CN111653122A - A vehicle collaborative collision warning system and its control method - Google Patents

Abstract

The invention discloses a vehicle cooperative collision early warning system and a control method thereof, wherein the system comprises a sensor module, a GPS positioning module, a base station, a communication module, a self-vehicle state estimation module, a vehicle state prediction module, a collision early warning judgment module, a collision early warning device and the like; the vehicle state estimation module carries out Kalman filtering estimation on vehicle signals acquired by the sensor module and the GPS positioning module; the vehicle state prediction module predicts the position states of the vehicle and other vehicles according to the received vehicle state estimation signal and other vehicle state signals acquired from the base station, and the collision early warning judgment module judges whether the vehicle has collision danger according to the received prediction result and decides whether to start a collision early warning device according to the collision danger, so that the running safety performance of the vehicle is improved.

Description

A vehicle collaborative collision warning system and its control method

technical field

The invention relates to the field of assisted driving of vehicles, in particular to a vehicle collaborative collision early warning system and a control method thereof.

Background technique

The vehicle collision warning system needs to obtain the information of the own vehicle, such as vehicle position, acceleration, yaw rate, etc., and also needs to perceive the information of other vehicles and obstacles in the surrounding environment. The traditional on-board sensors that can be used for environmental perception include visual sensors, aurora radar, inertial sensors, etc. These sensors can achieve the purpose of environmental perception alone or through multi-sensor fusion, but there are many methods for environmental perception based on on-board sensors. The shortcomings of the sensor, such as the data obtained by the sensor is often affected by the environment, there is a large error, and the cost of the sensor is high;

In addition, the vehicle collaborative collision early warning system is mainly designed based on the model. The early warning system predicts the position of the vehicle in the future time according to the vehicle position prediction model, and determines whether the vehicle is in danger of collision by calculating the relative distance of different vehicles in the future time. The accuracy of the position prediction model is directly related to the accuracy of the collision warning system. However, the current vehicle position prediction model ignores the change of vehicle speed and the lateral motion of the vehicle, and the vehicle model is simplified to a constant velocity and constant heading angle model or a constant acceleration and other yaw angular velocity model, which is inconsistent with reality, so the prediction accuracy is not ideal.

SUMMARY OF THE INVENTION

The technical problem to be solved by the present invention is to provide a vehicle cooperative collision early warning system and a control method thereof aiming at the defects involved in the background technology.

The present invention adopts the following technical solutions for solving the above-mentioned technical problems:

A vehicle collaborative collision early warning system, comprising a sensor module, a GPS positioning module, a base station, a communication module, a self-vehicle state estimation module, a vehicle state prediction module, a collision early warning judgment module, a collision early warning device, and the like;

The sensor module includes a vehicle speed sensor, an acceleration sensor, and a yaw rate sensor;

The vehicle speed sensor is installed in the wheel, and is used to obtain the vehicle speed signal and transmit it to the self-vehicle state estimation module;

The acceleration sensor is arranged at the center of mass of the vehicle, and is used to obtain the acceleration signal of the vehicle and transmit it to the self-vehicle state estimation module;

The yaw rate sensor is arranged at the center of mass of the vehicle, and is used to acquire the yaw rate signal of the vehicle and transmit it to the vehicle state estimation module;

The GPS positioning module is used to obtain the position signal and the heading angle signal of the own vehicle, and transmit them to the own vehicle state estimation module;

The self-vehicle state estimation module is used to perform Kalman filter estimation on the obtained vehicle speed signal, acceleration signal, yaw rate signal, position signal, and vehicle heading angle signal, and transmit the estimation result to the vehicle state prediction module;

The base station is used to receive the vehicle speed signal, acceleration signal, yaw rate signal, position signal, and vehicle heading angle signal of other vehicles, and transmit it to the vehicle state prediction module through the communication module;

The vehicle state prediction module predicts the position states of the own vehicle and other vehicles according to the received state signals of the own vehicle and other vehicles, and sends the prediction result to the collision warning judgment module;

The collision warning judging module judges whether the vehicle is in danger of collision according to the received prediction result, and decides whether to activate the collision warning device for collision warning based on this.

