CN111845737A - Curve target identification method of intelligent vehicle and danger level judgment mechanism thereof - Google Patents

Abstract

The invention discloses a curve target identification method of an intelligent vehicle, which comprises a starting vehicle, a key target identification unit M2, a curve key target identification unit M3; the invention also provides a system for judging the danger level of the curve target of the intelligent vehicle, which comprises data acquisition, data processing and danger marking; the invention overcomes the problem of transient disorder of the system caused by the phenomenon that the front target is disordered or lost when the vehicle enters a curve; meanwhile, a judgment mechanism and a display scheme of the front target danger level are provided, so that the surrounding traffic condition of the driver can be effectively reminded, and sufficient reaction time is provided for the driver.

Description

Curve target identification method of intelligent vehicle and danger level judgment mechanism thereof

Technical Field

The invention relates to the technical field of intelligent automobiles, in particular to a method for identifying a curve target of an intelligent vehicle, and also relates to a danger level judgment mechanism of the method for identifying the curve target of the intelligent vehicle.

Background

An intelligent automobile is a cross frontier science technology which integrates environmental perception, decision planning and control execution, and is also an important component of an Intelligent Transportation System (ITS). Aiming at the existing intelligent vehicle, the intelligent vehicle is generally formed by combining advanced sensor radar, a camera, a controller and other equipment, and is integrated with advanced network technology to realize the interaction of people, vehicles, roads and traffic environment information. The accurate recognition of the front key target vehicle is one of the most basic driving behaviors, and has important influence on road traffic efficiency, driving safety and the like.

In a stable lane, a Kalman filter of a yaw angular velocity is established based on a vehicle dynamics linear model and an online estimation of a road curvature is realized according to a document 1 (TaoXinlong, Mixueyu, Wang Chang. On the basis, whether the rear center of the front target vehicle is in the lane of the vehicle is taken as a judgment basis, and an identification model of the system target is established. However, since the vehicle is a complex nonlinear system, certain errors necessarily exist in the estimation of the target realized by using the linear kalman filter, and a specific method for judging the danger level of the front key target vehicle and a realization mechanism thereof are not provided.

For the problem of identifying the key target in front of the road, usually on the road section of a curve, the phenomenon that the target vehicle is disordered or lost is easy to happen, and the vehicle is also accelerated or decelerated suddenly in order to meet the tracking performance, so that the lateral stability of the vehicle is reduced suddenly, and the driving safety of the vehicle is seriously threatened.

Disclosure of Invention

The invention aims to provide a curve target identification method of an intelligent vehicle and a danger level judgment mechanism thereof, and the invention applies an extended Kalman filtering theory to carry out the identification research of key targets of a front curve. The method comprises the steps of establishing a transverse, longitudinal and transverse non-linear three-degree-of-freedom vehicle model, designing an extended Kalman filter of the transverse and longitudinal transverse angular velocity, and realizing the on-line real-time estimation of the road curvature. Meanwhile, a curve key target recognition model is established and a key target judgment basis is given on the basis of the estimation value. Finally, a danger level judgment mechanism of a key target vehicle is designed, and dangerous vehicles are judged in advance to deal with complex road conditions so as to solve the problems in the background technology.

In order to achieve the purpose, the invention provides the following technical scheme: a method for identifying a curve target of an intelligent vehicle specifically comprises the following steps:

s1: starting the vehicle, starting the vehicle-mounted sensor M1 to work, detecting the information of the area in front of the vehicle, and transmitting the information to the key target identification unit through the vehicle bus;

s2: the key target identification unit M2 receives the M1 transmitted multiple target vehicles i (i ═ 1, 2...... n) existing in the area ahead of the vehicle, and the transverse distance d between each target and the vehicle_xi(i 1, 2.... n), the longitudinal distance d of each target from the vehicle_yi(i 1, 2.... cndot., n), a direction angle θ of each target to the own vehicle_i(i 1, 2.... n), the relative speed v of each target to the own vehicle_rel,i(i ═ 1,2,..., n) and vehicle body state information;

