CN104269070B - Active vehicle safety pre-warning method and safety pre-warning system with same applied - Google Patents

Abstract

The invention relates to the technical field of vehicle active safety control and mobile interconnection, in particular to a vehicle active safety early warning method and a safety early warning system using the method. The safety early warning system includes mobile smart terminals, wireless communication networks and satellite positioning systems, and the active safety early warning method is to install three software modules: data preprocessing module, trajectory tracking and estimation module, and danger warning module on the mobile smart terminal. The self-vehicle can judge whether there is a risk of collision based on the information of pedestrians and other vehicles within the target range, and issue a safety alarm if necessary. Through the ingenious use of popular smart phones, low-cost vehicles and pedestrians can enjoy the protection of active safety technology without leaking personal privacy, high popularity, low cost, and easy to be accepted by the public.

Description

A vehicle active safety early warning method and a safety early warning system using the method

technical field

The invention relates to the technical field of vehicle active safety control and mobile interconnection, in particular to a vehicle active safety early warning method and a safety early warning system using the method.

Background technique

The vehicle active safety control system can realize vehicle collision warning and intervention in driving operation, making driving safer and easier. In recent years, the research enthusiasm in this technical field has continued to heat up. Active safety control systems such as ACC (Adaptive Cruise Control), EBA (Emergency Brake Assist), and LKA (Lane Keeping Assist) based on radar, camera and other environmental detection sensors have matured. , and is still being optimized. For example, Korean patent KR2007023392A (ADAPTIVE CRUISE CONTROL SYSTEM AND METHOD CONSIDERING DRIVING ENVIRONMENT, CAPABLE OF AUTOMATICALLY CONTROLLING AN INTER-CAR DISTANCE ESPECIALLY WHEN IT IS RAINY OR DARK) introduces the use of rain sensors to optimize the vehicle’s existing ACC system, and the night driving sensor to optimize the vehicle’s existing ACC system. The environment perception function of the vehicle can realize active safety control more accurately.

However, at this stage, it is still far from realizing the full sense of road active safety. First of all, on-board radars and cameras are mainly used to detect vehicles in the front and rear directions. For obstacles that are far away in the left and right directions, or within the blind spot of the driver's vision, or high-speed small motor vehicles that are blocked by obstacles, it is impossible to raise an alarm in advance. Secondly, the active safety control system is expensive, and it is currently only used in high-end models. For ordinary family cars, which account for the largest proportion of road vehicles, this function cannot be realized. Third, mini-cars, motorcycles and pedestrians on the road cannot enjoy the safety and comfort brought by active safety technology.

With the rise of the concept of the Internet of Vehicles, a new technology based on the Internet - V2V, that is, Vehicle-to-Vehicle (also known as Car-to-Car) has also been proposed. The so-called V2V is to realize the information exchange technology between vehicles through on-board sensors and other equipment. When a collision may occur, the driver will be reminded in time to avoid traffic accidents. The main focus of the automotive industry for V2V technology is on the vehicle components. However, although these related vehicle components have made great technological progress, in addition to the V2V technology itself, the popularity of the technology is also a very important factor. If it cannot be popularized, it will not be able to play the expected role at all.

With the rapid development of mobile Internet technology, people's lives are undergoing profound changes. Now, almost everyone carries a smart phone with various application software. People can know their geographical location at any time, and can use navigation technology to reach their destination smoothly. Send location information without leaking privacy (such as not sending personal information such as mobile phone number).

Contents of the invention

The object of the present invention is to provide a vehicle active safety early warning method and a safety early warning system using the method. It uses the widely popular smart phone to send out the location information of the smart phone holder positioned by the satellite positioning system, and calculates and predicts the speed and position of the location information of itself and others through the software installed on the smart phone and other mobile smart terminals. And when there is a risk of collision, the alarm has the function of active safety warning, and can be widely popularized.

