CN105667509A - Curve control system and method applied to automobile adaptive cruise control (ACC) system - Google Patents

Abstract

The present invention relates to a curve control system applied to an automobile adaptive cruise control (ACC) system. The curve control system applied to the ACC system comprises a data acquisition module, used for obtaining signals of each sensor from an automobile CAN bus in real time; a front automobile running state judging module, used for judging whether a front automobile is at a state of entering a curve; a vehicle trajectory road curvature calculation module, used for calculating the road curvature of the vehicle trajectory; an effective target selection module, used for screening effective objects ahead; a vehicle highest curve-passing speed calculation module, used for calculating the highest curve-passing speed to which the vehicle can reach at the radius of turning circle of the vehicle; a speed control module, used for performing reasonable control over the vehicle according to the running working condition of the vehicle, to improve the environmental awareness of the ACC system at the curve working condition.

Description

Curve control system and method for automobile adaptive cruise control system

Technical Field

The invention relates to the technical field of automobile control, in particular to a curve control system and method for an automobile adaptive cruise control system.

Background

In recent years, with the vigorous development of the automobile industry, automobiles provide great convenience for social transportation. With the rapid increase of the automobile holding capacity, the traffic flow is gradually increased, and the phenomena of traffic accidents, road congestion and environmental pollution caused by the traffic flow are more and more serious. Therefore, it has become a social problem concerned by people to improve the safety performance of automobiles and reduce road traffic accidents, and it is also a major problem to be solved by Intelligent Transportation Systems (ITS). As an important component of the man-vehicle-road traffic system, drivers have become a weak link of the system due to the limitation of their own conditions. According to statistics of national traffic accidents in 2014, 90.93% of accidents are caused by behaviors such as overspeed or illegal overtaking due to driver's misjudgment, and 3.79% of accidents are caused by misoperation due to driver's misjudgment.

As an important component of the driving assistance system, an Adaptive Cruise Control (ACC) system has attracted much attention from various countries. To date, the development of ACC systems has gone through three stages: the first stage is in the early 90 s, and aims at an ACC system of an expressway, and mainly realizes the functions of vehicle speed tracking and vehicle distance tracking. The second stage is an ACC system aiming at urban working conditions at the end of the 90 s, namely a Stop-Stop cruise system (SG, Stop & GoCruiseControl System), and the functions of automatic starting, stopping and low-speed following are realized. The third stage is a vehicle multi-target coordinated ACC system comprehensively considering fuel economy, tracking performance and driver feeling at the beginning of the 21 st century. To date, ACC systems have well proven to have the potential to reduce driver effort, improve vehicle driving safety, and increase road traffic, and are becoming more and more widely used.

However, research on the ACC system still has some defects, and one of the important problems is that the ACC system has poor adaptability to the environment, and is often directed at several typical driving conditions such as straight driving, and under other special driving conditions, the ACC system makes an erroneous judgment on the current driving environment, so as to control the vehicle to make a reaction uncomfortable for the driver, and even make the vehicle dangerous. For example, when the target vehicle has entered a curve and the vehicle is outside the curve, the target vehicle is out of the radar detection range of the vehicle, and the vehicle accelerates into the curve according to the conventional ACC control algorithm, which not only stresses the driver, but also may cause the vehicle to run out of the lane. When the vehicle enters the curve, the target vehicle in the curve may suddenly reenter the detection range of the radar, and the vehicle may suddenly decelerate, thereby causing the driver to feel uncomfortable. Therefore, in order to improve comfort, reliability and market acceptance of the ACC system, it is necessary to enhance the environmental awareness of the system, particularly the ability of the vehicle radar system to identify and track the target vehicle in a curve, so that it can make comprehensive judgment on various special driving conditions and automatically adjust the control and alarm strategies of the system in conjunction with changes in the driving conditions.

In addition, when the target vehicle enters a curve at a higher speed, the self-vehicle ACC system will also control the self-vehicle to enter the curve at a higher speed in order to ensure tracking performance. At this time, if the ACC controls the vehicle to brake or accelerate during the curve turning process, the vehicle dynamics may enter a nonlinear region due to large longitudinal and lateral acceleration, steering angle, load transfer and other factors, and if the adhesion between the tire and the ground reaches a limit value, the vehicle may have dangerous working conditions such as front and rear axle sideslip and even shock, which may reduce the driving safety of the vehicle. During steering, the preceding vehicle decelerates into a curve or accelerates out of a curve, and even during sudden emergency deceleration in a curve, the ACC system will control the own vehicle to decelerate, accelerate or brake suddenly, at which time the above-mentioned dangerous situation may occur.

