CN111717192B

CN111717192B - Control method and system for automatically driving vehicle

Info

Publication number: CN111717192B
Application number: CN201910205314.8A
Authority: CN
Inventors: 张凯; 和林; 甄龙豹; 葛建勇; 王天培; 刘洪亮; 鲁宁; 常仕伟; 魏松波
Original assignee: Haomo Zhixing Technology Co Ltd
Current assignee: Haomo Zhixing Technology Co Ltd
Priority date: 2019-03-18
Filing date: 2019-03-18
Publication date: 2022-01-28
Anticipated expiration: 2039-03-18
Also published as: CN111717192A

Abstract

The invention relates to the technical field of automatic driving, and provides a control method and a control system for an automatic driving vehicle. The control method comprises the following steps: receiving decision information about an expected lateral behavior and a longitudinal mode of the vehicle output by a decision system of the autonomous vehicle; in response to the expected lateral behavior, correspondingly performing one of lane keeping control, vehicle normal lane change control, and vehicle abnormal lane change control; in response to the longitudinal mode, correspondingly executing one of vehicle following control, vehicle cruise control, and AEB control; and respectively carrying out transverse control safety monitoring and longitudinal control safety monitoring when transverse control and longitudinal control are executed. The invention realizes the transverse control suitable for various complex working conditions, realizes the following control matched with various working conditions and more optimized cruise control, designs the safety monitoring strategy applied to the transverse control and the longitudinal control, and is more favorable for ensuring the stability and the safety of driving.

Description

Control method and system for automatically driving vehicle

Technical Field

The invention relates to the technical field of automatic driving, in particular to a control method and a control system for an automatic driving vehicle.

Background

The automatic Driving vehicle is an intelligent vehicle which senses road environment through a vehicle-mounted sensing System, automatically plans a Driving route and controls the vehicle to reach a preset destination, and the automatic Driving vehicle realizes the functions of the automatic Driving vehicle by means of an automatic Driving System (ADS for short). According to the development and design process of the ADS, the ADS can be divided into: the method comprises five parts of environment perception, data fusion, decision, control and execution.

Specifically, the environment sensing part is used for extracting current running environment information of vehicles such as vehicles, pedestrians, roads, traffic signs and the like through the vehicle-mounted sensing part; the data fusion part is used for screening, correlating, tracking, filtering and the like the data information of different sensors so as to obtain more accurate information such as a road, an environmental object target and the like; the decision part is used for logically judging and outputting the vehicle behavior of the unmanned vehicle according to the driving states, roads, environment information and the like of the vehicles in different environments output by the data fusion part; the control part is used for calculating and outputting the transverse and longitudinal control variable quantity of the current vehicle in real time according to the information output by the data fusion part and the decision part; the execution part is used for replacing the operation processes of a steering wheel, an acceleration pedal and a deceleration pedal of the vehicle by a driver according to the control quantity of steering, acceleration and the like output by the control part.

More specifically, the control part can be divided into transverse control and longitudinal control, the transverse control is to realize real-time steering control of the automatic driving vehicle through a series of control algorithms, the real-time steering control comprises the steps of enabling the vehicle to carry out abnormal lane changing, automatic lane changing, dynamic obstacle avoidance, turning around, turning and the like according to a known planned driving route, and the longitudinal control is mainly to control the acceleration and the deceleration of the vehicle, so that the automatic driving vehicle can longitudinally drive at a certain safe driving speed, and the automatic starting, stopping, following, cruising and the like are realized. Through the coupling of the transverse and longitudinal control, the steering and the speed of the vehicle can be automatically controlled at the same time.

However, in the existing transverse and longitudinal control of the automatic driving vehicle, there are problems that the vehicle positioning depends on high precision and a complex model, the complex working condition (such as abnormal lane change) cannot be handled, and the like, and further the stability and the safety of the vehicle are affected, so that a scheme of coupling capable of realizing correct transverse and longitudinal control needs to be comprehensively considered.

Disclosure of Invention

In view of the above, the present invention is directed to a method for controlling an autonomous vehicle, so as to solve the problem of implementing a correct lateral and longitudinal control for the autonomous vehicle.

In order to achieve the purpose, the technical scheme of the invention is realized as follows:

a control method of an autonomous vehicle is applied to an autonomous driving system of the autonomous vehicle, and includes:

receiving decision information about an expected lateral behavior and a longitudinal mode of the vehicle output by a decision system of the autonomous vehicle;

in response to the expected lateral behavior, correspondingly performing one of the following lateral controls: lane keeping control, vehicle normal lane changing control and vehicle abnormal lane changing control;

in response to the longitudinal mode, correspondingly performing one of the following longitudinal controls: vehicle following control, vehicle cruise control and automatic emergency braking AEB control of the vehicle; and

and respectively carrying out transverse control safety monitoring and longitudinal control safety monitoring when the transverse control and the longitudinal control are executed.

Compared with the prior art, the control method of the automatic driving vehicle has the following advantages: the scheme of the invention can carry out lane keeping control, vehicle normal lane changing control or vehicle abnormal lane changing control aiming at the expected transverse behavior of the vehicle in real time, is suitable for various complex working conditions, realizes following control matched with various working conditions and more optimized cruise control, and designs a safety monitoring strategy applied to transverse control and longitudinal control, thereby not only enabling the design of an automatic driving system to be more complete, but also being beneficial to ensuring the stability and the safety of driving.

Another object of the present invention is to provide a control system for an autonomous vehicle to solve the problem of achieving proper lateral and longitudinal control of the autonomous vehicle.

a control system of an autonomous vehicle, applied to an autonomous driving system of the autonomous vehicle, comprising:

the receiving unit is used for receiving decision information which is output by a decision system of the automatic driving vehicle and relates to the expected transverse behavior and the longitudinal mode of the vehicle;

a lateral control unit for, in response to the expected lateral behavior, correspondingly performing one of the following lateral controls: lane keeping control, vehicle normal lane changing control and vehicle abnormal lane changing control;

a longitudinal control unit for, in response to the longitudinal mode, correspondingly performing one of the following longitudinal controls: vehicle following control, vehicle cruise control and automatic emergency braking AEB control of the vehicle; and

and the safety monitoring unit is used for respectively carrying out transverse control safety monitoring and longitudinal control safety monitoring when the transverse control and the longitudinal control are executed.

Compared with the prior art, the control system of the automatic driving vehicle and the control method of the automatic driving vehicle have the same advantages, and are not described again.

It is another object of the present invention to provide a machine readable storage medium to address the problem of achieving proper lateral and longitudinal control of an autonomous vehicle.

a machine-readable storage medium having instructions stored thereon for causing a machine to perform the above-described method of controlling an autonomous vehicle.

The machine-readable storage medium has the same advantages as the above-described control method for an autonomous vehicle over the prior art, and is not described herein again.

Additional features and advantages of the invention will be set forth in the detailed description which follows.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate an embodiment of the invention and, together with the description, serve to explain the invention and not to limit the invention.

In the drawings:

FIG. 1 is a schematic flow chart diagram of a control method for an autonomous vehicle in accordance with an embodiment of the invention;

FIG. 2 is a schematic diagram of a course angle deviation and a lateral position deviation defined in an embodiment of the present invention;

FIG. 3 is a schematic flow chart of the method for obtaining the curvature of the pre-aimed road according to the embodiment of the invention;

FIG. 4 is a schematic diagram of the Ackerman steering principle;

FIG. 5 is a schematic diagram of a fuzzy logic control flow;

FIG. 6 is a schematic illustration of an abnormal lane change condition of the vehicle;

FIG. 7 is a schematic view of the vehicle following an arc of curvature R along a desired trajectory;

FIG. 8 is a flowchart illustrating a follow-up control according to an embodiment of the present invention;

FIG. 9 is a flowchart illustrating a cruise control of the vehicle according to the embodiment of the present invention;

FIG. 10 is a schematic flow chart of a lateral control security monitoring method according to an embodiment of the present invention; and

fig. 11 is a schematic configuration diagram of a control system of an autonomous vehicle according to an embodiment of the present invention.

Description of reference numerals:

1110. a receiving unit; 1120. a lateral control unit; 1130. a longitudinal control unit; 1140. a safety monitoring unit; 1121. a lane keeping control module; 1122. the lane changing control module is used for changing lanes in case of lane abnormality; 1131. a vehicle following control module; 1132. a vehicle cruise control module.

Detailed Description

In addition, the embodiments of the present invention and the features of the embodiments may be combined with each other without conflict.

The present invention will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.

Fig. 1 is a flowchart illustrating a control method of an autonomous vehicle according to an embodiment of the present invention, which is applied to an ADS of the autonomous vehicle and may include the steps of:

step S100, receiving decision information about the expected transverse behavior and longitudinal mode of the vehicle output by a decision system of the automatic driving vehicle.

Wherein the expected lateral behavior includes any one of lane keeping, normal lane changing of the vehicle, and abnormal lane changing of the vehicle, and the longitudinal mode includes any one of a cruise mode, a follow mode, and an AEB (automatic Emergency Braking) mode. The cruise mode is that when the automatic driving vehicle is in a cruise state without a front vehicle within an action distance (ActDis _ m), the highest vehicle speed capable of being driven by the automatic driving vehicle is adjusted, and when the vehicle speed is lower than the highest vehicle speed, the automatic driving vehicle accelerates to drive, otherwise decelerates. The following mode refers to a state that when the self-driving vehicle is in a following state that the self-driving vehicle is located in the action distance of the self-driving vehicle in the lane and moves along with the self-driving vehicle when the lane is not changed, the speed of the self-driving vehicle is adjusted, a certain safe distance and relative speed are kept between the self-driving vehicle and the self-driving vehicle on the premise that safe driving is guaranteed, and the self-driving vehicle can stably run along with the self-driving vehicle. The AEB mode refers to braking of the autonomous vehicle at a maximum deceleration when the longitudinal decision signals the AEB mode.