2.1: based on a nonlinear three-degree-of-freedom vehicle model, a yaw angular velocity extended Kalman filter is established, and the process is as follows:

on the basis of a traditional linear two-degree-of-freedom vehicle model, the longitudinal vehicle speed of the vehicle is introduced, and a nonlinear three-degree-of-freedom model is established by combining the yaw angular speed and the centroid slip angle, so that a state space equation and a measurement equation of the model are as follows:

wherein, w_rThe yaw angular velocity; beta is the centroid slip angle; is a front wheel corner; v. of _xIs the longitudinal vehicle speed; a is the distance of the centroid from the front axis; b is the distance of the centroid from the rear axis; k1 is front axle equivalent yaw stiffness; k2 is the rear axle equivalent cornering stiffness; i is_zIs the moment of inertia about the z-axis; m is the vehicle mass; a is_xIs the longitudinal acceleration; a is_yIs the lateral acceleration;

2.2: therefore, the yaw angular velocity, the longitudinal vehicle speed and the mass center slip angle of the automobile are estimated by a Kalman filtering (EKF) algorithm based on a three-degree-of-freedom vehicle model:

establishing a state equation and a measurement equation of the system:

in the formula f (x)_t,u_t,w_t)、h(x_t,v_t) Respectively, a state space equation and a measurement equation, and x (t) is a state variable of the system; y (t) is the measurement output; u (t) is a control variable; w (t) is system excitation noise; v (t) is measurement noise;

2.3: model linearization:

Φ(t)＝e^F(t)Δt≈I+F(t)·Δt

wherein F (t), H (t) are nonlinear functions f (x) respectively_t,u_t,w_t) And h (x)_t,v_t) Solving a Jacobian matrix of the partial derivatives of the state variables; phi (t) is a state transition matrix; i is an identity matrix; Δ t is the sampling time;

2.4: the prediction and update process of the extended kalman filter is as follows:

the state prediction equation:

error covariance prediction equation: p' (t +1) ═ Φ (t) p (t) Φ (t)^T+Q

Calculating a Kalman gain: k (t) ═ P' (t) H^T(t)(H(t)P'(t)H^T(t)+R)^-1

In the formula:

is a predictor variable at time t + 1; A. b is a state variable coefficient matrix of discretization of the prediction equation; k (t) is a transition variable at time t; h (t) is a Jacobian matrix for partial derivatives of the state variables h (x (t), v (t)); r is a covariance matrix of observation noise;

Updating the estimate by measurement:

error covariance update procedure: p (t) ([ I-k (t) h (t)) ] P' (t)

Above, the state variable x ═ w_r,β,]^T(ii) a The output variable is y ═ a_y]The controlled variable is u [, a [ ]_x]^TR, P, Q, I assigning initial values according to the original definition, respectively;

s3: identification of curve key targets:

establishing a filtering model of the yaw angular velocity by using an extended Kalman filter to realize real-time online estimation of the curvature radius of the vehicle running track;

when driving on a curve, the radius of curvature may be via w of the filter_rTo obtain:

taking the target i in front of the lane as an example, knowing that the vehicle is running on a curve, the target i is laterally offset from the center line of the lane by a distance d_off,i：

In the formula d_xi、d_yi(i 1, 2......, n) as above, obtained by M1;

the inverse of the curvature of the road at a target vehicle i (i ═ 1, 2.... n.), which is obtained by real-time online estimation of a yaw rate filtering model established by an extended Kalman filter;

according to the lateral distance d of the target i relative to the central line of the lane_off,iAnd determining whether the target i is located in the lane, wherein the judgment is as follows:

if it is

And d is_off,i> 0 and | d_off,i|＞d_threshold: the target vehicle is positioned on the right side of the lane;

if it is

And d is_off,i< 0 and | d_off,i|＞d_threshold: the target vehicle is positioned on the left side of the lane;

if it is

And d is_off,i> 0 and | d_off,i|＞d_threshold: the target vehicle is positioned on the left side of the lane;