For the active safety early warning method of the present invention, above-mentioned technical problem is solved like this:

A vehicle active safety early warning method based on location information of a mobile intelligent terminal, comprising the following steps:

(1) The mobile intelligent terminal of the mobile target within the target range of the vehicle transmits their position data positioned by the satellite positioning system to the data preprocessing module of the mobile intelligent terminal of the vehicle through the wireless communication network;

(2) The data preprocessing module on the mobile intelligent terminal of the own vehicle calculates the driving distance and driving speed of the own vehicle at each moment according to the position of the own vehicle, and calculates the driving of the moving target at each moment according to the position of the moving target within the target range. The distance and driving speed, and the obtained data of the moving target within the range of the vehicle and the target are transmitted to the trajectory tracking and estimation module on the mobile smart terminal of the vehicle;

(3) The trajectory tracking and estimation module on the mobile intelligent terminal of the self-vehicle predicts the position, speed and direction of movement of the self-vehicle at the next moment based on the position and speed information of the self-vehicle at the previous few moments. The position and speed information of the moving target within the range predicts the position, speed and direction of movement of the moving target within the target range at the next moment, and transmits the obtained data information of the own vehicle and the moving target within the target range to the mobile intelligent terminal of the own vehicle The hazard warning module on the

(4) The danger warning module on the mobile intelligent terminal of the self-vehicle judges whether there is a collision risk according to the position, speed and direction of movement of the self-vehicle and the position, speed and direction of movement of the moving target within the target range, and when it is judged that there is a risk of collision Hazard alert.

Further, the target range in the step (1) is a circular area with the ego vehicle as the center and a radius of 250 meters.

When the data preprocessing module on the self-vehicle mobile intelligent terminal in the step (2) detects that there are dense multi-machines running at the same speed and in the same direction in a small area, the target is judged as a group of dense multi-machines.

The data preprocessing module on the mobile intelligent terminal of the ego vehicle takes the direction of the line connecting the position of the moving target at the previous moment and the current moment within the target range as the speed direction when making intensive multi-machine group judgment.

It is judged that the distribution range of other aircraft running at the same speed and in the same direction when it is judged as a dense multi-machine group is not larger ^than a circular area with a radius of 6m2.

In the step (2), when the data preprocessing module judges that the other machine is a dense multi-machine group, the local coordinate system of the self-vehicle is used as a standard, and the farthest other machine from the self-vehicle is used as a bounding box to draw a rectangular frame line, And take the corner closest to the ego vehicle as the dangerous target point.

The speed calculation method of the moving target in the self-vehicle or the target range in the step (2) is: first call the position information of the current moment t and the previous moment t-1 from the memory chip of the mobile intelligent terminal, and then according to these two The geodetic coordinates (x _t , y _t ) and (x _t-1 , y _t-1 ) at the time are calculated by the distance formula between two points Calculate the distance D(t) between two points, and then calculate the current speed V(t) according to the speed calculation formula V(t)=D(t)/Δt, where Δt is the system calculation period.

In the step (2), the current moment velocity V(t) calculated by the vehicle or the moving target within the target range according to the velocity calculation formula needs to be filtered by low-pass filtering, and then the velocity V after low-pass filtering is processed '(t) is used as the speed at the current moment, and is input to the trajectory tracking and estimation module on the mobile intelligent terminal of the vehicle. The formula of the low-pass filter algorithm is: V'(t)=a ₀ V( t)+a ₁ V(t-1)+a ₂ V(t-2)+b ₁ V'(t-1)+b ₂ V'(t-2), where: a ₀ , a ₁ , a ₂ , b ₁ and b ₂ are CFC filter constants.

In the step (3), the position of the ego vehicle or the moving target within the range of the target at the next moment is estimated by the least squares trajectory prediction algorithm.