Step S200, in response to the expected lateral behavior, correspondingly performing one of the following lateral controls: lane keeping control, vehicle normal lane changing control and vehicle abnormal lane changing control.

Step S300, responding to the longitudinal mode, correspondingly executing any one of the following longitudinal control: vehicle following control, vehicle cruise control, and vehicle automatic emergency brake AEB control.

And step S400, respectively carrying out transverse control safety monitoring and longitudinal control safety monitoring when the transverse control and the longitudinal control are executed.

It should be noted that step S200, step S300, and step S400 are relatively independent, and there is no fixed execution sequence among them, and fig. 1 is only schematic. The following describes the solutions of the step S200, the step S300, and the step S400 for the lateral control, the longitudinal control, and the safety monitoring, respectively.

First and transverse control scheme

According to the expected transverse behaviors shown in the received decision information, the method is divided into three parts, namely lane keeping control, vehicle normal lane changing control and vehicle abnormal lane changing control.

Lane keeping control

In an embodiment of the present invention, a target steering wheel angle matching execution of the lane keeping control is determined based on a predicted road curvature, a course angle deviation, and a lateral position deviation of the autonomous vehicle, and satisfies: a first condition that the course angle deviation and the lateral position deviation are 0; and a second condition for the pre-line road curvature to reach an optimal road curvature that minimizes an error between an actual travel trajectory and a desired trajectory of the vehicle.

For the purpose of satisfying the first and second conditions, the method for performing lane protection control in the embodiment of the present invention may include the following steps S210A-S240A (not shown in the figure):

step S210A, obtaining the heading angle deviation and the transverse position deviation to predict the curvature of the road.

Specifically, the course angle refers to an included angle between the current course of the vehicle and a lane line where the vehicle is located, and the course angle deviation is an angle deviation between the current course angle and a target course angle; the lateral position deviation is a distance deviation between a current lateral position of the vehicle and the target running track, and is also referred to as a current lateral position deviation. FIG. 2 is a schematic diagram of a heading angle deviation and a lateral position deviation defined in an embodiment of the present invention, wherein d represents the lateral position deviation and α represents the heading angle deviation. In addition, during the lane keeping process, the target heading angle is consistent with the direction of the lane center line, and the target driving track can be, for example, the lane center line, so that in the ideal lane keeping process, d and α should be 0, so that the vehicle can keep driving safely on the lane center line.

Further, fig. 3 is a schematic flow chart of acquiring a curvature of a pre-aimed road according to an embodiment of the present invention. Referring to fig. 3, the following steps may be included:

step S211, obtaining a current lane line equation of the automatic driving vehicle.

For example, the fusion system of the automatic driving system outputs lane line information including lane line type, lane line width, lane line reliability, etc. by fitting the lane line information, a lane line equation in a vehicle coordinate system can be obtained, for example, as

y＝c0+c1*x+c2x²+c3x³ (1)

In the formula, (x, y) represents a lane line coordinate, c0-c3 represents a pending parameter, different parameter values represent different types of roads, and when c2 and c3 are 0, straight line segments are represented. Wherein, the values of c0-c3 can be extracted in the lane line fitting.

And step S212, acquiring the current pre-aiming distance of the automatic driving vehicle.

When a driver drives a vehicle, the driver usually pays attention to a distance in front of the driving direction of the vehicle so as to grasp the next position (namely the front view point of the driver) where the vehicle drives, the distance from the current position to the next position selected by the driver is the pre-aiming distance, and the corresponding next position is the pre-aiming point. Generally, in order to better simulate the driving process of a driver, when lane keeping control is performed on an autonomous vehicle, a distance is selected as a pre-line distance in front of the driving path of the autonomous vehicle.

In the embodiment of the invention, the pre-aiming distance of the vehicle can be obtained by the following formula:

in the formula, s represents the pre-aiming distance and has the unit of meter; m is a predetermined coefficient, and can be determined empirically by those skilled in the art in conjunction with the minimum turning radius of the vehicle, for example m can be 1.5; v represents the vehicle speed in kilometers/hour, and s is converted into meters/second because of the meter, and 3.6 in the formula is a related conversion parameter. d₀For fixed pre-aiming distance, it is typically 4m, based on road testing experience.

And S213, calculating the curvature of the pre-aimed road according to the current pre-aimed distance and the current lane line equation.

With the current pre-line distance and current lane line equation known, the pre-line road curvature can be calculated by:

ρ＝a1*c3*s+a2*c2 (3)

wherein a1 and a2 are conventional parameters, e.g. 6 and 2, respectively, c2 and c3 are obtained according to formula (1) above and s is obtained according to formula (2) above. Accordingly, the pre-line curvature of the vehicle is obtained.

In step S220A, a first controller, a second controller, and a third controller are provided.

Wherein the first controller inputs the pre-line road curvature and outputs a first target steering wheel angle at which the pre-line road curvature reaches the optimal road curvature. Here, the input parameter is the pre-aiming road curvature, so the first controller is essentially a pre-aiming tracking controller, which is based on the pre-aiming following theory, the vehicle-driver forms a closed loop system, and the pre-aiming distance of the forward sight is estimated according to the current lane line information and the vehicle motion state, so that the error of the vehicle relative to the expected track in the route is minimized, and the required optimal pre-aiming curvature is achieved. That is, if the driver can grasp the mapping relationship between the trajectory curvature and the steering wheel angle in the continuous driving practice, the corresponding steering wheel angle is naturally determined when the driver observes a specific road curvature. Therefore, it is important to establish a mapping relationship between the target steering wheel angle and the curvature of the pre-line road in the control process of the first controller.

Preferably, in the embodiment of the present invention, a mapping relationship between the target steering wheel angle and the curvature of the pre-aimed road may be established by a steering principle based on Ackerman (Ackerman) steering principle, so that the optimal steering wheel angle required for lane keeping is obtained according to the mapping relationship. FIG. 4 is a schematic view of the Ackerman steering principle, where L is the wheel base, R is the vehicle turning radius, and δ_fIs the corner of the front wheel. Under the condition that the vehicle G is in low-speed steering, the turning radius R of the vehicle is only equal to the front wheel steering angle delta_fAnd the Ackerman principle is satisfied. For convenience of description, the four-wheel vehicle model is simplified to a two-wheel model, that is, the turning angles of the inner and outer wheels when the vehicle turns are considered to be equal, so that the four-wheel vehicle model is based on L, R and delta_fThe geometrical relationship of the three can be obtained:

further, the front wheel turning angle δ_fSteering wheel angle sigma and steering system gear ratio G of vehicle_iThere is a mapping relationship between:

σ＝G_i*δ_f (5)

further, it is known that the pre-line road curvature ρ is 1/R, and the mapping relationship between the target steering wheel angle σ and the pre-line road curvature ρ is obtained by combining the formula (4) and the formula (5):

σ＝arctan(L.ρ)*G_i (6)

therefore, the preview following theory is combined with the Ackerman steering principle, so that the target control quantity (namely the first target steering wheel angle) is compensated, and the nonlinear motion working condition of closed-loop response can be met.

Wherein the second controller inputs the lateral position deviation and outputs a second target steering wheel angle at which the lateral position deviation is 0. The second controller can adopt a PID controller, and because the PID controller is required to be capable of self-tuning P (proportion), I (integral) and D (differential) parameters according to the change of a controlled object, a plurality of groups of P, I, D parameters are required to be adjusted according to different vehicle speeds, and then corresponding parameters are obtained according to different table look-up tables of the vehicle speeds. However, the parameters obtained by the table lookup are not continuous, and the vehicle speed is continuously varied, so that parameters that cannot be found in the table are always encountered.

In this regard, in a preferred embodiment, it is contemplated that the fuzzy logic control and the PID control are combined to design a fuzzy PID controller to eliminate the lateral position deviation. Fig. 5 is a schematic diagram of a fuzzy logic control flow, and as shown in fig. 5, a corresponding fuzzy logic control module can be divided into four components: an input quantity fuzzification interface, an output quantity defuzzification (clarification) interface, fuzzy reasoning and a knowledge base. In the embodiment of the invention, the input (vehicle speed) and the output (P, D parameters) of the fuzzy logic control module are fuzzified to obtain a fuzzy domain. And selecting a triangular function as a membership function of the input and output fuzzy subsets for mathematical expression and simple operation.

The fuzzy subset of inputs (vehicle speed) is:

[0，5]；[5，10]；[10，15]；[15，20]；[20，25]；[25，30]；[30，35]；

[35，40]；[40，45]；[45，50]；[50，55]；[55，60]；[60，65]；[65，

70]；[70，75]；[75，80]；[80，85]；[85，90]；[90，95]；[95，100]；

[105，110]；[110，115]；[115，120]

the knowledge base comprises a database and a rule base, wherein P, I, D parameters are calibrated according to different vehicle speeds, and the fuzzy rules are in one-to-one correspondence with the knowledge base. The fuzzy solution calculation adopts a weighted average method, according to the antecedent condition of each rule and the input fuzzy set,according to a determined degree of membership k_iAs a weight, the value z is represented for the back part_iWeighted average is made and clear value z is output₀The formula is as follows:

in the formula, z_iFor the endpoint values of the fuzzy subset of output quantities, k_iFor degree of membership, z, of input quantities in the fuzzy subset concerned₀Is the exact value of the output. The fuzzy logic control module realizes the self-adaptive function of the PID parameters to the vehicle speed, and finally realizes the self-adaptive control.