If it is

And d is_off,i< 0 and | d_off,i|＞d_threshold: the target vehicle is positioned on the right side of the lane;

if | d_off,i|≤d_thresholdAnd then: the target vehicle is positioned in the lane;

in the above judgment bases, d_threshold(m) is a set threshold value, usually takes a half lane width or less, and can be set according to actual conditions;

s4: a display unit M3 for receiving the target vehicle i (i ═ 1,2,...... multidot.n) in the three directions of the front, the front left and the front right, which are transmitted by the M2, through the automobile bus; then, each target vehicle is associated with itself according to the target vehicleTransverse distance d of vehicle_xi(i 1, 2.... n), the longitudinal distance d of each target from the vehicle_yi(i 1, 2.... cndot., n), a direction angle θ of each target to the own vehicle_i(i 1,2, a.. n, n), and vehicle body state information, and deciding one target of each lane (left lane, self lane, right lane) which is closest to the self vehicle, wherein the target is respectively arranged in the left front area, the right front area and the right front area of the display;

4.1: a display area division for displaying the left front on the vehicle-mounted display when the target is divided into the left lane of the M2 range, and displaying the right front on the vehicle-mounted display when the target is divided into the right lane of the M2 range, otherwise, displaying the right front area;

4.2: judging the danger level by calculating the longitudinal distance of the target and the time distance tau between longitudinal vehicles _ttc：

Based on the above, dividing the danger level; the smaller the relative distance and the time interval of the longitudinal workshop, the higher the danger level;

area directly in front:

d_xie (180, + ∞) m, class 0; d_xi∈(150,180]m, grade 1; d_xi∈(90,150]m, 2 grade; v. of_rel,i< -30(m/s) or τ_ttc2.5s, grade 3; tau is_ttc< 1.5s, level 4;

left and right front regions:

d_xie (100, + ∞) m, class 0; d_xiE (90, + ∞) m, level 1; v. of_rel,i< -30(m/s) or τ_ttc2 < 2.5s, grade 2; and τ_ttc< 1.5s, grade 3;

4.3: the vehicle-mounted display receives the target danger level information, and the target vehicle danger level information is displayed in a layered mode or different colors or sounds and the like.

The invention also provides a danger level judgment mechanism of the curve target of the intelligent vehicle, which comprises the following steps:

A. data acquisition: acquiring related information in front of a vehicle body through a vehicle-mounted sensor radar;

B. data processing: receiving all target data output by a radar from an automobile bus, and finally obtaining a target closest to the automobile in the same lane and targets closest to the automobile in adjacent lanes (namely left and right lanes of the automobile lane) through a series of filtering, verifying and combing;

C. and (4) danger marking: after the data processing of step B, the vehicle-mounted display identifies the target information obtained from the target processing system of S2 by color level or sound level.

Compared with the prior art, the invention has the beneficial effects that:

1. the invention provides a method for improving the stability of an intelligent vehicle, which solves the problem of transient disorder of a system caused by the fact that a front target is disordered or lost when a vehicle enters a curve; meanwhile, a judgment mechanism and a display scheme of the front target danger level are provided, so that the surrounding traffic condition of the driver can be effectively reminded, and sufficient reaction time is provided for the driver.

Drawings

FIG. 1 is a block diagram of a risk level determination mechanism of the present invention;

FIG. 2 is a flow chart of an identification method of the present invention;

FIG. 3 is a schematic diagram of a method for displaying a risk level according to the present invention.