The operation process of the least squares trajectory prediction algorithm is: call the position data of the measured object at k moments before the current moment, and assume that the trajectory of these k points is a straight line, and obtain the position data of the measured object at the next moment The expressions of x-coordinate and y-coordinate are: x=a'+b't, y=a″+b″t, and the a that makes the trajectory point estimation error minimum at k time points is calculated by the least squares trajectory prediction algorithm ', b', a ", b ", so as to obtain the estimated position at the next moment, wherein a', b', a ", b " are respectively calculated by the least squares trajectory prediction algorithm as follows:

Among them, t _i is the time interval from the i-th time to the initial calculation time, t+1 is the time interval from the next time to the initial calculation point, x _i and y _i are the time interval of the measured object at the i-th time before actual coordinates.

In the step (3), the velocity of the ego vehicle or the moving target within the target range at the next moment is the velocity V'(t) at the current moment after the low-pass filtering process.

In the step (3), the speed direction of the ego vehicle or the moving target within the target range at the next moment is the linear direction predicted by the least square method.

The judgment method of the collision risk in the step (4) is: calculate the collision point according to the speed information and position of the vehicle at the next moment and the speed information and the position of the moving target within the target range, if the travel time from the collision point is less than the danger When the alarm time is set, the dangerous working condition flag is set to 1, and the danger warning module makes multiple judgments on dangerous working conditions in a short period of time. If the dangerous working condition flag is set to 1 for 5 consecutive times, a dangerous alarm is issued.

For the safety early warning system of the present invention, above-mentioned technical problem is solved like this:

Including the mobile intelligent terminal, which is used to send the position information of the vehicle to the outside, receive the position information of the moving target within the target range, and perform speed calculation, trajectory estimation and danger alarm on the moving target position within the target range;

The wireless communication network is used for the mutual transmission of the position of the mobile target within the range of the vehicle and the target;

The satellite positioning system is used to determine the location of the mobile intelligent terminal.

Further, the mobile intelligent terminal is one or more of a smart phone, a vehicle-mounted information collection device T-Box, a tablet computer, and a notebook computer.

The beneficial effect of the present invention is that: the present invention is based on the widely popularized smart phone capable of sending geographical location information to the outside, and by installing a data preprocessing module, a trajectory tracking and estimation module, and a danger warning module on mobile smart terminals such as smart phones. Three pieces of software enable the vehicle to judge whether there is a risk of collision based on the information of pedestrians and other vehicles within the target range, and issue a safety alarm if necessary. Through the ingenious use of popular smart phones, low-cost vehicles and pedestrians can enjoy the protection of active safety technology without leaking personal privacy, high popularity, low cost, and easy to be accepted by the public.

Description of drawings

Fig. 1 is the control main flowchart of the present invention;

Fig. 2 is the processing flowchart of data preprocessing module;

Fig. 3 is a flow chart of trajectory tracking and estimation module processing;

Fig. 4 is the processing flowchart of danger early warning module;

Figure 5 is a schematic diagram of dense multi-machine grouping;

Fig. 6 is a schematic diagram of the position information of the vehicle tracked and predicted;

Fig. 7 is a schematic diagram of collision danger point prediction;

In the figure: 1—data preprocessing module, 2—trajectory tracking and estimation module, 3—danger warning module.

detailed description

In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. The specific embodiments described here are only used to explain the present invention, not to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not constitute a conflict with each other.

The active safety early warning system of the present invention includes a mobile intelligent terminal, a wireless communication network and a satellite positioning system. The mobile smart terminal is a smart phone, a tablet computer, a vehicle-mounted information collection device (T-Box), a notebook computer, etc., which have a user-friendly operation interface and are intelligent carriers capable of installing the software required by the present invention. The self-car and other cars or mobile smart terminals of other people can be positioned through the satellite positioning system, and send their own geographical location information to the outside world through the wireless communication network, and at the same time do not disclose their personal privacy such as their mobile phone numbers, that is, the movement of pedestrians and vehicles. Smart terminals can simultaneously send their own location information and receive other people's location information. These active safety early warning software, data preprocessing module 1, trajectory tracking and estimation module 2 and danger warning module 3, are also installed on these mobile smart terminals. After the mobile intelligent terminal of the ego vehicle receives the geographic location information of others, it processes the data preprocessing module 1, track tracking and estimation module 2 and danger warning module 3 installed on it, and passes the matching map information , on the display screen of the mobile intelligent terminal of the vehicle to display the position and trajectory of the vehicle and other vehicles in real time.