In practical applications, the embodiment of the present invention, in which the second controller is adopted to make the lateral position deviation be 0, may specifically include:

1) PID control section

Firstly, setting the current lateral position deviation as e (t), and performing PD operation on the deviation e (t):

y＝kp*e(t)+kd*de(t)/dt (8)

where kp is the proportional (P) parameter and kd is the derivative (D) parameter, which is obtained by means of modulus control, as will be described in detail in the fuzzy control section.

Then, it is determined whether the lateral position deviation is e (t) is 0, if so, the flow is ended, otherwise, the equation (8) is returned, and the target control amount y is adjusted until the lateral position deviation is 0.

2) Fuzzy control part

The part mainly obtains the control coefficients kp and kd of the PID controller of the formula (8) through the vehicle speed which changes in real time, and specifically comprises the following steps:

firstly, fuzzifying an input vehicle speed v to obtain a fuzzy domain, and calculating the membership degree of the input vehicle speed, wherein the main membership calculation formula is as follows:

lsd1＝(v-v(i))/(v(i+1)-v(i))

lsd2＝(v(i+1)-v)/(v(i+1)-v(i)) (9)

secondly, fuzzy reasoning is carried out, wherein the specific reasoning formula is as follows:

kp (i) < kp < kp (i +1) and kd (i) < kd < kd (i +1), if v (i) < v < v (i +1) (10)

And finally, carrying out deblurring, wherein the specific deblurring formula is as follows:

kp＝(lsd1*kp(i)+lsd2*kp(i+1))/(lsd1+lsd2)

kd＝(lsd1*kd(i)+lsd2*kd(i+1))/(lsd1+lsd2) (11)

accordingly, the fuzzy control portion supplies the PID control portion with the control coefficients kp and kd adaptive to the vehicle speed, so that the PID control portion performs PD control of the lateral position deviation based on the determined control coefficients kp, kd, resulting in a control amount for making the lateral position deviation 0.

And the third controller inputs the course angle deviation and outputs a third target steering wheel rotating angle which enables the course angle deviation to be 0. The third controller may employ a PID controller. Since the vehicle is traveling, it is always desirable that the vehicle direction and the lane line direction coincide, i.e., the target heading angle is 0. Because, through PID control, the course angle deviation is used as input, and PID operation is carried out on the course angle deviation to obtain the controlled variable. Preferably, in order to enable the designed PID controller to achieve the effect of quick response, only P control can be adopted for the course angle deviation, and an ideal P parameter is obtained through calibration and correction of a real vehicle test.

In practical applications, the embodiment of the present invention, where the third controller is adopted to make the heading angle deviation be 0, may include:

firstly, the current course angle deviation is calculated, and a PID parameter table is looked up according to the actual vehicle speed.

For example, if the target heading angle is 0 degrees and the current heading angle is denoted by HeadingAngle, the heading angle deviation e (t) is-HeadingAngle. In addition, the PID parameter table is obtained by actual vehicle test calibration and correction, and shows the optimal kp values corresponding to different vehicle speeds.

And secondly, performing P control operation on the course angle deviation.

The operation expression can be expressed as y ═ kp @ (e) (t), y represents the control quantity, kp is the proportional coefficient controlled by P, and kp is obtained by inquiring a PID parameter table.

And finally, judging whether the course angle deviation is e (t) is 0, if so, ending the process, otherwise, returning to the previous step, and adjusting the target control quantity y until the course angle deviation is 0.

Therefore, the course angle deviation is controlled to be 0 through P control on the course angle deviation, and the PID controller can achieve the effect of quick response.

Step S230A, determining a final target steering wheel angle according to the first target steering wheel angle, the second target steering wheel angle, and the third target steering wheel angle.

For example, the pre-aiming tracking control is performed on the curvature of the pre-aiming road to obtain a first target steering wheel angle y1, the PD calculation is performed on the lateral position deviation to obtain a second target steering wheel angle y2, and the P calculation is performed on the heading angle deviation to obtain a third target steering wheel angle y3, so that the final target control steering wheel angle may be represented as y1+ y2+ y 3.

And step S240A, controlling the autonomous vehicle to perform lane keeping according to the final target steering wheel angle.

For example, a steering wheel angle command may be generated according to a target steering wheel angle, the steering wheel angle command may be sent to the steering wheel controller, and the steering wheel controller may receive the steering wheel angle command, analyze the corresponding target steering wheel angle, and adjust the angle and direction of rotation of the steering wheel accordingly, so that the autonomous vehicle may keep running on the center line of the current driving lane.

In conjunction with the above-mentioned steps S210A-S240A, the lane keeping control scheme according to the embodiment of the present invention has the following advantages:

1) the algorithm is designed according to vehicle kinematics, fuzzy-PID and PID control are respectively applied to the horizontal position deviation and the course angle deviation through preview tracking control, the real-time control requirement is met, the lane keeping effect is good, and the algorithm has very high transportability after encapsulation.

2) Compared with the scheme that high-precision positioning equipment needs to be installed on the vehicle, the same function can be realized through the lane information output by the high-definition camera, and the system cost is greatly reduced.

3) Through the optimal pre-aiming road curvature, the initial mastering and utilization of the front road information can be realized in the feed-forward control, the automatic driving vehicle can perform corresponding actions in advance according to the first target steering wheel corner output by the first controller, the driving process is more comfortable and smooth, the driving condition is better met, the requirement of lane keeping of the vehicle in the straight road condition, the curve road condition and other road conditions during safe driving is met, the problems of over-steering and understeering caused by improper operation are avoided, and the stability and the safety of curve driving are improved.

4) Fuzzy reasoning of self-tuning P, I, D parameters is designed according to the change of the vehicle speed, and the fuzzy reasoning is combined with PID control to realize self-adaptive PID control, so that the feasibility is strong, and the operation efficiency is high.

(II) normal lane change control of vehicle

It should be noted that the normal lane change and the abnormal lane change of the vehicle in the embodiment of the present invention are relative concepts, the normal lane change is a conventional lane change according to road conditions and driving requirements, such as lane change of passing, lane change, and the like, and the abnormal lane change relates to more complicated working conditions, such as driving into a target ramp under a forced cut-in working condition, abnormal driving in the vehicle lane, abnormal driving across lanes under a front obstacle or road repair condition, and the like.

And determining the target steering wheel rotation angle matched with the execution of the normal lane changing control of the vehicle based on the vehicle speed, the transverse position deviation and the course angle deviation. The related calculation schemes are well described in the prior art, and will not be described in detail herein, for example, the patent with application number 201810652866.9, which was filed by the inventor of the present application before the present application, is a lane change control method and apparatus, and the entire content of the patent is incorporated herein by reference to explain the normal lane change control method of the embodiment 201810652866.9 of the present invention.

(III) abnormal lane-changing control of vehicle

In the embodiment of the present invention, the target steering wheel angle that matches the execution of the abnormal lane change control of the vehicle is determined based on the current lateral state value of the vehicle and the target line to be traveled to corresponding to the current lateral state value, and the first condition and the second condition are satisfied.

Fig. 6 is a schematic diagram of a working condition of an Abnormal Lane Change (ALC) of the vehicle, and it can be known that the ALC includes working conditions such as driving into a target ramp under a forced cut-in working condition, Abnormal driving in the own Lane, Abnormal driving across lanes under a condition that an obstacle exists in front of the own Lane or road repair is performed. Referring to fig. 6, the following steps S210B-240B (not shown) may be employed in the embodiment of the present invention to perform ALC:

in step S210B, a current lateral state value of the autonomous vehicle and a target line to which the autonomous vehicle is to move corresponding to the current lateral state value are acquired.

Where different lateral state values are used to show different lateral states, it may be defined, for example, that when the lateral state value is 0, the vehicle is in a lane keeping state. And, each lateral state value is preconfigured to correspond to a different said target line, including for example: when the transverse state value is a first numerical value, the target line is the center line of the current lane; when the transverse state value is a second numerical value, the target line is a left lane central line; when the transverse state value is a third numerical value, the target line is the center line of the right lane; when the transverse state value is a fourth numerical value, the target line is a dynamic deviation line of the current lane; when the transverse state value is a fifth numerical value, the target line is a lane crossing dynamic deviation line; when the transverse state value is a sixth numerical value, the target line is a left safety offset line; and when the transverse state value is a seventh numerical value, the target line is a right safety offset line.

The first to seventh values are different and may be set arbitrarily, which is not limited in the embodiment of the present invention. For example, table 1 gives examples of the first to seventh numerical values and their correspondence with the target line.

TABLE 1 correspondence of lateral State values to target lines

According to table 1, when the lateral state values output by the decision system of the autonomous vehicle are 2, 3, 4, -4, it is indicated that the vehicle is in an abnormal lane change state. Therefore, according to the rule defined in table 1, the corresponding target line should be selected to control the vehicle to move to the selected target line, so as to accomplish the adaptive ALC control.

Step S220B, determining a desired trajectory of the autonomous vehicle from the target line.

According to the information of the lane line where the vehicle is located and the information of the environment of the vehicle, under the condition that the current transverse state and the target line of the vehicle are known, the expected track of the vehicle is easy to determine. For example, when there is an obstacle in the own lane of the vehicle, the current lateral state is that the own lane is abnormal, and the target line is the own lane dynamic deviation line, it is easy to determine the expected trajectory of the vehicle that deviates from the center line of the own lane to the target line.

And step S230B, performing preview tracking control on the automatic driving vehicle based on the expected track to obtain a target steering wheel angle.

The preview tracking control is performed based on the preview following theory, and mainly comprises the following steps: determining a preview point; calculating the distance between the point closest to the pre-aiming point in the expected track and the pre-aiming point, and taking the distance as a pre-aiming error; and determining a transfer function relation between the preview error and the steering wheel rotation angle according to a vehicle dynamic model, a motion rule, a preview distance and a vehicle speed of the automatic driving vehicle.