As a further optimized solution of a vehicle collaborative collision early warning system of the present invention, the collision early warning device adopts a speaker or a light alarm.

The present invention also provides a control method of the vehicle collaborative collision warning system, comprising the following steps:

Step 1.1), the self-vehicle state estimation module obtains the vehicle speed signal, the acceleration signal, and the yaw rate signal of the self-vehicle through the vehicle speed sensor, the acceleration sensor, and the yaw rate sensor respectively;

Step 1.2), the self-vehicle state estimation module obtains the position signal and the heading angle signal of the self-vehicle through the GPS positioning module;

Step 1.3), the self-vehicle state estimation module performs Kalman filter estimation on the obtained self-vehicle speed signal, acceleration signal, yaw rate signal, position signal, and vehicle heading angle signal, and transmits the estimation result to the vehicle state prediction module;

Step 1.4), the vehicle state prediction module calculates the position information of the own vehicle and other vehicles at the time t=n*T in the future according to the state information of the own vehicle and the state information of other vehicles, and sends the calculation results to the collision warning judgment. module, n is the number of steps at the current moment, and T is the preset time step;

Step 1.5), the collision warning judgment module judges the distance S between the own vehicle and other vehicles at time t, the size of the preset distance threshold value S*, and the time t and the preset time threshold value t according to the predicted position information of the own vehicle and other vehicles. *the size of;

When S≤S*, there is a danger of collision, and the collision warning device is activated after time t;

When S>S* and t<t*, there is no danger of collision, n=n+1, continue to return to step 1.4);

When t≥t*, return to step 1.1) and re-enter the next moment.

As a further optimization scheme of the control method of the vehicle collaborative collision warning system of the present invention, the Kalman filter in step 1.3) is divided into two parts: the time update equation and the measurement update equation, the time update equation and the measurement update equation, wherein the time update equation It is used to estimate the estimated value of the state variables and covariance at time k, and provides a priori estimate for the state at time k; the measurement update equation is used for feedback, combining the prior estimate and new measurement variables to provide an improved post-test for the state at time k. The specific steps are as follows:

Step 2.1), estimate the predicted value at time k from the optimal value estimate at time k-1:

x(k|k-1)=Ax(k-1|k-1)+Bu(k)

In the formula, x(k-1|k-1) is the optimal estimated value at time k-1, x(k|k-1) is the predicted value at time k obtained by using the state at time k-1, u(k) is the control quantity at time k, A and B are the system gain matrix;

Step 2.2), predict the estimated error at time k by the error covariance and process noise at time k-1:

P(k|k-1)=AP(k-1|k-1)A ^T +Q;

In the formula, P(k|k-1) is the covariance corresponding to x(k|k-1), and P(k-1|k-1) is the covariance corresponding to x(k-1|k-1) , A ^T represents the transpose matrix of A, and Q is the covariance of the system process noise;

Step 2.3), calculate the Kalman gain matrix:

K _k =P(k|k-1)H ^T /(HP(k|k-1)H ^T +R)

In the formula, K _k is the Kalman gain at time k, R is the covariance of the system measurement noise; H is the system measurement matrix;

Step 2.4), correct and update the optimal estimated value at the current moment:

x(k|k)=x(k|k-1)+K _k (Z(k)-Hx(k|k-1))

In the formula, Z(k) is the measured value at time k, and x(k|k) is the optimal estimated value at time k;

Step 2.5), update the optimal estimation error for the next sampling period:

P(k|k)=(IK _k H)P(k|k-1)

where P(k|k) is the covariance of x(k|k) at time k, and I is the identity matrix.