Detailed Description

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to fig. 1 to 3 in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

Referring to fig. 2, the present invention provides a technical solution: a method for identifying a curve target of an intelligent vehicle specifically comprises the following steps:

S1: starting the vehicle, starting the vehicle-mounted sensor M1 to work, detecting the information of the area in front of the vehicle, and transmitting the information to the key target identification unit through the vehicle bus;

s2: the key target identification unit M2 receives the M1 transmitted multiple target vehicles i (i ═ 1, 2...... n) existing in the area ahead of the vehicle, and the transverse distance d between each target and the vehicle_xi(i 1, 2.... n), the longitudinal distance d of each target from the vehicle_yi(i 1, 2.... cndot., n), a direction angle θ of each target to the own vehicle_i(i 1, 2.... n), the relative speed v of each target to the own vehicle_rel,i(i ═ 1,2,..., n) and vehicle body state information;

2.1: based on a nonlinear three-degree-of-freedom vehicle model, a yaw angular velocity extended Kalman filter is established, and the process is as follows:

on the basis of a traditional linear two-degree-of-freedom vehicle model, the longitudinal vehicle speed of the vehicle is introduced, and a nonlinear three-degree-of-freedom model is established by combining the yaw angular speed and the centroid slip angle, so that a state space equation and a measurement equation of the model are as follows:

wherein, w_rThe yaw angular velocity; beta is the centroid slip angle; is a front wheel corner; v. of_xIs the longitudinal vehicle speed; a is the distance of the centroid from the front axis; b is the distance of the centroid from the rear axis; k1 is front axle equivalent yaw stiffness; k2 is the rear axle equivalent cornering stiffness; i is _zIs the moment of inertia about the z-axis; m is the vehicle mass; a is_xIs the longitudinal acceleration; a is_yIs the lateral acceleration;

2.2: therefore, the yaw angular velocity, the longitudinal vehicle speed and the mass center slip angle of the automobile are estimated by a Kalman filtering (EKF) algorithm based on a three-degree-of-freedom vehicle model:

establishing a state equation and a measurement equation of the system:

in the formula f (x)_t,u_t,w_t)、h(x_t,v_t) Respectively, a state space equation and a measurement equation, and x (t) is a state variable of the system; y (t) is the measurement output; u (t) is a control variable; w (t) is system excitation noise; v (t) is measurement noise;

2.3: model linearization:

Φ(t)＝e^F(t)Δt≈I+F(t)·Δt

wherein F (t), H (t) are nonlinear functions f (x) respectively_t,u_t,w_t) And h (x)_t,v_t) Solving a Jacobian matrix of the partial derivatives of the state variables; phi (t) is a state transition matrix; i is an identity matrix; Δ t is the sampling time;

2.4: the prediction and update process of the extended kalman filter is as follows:

the state prediction equation:

error covariance prediction equation: p' (t +1) ═ Φ (t) p (t) Φ (t)^T+Q

Calculating a Kalman gain: k (t) ═ P' (t) H^T(t)(H(t)P'(t)H^T(t)+R)^-1

In the formula:

is a predictor variable at time t + 1; A. b is a state variable coefficient matrix of discretization of the prediction equation; k (t) is a transition variable at time t; h (t) is a Jacobian matrix for partial derivatives of the state variables h (x (t), v (t)); r is a covariance matrix of observation noise;

Updating the estimate by measurement:

error covariance update procedure: p (t) ([ I-k (t) h (t)) ] P' (t)

Above, the state variable x ═ w_r,β,]^T(ii) a The output variable is y ═ a_y]The controlled variable is u [, a [ ]_x]^TR, P, Q, I assigning initial values according to the original definition, respectively;

s3: identification of curve key targets:

establishing a filtering model of the yaw angular velocity by using an extended Kalman filter to realize real-time online estimation of the curvature radius of the vehicle running track;

when driving on a curve, the radius of curvature may be via w of the filter_rTo obtain:

taking the target i in front of the lane as an example, knowing that the vehicle is running on a curve, the target i is laterally offset from the center line of the lane by a distance d_off,i：

In the formula d_xi、d_yi(i 1, 2......, n) as above, obtained by M1;

the inverse of the curvature of the road at a target vehicle i (i ═ 1, 2.... n.), which is obtained by real-time online estimation of a yaw rate filtering model established by an extended Kalman filter;

according to the lateral distance d of the target i relative to the central line of the lane_off,iAnd determining whether the target i is located in the lane, wherein the judgment is as follows:

if it is

And d is_off,i> 0 and | d_off,i|＞d_threshold: the target vehicle is positioned on the right side of the lane;

if it is

And d is_off,i< 0 and | d_off,i|＞d_threshold: the target vehicle is positioned on the left side of the lane;

if it is

And d is_off,i> 0 and | d_off,i|＞d_threshold: the target vehicle is positioned on the left side of the lane;