Specifically, fig. 7 is a schematic diagram of the circular arc motion of the vehicle along the desired track with curvature R, where d is the preview distance, l is the preview error, h is the distance from the preview point to the center of the curve, R is the curvature radius of the desired track, w is the yaw rate, u is the longitudinal vehicle speed of the vehicle, and v is the lateral speed. With reference to fig. 7, the embodiment of the present invention adopts a two-degree-of-freedom vehicle dynamics model, neglecting the effects of the steering system and the suspension, assuming that the displacement of the vehicle about the z-axis and the pitch angle and roll angle of the vehicle about the y-axis and the x-axis are both zero, and the forward speed of the vehicle along the x-axis is regarded as constant. Therefore, the whole vehicle comprises two degrees of freedom of lateral direction and transverse swing, and the dynamic differential equation of the whole vehicle is as follows:

where δ is the front wheel angle, I_zIs the moment of inertia of the vehicle around the z-axis, m is the vehicle mass, a is the distance from the vehicle center of mass to the front axle, b is the distance from the vehicle center of mass to the rear axle, K_afFront wheel cornering stiffness; k_arIs rear wheel cornering stiffness.

Assuming that the vehicle is travelling along the desired curve with a steady state following error of zero, in steady state conditions,

the vehicle model equation obtained according to the above equation of differential is:

according to the above equation, the lateral velocity v of the vehicle in the steady state situation can be expressed as a relation expressed in yaw rate w:

ν＝e·w(15)

wherein e is a matrix type which can be obtained from the formula (14).

According to the law of the steady circular motion, the following relation can be obtained:

V＝R·w (17)

in the formula, V is the speed of the circular motion of the vehicle. Due to the complexity of road working conditions, the selection of the pre-aiming distance has great influence on the pre-aiming following effect, and when the vehicle speed is low, if the pre-aiming distance is too large, the information of the road ahead cannot be well utilized; when the speed of a motor vehicle is higher, if the preview distance is too short, the road information in the front can be lost, the above conditions are integrated, and the preview distance is selected as follows:

wherein K is the preview coefficient, u is the longitudinal speed (km/h) of the vehicle, and d₀For fixed pre-aiming distance, the experience is generally 4m according to road test, and 3.6 is related conversion parameters.

In addition, referring to fig. 7, the corresponding relationship between the preview distance d and the preview error l in the figure can be obtained, and the front wheel turning angle δ and the steering wheel turning angle

And steering system gear ratio G of the vehicle_iThere is a mapping relationship between:

finally, determining the transfer function between the desired steering wheel angle and the preview error according to the above equation and law of motion can be expressed as:

and step S234, calculating a target steering wheel rotation angle corresponding to the current preview error according to the transfer function relation.

For example, based on equation (20), after the current preview error is known, the corresponding target steering wheel angle may be calculated.

In other embodiments of the present invention, the target steering wheel angle may also be obtained according to the preview curvature of the vehicle, that is, the scheme of the formula (3) to the formula (6) in the foregoing step S213.

And step S240B, controlling the automatic driving vehicle to carry out abnormal lane changing of the vehicle according to the target steering wheel angle.

For example, a steering wheel angle command may be generated according to a target steering wheel angle, the steering wheel angle command may be sent to a steering wheel controller, and the steering wheel controller may receive the steering wheel angle command, analyze a corresponding target steering wheel angle, and adjust a steering wheel rotation angle and direction accordingly, so that the autonomous vehicle may safely and stably travel to a target line.

The transfer function based on the preview error and the steering wheel angle is established, so that the feedforward control of the steering wheel angle is realized, but in the transverse control process, due to model errors and various external interferences, the good control effect and stability are difficult to ensure only the feedforward control. Therefore, the method for controlling the abnormal lane change of the vehicle, provided by the embodiment of the invention, also adds the feedback control based on the deviation of the heading angle.

Specifically, during the course following process, the heading angle of the vehicle reflects the tangential direction of the vehicle tracking the course, since the vehicle driving direction is always expected to be consistent with the selected target line direction during the vehicle driving process, i.e. the target heading angle should be 0 degree. Accordingly, the control method for the abnormal lane change of the vehicle in the embodiment of the invention further comprises the following steps: the method comprises the steps of obtaining course angle deviation between a current course angle and a target course angle of an automatic driving vehicle, carrying out PID operation on the course angle deviation to obtain a control increment aiming at a steering wheel corner, and correcting the target steering wheel corner based on the control increment.

For example, if the target heading angle is 0 degrees and the current heading angle is denoted by HeadingAngle, the heading angle deviation e (t) is-HeadingAngle. When the lateral state values are 2, 3, 4 and-4 in table 1, a higher demand is placed on the control in the emergency state, and in order to achieve the effect of quick response by the controller designed, the P control may be adopted only for the vehicle heading angle deviation. Accordingly, the operation expression of P control can be expressed as y ═ kp × e (t), y represents the control increment, kp is the proportional coefficient of P control, and kp is obtained by querying the PID parameter table. And judging whether the heading angle deviation is 0 or not, if the deviation is 0, correcting the target steering wheel angle, and otherwise, adjusting the control increment y until the heading angle deviation is 0 through y-kp-e (t). Further, when the initial target steering wheel angle y1 is obtained and the heading angle deviation P is calculated to obtain the control increment y2 when the preview tracking control is performed on the preview error, the final target control steering wheel angle may be represented as y1+ y 2.

The automatic driving vehicle can be influenced by various complex working conditions and different road scenes in the driving process, and the abnormal lane changing control scheme of the vehicle has self-adaptability, so that the automatic driving vehicle can cope with various abnormal road working conditions. In addition, the abnormal lane change control scheme of the vehicle provided by the embodiment of the invention has a wide application range, can be suitable for automatic driving systems under curved roads and straight roads with different curvatures, and meets the requirements on the operation stability and the safety of the vehicle.

Therefore, the transverse control method provided by the embodiment of the invention can be used for carrying out lane keeping control, normal lane changing control or abnormal lane changing control on the expected transverse behavior of the vehicle in real time, ensuring that the target steering wheel corner in each control state meets the condition of transverse safe driving of the vehicle, and being suitable for various complex working conditions.

Second, longitudinal control scheme

According to the longitudinal mode shown in the received decision information, the control method is divided into three parts, namely vehicle following control, vehicle cruise control and AEB control.

Vehicle follow-up control

Fig. 8 is a flowchart illustrating the follow-up control in the embodiment of the present invention. As shown in fig. 8, the follow-up control may include the steps of:

step S310, detecting the current working condition of the automatic driving vehicle in the following mode.

And S320, matching a control algorithm corresponding to the current working condition according to the corresponding relation between different working conditions and different control algorithms of the automatic driving vehicle in the following mode, wherein the control algorithm is used for controlling the speed change of the automatic driving vehicle in the corresponding working conditions.

And step S330, controlling the automatic driving vehicle to follow the motion according to the matched control algorithm.

According to the embodiment of the invention, the vehicle is divided into five working conditions according to the running condition of the vehicle in the following mode, and different working conditions are configured to have different control algorithms.

The corresponding five operating conditions can be described simply as follows:

1) under the first working condition, the front vehicle is slow and the relative distance is short, wherein the slow front vehicle means that the speed of the front vehicle is smaller than the speed of the front vehicle, and the short relative distance means that the actual distance between the front vehicle and the front vehicle is smaller than the expected distance.

2) And under the second working condition, the front vehicle is slow and has a long relative distance, wherein the long relative distance means that the actual distance between the front vehicle and the vehicle is greater than the expected distance.

3) And under a third working condition, the front vehicle is fast and has a long relative distance, wherein the fast front vehicle means that the speed of the front vehicle is greater than that of the vehicle.

4) And under the fourth working condition, the front vehicle is fast and the relative distance is short.

5) And under the fifth working condition, the vehicle can be stably followed.

In a preferred embodiment, corresponding to the above five operating conditions, the corresponding relationship between different operating conditions and different control algorithms of the autonomous vehicle in the following mode includes any one or more of the following.

1. First working condition and corresponding first control algorithm

The first control algorithm is used for controlling the vehicle to decelerate at a first acceleration under the first working condition, namely when the vehicle is in the first working condition, the vehicle is adjusted to decelerate. Those skilled in the art will appreciate that "acceleration" is not limited to indicating a vehicle acceleration scenario, but may also indicate a vehicle deceleration scenario, and "deceleration with acceleration" and "deceleration" may both indicate a vehicle deceleration scenario.

In a more preferred embodiment, a specific procedure for calculating the first acceleration is given, and before the procedure is described, a concept of a safe distance (SfDis _ m) which is a minimum distance required to be maintained between the two vehicles when the speed of the host vehicle is the same as that of the preceding vehicle and a braking distance (AEBDis _ m) which is a threshold value of the distance between the two vehicles when the autonomous vehicle switches from the following mode to the AEB mode is introduced.

In the embodiment of the present invention, the braking distance AEBDis _ m needs to be calculated by calculating the TTC (Time to collision Time). TTC is calculated as follows:

in the formula, VehSpd _ kph is the speed of the vehicle, FroVehSpd _ kph is the speed of the vehicle ahead, and RelaDis _ m is the actual distance between the two vehicles.

Further, the braking distance AEBDis _ m is calculated using the following equation:

AEBDis_m＝(VehSpd_kph-FroVehSpd_kph)*TTC/3.6 (22)

further, the safety distance SfDis _ m is calculated using the following formula:

SfDis_m＝0.8509*FroVehSpd_kph+c (23)

in the formula, c is a standard amount, for example, c ═ 8.

Based on the above equations (21) to (23), the embodiment of the present invention calculates the first acceleration a1 using the following equation:

where ExpDis _ m is the desired distance.