As a further optimization scheme of the control method of a vehicle collaborative collision warning system of the present invention, step 1.4) The vehicle state prediction module adopts the yaw rate change rate model of equal acceleration rate of change and other yaw rate change rate models, respectively, for the own vehicle and other vehicles in the future t=n*T Predict the location information at the moment:

分别为t和t-1时刻车辆的航向角，a_t、a_t-1分别为t和t-1时刻车辆的加速度，ω_t、ω_t-1分别为t和t-1时刻车辆的横摆角速度，

为t-1时刻车辆的加速度变化率，

为t-1时刻车辆的横摆角速度变化率。In the formula, x _t and x _t-1 are the abscissas of the vehicle at t and t-1, respectively, y _t and y _t-1 are the ordinates of the vehicle at t and t-1, respectively, v _t , v _t-1 are the vehicle speeds at time t and t-1, respectively,

are the heading angles of the vehicle at t and t-1, respectively, a _t and a _t-1 are the accelerations of the vehicle at t and t-1, respectively, and ω _t and ω _t-1 are the lateral directions of the vehicle at t and t-1, respectively. Swing angular velocity,

is the acceleration rate of change of the vehicle at time t-1,

is the rate of change of the yaw rate of the vehicle at time t-1.

As a further optimization scheme for the control method of the vehicle collaborative collision warning system of the present invention, the preset distance threshold S*=3 meters and the preset time threshold t*=2.5 seconds in step 1.5).

As a further optimization scheme for the control method of a vehicle collaborative collision warning system of the present invention, the calculation method of the distance S between the own vehicle and other vehicles in step 1.5) is:

In the formula, x _t1 and x _t2 are the abscissas of the own vehicle and other vehicles at time t, respectively, and y _t1 and y _t2 are the ordinates of the own vehicle and other vehicles at time t, respectively.

Compared with the prior art, the present invention adopts the above technical scheme, and has the following technical effects:

(1) Use Kalman filter to estimate the six state variables of the self-vehicle collected by the sensor, reduce the influence of noise, etc., and ensure the accuracy of subsequent vehicle position prediction;

(2) Obtaining the status information of other vehicles from the base station through the vehicle-vehicle coordination system and judging whether the vehicle has a collision risk based on this can greatly save the cost of obtaining the information of surrounding vehicles through the own vehicle sensors.

(3) Using the vehicle position prediction model with the yaw rate change rate and other yaw rate change rate considering factors such as vehicle speed change and lateral motion, the vehicle position information can be predicted more accurately;

Description of drawings

1 is a schematic diagram of a control flow of a vehicle collaborative collision warning system provided by an embodiment of the present invention;

Detailed ways

Below in conjunction with accompanying drawing, the technical scheme of the present invention is described in further detail:

The present invention may be embodied in many different forms and should not be considered limited to the embodiments described herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art. In the drawings, components are exaggerated for clarity.

As shown in FIG. 1 , the present invention discloses a vehicle collaborative collision warning system, including a sensor module, a GPS positioning module, a base station, a communication module, a vehicle state estimation module, a vehicle state prediction module, a collision warning judgment module, and a collision warning device. Wait;

The sensor module includes a vehicle speed sensor, an acceleration sensor, and a yaw rate sensor;

The vehicle speed sensor is installed in the wheel, and is used to obtain the vehicle speed signal and transmit it to the self-vehicle state estimation module;

The acceleration sensor is arranged at the center of mass of the vehicle, and is used to obtain the acceleration signal of the vehicle and transmit it to the self-vehicle state estimation module;

The yaw rate sensor is arranged at the center of mass of the vehicle, and is used to acquire the yaw rate signal of the vehicle and transmit it to the vehicle state estimation module;

The GPS positioning module is used to obtain the position signal and the heading angle signal of the own vehicle, and transmit them to the own vehicle state estimation module;

The self-vehicle state estimation module is used to perform Kalman filter estimation on the obtained vehicle speed signal, acceleration signal, yaw rate signal, position signal, and vehicle heading angle signal, and transmit the estimation result to the vehicle state prediction module;

The base station is used to receive the vehicle speed signal, acceleration signal, yaw rate signal, position signal, and vehicle heading angle signal of other vehicles, and transmit it to the vehicle state prediction module through the communication module;

The vehicle state prediction module predicts the position states of the own vehicle and other vehicles according to the received state signals of the own vehicle and other vehicles, and sends the prediction result to the collision warning judgment module;

The collision warning judging module judges whether the vehicle is in danger of collision according to the received prediction result, and decides whether to activate the collision warning device for collision warning based on this.