Assuming that a certain vehicle is running on the road, driver A runs the software of the present invention in the smart phone, and the active safety warning enters the starting state. As shown in Figures 1 and 2, the data preprocessing module 1 in the driver's smart phone starts to receive the location information of other aircraft within a radius of 250m, and assigns a unique temporary ID number to each other aircraft entering the monitoring range. The location information generated by the mobile phone is stored in the data bit corresponding to the ID of the smart phone memory chip. When other machines move out of the monitoring range, the corresponding ID number and stored location information will be cleared automatically. Data preprocessing module 1 first calls the position of each ID object in the smart phone storage chip at the current moment and the previous moment (t-1, t), and calculates the speed of the own machine and the moving target within the target range, that is, all other machines , since the processing and calculation methods of the speed, position and other information of the self and other machines in several modules are the same, for the convenience of description, the self-machine is used as an example for illustration.

The geodetic coordinates of the self-machine at two moments t-1 and t are (x _t , y _t ) and (x _t-1 , y _t-1 ) respectively, then the two-point distance formula The distance D(t) traveled by the self-vehicle from time t-1 to the current time t can be calculated, and then the speed V(t) at the current moment can be calculated according to the speed calculation formula V(t)=D(t)/Δt, where Δt is the calculation cycle of the system. In order to avoid large fluctuations in the vehicle speed data, a low-pass filter algorithm can be used to eliminate the peaks and valleys of the curve and make the vehicle speed curve smoother. The formula of the low-pass filter algorithm is:

V'(t)＝a ₀ V(t)+a ₁ V(t-1)+a ₂ V(t-2)+b ₁ V'(t-1)+b ₂ V'(t-2) ,

Among them: a ₀ , a ₁ , a ₂ , b ₁ , b ₂ are CFC filter constants, and the calculation formula of this series of filter constants is as follows:

ω _d =2π·1.25·CutOff_Frq

a ₁ =2a ₀

a ₂ =a ₀

Among them, CutOff_Frq is the cutoff frequency of the filter, the default value is 5, and the specific value can be adjusted according to the specific fluctuation of the vehicle speed curve.

V(t) is the speed without low-pass filtering processing, and the data preprocessing module 1 takes the speed V'(t) after low-pass filtering processing as the speed at the current moment, and inputs it to the mobile intelligent terminal of the vehicle Go in module 2 of trajectory tracking and estimation. In the data preprocessing module 1, the traveling distance and traveling speed of the own vehicle and the moving target within the target range are calculated in a timely manner according to the calculation period of the system. And when performing this series of calculations, other confidential and intensive multi-machine group judgments will also be carried out.

As shown in Figure 5, the direction of motion in the data preprocessing module 1 is determined as the direction of the line connecting the position between the current moment and the previous moment (the judgment of speed and direction here is only used in the judgment of dense multi-machine groups, and it is used in trajectory tracking and forecasting). will be re-judged in the evaluation module). If at the same moment, four other machines whose calculated ID numbers are 5, 6, 7, and 8 are running at the same speed and in the same direction within a range not greater than 6m ² . At this time, the system judges that the four other machines whose ID numbers are 5, 6, 7, and 8 are a grouping, which is a dense multi-machine group, and gives the grouping ID number 5*6*7*8=1680, and at the same time Delete the data of three other machines whose ID numbers are 6, 7, and 8. At the same time, with the local coordinate system of the own vehicle as the standard, draw a rectangular wire frame with the other machine farthest from the own machine as the bounding box, and use the corner of the rectangular wire frame closest to the own machine as the dangerous target point, the movement speed of grouping and The direction of movement is the same as that of the other machine with ID number 5.