Disclosure of Invention

The technical problem to be solved by the invention is as follows: how to solve present car self-adaptation cruise control system and be used for the bend operating mode exist more than not enough to according to the vehicle operating mode, make reasonable control to the vehicle.

In order to solve the above technical problem, the present invention provides a curve control system for an adaptive cruise control system of an automobile, comprising:

the data acquisition module is used for acquiring signals of an automobile steering angle sensor, a wheel speed sensor, a yaw angle sensor and a radar sensor from an automobile CAN bus in real time;

the front vehicle driving state judging module is used for judging whether the front vehicle is in a state of entering a curve or not;

the road curvature calculation module of the vehicle running track is used for calculating the road curvature of the vehicle running track;

the effective target screening module is used for screening the front effective target according to the curvature of the road of the driving track of the vehicle to obtain the front effective target positioned in the driving track of the vehicle;

the vehicle highest over-curve speed calculation module is used for calculating the highest over-curve speed which can be reached by a vehicle passing through a curve under the turning radius of the vehicle;

the vehicle speed control module is used for controlling the vehicle speed to be the lower value of the highest over-curve vehicle speed and the vehicle speed set by a driver when the vehicle enters a curve and no vehicle exists in front of the curve; when a vehicle exists in front of the vehicle and does not enter a curve, controlling the speed of the vehicle to be consistent with that of a front vehicle; when a vehicle exists in front of the vehicle and the vehicle enters a curve, the vehicle speed of the vehicle is controlled to be the smaller value of the vehicle speed of the front vehicle and the highest curve passing speed.

The invention also provides a curve control method for the automobile adaptive cruise control system, which comprises the following steps of:

scattered data acquisition:

acquiring data of an automobile steering angle sensor, a wheel speed sensor, a yaw angle sensor and a radar sensor from an automobile CAN bus in real time;

judging whether the front vehicle is in the process of entering a bend by the aid of waste rocks:

when the front vehicle enters a curve and the vehicle is still in a straight road or the front vehicle drives out of the curve and the vehicle is still in the curve, the azimuth angle of the front vehicle relative to the vehicle and the transverse component of the relative speed of the two vehicles satisfy the following relational expression:

$\mathrm{\θ}=b_0+b_1{\mathrm{\Δv}}_{lat}+b_2{\mathrm{\Δv}}_{lat}^2$

wherein theta is the azimuth angle of the front vehicle relative to the vehicle; Δ v_latLateral relative velocity, unit: m/s;

$b_0=\frac{d}{2R_H^2(1/R_T-1/R_H)}+\frac{d}{2R_H}$

$b_1=-\frac{1}{v_x{R}_H(1/R_T-1/R_H)}$

$b_2=\frac{1}{2v_x^{2}d(1/R_T-1/R_H)}$

wherein v is_xLongitudinal vehicle speed of the vehicle, unit: m/s; d is the relative distance between the two vehicles, unit: m; r_TThe unit is the radius of the road where the front vehicle is located: m; r_HThe radius of the road where the vehicle is located is as follows: m;

suppose that a radar sensor is measured at a series of points (θ [ i ]]，Δv_lat[i]) The fitting coefficient obtained by the least square method isNamely, the regression equation expression of the in-out curve obtained by fitting is as follows:

${\mathrm{\θ}}^{*}=b_0^{*}+b_1^{*}{\mathrm{\Δv}}_{lat}+b_2^{*}{\mathrm{\Δv}}_{lat}^2$

and (3) carrying out goodness-of-fit test on the above formula by using a correlation coefficient method, wherein the corresponding full correlation coefficient expression is as follows:

$r_{\mathrm{\θ}}=\sqrt{\frac{{\Σ}_{i=1}^n{({\mathrm{\θ}}_i^{*}-\stackrel{\‾}{\mathrm{\θ}})}^2}{{\Σ}_{i=1}^n{({\mathrm{\θ}}_i-\stackrel{\‾}{\mathrm{\θ}})}^2}}$

wherein,for values calculated according to the regression equation, theta_iIn order to measure the azimuth angle,the confidence coefficient judgment condition is that n is the number of measured values used for each fitting, which is the measured value average value:

r_θ＞r_0，05，17

in summary, it can be seen that:

$r_x=\frac{b_1^{*}}{h_1^2-4b_0^{*}{b}_2^{*}}$

the additional criteria for extracting the high goodness-of-fit are:

$\left|\frac{v_x-\frac{b_1^{*}}{b_1^2-4b_0^{*}{b}_2^{*}}}{v_x}\right|<c_v$

c_vfor judging the threshold value, the smaller the value is, the stricter the additional judgment condition is;

when a series of data measured by the radar sensor simultaneously meet the confidence degree judgment condition and the additional judgment condition, the front vehicle is considered to be in the process of entering a curve;

calculating the road curvature of a vehicle running track:

after the front vehicle enters the curve, calculating the road curvature of the driving track of the vehicle by the following formula:

when the vehicle speed is lower than 1m/s,

when the vehicle speed is higher than 1.5m/s,

in the above formula, τ is the curvature, unit: 1/m; l is the vehicle wheel base, unit: m;_vis the vehicle front wheel angle, unit: rad;yaw rate, unit: rad/s; v. of_egoIs the speed of the vehicle, unit: m/s;

screening effective targets in front:

after the road curvature of the driving track of the vehicle is obtained, the front effective target is screened, and the screening process is as follows:

the method includes the following relation to the motion state of the vehicle according to the road curvature tau calculated in the step of:

x²+y²＝(1/τ)²

wherein x is an abscissa value, y is an ordinate value, and the unit is: m;

for the ith object detected by the radar, the following relation exists in the x and y coordinate systems:

x_i＝x-d*cos(θ_i),y_i＝y+d*sin(θ_i)

in the formula x_i、y_iCoordinates in x abscissa and y ordinate for the ith target, in units: m; d is the relative distance between the two vehicles, unit: m; theta_iIn azimuth, the unit: degree;

at this time, if x_iAnd y_iSatisfies the following conditions:

x_i ²+y_i ²＝(1/τ±r)²

where r is half the lane width, unit: m;

if a plurality of targets meet the condition of the formula, selecting the target with the minimum relative distance as an effective target;

calculating the highest bending passing speed of the vehicle:

calculating the highest over-bending speed which can be reached by the vehicle passing through the curve under the turning radius of the vehicle, wherein the highest over-bending speed is calculated by the following formula:

$V_{max}=\sqrt{\frac{a_{ymax}}{\mathrm{\&tau;}}}$

in the formula, a_ymaxMaximum lateral acceleration in m/s²τ is the curvature, unit: 1/m;

fourth vehicle speed control:

if the vehicle enters a curve and no vehicle exists in front of the curve, controlling the vehicle speed to be the lower value of the highest over-curve vehicle speed and the vehicle speed set by a driver; and if the vehicle exists in front of the vehicle and enters a curve, controlling the vehicle speed to be the smaller value of the vehicle speed of the front vehicle and the highest curve passing speed.

The invention relates to an automobile self-adaptive cruise control system, which comprises a CAN bus of an automobile, a radar sensor, a wheel speed sensor, a yaw angle sensor, a gearbox control unit, a steering angle sensor, an engine management system, an electronic stability program, an ACC switch, an on-board instrument and the like, wherein the radar sensor, the wheel speed sensor, the yaw angle sensor, the gearbox control unit, the steering angle sensor, the engine management system, the electronic stability program, the ACC.

When the automobile is in a straight road environment, if no effective target exists in front of the automobile, the system runs at a speed set by a driver; if an effective target exists in front of the vehicle and the speed of the target vehicle is lower than the set speed of the driver, the system adjusts the speed of the vehicle to be consistent with the speed of the vehicle ahead by controlling an engine and a braking system.

The invention has the beneficial effects that: the method includes the steps that the curve entering and exiting working conditions of the front vehicle are identified based on the relative speed and the azimuth angle relation between the self vehicle and the front vehicle, and the environment perception capability of the self-adaptive cruise control system under the curve working conditions is improved; according to vehicle dynamics and kinematics, the curvature of the vehicle running track is estimated in a segmented mode according to the motion state of the vehicle, and the control precision is improved; and thirdly, a control mode is reasonably selected according to the lateral motion state of the vehicle, so that the vehicle is always kept in a safe and controllable state.