The collision warning device may use a loudspeaker or a light alarm.

The invention also discloses a control method of the vehicle collaborative collision warning system, comprising the following steps:

Step 1.1), the self-vehicle state estimation module obtains the vehicle speed signal, the acceleration signal, and the yaw rate signal of the self-vehicle through the vehicle speed sensor, the acceleration sensor, and the yaw rate sensor respectively;

Step 1.2), the self-vehicle state estimation module obtains the position signal and the heading angle signal of the self-vehicle through the GPS positioning module;

Step 1.3), the self-vehicle state estimation module performs Kalman filter estimation on the obtained self-vehicle speed signal, acceleration signal, yaw rate signal, position signal, and vehicle heading angle signal, and transmits the estimation result to the vehicle state prediction module;

Step 1.4), the vehicle state prediction module calculates the position information of the own vehicle and other vehicles at the time t=n*T in the future according to the state information of the own vehicle and the state information of other vehicles, and sends the calculation results to the collision warning judgment. module, n is the number of steps at the current moment, T is the preset time step and T=0.05 seconds;

Step 1.5), the collision warning judgment module judges the distance S between the own vehicle and other vehicles at time t, the size of the preset distance threshold value S*, and the time t and the preset time threshold value t according to the predicted position information of the own vehicle and other vehicles. *the size of;

When S≤S*, there is a danger of collision, and the collision warning device is activated after time t;

When S>S* and t<t*, there is no danger of collision, n=n+1, continue to return to step 1.4);

When t≥t*, return to step 1.1) and re-enter the next moment.

In step 1.3), the Kalman filter is divided into two parts: the time update equation and the measurement update equation. The time update equation part mainly plays a predictive role and is responsible for estimating the estimated value of the state variable and covariance at time k to provide the state at time k. Priori estimation; the measurement update equation part mainly plays the role of correction and is responsible for feedback, combining the priori estimation with the new measurement variables to provide an improved posteriori estimation for the state at time k. The steps are:

Step 2.1), estimate the predicted value at time k from the optimal value estimate at time k-1:

x(k|k-1)=Ax(k-1|k-1)+Bu(k)

In the formula, x(k-1|k-1) is the optimal estimated value at time k-1, x(k|k-1) is the predicted value at time k obtained by using the state at time k-1, u(k) is the control quantity at time k, A and B are the system gain matrix;

Step 2.2), predict the estimated error at time k by the error covariance and process noise at time k-1:

P(k|k-1)=AP(k-1|k-1)A ^T +Q;

In the formula, P(k|k-1) is the covariance corresponding to x(k|k-1), and P(k-1|k-1) is the covariance corresponding to x(k-1|k-1) , A ^T represents the transpose matrix of A, and Q is the covariance of the system process noise;

Step 2.3), calculate the Kalman gain matrix:

K _k =P(k|k-1)H ^T /(HP(k|k-1)H ^T +R)

In the formula, K _k is the Kalman gain at time k, R is the covariance of the system measurement noise; H is the system measurement matrix;

Step 2.4), correct and update the optimal estimated value at the current moment:

x(k|k)=x(k|k-1)+K _k (Z(k)-Hx(k|k-1))

In the formula, Z(k) is the measured value at time k, and x(k|k) is the optimal estimated value at time k;

Step 2.5), update the optimal estimation error for the next sampling period:

P(k|k)=(IK _k H)P(k|k-1)

where P(k|k) is the covariance of x(k|k) at time k, and I is the identity matrix.