If it is

And d is_off,i< 0 and | d_off,i|＞d_threshold: the target vehicle is positioned on the right side of the lane;

if | d_off,i|≤d_thresholdAnd then: the target vehicle is positioned in the lane;

in the above judgment bases, d_threshold(m) is a set threshold value, usually takes a half lane width or less, and can be set according to actual conditions;

s4: a display unit M3 for receiving the target vehicle i (i ═ 1,2,...... multidot.n) in the three directions of the front, the front left and the front right, which are transmitted by the M2, through the automobile bus; then, according to the lateral distance d between each target and the self vehicle of the target vehicle_xi(i 1, 2.... n), the longitudinal distance d of each target from the vehicle_yi(i 1, 2.... cndot., n), a direction angle θ of each target to the own vehicle_i(i 1, 2.... cndot., n), and vehicle body state information, deciding one target of each lane (left lane, own lane, right lane) closest to the own vehicle, and respectively placing the targets in the left front area, the right front area and the right front area of the display, as shown in fig. 3;

4.1: a display area division for displaying the left front on the vehicle-mounted display when the target is divided into the left lane of the M2 range, and displaying the right front on the vehicle-mounted display when the target is divided into the right lane of the M2 range, otherwise, displaying the right front area;

4.2: judging the danger level by calculating the longitudinal distance of the target and the time distance tau between longitudinal vehicles _ttc：

Based on which the danger level is divided(ii) a The smaller the relative distance and the time interval of the longitudinal workshop, the higher the danger level;

area directly in front:

d_xie (180, + ∞) m, class 0; d_xi∈(150,180]m, grade 1; d_xi∈(90,150]m, 2 grade; v. of_rel,i< -30(m/s) or τ_ttc2.5s, grade 3; tau is_ttc< 1.5s, level 4;

left and right front regions:

d_xie (100, + ∞) m, class 0; d_xiE (90, + ∞) m, level 1; v. of_rel,i< -30(m/s) or τ_ttc2 < 2.5s, grade 2; and τ_ttc< 1.5s, grade 3;

4.3: the vehicle-mounted display receives the target danger level information, and the target vehicle danger level information is displayed in a layered mode or different colors or sounds and the like.

As shown in fig. 1, the present invention further provides a danger level determination mechanism for a curve target of an intelligent vehicle, comprising the following steps:

A. data acquisition: acquiring related information in front of a vehicle body through a vehicle-mounted sensor radar;

B. data processing: receiving all target data output by a radar from an automobile bus, and finally obtaining a target closest to the automobile in the same lane and targets closest to the automobile in adjacent lanes (namely left and right lanes of the automobile lane) through a series of filtering, verifying and combing;

C. and (4) danger marking: after the data processing of step B, the vehicle-mounted display identifies the target information obtained from the target processing system of S2 by color level or sound level.

In summary, the following steps: according to the scheme, the vehicle-mounted sensor is used for acquiring the workshop state information in real time; judging the target of the lane and the adjacent lane according to the target information and the state of the vehicle; and making a relevant prompt on a corresponding vehicle-mounted display according to the position of the target relative to the vehicle.

By acquiring workshop information, a yaw angular velocity extended Kalman filter is established, and the curvature radius of the curve is estimated on line, so that the effect of stably tracking a front target is achieved.

The vehicle-mounted display makes related prompts, and the display can remind the danger degree of the target through the same color or multiple colors or the buzzer through sound according to the processed information.

And according to the tracking effect, providing a judgment basis for the position of the target, namely the position of the lane where the target is located.

And obtaining information according to the display, and giving a method for judging the danger level of the target vehicle according to the time distance and the relative distance between the vehicles.