According to the equation (24), the first acceleration a1 can be calculated in three cases, from which the range of the first acceleration a1 can be determined as a1 e [ -8, 1], in the equation (24):

when the distance between the two vehicles is SfDis _ m<RelaDis_m<ExpDI _ m, at this time

When the distance between the two vehicles is AEBDis _ m<RelaDis_m<sfDis _ m, at this time

When the distance between the two vehicles is ReLaDis _ m<Switching from the following mode to the AEB mode when AEBDis _ m, and performing AEB control with acceleration of-8 m/s²。

In the calculation formula of the embodiment of the present invention, the acceleration value with a minus sign "-" indicates "deceleration with acceleration" or "deceleration".

2. Second operating mode and corresponding second control algorithm

The second control algorithm is used for controlling the vehicle to decelerate at a second acceleration under the second working condition, namely when the vehicle is under the second working condition, the vehicle should be adjusted to decelerate.

In a more preferred embodiment, a specific procedure for calculating the second acceleration is given, that is, the second acceleration a2 is calculated using the following equation:

in the formula, K₁Is a constant used to compensate for the delay effects of the control algorithm. Specifically, when a Vehicle Longitudinal Control (VLC) system of an autonomous Vehicle performs Longitudinal Control on the Vehicle, there is a delay response, K₁Then it is used to compensate for the delayed response of the VLC system.

3. Third operating mode and corresponding third control algorithm

The third control algorithm is used for controlling the vehicle to accelerate at a third acceleration under the third working condition, namely when the vehicle is under the third working condition, the vehicle is adjusted to accelerate.

In a preferred embodiment, in the third control algorithm, further comprises calculating the third acceleration a3 using the following formula:

in the formula, C31-C34 represent different acceleration states, and k31-k34 represent acceleration values corresponding to the different acceleration states. Specifically, according to the speed of the front vehicle, the distance between the front vehicle and the vehicle, and the like, the acceleration state of the third working condition can be divided into four sub-states C31-C34, wherein different sub-states correspond to corresponding accelerations, namely k31-k 34.

In other embodiments, the third acceleration a3 is also calculated using the following equation:

here, K₁Is constant, e.g. K₁Under the third condition, the range of the third acceleration a3 is a3 e [0, 0.8 ∈ under the third condition]。

4. Fourth operating mode and corresponding fourth control algorithm

The fourth control algorithm is used for controlling the vehicle to decelerate at a fourth acceleration under the fourth working condition, that is, when the vehicle is in the fourth working condition, the vehicle should be adjusted to decelerate.

In a preferred embodiment, the fourth control algorithm further comprises calculating the fourth acceleration a4 using the following formula:

wherein C41-C44 represent different deceleration states, and k41-k44 represent acceleration values corresponding to different deceleration states

In other embodiments, a specific procedure for calculating the fourth acceleration is given, that is, the fourth acceleration a4 is calculated by the following equation:

here, K₁Is constant, e.g. K₁0.2, and in the fourth condition, the fourth acceleration a4 is in the range of a4 e [ -0.8, 1 ∈]。

5. Fifth operating mode and fifth control algorithm

In the fifth working condition, the speed difference between the speed of the front vehicle and the speed of the vehicle is within the set speed threshold range, and the actual distance between the front vehicle and the vehicle is also within the set distance threshold range, so that the vehicle stably follows the front vehicle at an approximately constant speed.

And the fifth control algorithm is used for controlling the vehicle to stably follow the front vehicle to run under the fifth working condition.

In other embodiments, where the requirement for a stable following state of the host vehicle relative to the preceding vehicle is not high, the fifth algorithm may consider controlling the host vehicle to travel at a fifth rate a5 to stably follow the preceding vehicle, and may calculate the fifth rate a5 using the following equation:

a5＝(FroVehSpd_kph-VehSpd_kph)*k_p

wherein VehSpd _ kph is the speed of the vehicle, FroVehSpd _ kph is the speed of the front vehicle, k_pIs a scaling factor.

Therefore, in the following state, the control algorithm under the above five working conditions can be matched according to the speed of the front vehicle, the speed of the vehicle and the actual distance between the two vehicles, the control algorithm corresponding to the working condition is executed under which working condition is met, a good control effect is achieved, and the efficiency, safety and comfort of the control algorithm can be improved.

Cruise control of vehicle

Fig. 9 is a flowchart of the vehicle cruise control according to the embodiment of the present invention. As shown in fig. 9, the vehicle cruise control may include the steps of:

step S410, acquiring the current vehicle speed when the vehicle is in a cruising mode, and calculating the relative speed difference between the current vehicle speed and the highest vehicle speed which can be driven by the vehicle.

The current vehicle speed may be obtained from a vehicle sensor (e.g., an inertial navigation device) of the vehicle itself, or may be obtained through an Electronic Control Unit (ECU) of the vehicle in the CAN bus.

The maximum vehicle speed that the vehicle can run is derived from a maximum vehicle speed management module in the ADS of the automatic driving vehicle. The maximum vehicle speed management unit belongs to a decision system of an automatic driving vehicle and is used for acquiring the maximum driving speed which corresponds to the driving scene information, the driving environment information and the road condition information and can be driven by the vehicle to form a maximum speed set, and deciding a corresponding reasonable maximum vehicle speed according to the actual situation to be used as the maximum vehicle speed which can be driven by the vehicle.

The function of the maximum vehicle speed management unit is specifically described below with respect to a driving scene, a driving environment, and a road condition, respectively.

1) For different driving scenarios, the maximum vehicle speed is affected by the following conditions: ADS presets the highest vehicle speed V1; setting different maximum vehicle speeds V2 in different widths in a drivable area in front of the automatically driven vehicle; thirdly, pedestrians exist in the set range of the current adjacent lane (non-emergency lane) of the automatic driving vehicle, the vehicle is decelerated, and the maximum vehicle speed V3 is set; fourthly, automatically driving the vehicle to be away from the exit of the highway (the distance of the ramp exit, the distance of a toll station and the like), and setting different highest speed limit values V4 according to different distances; when the distance of the automatic driving vehicle is set at the distance sink point, the automatic driving vehicle starts to decelerate to the maximum vehicle speed V5; sixthly, the automatic driving vehicle needs to forcibly cut into an adjacent lane under specific working conditions (such as parallel flow, shunt, high speed of driving away, turnout and the like), and the maximum vehicle speed V6 of the automatic driving vehicle is set according to the vehicle speed of the left and right front areas; seventhly, setting a highest vehicle speed V7 according to a target right ahead of the automatically-driven vehicle in the lane changing process of the automatically-driven vehicle; 8) the autonomous vehicles include side-by-side running vehicles, and the maximum vehicle speed V8 is the maximum vehicle speed when the autonomous vehicles run side-by-side over adjacent lanes.

2) For different driving environments, the maximum vehicle speed is affected by the following conditions: firstly, different driving roads and road sections have different road speed limits, and the highest speed limit V9 of the output road is identified according to a high-precision map (HDM) and a visual sensor; secondly, the maximum speed V10 of the automatic driving vehicle is limited according to different environmental conditions by the running environmental factors (light and shade degree, fog, rain and snow, hail and the like) of the automatic driving vehicle.

3) For different road conditions, the maximum vehicle speed is affected by the following conditions: firstly, the curvature of a road limits the highest vehicle speed V11 of a vehicle according to the completeness of different roads; secondly, the road surface unevenness is carried out, and the highest vehicle speed V12 of the vehicle is limited according to the dynamic information (such as vertical acceleration, transverse force, gradient and the like) of the vehicle; and thirdly, the road surface adhesion coefficient is detected according to a sensor arranged on the automatic driving vehicle, and the highest vehicle speed V13 of the vehicle is limited. Accordingly, the highest speed set { V1, … …, V13} is obtained.

And selecting a proper speed V0 as the highest vehicle speed V allowed by the automatic driving vehicle when the automatic driving vehicle runs on the current road according to actual conditions. If V0 is caused by environmental factors (rain, snow, fog, etc.) or road surface adhesion coefficients, if there are neighboring vehicles around the vehicle at this time, V0 is actively reduced by 10%, and the maximum vehicle speed V of the system is 0.9 × V0; if the V0 is not caused by environmental factors (rain, snow, fog, etc.) and road surface adhesion coefficients, if an adjacent vehicle exists around the vehicle at the moment, the maximum vehicle speed V of the system is equal to V8, otherwise, V is equal to V0.

The highest vehicle speed determined by the embodiment of the invention has stronger practicability, covers more scenes, better accords with the driving habits of people, avoids the accidents of violation of regulations or collision, slippage and the like caused by overhigh driving speed of the vehicle, and ensures the driving safety and riding comfort of the automatic driving vehicle.

Step S420, correcting the relative speed difference so that a variation range of the relative speed difference in the control period is within a preset range.

For this step S420, in a preferred embodiment, the relative speed difference is corrected by configuring a ratio clipping module. Wherein the rate limiter module is, for example, the rate limiter module in simulink. The purpose of configuring the ratio amplitude limiting module is to correct the change of the highest vehicle speed which can be driven by the vehicle, so that the relative speed difference has larger change in one operation period, and the comfort of the whole vehicle cruise control is influenced. The ratio amplitude limiting module can avoid the condition after being introduced, and the cruise control effect can be greatly improved through simulation and real vehicle test verification. In addition, the parameter setting in the rate limiting module is also calibrated through real-time test.

Step S430, calculating the vehicle acceleration according to the corrected relative speed difference.

In a preferred embodiment, the vehicle acceleration a is calculated using the following equation,

a＝(TopSpd_kph-VehSpd_kph)*Kp

wherein TopSpd _ kph represents the maximum vehicle speed, VehSpd _ kph represents the current vehicle speed, and Kp is a proportional parameter for P control. The P control is P control in the classical PID (proportional-integral-derivative) control.

