current position of the remote vehicle in the second coordinate system Predict the next position of a distant vehicle in, is the current speed of the remote vehicle in the second coordinate system, is the yaw angle of the remote vehicle in the second coordinate system at the current moment; In k ∈N(0,0.1) Calculating the covariance matrix of the remote vehicle state equation; According to the covariance matrix, calculate the Kalman gain; The prediction results are corrected according to the Kalman gain.

9. A vehicle collision prediction device based on V2V, characterized in that: include: Memory for storing computer programs; A processor, configured to implement the steps of the V2V-based vehicle collision prediction method as described in any one of claims 1 to 4 when executing the computer program.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the V2V-based vehicle collision prediction method according to any one of claims 1 to 4 are implemented.

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