The invention provides a method for improving the stability of an intelligent vehicle, which solves the problem of transient disorder of a system caused by the fact that a front target is disordered or lost when a vehicle enters a curve; meanwhile, a judgment mechanism and a display scheme of the front target danger level are provided, so that the surrounding traffic condition of the driver can be effectively reminded, and sufficient reaction time is provided for the driver.

Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.

Claims (2)

1. A method for identifying a curve target of an intelligent vehicle is characterized by comprising the following steps:

s1: starting the vehicle, starting the vehicle-mounted sensor M1 to work, detecting the information of the area in front of the vehicle, and transmitting the information to the key target identification unit through the vehicle bus;

s2: the key target identification unit M2 receives the M1 transmitted multiple target vehicles i (i ═ 1, 2...... n) existing in the area ahead of the vehicle, and the transverse distance d between each target and the vehicle_xi(i 1, 2.... n), the longitudinal distance d of each target from the vehicle_yi(i 1, 2.... cndot., n), a direction angle θ of each target to the own vehicle_i(i 1, 2.... n), the relative speed v of each target to the own vehicle_rel,i(i ═ 1,2,..., n) and vehicle body state information;

2.1: based on a nonlinear three-degree-of-freedom vehicle model, a yaw angular velocity extended Kalman filter is established, and the process is as follows:

On the basis of a traditional linear two-degree-of-freedom vehicle model, the longitudinal vehicle speed of the vehicle is introduced, and a nonlinear three-degree-of-freedom model is established by combining the yaw angular speed and the centroid slip angle, so that a state space equation and a measurement equation of the model are as follows:

wherein, w_rThe yaw angular velocity; beta is the centroid slip angle; is a front wheel corner; v. of_xIs the longitudinal vehicle speed; a is the distance of the centroid from the front axis; b is the distance of the centroid from the rear axis; k1 is front axle equivalent yaw stiffness; k2 is the rear axle equivalent cornering stiffness; i is_zIs the moment of inertia about the z-axis; m is the vehicle mass; a is_xIs the longitudinal acceleration; a is_yIs the lateral acceleration;

2.2: therefore, the yaw angular velocity, the longitudinal vehicle speed and the mass center slip angle of the automobile are estimated by a Kalman filtering (EKF) algorithm based on a three-degree-of-freedom vehicle model:

establishing a state equation and a measurement equation of the system:

in the formula f (x)_t,u_t,w_t)、h(x_t,v_t) Respectively, a state space equation and a measurement equation, and x (t) is a state variable of the system; y (t) is the measurement output; u (t) is a control variable; w (t) is system excitation noise; v (t) is measurement noise;

2.3: model linearization:

Φ(t)＝e^F(t)Δt≈I+F(t)·Δt

wherein F (t), H (t) are nonlinear functions f (x) respectively_t,u_t,w_t) And h (x)_t,v_t) Solving a Jacobian matrix of the partial derivatives of the state variables; phi (t) is a state transition matrix; i is an identity matrix; Δ t is the sampling time;

2.4: the prediction and update process of the extended kalman filter is as follows:

the state prediction equation:

error covariance prediction equation: p' (t +1) ═ Φ (t) p (t) Φ (t)^T+Q

Calculating a Kalman gain: k (t) ═ P' (t) H^T(t)(H(t)P'(t)H^T(t)+R)^-1

In the formula:

is a predictor variable at time t + 1; A. b is a state variable coefficient matrix of discretization of the prediction equation; k (t) is a transition variable at time t; h (t) is a Jacobian matrix for partial derivatives of the state variables h (x (t), v (t)); r is a covariance matrix of observation noise;

updating the estimate by measurement:

error covariance update procedure: p (t) ([ I-k (t) h (t)) ] P' (t)