As shown in Figure 3, the trajectory tracking and estimation module 2 uses the low-pass filtered current velocity V'(t) input by the data preprocessing module 1 as the predicted next moment velocity information of the own vehicle, and for The position estimation of the ego vehicle at the next moment is predicted by the least squares trajectory prediction algorithm. As shown in Figure 6, the trajectory tracking and estimation module 2 calls the position information of the car at the previous k (k<=10) moments in the storage chip of the smart phone. Since the calculation frequency of this system is above 10Hz, the first k moments The trajectory of the movement can be regarded as a straight line, and the x(t+1) and y(t+1) coordinates of the position at the next moment can be based on the formula x _(t+1) = a'+b'(t+1), y _(t+1) = a″+b″(t+1) to obtain. Since the motion trajectory cannot be a straight line in fact, there will be errors, and the error formulas of x coordinates and y coordinates are: ε _i '=x _i -a'-b't _i , ε _i ″=y _i -a ″-b″t _i , the variances of the positions of k points on the x and y coordinates are: In order to minimize the error, it is necessary to perform partial differentiation on a', b', a", and b". Finally, the expressions of a', b', a", and b" when the error is minimized are:

In the formula, t _i is the time interval from the i-th time to the initial calculation time, t+1 is the time interval from the next time to the initial calculation point, x _i and y _i are the time interval of the measured object at the i-th time before actual coordinates.

Substitute a', b', a", and b" into the formula x _(t+1) = a'+b'(t+1), y _(t+1) = a"+b"(t+1) , so as to obtain the position at the next moment when the car exits. The direction of motion of the ego vehicle at the next moment is the linear direction estimated by the least squares trajectory prediction algorithm. The speed, direction and position of the moving target at the next moment within the target range in the trajectory tracking and estimation module 2 are also calculated and processed in the same manner. The trajectory tracking and estimation module 2 inputs the obtained next moment speed, direction and position of the ego vehicle and the moving target within the target range into the danger warning module 3 .

As shown in Figure 4, the danger warning module 3 predicts the location of the collision according to the running track and running speed of the self-vehicle and other vehicles. The position of the working condition flag is 1. For example, the danger warning module 3 calculates that a collision will occur at the position of the earth coordinate system point P (x0, y0) according to the location information and speed information of the own machine and the other machine numbered 3 at the next moment (as shown in Figure 7 ), and the running time Δt' of the self-machine departure point P is 2.8s. At this time, the danger warning module 4 stores the position of the dangerous working condition flag as 1 and stores it in the memory chip, and calls the previous four moments in the memory chip at the same time. Hazardous working condition sign. If it is found that the dangerous working condition flags are all 1 at the first four moments, then the dangerous warning module 4 sends a dangerous warning command, and the control system sends a dangerous warning.

When the vehicle stops, the computer calculates that the vehicle speed signal is 0, judging that the vehicle has stopped, and the system automatically exits the operation.

It should be understood that those skilled in the art can make improvements or changes based on the above description, and all these improvements and changes should belong to the protection scope of the appended claims of the present invention.

Claims (14)