Step 1.4) The vehicle state prediction module adopts the yaw rate change rate model such as equal acceleration rate of change and other yaw rate change rate models respectively for the own vehicle and other vehicles at time t=n*T in the future (where n is the number of steps at the current moment, T is the step size and T = 0.05 seconds) position information to predict:

分别为t和t-1时刻车辆的航向角，a_t、a_t-1分别为t和t-1时刻车辆的加速度，ω_t、ω_t-1分别为t和t-1时刻车辆的横摆角速度，

为t-1时刻车辆的加速度变化率，

为t-1时刻车辆的横摆角速度变化率。In the formula, x _t and x _t-1 are the abscissas of the vehicle at t and t-1, respectively, y _t and y _t-1 are the ordinates of the vehicle at t and t-1, respectively, v _t , v _t-1 are the vehicle speeds at time t and t-1, respectively,

are the heading angles of the vehicle at t and t-1, respectively, a _t and a _t-1 are the accelerations of the vehicle at t and t-1, respectively, and ω _t and ω _t-1 are the lateral directions of the vehicle at t and t-1, respectively. Swing angular velocity,

is the acceleration rate of change of the vehicle at time t-1,

is the rate of change of the yaw rate of the vehicle at time t-1.

The preset distance threshold S* is preferentially 3 meters, and the preset time threshold t* is preferentially 2.5 seconds.

A control method of a vehicle collaborative collision warning system, the calculation method of the distance S between the own vehicle and other vehicles in step 1.5) is:

In the formula, x _t1 and x _t2 are the abscissas of the own vehicle and other vehicles at time t, respectively, and y _t1 and y _t2 are the ordinates of the own vehicle and other vehicles at time t, respectively.

It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It should also be understood that terms such as those defined in general dictionaries should be understood to have meanings consistent with their meanings in the context of the prior art and, unless defined as herein, are not to be taken in an idealized or overly formal sense. explain.

The specific embodiments described above further describe the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above descriptions are only specific embodiments of the present invention, and are not intended to limit the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims (7)