Kp differs because cruise control involves acceleration and deceleration control, and when the vehicle speed is lower than the maximum vehicle speed, the vehicle accelerates, whereas the vehicle decelerates, and because the response speeds and accuracies of the actuators that accelerate and decelerate differ. Preferably, the proportional parameter Kp is determined by:

wherein, K₀Is a first parameter, K, of the optimal riding experience corresponding to the vehicle acceleration condition determined in the real vehicle test₁Is a second parameter of the optimal ride experience corresponding to the deceleration condition of the vehicle determined in the real vehicle test, the K₀And K₁Are all calibrated values determined by real vehicle testing.

And step S440, adjusting the speed of the vehicle in the cruise mode based on the acceleration of the vehicle.

After determining the appropriate acceleration through steps S410-S430, the vehicle speed of the host vehicle in the cruise mode is adjusted through controlling the acceleration and deceleration of the host vehicle in step S440.

In some cases, the acceleration of the vehicle calculated in step S430 may still be less than ideal, for example, greater than the acceleration actually emitted by the driver under the same conditions in a certain speed range. In this regard, in a preferred embodiment, the step S440 is configured to: and carrying out amplitude limiting correction on the vehicle acceleration, and then adjusting the vehicle speed of the vehicle in the cruise mode based on the vehicle acceleration after the amplitude limiting correction. Accordingly, the acceleration value for vehicle speed control is made more appropriate by the acceleration limit.

Further, the upper limit value Up of the acceleration limiter is determined by a table look-Up method, the maximum acceleration executed by the vehicle is limited according to the vehicle speed of the vehicle, namely, when the acceleration a emitted by the vehicle is greater than the upper limit value Up, the acceleration a emitted by the vehicle is executed according to the upper limit value Up, otherwise, the acceleration a emitted by the vehicle is executed_TaracceMIN (Up, a). More specifically, the upper limit value Up is determined by the following equation:

similarly, the lower limit of the acceleration slice is treated in the same way, and b1 to b6 are constants which are sequentially increased and less than 1, for example, 0.35, 0.45, 0.55, 0.65, 0.75, and 0.85, a₁To a₈Are successively lower set values.

Therefore, the embodiment of the invention optimizes the control comfort on the basis of realizing the cruise control, has better control effect by analyzing test data through real vehicles and simulation tests, and improves the high efficiency, safety and comfort of the cruise control algorithm and the whole longitudinal control algorithm.

(III) AEB control

In the embodiment of the present invention, AEB control may include: when the longitudinal decision signals the AEB mode, the autonomous vehicle brakes at maximum deceleration, requesting a braking deceleration of between 0.7 and 0.9g (where g is acceleration due to gravity), e.g., a deceleration of-8 m/s, regardless of comfort requirements for the vehicle braking process²。

Therefore, the longitudinal control method of the automatic driving vehicle disclosed by the embodiment of the invention executes the relative control algorithm according to the instruction sent by the longitudinal decision, so that the acceleration and the deceleration of the vehicle are controlled, the speed of the vehicle is further adjusted, and the control of a cruise mode, a following mode and an AEB mode is realized.

Third, safety monitoring scheme

In the embodiment of the invention, the safety monitoring scheme mainly comprises a transverse control safety monitoring part and a longitudinal control safety monitoring part.

Safety monitoring of transverse control

In the embodiment of the present invention, lateral acceleration is selected as a stability control target of a vehicle, and as shown in fig. 10, the lateral control safety monitoring method in the embodiment of the present invention includes the following steps:

and step S510, establishing a corresponding relation between the lateral acceleration of the vehicle, the longitudinal speed and the steering wheel angle.

In a first preferred embodiment, the corresponding relationship is established according to a vehicle kinematic model and vehicle parameters as follows:

wherein, delta_swFor the steering wheel angle, L is the vehicle wheelbase, a_yFor the lateral acceleration, u is the longitudinal speed, G_iIs the steering system gear ratio.

Specifically, the lateral acceleration of the vehicle when turning is known from the kinematic principle:

in the formula, a_yIs the vehicle lateral acceleration, u is the longitudinal vehicle speed, and R is the turning radius of the vehicle.

Again according to ackermann steering theorem (i.e., equation (4) above), it is known that the vehicle front wheel slip angle and the steering wheel angle satisfy:

δ_sw＝δG_i (30)

in the formula: delta_swIs the steering wheel angle, delta is the front wheel angle, G_iIs the steering system gear ratio.

According to the expressions (29) to (30), the correspondence relationship between the lateral acceleration and the longitudinal vehicle speed, the steering wheel angle, that is, the above-described expression (28), can be easily established.

In a second preferred embodiment, based on a vehicle dynamics model and vehicle parameters, the corresponding relationship between the lateral acceleration and the longitudinal vehicle speed, the front wheel steering angle and the vehicle parameters is established as follows:

wherein, a_yIs said lateral acceleration, k₁Is front axle equivalent yaw stiffness, k₂The equivalent cornering stiffness of the rear axle is shown as a, the distance from the center of mass to the front axle, b, the distance from the center of mass to the rear axle, u, the longitudinal speed and delta, and the rotation angle of the front wheel.

This formula (31) can be obtained by the following procedure:

firstly, the basic characteristics of vehicle motion can be effectively mastered by simplifying the vehicle into a linear two-degree-of-freedom bicycle model for research. According to the established two-degree-of-freedom model:

wherein k is₁Is front axle equivalent yaw stiffness, k₂For equivalent yaw stiffness of the rear axle, a is the distance from the center of mass to the front axle, b is the distance from the center of mass to the rear axle, m is the vehicle mass, I_ZIs the moment of inertia, u is the longitudinal vehicle speed, v is the lateral vehicle speed, w_rThe yaw rate is shown, and δ is the front wheel angle.

It should be noted that the lateral vehicle speed v is generally small, and in the embodiment of the present invention, the influence on the steering angle is also small, so that only the longitudinal vehicle speed may be used in some cases.

Secondly, according to the optimal preview theory, suppose thatThe vehicle always stably runs along a curve on the road, the following error is zero, and the vehicle is in a stable state

This is obtained according to equation (32):

from the expressions (32) to (34), the above-described expression (31) can be obtained as a correspondence relationship between the lateral acceleration and the longitudinal vehicle speed, the front wheel steering angle, and the vehicle parameter.

In this case, the first preferred embodiment establishes the relationship between the lateral acceleration and the longitudinal vehicle speed and the steering wheel angle based on a vehicle kinematic model,

and step S520, acquiring the maximum steering wheel angle corresponding to the given maximum lateral acceleration at different vehicle speeds according to the corresponding relation, and taking the maximum steering wheel angle as a steering angle threshold value.

In accordance with the first preferred embodiment in step S510, the maximum vehicle lateral acceleration a is given at different vehicle speeds according to equation (28)_vmaxThen, the maximum steering wheel angle δ can be obtained_swmaxThe calculation formula is:

the maximum steering wheel angle delta_swmaxIs the corner threshold.

In addition, the maximum lateral acceleration corresponding to different vehicle speeds needs to be controlled to be smaller than the limit lateral acceleration value (preferably 0.25 g).

In accordance with the second preferred embodiment of step S510, according to equation (31), the maximum vehicle lateral acceleration a can be given at different vehicle speeds_ymaxThen, the maximum steering wheel angle δ can be obtained_swmaxThe calculation formula is:

step S530, the steering wheel angle is monitored in real time, whether the monitored steering wheel angle is larger than the steering wheel threshold value or not is judged, if yes, the steering wheel angle is limited to the steering wheel threshold value and then output for vehicle transverse control, and if not, the steering wheel angle is normally output for vehicle transverse control.

Specifically, the automatic driving system may utilize an Electric Power Steering (EPS) for lateral control, so after the environmental awareness module, the data fusion module or the decision planning module obtains the target Steering wheel angle, the Steering wheel angle is sent to the EPS by using the Steering angle threshold δ before being sent to the EPS_swmaxLimiting, and when the calculated target steering wheel angle is larger than the steering angle threshold value at a certain moment, making the target steering wheel angle equal to the steering angle threshold value delta_swmaxOtherwise, outputting normally.

The actual measurement results of the lateral control safety monitoring method according to the embodiment of the present invention are illustrated by way of example. In this example, the maximum steering wheel angles under different vehicle speeds are obtained according to certain vehicle type setting parameters, and it can be known that the larger the vehicle speed is, the smaller the corresponding maximum steering wheel angle is, so that rollover caused by an excessively large vehicle speed can be avoided. Therefore, the transverse control safety monitoring step of the embodiment of the invention can achieve the purpose of safety monitoring (especially under a high-speed working condition), further improves the safety of ADS, and is more suitable for different working conditions and continuously changing vehicle states compared with the traditional scheme of directly providing an extreme value limiting value.

(II) longitudinal control safety monitoring

In the embodiment of the invention, the longitudinal control safety monitoring is aimed at monitoring whether the target acceleration of the vehicle can ensure the stable operation of the vehicle for the cruise mode, the follow mode, and the AEB mode, and therefore it is substantially in accordance with the purpose of the longitudinal control of the vehicle aimed at calculating the target acceleration to ensure the stable operation of the vehicle. Therefore, the longitudinal control safety monitoring scheme of the embodiment of the invention corresponds to the longitudinal control scheme described above.