Above, the state variable x ═ w_r,β,]^T(ii) a The output variable is y ═ a_y]The controlled variable is u [, a [ ]_x]^TR, P, Q, I assigning initial values according to the original definition, respectively;

s3: identification of curve key targets:

establishing a filtering model of the yaw angular velocity by using an extended Kalman filter to realize real-time online estimation of the curvature radius of the vehicle running track;

when driving on a curve, the radius of curvature may be via w of the filter_rTo obtain:

taking the target i in front of the lane as an example, knowing that the vehicle is running on a curve, the target i is laterally offset from the center line of the lane by a distance d_off,i：

In the formula d_xi、d_yi(i 1, 2......, n) as above, obtained by M1;

the inverse of the curvature of the road at a target vehicle i (i ═ 1, 2.... n.), which is obtained by real-time online estimation of a yaw rate filtering model established by an extended Kalman filter;

According to the lateral distance d of the target i relative to the central line of the lane_off,iAnd determining whether the target i is located in the lane, wherein the judgment is as follows:

if it is

And d is_off,i> 0 and | d_off,i|＞d_threshold: the target vehicle is positioned on the right side of the lane;

if it is

And d is_off,i< 0 and | d_off,i|＞d_threshold: the target vehicle is positioned on the left side of the lane;

if it is

And d is_off,i> 0 and | d_off,i|＞d_threshold: the target vehicle is positioned on the left side of the lane;

if it is

And d is_off,i< 0 and | d_off,i|＞d_threshold: the target vehicle is positioned on the right side of the lane;

if | d_off,i|≤d_thresholdAnd then: the target vehicle is positioned in the lane;

in the above judgment bases, d_threshold(m) is a set threshold value, usually takes a half lane width or less, and can be set according to actual conditions;

s4: a display unit M3 for receiving the target vehicle i (i ═ 1,2,...... multidot.n) in the three directions of the front, the front left and the front right, which are transmitted by the M2, through the automobile bus; then, according to the lateral distance d between each target and the self vehicle of the target vehicle_xi(i 1, 2.... n), the longitudinal distance d of each target from the vehicle_yi(i 1, 2.... cndot., n), a direction angle θ of each target to the own vehicle_i(i 1,2, a.. n, n), and vehicle body state information, and deciding one target of each lane (left lane, self lane, right lane) which is closest to the self vehicle, wherein the target is respectively arranged in the left front area, the right front area and the right front area of the display;

4.1: a display area division for displaying the left front on the vehicle-mounted display when the target is divided into the left lane of the M2 range, and displaying the right front on the vehicle-mounted display when the target is divided into the right lane of the M2 range, otherwise, displaying the right front area;

4.2: judging the danger level by calculating the longitudinal distance of the target and the time distance tau between longitudinal vehicles_ttc：

Based on the above, dividing the danger level; the smaller the relative distance and the time interval of the longitudinal workshop, the higher the danger level;

area directly in front:

d_xie (180, + ∞) m, class 0; d_xi∈(150,180]m, grade 1; d_xi∈(90,150]m, 2 grade; v. of_rel,i< -30(m/s) or τ_ttc2.5s, grade 3; tau is_ttc< 1.5s, level 4;

left and right front regions:

d_xie (100, + ∞) m, class 0; d_xiE (90, + ∞) m, level 1; v. of_rel,i< -30(m/s) or τ_ttc2 < 2.5s, grade 2; and τ_ttc< 1.5s, grade 3;

4.3: the vehicle-mounted display receives the target danger level information, and the target vehicle danger level information is displayed in a layered mode or different colors or sounds and the like.

2. A danger level judging mechanism of a curve target of a smart vehicle according to claim 1, characterized in that: the method specifically comprises the following steps:

A. data acquisition: acquiring related information in front of a vehicle body through a vehicle-mounted sensor radar;

B. data processing: receiving all target data output by a radar from an automobile bus, and finally obtaining a target closest to the automobile in the same lane and targets closest to the automobile in adjacent lanes (namely left and right lanes of the automobile lane) through a series of filtering, verifying and combing;

C. And (4) danger marking: after the data processing of step B, the vehicle-mounted display identifies the target information obtained from the target processing system of S2 by color level or sound level.

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