1. A vehicle active safety early warning method, is characterized in that, comprises the steps: (1) The mobile intelligent terminal of the mobile target within the target range of the vehicle transmits their positions positioned by the satellite positioning system to the data preprocessing module of the mobile intelligent terminal of the vehicle through the wireless communication network; (2) The data preprocessing module on the mobile intelligent terminal of the own vehicle calculates the driving distance and driving speed of the own vehicle at each moment according to the position of the own vehicle, and calculates the driving of the moving target at each moment according to the position of the moving target within the target range. The distance and driving speed, and the obtained data of the moving target within the range of the vehicle and the target are transmitted to the trajectory tracking and estimation module on the mobile smart terminal of the vehicle; (3) The trajectory tracking and estimation module on the mobile intelligent terminal of the self-vehicle predicts the position, speed and direction of movement of the self-vehicle at the next moment based on the position and speed information of the self-vehicle at the previous few moments. The position and speed information of the moving target within the range predicts the position, speed and direction of movement of the moving target within the target range at the next moment, and transmits the obtained data information of the own vehicle and the moving target within the target range to the mobile intelligent terminal of the own vehicle The hazard warning module on the (4) The danger warning module on the mobile intelligent terminal of the self-vehicle judges whether there is a collision risk according to the position, speed and direction of movement of the self-vehicle and the position, speed and direction of movement of the moving target within the target range, and when it is judged that there is a risk of collision sound a danger alert; When the data preprocessing module on the self-vehicle mobile intelligent terminal in the step (2) detects that there are dense multi-machines running at the same speed and in the same direction in a small area, the target is judged as a group of dense multi-machines. 2. A kind of vehicle active safety early warning method as claimed in claim 1, is characterized in that: in the described step (1), the target range is a circular area with the self-vehicle as the center and a radius of 250 meters. 3. A kind of vehicle active safety early warning method as claimed in claim 1, is characterized in that: the data preprocessing module on the mobile intelligent terminal of described self-vehicle is when carrying out intensive multi-machine group judgment, moves the target within the target range The direction of the line connecting the position at the previous time and the current time is taken as the speed direction. 4. A vehicle active safety early warning method according to claim 1, characterized in that: said small range is a circular area with a radius not greater than 6m. 5. A kind of vehicle active safety early warning method as claimed in claim 1, is characterized in that: in described step (2), when data preprocessing module judges that other machine is dense multi-machine group, with own vehicle local coordinate system As a standard, the other machine farthest from the self-vehicle is used as the bounding box to draw a rectangular frame line, and the corner of the rectangular frame line closest to the self-vehicle is used as the dangerous target point. 6. A kind of vehicle active safety early warning method as claimed in claim 1, is characterized in that: in described step (2), the speed calculation method of self-vehicle or target range is: first from the memory chip of mobile intelligent terminal call the position of the current time t and the previous time t-1, and then according to the earth coordinates (x _t , y _t ), (x _t-1 , y _t-1 ) through the two-point distance formula Calculate the distance D(t) between two points, and then calculate the current speed V(t) according to the speed calculation formula V(t)=D(t)/Δt, where Δt is the system calculation cycle. 7. A kind of vehicle active safety early warning method as claimed in claim 1, is characterized in that: in said step (2), the current moment velocity V (t) calculated according to the velocity calculation formula of the moving target in the self-vehicle or target range Through low-pass filtering algorithm: V'(t)=a ₀ V(t)+a ₁ V(t-1)+a ₂ V(t-2)+b ₁ V'(t-1)+b ₂ V '(t-2) processing, and the processed speed V'(t) is used as the speed at the current moment, and input to the trajectory tracking and estimation module on the mobile intelligent terminal of the vehicle, where: a ₀ , a ₁ , a ₂ , b ₁ , b ₂ are filtering constants, the V(t-1) and V(t-2) are respectively the speed of the moving target at the last moment and the speed of the moving target at the last two moments, V' (t-1) and V'(t-2) are respectively the speed of the moving target at the last moment and the speed of the last two moments after low-pass filtering. 8. A kind of vehicle active safety early warning method as claimed in claim 1, is characterized in that: in described step (3), the position of moving target next moment in self-vehicle or target range is to carry out by least square trajectory prediction algorithm Estimated. 