Specifically, for the cruise mode, the longitudinal control adopts the P control algorithm involved in step S430, but when the difference between the two values is large, the acceleration output by the P control exceeds the control range to which the vehicle should respond, so the embodiment of the invention introduces the longitudinal monitoring strategy. Different proportional parameters Kp have different control effects, if different proportional coefficients Kp at different speeds are adopted, the calibration work of the proportional coefficients Kp is a huge project, a unified judgment standard does not exist, and if parameters and conditions of a system in the later development stage are changed, the work needs to be calibrated again, so that a large amount of manpower and material resources are consumed. Therefore, the embodiment of the invention adopts a fixed proportionality coefficient mode, adds a monitoring strategy of a cruise mode for correction, and can complete the cruise control of the vehicle.

For the following mode, its longitudinal control refers to the description above in relation to fig. 10. On this basis, the longitudinal control safety monitoring of the embodiment of the invention may include the following steps:

firstly, aiming at different longitudinal control states, acquiring acceleration threshold values corresponding to given maximum target acceleration under different vehicle speeds.

Preferably, the acceleration threshold is determined using the following equation

Wherein b 1-b 7 are constants which are sequentially increased and are less than 1, a₁₁To a₁₇Are successively lower set values. In the example, b1 to b6 increase sequentially in the manner of an arithmetic sequence, a₁To a₇For decreasing in sequence by means of an arithmetic sequence, for example, b1 to b7 are respectively 0.35, 0.45, 0.55, 0.65, 0.75 and 0.85, which respectively result in decreasing a₁₁To a₁₇。

It should be noted that the determination of the acceleration threshold for the AEB mode is similar to this, and therefore, the detailed description thereof is omitted.

Secondly, monitoring the acquired target acceleration in real time, and judging whether the target acceleration is greater than the acceleration threshold, if so, limiting the target acceleration after the acceleration threshold and outputting the target acceleration for longitudinal control of the vehicle, otherwise, normally outputting the target acceleration for longitudinal control of the vehicle.

That is, the longitudinal Control algorithm acceleration is compared with corresponding acceleration thresholds at different vehicle speeds, and the smaller value of the two is transmitted to a vehicle actuator, such as a vehicle stability Control unit (ESP), an Engine Control Module (ECM), and the like, to perform longitudinal Control.

Therefore, the longitudinal control safety monitoring step of the embodiment of the invention monitors and limits the output target acceleration, so that on one hand, the vehicle can be prevented from suddenly accelerating, and on the other hand, the vehicle actuator can be ensured to normally respond within the limit capacity of the vehicle actuator.

Therefore, the embodiment of the invention designs the corresponding safety monitoring strategy from the safety of the transverse control and the longitudinal control respectively, so that the ADS design has more integrity. In addition, compared with the traditional method of 'cutting one' directly (namely, giving a constraint extreme value), the safety monitoring strategy for the transverse control is more reasonable, the longitudinal control state of the vehicle is considered in real time aiming at the safety monitoring strategy for the longitudinal control, the hidden danger that the longitudinal control algorithm recalibrates the coefficient due to the change of parameters or conditions is avoided, the algorithm is more convenient and faster, and the later development and maintenance are easy.

In summary, the control method of the automatically driven vehicle according to the embodiment of the present invention can perform lane keeping control, normal lane changing control or abnormal lane changing control of the vehicle in real time according to the expected lateral behavior of the vehicle, is suitable for various complex working conditions, and realizes following control and more optimized cruise control matching various working conditions, and designs a safety monitoring strategy applied to lateral control and longitudinal control, thereby not only improving the design of the automatically driven system, but also being beneficial to ensuring the stability and safety of driving.

Fig. 11 is a schematic structural diagram of a control system of an autonomous vehicle according to another embodiment of the present invention, which is based on the same inventive concept as the above-described control method. As shown in fig. 11, the lateral control system of the embodiment of the present invention includes:

a receiving unit 1110 for receiving decision information regarding an expected lateral behavior and a longitudinal mode of the vehicle output by a decision system of the autonomous vehicle;

a lateral control unit 1120 for, in response to the expected lateral behavior, correspondingly performing one of the following lateral controls: lane keeping control, vehicle normal lane changing control and vehicle abnormal lane changing control;

a vertical control unit 1130, configured to, in response to the vertical mode, correspondingly perform any one of the following vertical controls: vehicle following control, vehicle cruise control and automatic emergency braking AEB control of the vehicle; and

a safety monitoring unit 1140, configured to perform safety monitoring for lateral control and safety monitoring for longitudinal control when the lateral control and the longitudinal control are performed, respectively.

In a preferred embodiment, the lateral control unit 1120 may include:

a lane keeping control module 1121 for performing the lane keeping control, including: acquiring course angle deviation, transverse position deviation and pre-aiming road curvature of a vehicle; setting a first controller, wherein the first controller inputs the curvature of the pre-aimed road and outputs a first target steering wheel angle which enables the curvature of the pre-aimed road to reach an optimal road curvature which enables the error between the actual running track of the vehicle and the expected track to be minimum; setting a second controller that inputs the lateral position deviation and outputs a second target steering wheel angle at which the lateral position deviation is 0; setting a third controller, wherein the third controller inputs the course angle deviation and outputs a third target steering wheel rotating angle which enables the course angle deviation to be 0; determining a final target steering wheel angle according to the first target steering wheel angle, the second target steering wheel angle and the third target steering wheel angle; controlling the automatic driving vehicle to keep a lane according to the final target steering wheel angle;

the lane change abnormal control module 1122 is configured to execute the vehicle lane change abnormal control, and includes: acquiring the current lateral state values and the target lines, wherein each lateral state value is preconfigured to correspond to a different target line; determining a desired trajectory of the autonomous vehicle from the target line; pre-target tracking control of the autonomous vehicle to a target steering wheel angle based on the desired trajectory; and controlling the automatic driving vehicle to carry out abnormal lane changing of the vehicle according to the target steering wheel angle.

In addition, corresponding to the above-described embodiment of the control method of the autonomous vehicle, the lateral control unit 1120 may further include: and a lane normal lane change control module (not shown in the figure).

In a preferred embodiment, the longitudinal control unit 1130 may include:

a vehicle following control module 1131 for performing the vehicle following control when the longitudinal mode shows that the autonomous vehicle is in a following mode, comprising: matching a control algorithm corresponding to the current working condition according to the corresponding relation between different working conditions and different control algorithms of the automatic driving vehicle in the following mode, wherein the control algorithm is used for controlling the speed change of the automatic driving vehicle under the corresponding working conditions; controlling the automatic driving vehicle to follow according to the matched control algorithm;

a vehicle cruise control module 1132 for performing the vehicle cruise control when the autonomous vehicle is in the cruise mode, comprising: the method comprises the steps of obtaining the current speed of a vehicle in a cruising mode, and calculating the relative speed difference between the current speed and the highest speed at which the vehicle can run; correcting the relative speed difference to enable the variation amplitude of the relative speed difference in a control period to be in a preset range; calculating the acceleration of the vehicle according to the corrected relative speed difference; and adjusting the speed of the vehicle in the cruise mode based on the acceleration of the vehicle.

In addition, corresponding to the above-described embodiment of the control method of the autonomous vehicle, the longitudinal control unit 1130 may further include: an AEB control module (not shown).

In a preferred embodiment, the safety monitoring unit 1140 may include:

a lateral control safety monitoring module 1141, configured to perform the lateral control safety monitoring, including: establishing a corresponding relation between the lateral acceleration of the vehicle, the longitudinal speed and the steering wheel angle; according to the corresponding relation, obtaining the maximum steering wheel corner corresponding to the given maximum lateral acceleration under different vehicle speeds, and taking the maximum steering wheel corner as a corner threshold value; monitoring the steering wheel angle in real time, judging whether the monitored steering wheel angle is larger than the steering wheel threshold value, if so, limiting the steering wheel angle to the steering wheel threshold value and then outputting the steering wheel angle for vehicle transverse control, otherwise, normally outputting the steering wheel angle for vehicle transverse control;

a longitudinal control safety monitoring module 1142, configured to perform the longitudinal control safety monitoring, including: acquiring acceleration thresholds corresponding to given maximum target acceleration under different vehicle speeds according to different longitudinal control states; and monitoring the acquired target acceleration in real time, judging whether the target acceleration is greater than the acceleration threshold, if so, limiting the target acceleration after the acceleration threshold and outputting the target acceleration for longitudinal control of the vehicle, otherwise, normally outputting the target acceleration for longitudinal control of the vehicle.

Other implementation details and effects of the control system of the autonomous vehicle according to the embodiment of the present invention may refer to the embodiment of the control method of the autonomous vehicle, and are not described herein again.

Another embodiment of the present invention also provides a machine-readable storage medium having stored thereon instructions for causing a machine to execute the above-described control method of an autonomous vehicle. The machine-readable storage medium includes, but is not limited to, phase change Memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), Flash Memory (Flash Memory) or other Memory technologies, compact disc read only Memory (CD-ROM), Digital Versatile Disks (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, and the like, which can store program code.

The machine-readable storage medium CAN be executed by a processor on the vehicle, and the processor CAN utilize a vehicle CAN bus and the like to acquire required vehicle information, lane line information, environment information and the like from an environment sensing part and the like of the ADS to judge whether the vehicle is in a lane keeping state, a vehicle normal lane changing state or a vehicle abnormal lane changing state and the like, and correspondingly execute the instructions stored in the machine-readable storage medium.

The Processor may be an ECU (Electronic Control Unit) of the vehicle, or may be an independently configured conventional controller, such as a CPU, a single Chip, a DSP (Digital Signal Processor), an SOC (System On a Chip), and the like, and it is understood that these independent controllers may also be integrated into the ECU. The processor is preferably configured by adopting a controller with high operation speed and abundant I/O port equipment, and is required to be provided with an input/output port capable of communicating with the CAN of the whole vehicle, an input/output port for switching signals, a network cable interface and the like.

The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.