1. A vehicle collaborative collision early warning system is characterized in that, comprising a sensor module, a GPS positioning module, a base station, a communication module, a self-vehicle state estimation module, a vehicle state prediction module, a collision early warning judgment module and a collision early warning device; The sensor module includes a vehicle speed sensor, an acceleration sensor, and a yaw rate sensor; The vehicle speed sensor is installed in the wheel, and is used to obtain the vehicle speed signal and transmit it to the self-vehicle state estimation module; The acceleration sensor is arranged at the center of mass of the vehicle, and is used to obtain the acceleration signal of the vehicle and transmit it to the self-vehicle state estimation module; The yaw rate sensor is arranged at the center of mass of the vehicle, and is used to acquire the yaw rate signal of the vehicle and transmit it to the vehicle state estimation module; The GPS positioning module is used to obtain the position signal and the heading angle signal of the own vehicle, and transmit them to the own vehicle state estimation module; The self-vehicle state estimation module is used to perform Kalman filter estimation on the obtained vehicle speed signal, acceleration signal, yaw rate signal, position signal, and vehicle heading angle signal, and transmit the estimation result to the vehicle state prediction module; The base station is used to receive the vehicle speed signal, acceleration signal, yaw rate signal, position signal, and vehicle heading angle signal of other vehicles, and transmit it to the vehicle state prediction module through the communication module; The vehicle state prediction module calculates the position states of the own vehicle and other vehicles according to the received state signals of the own vehicle and other vehicles, and sends the calculation result to the collision warning judgment module; The collision warning judging module judges whether the vehicle is in danger of collision according to the received prediction result, and decides whether to activate the collision warning device for collision warning based on this. 2 . The vehicle cooperative collision warning system according to claim 1 , wherein the collision warning device adopts a speaker or a light alarm. 3 . 3. The control method of the vehicle collaborative collision warning system according to claim 1, characterized in that, comprising the following steps: Step 1.1), the self-vehicle state estimation module obtains the vehicle speed signal, the acceleration signal, and the yaw rate signal of the self-vehicle through the vehicle speed sensor, the acceleration sensor, and the yaw rate sensor respectively; Step 1.2), the self-vehicle state estimation module obtains the position signal and the heading angle signal of the self-vehicle through the GPS positioning module; Step 1.3), the self-vehicle state estimation module performs Kalman filter estimation on the obtained self-vehicle speed signal, acceleration signal, yaw rate signal, position signal, and vehicle heading angle signal, and transmits the estimation result to the vehicle state prediction module; Step 1.4), the vehicle state prediction module calculates the position information of the own vehicle and other vehicles at the time t=n*T in the future according to the state information of the own vehicle and the state information of other vehicles, and sends the calculation results to the collision warning judgment. module, n is the number of steps at the current moment, and T is the preset time step; Step 1.5), the collision warning judgment module judges the distance S between the own vehicle and other vehicles at time t, the size of the preset distance threshold value S*, and the time t and the preset time threshold value t according to the predicted position information of the own vehicle and other vehicles. *the size of; When S≤S*, there is a danger of collision, and the collision warning device is activated after time t; When S>S* and t<t*, there is no danger of collision, n=n+1, continue to return to step 1.4); When t≥t*, return to step 1.1) and re-enter the next moment. 4. the control method of vehicle collaborative collision warning system according to claim 3, is characterized in that, Kalman filter is divided into two parts in step 1.3): time update equation and measurement update equation, wherein time update equation is used for reckoning The estimated values of the state variables and covariance at time k provide a priori estimate for the state at time k; the measurement update equation is used for feedback, combining the prior estimate with the new measurement variable to provide an improved posterior estimate for the state at time k, Specific steps are as follows: Step 2.1), estimate the predicted value at time k from the optimal value estimate at time k-1: x(k|k-1)=Ax(k-1|k-1)+Bu(k) In the formula, x(k-1|k-1) is the optimal estimated value at time k-1, x(k|k-1) is the predicted value at time k obtained by using the state at time k-1, u(k) is the control quantity at time k, A and B are the system gain matrix; Step 2.2), predict the estimated error at time k by the error covariance and process noise at time k-1: P(k|k-1)=AP(k-1|k-1)A ^T +Q; In the formula, P(k|k-1) is the covariance corresponding to x(k|k-1), and P(k-1|k-1) is the covariance corresponding to x(k-1|k-1) , A ^T represents the transpose matrix of A, and Q is the covariance of the system process noise; Step 2.3), calculate the Kalman gain matrix: K _k =P(k|k-1)H ^T /(HP(k|k-1)H ^T +R) In the formula, K _k is the Kalman gain at time k, R is the covariance of the system measurement noise; H is the system measurement matrix; Step 2.4), correct and update the optimal estimated value at the current moment: x(k|k)=x(k|k-1)+K _k (Z(k)-Hx(k|k-1)) In the formula, Z(k) is the measured value at time k, and x(k|k) is the optimal estimated value at time k; Step 2.5), update the optimal estimation error for the next sampling period: P(k|k)=(IK _k H)P(k|k-1) where P(k|k) is the covariance of x(k|k) at time k, and I is the identity matrix. 5. the control method of the vehicle collaborative collision warning system according to claim 3, it is characterized in that, step 1.4) vehicle state prediction module adopts the yaw rate change rate model such as equal acceleration rate of change and so on respectively to own vehicle and other vehicles in the future. Predict the position information at time t=n*T:

In the formula, x _t and x _t-1 are the abscissas of the vehicle at t and t-1, respectively, y _t and y _t-1 are the ordinates of the vehicle at t and t-1, respectively, v _t , v _t-1 are the vehicle speeds at time t and t-1, respectively,

are the heading angles of the vehicle at t and t-1, respectively, a _t and a _t-1 are the accelerations of the vehicle at t and t-1, respectively, and ω _t and ω _t-1 are the lateral directions of the vehicle at t and t-1, respectively. Swing angular velocity,

is the acceleration rate of change of the vehicle at time t-1,

is the rate of change of the yaw rate of the vehicle at time t-1. 6 . The control method of the vehicle collaborative collision warning system according to claim 3 , wherein the preset distance threshold S*=3 meters in step 1.5) and the preset time threshold t*=2.5 seconds. 7 . 7. the control method of vehicle collaborative collision warning system according to claim 3, is characterized in that, in step 1.5), the distance S calculation method of self-vehicle and other vehicles is:

In the formula, x _t1 and x _t2 are the abscissas of the own vehicle and other vehicles at time t, respectively, and y _t1 and y _t2 are the ordinates of the own vehicle and other vehicles at time t, respectively.

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