9. A kind of vehicle active safety early warning method as claimed in claim 8, is characterized in that: the operation process of described least square trajectory prediction algorithm is: call the location data of measured object at k moments before the current moment, And assuming that the trajectory of these k points is a straight line, the expressions of the x-coordinate and y-coordinate of the measured object at the next moment are respectively: x _(t+1) = a'+b'(t+1), y _(t+1) ＝a”+b”(t+1), through the least squares trajectory prediction algorithm, calculate the values of a’, b’, a”, and b” that minimize the estimation error of trajectory points at k times value, so as to obtain the estimated position at the next moment, where a', b', a", b" are calculated by the least squares trajectory prediction algorithm as follows: ${\mathrm{<span\; class="notranslate">\; a</span>}}^{<span\; class="notranslate">\; \&prime;</span>}<span\; class="notranslate">\; =</span>\frac{\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; k</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}{{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; k</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; -</span>\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; k</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; k</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}{\mathrm{<span\; class="notranslate">\; k</span>}\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; k</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; -</span>{<span\; class="notranslate">\; (</span>\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; k</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; b</span>}}^{<span\; class="notranslate">\; \&prime;</span>}<span\; class="notranslate">\; =</span>\frac{\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; k</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; k</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; k</span>}\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; k</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}{\mathrm{<span\; class="notranslate">\; k</span>}\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; k</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; -</span>{<span\; class="notranslate">\; (</span>\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; k</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}<span\; class="notranslate">\; ,</span>$ ${\mathrm{<span\; class="notranslate">\; a</span>}}^{<span\; class="notranslate">\; \&prime;</span><span\; class="notranslate">\; \&prime;</span>}<span\; class="notranslate">\; =</span>\frac{\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; k</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}{{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; k</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}{\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; -</span>\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; k</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; k</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}{\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}{\mathrm{<span\; class="notranslate">\; k</span>}\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; k</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; -</span>{<span\; class="notranslate">\; (</span>\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; k</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; b</span>}}^{<span\; class="notranslate">\; \&prime;</span><span\; class="notranslate">\; \&prime;</span>}<span\; class="notranslate">\; =</span>\frac{\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; k</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; k</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}{\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; k</span>}\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; k</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}{\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}{\mathrm{<span\; class="notranslate">\; k</span>}\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; k</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; -</span>{<span\; class="notranslate">\; (</span>\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; k</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}<span\; class="notranslate">\; ,</span>$ Among them, t _i is the time interval from the i-th time to the initial calculation time, t+1 is the time interval from the next time to the initial calculation point, x _i and y _i are the time interval of the measured object at the i-th time before actual coordinates. 10. A kind of vehicle active safety early warning method as claimed in claim 1, is characterized in that: in the described step (3), the speed of the moving target in the self-vehicle or the target range at the next moment is after the processing of the low-pass filter algorithm. The current moment velocity V'(t). 11. A kind of vehicle active safety early-warning method as claimed in claim 1, is characterized in that: in described step (3), the velocity direction of the moving target in the self-vehicle or the target range at the next moment is a straight line predicted by the least squares method direction. 12. A vehicle active safety early warning method according to claim 1, characterized in that: the judgment method of the collision risk in the step (4) is: according to the speed information and position of the own vehicle at the next moment and the target range The collision point is calculated from the speed information and position of the moving target. If the travel time from the collision point is less than the danger alarm time, the position of the dangerous working condition flag is set to 1, and the danger warning module performs multiple dangerous working condition judgments in a short period of time. If the working condition flag is set to 1 consecutively for 5 times, a danger alarm will be issued. 13. A safety warning system using the vehicle active safety warning method according to any one of claims 1-12, characterized in that: it includes a mobile intelligent terminal, which is used to send the position information of the vehicle to the outside, and receive the moving target position within the target range Information and perform speed calculation, trajectory estimation and danger alarm on the moving target position within the target range; The wireless communication network is used for the mutual transmission of the position of the mobile target within the range of the vehicle and the target; The satellite positioning system is used to determine the location of the mobile intelligent terminal. 14. The safety early warning system according to claim 13, characterized in that: said mobile intelligent terminal is one or more of a smart phone, a vehicle-mounted information collection device T-Box, a tablet computer, and a notebook computer.