Claims

1. A control method of an autonomous vehicle, applied to an autonomous system of the autonomous vehicle, comprising:

in response to the longitudinal mode, correspondingly performing one of the following longitudinal controls: the automatic emergency braking control method comprises vehicle following control, vehicle cruise control and automatic emergency braking AEB control, wherein in a cruise mode, when an automatic driving vehicle is within an action distance and in a cruise state without a front vehicle, the highest vehicle speed which can be driven by the automatic driving vehicle is adjusted, when the vehicle speed is lower than the highest vehicle speed, the automatic driving vehicle accelerates, and when the vehicle speed is not lower than the highest vehicle speed, the automatic driving vehicle decelerates to drive; and

2. The control method of an autonomous vehicle according to claim 1, characterized in that executing the lane-keeping control includes:

acquiring course angle deviation, transverse position deviation and pre-aiming road curvature of a vehicle;

setting a first controller, wherein the first controller inputs the curvature of the pre-aimed road and outputs a first target steering wheel angle which enables the curvature of the pre-aimed road to reach an optimal road curvature which enables the error between the actual running track of the vehicle and the expected track to be minimum;

setting a second controller that inputs the lateral position deviation and outputs a second target steering wheel angle at which the lateral position deviation is 0;

setting a third controller, wherein the third controller inputs the course angle deviation and outputs a third target steering wheel rotating angle which enables the course angle deviation to be 0;

determining a final target steering wheel angle according to the first target steering wheel angle, the second target steering wheel angle and the third target steering wheel angle; and

controlling the automatic driving vehicle to keep a lane according to the final target steering wheel angle;

preferably, the first controller is configured to establish a mapping relationship between the target steering wheel angle and the pre-line road curvature based on ackermann steering principles; and/or the second controller is configured to determine a control coefficient of the second controller for fuzzy PID control according to the real-time vehicle speed of the automatic driving vehicle.

3. The control method of an autonomous vehicle as claimed in claim 1, characterized in that the executing of the vehicle abnormal lane change control includes:

acquiring current horizontal state values and target lines, wherein each horizontal state value is preconfigured to correspond to a different target line;

determining a desired trajectory of the autonomous vehicle from the target line;

pre-target tracking control of the autonomous vehicle to a target steering wheel angle based on the desired trajectory; and

and controlling the automatic driving vehicle to carry out abnormal lane changing of the vehicle according to the target steering wheel angle.

4. The control method of an autonomous-capable vehicle as recited in claim 3, wherein the each lateral state value being preconfigured to correspond to a different one of the target lines comprises:

when the transverse state value is a first numerical value, the target line is the center line of the current lane;

when the transverse state value is a second numerical value, the target line is a left lane central line;

when the transverse state value is a third numerical value, the target line is the center line of the right lane;

when the transverse state value is a fourth numerical value, the target line is a dynamic deviation line of the current lane;

when the transverse state value is a fifth numerical value, the target line is a lane crossing dynamic deviation line;

when the transverse state value is a sixth numerical value, the target line is a left safety offset line; and

and when the transverse state value is a seventh numerical value, the target line is a right safety offset line.

5. The control method of an autonomous vehicle according to claim 1, characterized in that executing the vehicle following control includes:

performing, when the longitudinal mode shows that the autonomous vehicle is in a following mode:

matching a control algorithm corresponding to the current working condition according to the corresponding relation between different working conditions and different control algorithms of the automatic driving vehicle in the following mode, wherein the control algorithm is used for controlling the speed change of the automatic driving vehicle under the corresponding working conditions; and

controlling the automatic driving vehicle to follow according to the matched control algorithm;

while the autonomous vehicle is in the cruise mode, performing:

the method comprises the steps of obtaining the current speed of a vehicle in a cruising mode, and calculating the relative speed difference between the current speed and the highest speed at which the vehicle can run;

correcting the relative speed difference to enable the variation amplitude of the relative speed difference in a control period to be in a preset range;

calculating the acceleration of the vehicle according to the corrected relative speed difference; and

and adjusting the speed of the vehicle in the cruise mode based on the acceleration of the vehicle.

6. The control method of an autonomous-capable vehicle as claimed in claim 5, characterized in that the correspondence between different conditions and different control algorithms of the autonomous-capable vehicle in the following mode comprises any one or more of:

the speed of the front vehicle is smaller than the speed of the vehicle, the actual distance between the front vehicle and the vehicle is smaller than the expected distance, and a first control algorithm for controlling the vehicle to decelerate at a first acceleration under the first working condition;

the speed of the front vehicle is smaller than the speed of the vehicle, the actual distance between the front vehicle and the vehicle is larger than the expected distance, and a second control algorithm is used for controlling the vehicle to decelerate at a second acceleration under the second working condition;

the speed of the front vehicle is greater than the speed of the vehicle, the actual distance between the front vehicle and the vehicle is greater than the expected distance, and a third control algorithm is used for controlling the vehicle to accelerate at a third acceleration under the third working condition;

the speed of the front vehicle is greater than the speed of the vehicle, and the actual distance between the front vehicle and the vehicle is smaller than the expected distance, and a fourth control algorithm for controlling the vehicle to decelerate at a fourth acceleration under the fourth condition; and

the speed difference between the speed of the front vehicle and the speed of the main vehicle is within a set speed threshold range, and the actual distance between the front vehicle and the main vehicle is within a set distance threshold range, and a fifth control algorithm for controlling the main vehicle to stably follow the front vehicle under the fifth working condition.

7. The control method of an autonomous vehicle as claimed in claim 1, characterized in that performing the lateral control safety monitoring comprises:

establishing a corresponding relation between the lateral acceleration of the vehicle, the longitudinal speed and the steering wheel angle;

according to the corresponding relation, obtaining the maximum steering wheel corner corresponding to the given maximum lateral acceleration under different vehicle speeds, and taking the maximum steering wheel corner as a corner threshold value; and

and monitoring the steering wheel angle in real time, judging whether the monitored steering wheel angle is larger than the steering wheel threshold value, if so, limiting the steering wheel angle to the steering wheel threshold value and then outputting the steering wheel angle for vehicle transverse control, otherwise, normally outputting the steering wheel angle for vehicle transverse control.

8. The control method of an autonomous-capable vehicle as claimed in claim 7, wherein the establishing correspondence between lateral acceleration of the vehicle and longitudinal vehicle speed and steering wheel angle includes:

according to the vehicle kinematic model and the vehicle parameters, establishing the corresponding relation as follows:

wherein the content of the first and second substances,

is the steering wheel angle, L is the vehicle wheelbase,a _yin order to be said for the lateral acceleration,uin order to be the longitudinal vehicle speed,

is the steering system gear ratio; and/or

According to a vehicle dynamic model and vehicle parameters, establishing corresponding relations among the lateral acceleration, the longitudinal speed, the front wheel rotation angle and the vehicle parameters as follows:

wherein k is₁Is front axle equivalent yaw stiffness, k₂Is the equivalent lateral deflection stiffness of the rear axle, a is the distance from the center of mass to the front axle, b is the distance from the center of mass to the rear axle,

is a front wheel corner; and then determining the corresponding relation between the lateral acceleration and the longitudinal speed as well as the steering wheel angle according to the following conversion relation between the front wheel angle and the steering wheel angle:

。

9. the control method of an autonomous vehicle as claimed in claim 1, characterized in that performing the longitudinal control safety monitoring comprises:

acquiring acceleration thresholds corresponding to given maximum target acceleration under different vehicle speeds according to different longitudinal control states; and

and monitoring the acquired target acceleration in real time, judging whether the target acceleration is greater than the acceleration threshold, if so, limiting the target acceleration after the acceleration threshold and outputting the target acceleration for longitudinal control of the vehicle, otherwise, normally outputting the target acceleration for longitudinal control of the vehicle.

10. A control system for an autonomous vehicle, applied to an autonomous system for an autonomous vehicle, comprising:

a lateral control unit for, in response to the expected lateral behavior, correspondingly performing one of the following lateral controls: the automatic lane changing control system comprises lane keeping control, vehicle normal lane changing control and vehicle abnormal lane changing control, wherein in the cruising mode, when the automatic driving vehicle is within the action distance and in the cruising state that no front vehicle exists, the highest vehicle speed which can be driven by the automatic driving vehicle is adjusted, when the vehicle speed is lower than the highest vehicle speed, the automatic driving vehicle accelerates, and when the vehicle speed is not lower than the highest vehicle speed, the automatic driving vehicle decelerates to drive;

11. The control system of an autonomous vehicle as claimed in claim 10, characterized in that the lateral control unit comprises:

a lane-keeping control module for performing the lane-keeping control, comprising:

controlling the automatic driving vehicle to keep a lane according to the final target steering wheel angle; and

the lane changing control module is used for executing the vehicle lane changing control and comprises:

12. The control system of an autonomous vehicle as claimed in claim 10, characterized in that the longitudinal control unit comprises:

a vehicle following control module for executing the vehicle following control when the longitudinal mode shows that the autonomous vehicle is in a following mode, including:

a vehicle cruise control module for performing the vehicle cruise control when the autonomous vehicle is in the cruise mode, comprising:

13. The control system of an autonomous vehicle as claimed in claim 10, characterized in that the safety monitoring unit comprises:

the lateral control safety monitoring module is used for carrying out the lateral control safety monitoring, and comprises:

monitoring the steering wheel angle in real time, judging whether the monitored steering wheel angle is larger than the steering wheel threshold value, if so, limiting the steering wheel angle to the steering wheel threshold value and then outputting the steering wheel angle for vehicle transverse control, otherwise, normally outputting the steering wheel angle for vehicle transverse control;

a longitudinal control safety monitoring module for performing the longitudinal control safety monitoring, comprising:

14. A machine-readable storage medium having stored thereon instructions for causing a machine to execute the method of controlling an autonomous vehicle of any of claims 1 to 9.