Drawings

FIG. 1 is a block diagram of an adaptive cruise control system according to an embodiment of the present invention.

Fig. 2 is a schematic view of the recognition of the motion state of the front vehicle by the adaptive cruise control system according to the embodiment of the invention.

FIG. 3 is a flowchart of a curve control method of the adaptive cruise control system according to an embodiment of the present invention.

Detailed Description

Example 1

The present embodiment provides a curve control system for an adaptive cruise control system of a vehicle, which is connected as shown in fig. 1, and comprises a CAN bus of the vehicle, a radar sensor 11, a wheel speed sensor 13, a yaw angle sensor 12, a transmission control unit 17, a steering angle sensor 20, an engine management system 14, an electronic stability program 15, an ACC switch 18, an on-board meter 19, and a controller 16, which are electrically connected to the CAN bus; in which the radar sensor 11 is mounted on the head of the car.

The system of this implementation includes:

the data acquisition module is used for acquiring signals of an automobile steering angle sensor, a wheel speed sensor, a yaw angle sensor and a radar sensor from an automobile CAN bus in real time;

the front vehicle driving state judging module is used for judging whether the front vehicle is in a state of entering a curve or not;

the road curvature calculation module of the vehicle running track is used for calculating the road curvature of the vehicle running track;

the effective target screening module is used for screening the front effective target according to the curvature of the road of the driving track of the vehicle to obtain the front effective target positioned in the driving track of the vehicle;

the vehicle highest over-curve speed calculation module is used for calculating the highest over-curve speed which can be reached by a vehicle passing through a curve under the turning radius of the vehicle;

the vehicle speed control module is used for controlling the vehicle speed to be the lower value of the highest over-curve vehicle speed and the vehicle speed set by a driver when the vehicle enters a curve and no vehicle exists in front of the curve; when a vehicle exists in front of the vehicle and does not enter a curve, controlling the speed of the vehicle to be consistent with that of a front vehicle; when a vehicle exists in front of the vehicle and the vehicle enters a curve, the vehicle speed of the vehicle is controlled to be the smaller value of the vehicle speed of the front vehicle and the highest curve passing speed.

By applying the steering angle sensor 20, the yaw angle sensor 12, the transmission control unit 17, the engine management system 14 and the wheel speed sensor 13, the radar sensor 11, the ACC switch 18, the electronic stability program 15 and the controller 16 to cooperate together. The ACC system obtains signals of various sensors on the automobile in real time from a CAN bus by means of the CAN bus in the automobile, and after the electronic control unit 16 acquires relevant information, the data are intelligently processed and judged, and a control instruction is sent according to a judgment result to automatically control the cruising speed of the ACC system and the distance between the automobile and a target.

Specifically, aiming at the comfort and convenience of driving of a driver in the daily driving process of the automobile and the dangerous state of the automobile, the ACC system provided by the embodiment of the invention can identify the curve and adjust the speed or the distance between the automobile and the front automobile according to the environment of the curve.

The curve control method for the automobile adaptive cruise control system comprises the following steps:

scattered data acquisition:

acquiring data of an automobile steering angle sensor, a wheel speed sensor, a yaw angle sensor and a radar sensor from an automobile CAN bus in real time;

judging whether the front vehicle is in the process of entering a bend by the aid of waste rocks:

as shown in fig. 2, when the leading vehicle enters a curve and the vehicle is still in a straight road, or the leading vehicle has exited the curve and the vehicle is still in the curve, the azimuth angle of the leading vehicle relative to the vehicle and the lateral component of the relative speed of the two vehicles satisfy the following relational expression:

$\mathrm{\&theta;}=b_0+b_1{\mathrm{\&Delta;v}}_{lat}+b_2{\mathrm{\&Delta;v}}_{lat}^2$

wherein theta is the azimuth angle of the front vehicle relative to the vehicle; Δ v_latLateral relative velocity, unit: m/s;

$b_0=\frac{d}{2R_H^2(1/R_T-1/R_H-1/R_H)}+\frac{d}{2R_H}$

$b_1=-\frac{x}{v_x{R}_H(1/R_T-1/R_H)}$

$b_2=\frac{1}{2v_x^{2}d(1/R_T-1/R_H)}$

wherein v is_xLongitudinal vehicle speed of the vehicle, unit: m/s; d is the relative distance between the two vehicles, unit: m; r_TThe unit is the radius of the road where the front vehicle is located: m; r_HThe radius of the road where the vehicle is located is as follows: m;

suppose that a radar sensor is measured at a series of points (θ [ i ]]，Δv_lat[i]) Fitting by least square method to obtain simulationA resultant coefficient ofNamely, the regression equation expression of the in-out curve obtained by fitting is as follows:

${\mathrm{\&theta;}}^{*}=b_0^{*}+b_1^{*}{\mathrm{\&Delta;v}}_{lat}+b_2^{*}{\mathrm{\&Delta;v}}_{lat}^2$

and (3) carrying out goodness-of-fit test on the above formula by using a correlation coefficient method, wherein the corresponding full correlation coefficient expression is as follows:

$r_{\mathrm{\&theta;}}=\sqrt{\frac{{\&Sigma;}_{i=1}^n{({\mathrm{\&theta;}}_i^{*}-\stackrel{\&OverBar;}{\mathrm{\&theta;}})}^2}{{\&Sigma;}_{i=1}^n{({\mathrm{\&theta;}}_i-\stackrel{\&OverBar;}{\mathrm{\&theta;}})}^2}}$

wherein,for values calculated according to the regression equation, theta_iIn order to measure the azimuth angle,the confidence coefficient judgment condition is that n is the number of measured values used for each fitting, which is the measured value average value:

r_θ＞r_0，05，17

in summary, it can be seen that:

$v_x=\frac{b_1^{*}}{b_1^2-4b_0^{*}{b}_2^{*}}$

the additional criteria for extracting the high goodness-of-fit are:

$\left|\frac{v_x-\frac{b_1^{*}}{b_1^2-4b_0^{*}{b}_2^{*}}}{v_x}\right|<c_v$

c_vfor judging the threshold value, the smaller the value is, the stricter the additional judgment condition is;

when a series of data measured by the radar sensor simultaneously meet the confidence degree judgment condition and the additional judgment condition, the front vehicle is considered to be in the process of entering a curve;

calculating the road curvature of a vehicle running track:

after the front vehicle enters the curve, calculating the road curvature of the driving track of the vehicle by the following formula:

when the vehicle speed is lower than 1m/s,

when the vehicle speed is higher than 1.5m/s,

in the above formula, τ is the curvature, unit: 1/m; l is the vehicle wheel base, unit: m;_vis the front wheel corner, unit: rad;yaw rate, unit: rad/s; v. of_egoIs the speed of the vehicle, unit: m/s;

screening effective targets in front:

after the road curvature of the driving track of the vehicle is obtained, the front effective target is screened, and the screening process is as follows:

firstly, according to the road curvature tau obtained by calculation in the step three, the following relational expression exists for the motion state of the vehicle:

x²+y²＝(1/τ)²

wherein x and y coordinates are in accordance with FIG. 2 and are in the following units: m;

for the ith object detected by the radar, the following relation exists in the coordinate system in fig. 2:

x_i＝x-d*cos(θ_i),y_i＝y+d*sin(θ_i)

in the formula x_i、y_iCoordinates of the ith target in FIG. 2, in units: m; d is the relative distance between the two vehicles, unit: m; theta_iIn azimuth, the unit: degree;

at this time, if x_iAnd y_iSatisfies the following conditions:

x_i ²+y_i ²＝(1/τ±r)²

where r is half the lane width, unit: m; if a plurality of targets meet the condition of the formula, selecting the target with the minimum relative distance as an effective target;

calculating the highest bending passing speed of the vehicle:

calculating the highest over-bending speed which can be reached by the vehicle passing through the curve under the turning radius of the vehicle, wherein the highest over-bending speed is calculated by the following formula:

$V_{max}=\sqrt{\frac{a_{ymax}}{\mathrm{\&tau;}}}$

in the formula, a_ymaxMaximum lateral acceleration in m/s²τ is the curvature, unit: 1/m;

fourth vehicle speed control:

specifically, as shown in fig. 3, if the vehicle enters a curve and no vehicle exists in front of the curve, the vehicle speed is controlled to be the lower value of the highest over-curve speed and the vehicle speed set by the driver; and if the vehicle exists in front of the vehicle and enters a curve, controlling the vehicle speed to be the smaller value of the vehicle speed of the front vehicle and the highest curve passing speed. Firstly, calculating the highest bend-passing speed which can be reached by a vehicle passing through a bend under the turning radius, and if the vehicle speed is higher than the highest bend-passing speed in the current state, sending a deceleration instruction to an electronic stability program by a controller; and if the controller judges that the target vehicle exists in the expected running track of the vehicle, the controller judges the smaller value of the target vehicle and the highest turning speed, and the vehicle adjusts the vehicle speed according to the smaller value of the target vehicle and the highest turning speed.

In addition to the above embodiments, the present invention may have other embodiments. All technical solutions formed by adopting equivalent substitutions or equivalent transformations fall within the protection scope of the claims of the present invention.

Claims (4)

1. A curve control system for an automotive adaptive cruise control system, characterized by: the method comprises the following steps:

the data acquisition module is used for acquiring signals of an automobile steering angle sensor, a wheel speed sensor, a yaw angle sensor and a radar sensor from an automobile CAN bus in real time;

the front vehicle driving state judging module is used for judging whether the front vehicle is in a state of entering a curve or not;

the road curvature calculation module of the vehicle running track is used for calculating the road curvature of the vehicle running track;

the effective target screening module is used for screening the front effective target according to the curvature of the road of the driving track of the vehicle to obtain the front effective target positioned in the driving track of the vehicle;

the vehicle highest over-curve speed calculation module is used for calculating the highest over-curve speed which can be reached by a vehicle passing through a curve under the turning radius of the vehicle;

the vehicle speed control module is used for controlling the vehicle speed to be the lower value of the highest over-curve vehicle speed and the vehicle speed set by a driver when the vehicle enters a curve and no vehicle exists in front of the curve; when a vehicle exists in front of the vehicle and does not enter a curve, controlling the speed of the vehicle to be consistent with that of a front vehicle; when a vehicle exists in front of the vehicle and the vehicle enters a curve, the vehicle speed of the vehicle is controlled to be the smaller value of the vehicle speed of the front vehicle and the highest curve passing speed.

2. A curve control system for an adaptive cruise control system for an automobile according to claim 1, characterized in that: the radar sensor is mounted on the head of the automobile.

3. A curve control method for an automobile adaptive cruise control system is characterized by comprising the following steps: the method comprises the following steps:

scattered data acquisition:

acquiring data of an automobile steering angle sensor, a wheel speed sensor, a yaw angle sensor and a radar sensor from an automobile CAN bus in real time;

judging whether the front vehicle is in the process of entering a bend by the aid of waste rocks:

when the front vehicle enters a curve and the vehicle is still in a straight road or the front vehicle drives out of the curve and the vehicle is still in the curve, the azimuth angle of the front vehicle relative to the vehicle and the transverse component of the relative speed of the two vehicles satisfy the following relational expression:

$\mathrm{\&theta;}=b_0+b_1{\mathrm{\&Delta;v}}_{lat}+b_2{\mathrm{\&Delta;v}}_{lat}^2$

wherein theta is the azimuth angle of the front vehicle relative to the vehicle; Δ v_latIs the lateral relative speed in m/s;

$b_0=\frac{d}{2R_H^2(1/R_T-1/R_H)}+\frac{d}{2R_H}$

$b_1=-\frac{1}{v_x{R}_H\left(1/R_T-1/R_H\right)}$

$b_2=\frac{1}{2v_x^{2}d(1/R_T-1/R_H)}$

wherein v is_xLongitudinal vehicle speed of the vehicle, unit: m/s; d is the relative distance between the two vehicles, unit: m; r_TThe unit is the radius of the road where the front vehicle is located: m; r_HThe radius of the road where the vehicle is located is as follows: m;

suppose that a radar sensor is measured at a series of points (θ [ i ]]，Δv_lat[i]) The fitting coefficient obtained by the least square method isNamely, the regression equation expression of the in-out curve obtained by fitting is as follows:

${\mathrm{\&theta;}}^{*}=b_0^{*}+b_1^{*}{\mathrm{\&Delta;v}}_{lat}+b_2^{*}{\mathrm{\&Delta;v}}_{lat}^2$

and (3) carrying out goodness-of-fit test on the above formula by using a correlation coefficient method, wherein the corresponding full correlation coefficient expression is as follows:

$r_{\mathrm{\&theta;}}=\sqrt{\frac{{\&Sigma;}_{i=1}^n{\left({\mathrm{\&theta;}}_i^{*}-\stackrel{\&OverBar;}{\mathrm{\&theta;}}\right)}^2}{{\&Sigma;}_{i=1}^n{\left({\mathrm{\&theta;}}_i-\stackrel{\&OverBar;}{\mathrm{\&theta;}}\right)}^2}}$

wherein,for values calculated according to the regression equation, theta_iIn order to measure the azimuth angle,the confidence coefficient judgment condition is that n is the number of measured values used for each fitting, which is the measured value average value:

r_θ＞r_0.05.17

in summary, it can be seen that:

$v_x=\frac{b_1^{*}}{b_1^2-4b_0^{*}{b}_2^{*}}$

the additional criteria for extracting the high goodness-of-fit are:

$\left|\frac{v_x-\frac{b_1^{*}}{b_1^2-4b_0^{*}{b}_2^{*}}}{v_x}\right|<c_v$

c_vfor judging the threshold value, the smaller the value is, the stricter the additional judgment condition is;

when a series of data measured by the radar sensor simultaneously meet the confidence degree judgment condition and the additional judgment condition, the front vehicle is considered to be in the process of entering a curve;

calculating the road curvature of a vehicle running track:

after the front vehicle enters the curve, calculating the road curvature of the driving track of the vehicle by the following formula:

when the vehicle speed is lower than 1m/s,

when the vehicle speed is higher than 1.5m/s,

in the above formula, τ is the curvature, unit: 1/m; l is the vehicle wheel base, unit: m;_vis the vehicle front wheel angle, unit: rad;yaw rate, unit: rad/s; v. of_egoIs the speed of the vehicle, unit: m/s;

screening effective targets in front:

after the road curvature of the driving track of the vehicle is obtained, the front effective target is screened, and the screening process is as follows:

the method includes the following relation to the motion state of the vehicle according to the road curvature tau calculated in the step of:

x²+y²＝(1/τ)²

wherein x is an abscissa value, y is an ordinate value, and the unit is: m;

for the ith object detected by the radar, the following relation exists in the x and y coordinate systems:

x_i＝x-d*cos(θ_i),y_i＝y+d*sin(θ_i)

in the formula x_i、y_iCoordinates in x abscissa and y ordinate for the ith target, in units: m; d is the relative distance between the two vehicles, unit: m; theta_iIn azimuth, the unit: degree;

at this time, if x_iAnd y_iSatisfies the following conditions:

x_i ²+y_i ²＝(1/τ±r)²

where r is half the lane width, unit: m;

if a plurality of targets meet the condition of the formula, selecting the target with the minimum relative distance as an effective target;

calculating the highest bending passing speed of the vehicle:

calculating the highest over-bending speed which can be reached by the vehicle passing through the curve under the turning radius of the vehicle, wherein the highest over-bending speed is calculated by the following formula:

$V_{max}=\sqrt{\frac{a_{ymax}}{\mathrm{\&tau;}}}$

in the formula, a_ymaxMaximum lateral acceleration in m/s²(ii) a τ is the curvature, in units: 1/m;

fourth vehicle speed control:

if the vehicle enters a curve and no vehicle exists in front of the curve, controlling the vehicle speed to be the lower value of the highest over-curve vehicle speed and the vehicle speed set by a driver; and if the vehicle exists in front of the vehicle and enters a curve, controlling the vehicle speed to be the smaller value of the vehicle speed of the front vehicle and the highest curve passing speed.

4. A curve control method for an adaptive cruise control system for an automobile according to claim 3, characterized in that: and step four, if the vehicle does not enter the curve and no vehicle exists in front, controlling the vehicle to run at the speed set by the driver, and if a front vehicle exists in front and has the speed lower than the speed set by the driver, controlling the vehicle to run with the front vehicle at the front vehicle speed.