CN113978548B - Steering cooperative control method, device, equipment and medium applied to unmanned vehicle - Google Patents
Steering cooperative control method, device, equipment and medium applied to unmanned vehicle Download PDFInfo
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Abstract
The embodiment of the invention discloses a steering cooperative control method, a steering cooperative control device, steering cooperative control equipment and a steering cooperative control medium, wherein the method comprises the following steps: acquiring current steering angle information of an unmanned vehicle and a man-machine control state of the unmanned vehicle; determining a target control gain coefficient corresponding to the unmanned vehicle according to the current steering angle information and the man-machine control state; and determining the cooperative control parameters of the unmanned vehicle according to the target control gain coefficient and a preset state feedback matrix so as to control the unmanned vehicle based on the cooperative control parameters. According to the technical scheme of the embodiment of the invention, the control of the steering behavior of the unmanned vehicle by the automatic driving system is realized, the divergence of human-machine decision is reduced, and the human-machine coordination in the co-driving system is improved.
Description
Technical Field
The embodiment of the invention relates to the technical field of computers, in particular to a steering cooperative control method, device, equipment and medium applied to an unmanned vehicle.
Background
At present, a common driving system can be arranged in an unmanned vehicle, and the common driving system also comprises two sets of human-machine decisions. Based on the above, by using the existing common driving system, on one hand, the driver can autonomously control the unmanned vehicle, and on the other hand, the automatic driving system can judge the driving state of the unmanned vehicle based on specific information and issue a control command to the unmanned vehicle, thereby realizing the functions of automatic driving or auxiliary driving.
When the present invention is implemented based on the above-described embodiments, the inventors have found that the following problems occur:
when two sets of human-machine decisions exist in the co-driving system, decision divergence may occur. For example, in the process of operating an unmanned vehicle by a driver, the real-time intention of the driver provides important information for an automatic driving system to predict driver behaviors, evaluate driving risks of the unmanned vehicle and understand complex driving scenes, and if the automatic driving system misjudges or ignores the real intention of the driver, serious man-machine conflict can be caused, so that the driving experience of the driver and the reliability of the automatic driving system can be reduced, the tracking accuracy of the track of the unmanned vehicle can be reduced, and the driving safety of the unmanned vehicle can be threatened.
Therefore, in the existing co-driving system, an effective solution and a way for solving the problem of human-machine decision divergence are lacked.
Disclosure of Invention
The invention provides a steering cooperative control method, a steering cooperative control device, steering cooperative control equipment and a steering cooperative control medium applied to an unmanned vehicle, which are used for realizing the control of an automatic driving system on the steering behavior of the unmanned vehicle, reducing the divergence of human-computer decision and improving the human-computer coordination in a common driving system.
In a first aspect, an embodiment of the present invention provides a steering cooperative control method applied to an unmanned vehicle, where the method includes:
acquiring current steering angle information of an unmanned vehicle and a man-machine control state of the unmanned vehicle;
determining a target control gain coefficient corresponding to the unmanned vehicle according to the current steering angle information and the man-machine control state; the target control gain coefficient is used for controlling the transverse driving and the longitudinal driving of the unmanned vehicle;
and determining the cooperative control parameters of the unmanned vehicle according to the target control gain coefficient and a preset state feedback matrix so as to control the unmanned vehicle based on the cooperative control parameters.
In a second aspect, an embodiment of the present invention further provides a steering cooperative control apparatus applied to an unmanned vehicle, where the apparatus includes:
the information acquisition module is used for acquiring the current steering angle information of the unmanned vehicle and the man-machine control state of the unmanned vehicle;
the gain coefficient determining module is used for determining a target control gain coefficient corresponding to the unmanned vehicle according to the current steering angle information and the man-machine control state; the target control gain coefficient is used for controlling the transverse driving and the longitudinal driving of the unmanned vehicle;
and the cooperative control module is used for determining cooperative control parameters of the unmanned vehicle according to the target control gain coefficient and a preset state feedback matrix so as to control the unmanned vehicle based on the cooperative control parameters.
In a third aspect, an embodiment of the present invention further provides an electronic device, where the electronic device includes:
one or more processors;
a storage device to store one or more programs,
when the one or more programs are executed by the one or more processors, the one or more processors implement the steering cooperative control method applied to the unmanned vehicle according to any one of the embodiments of the present invention.
In a fourth aspect, the present invention further provides a storage medium containing computer-executable instructions, which when executed by a computer processor, are configured to perform the steering cooperative control method applied to an unmanned vehicle according to any one of the embodiments of the present invention.
According to the technical scheme of the embodiment of the invention, the current steering angle information of the unmanned vehicle and the man-machine control state of the unmanned vehicle are firstly obtained, then the target control gain coefficient corresponding to the unmanned vehicle is determined according to the current steering angle information and the man-machine control state, so that the parameters are quantized, the cooperative control parameters of the unmanned vehicle are determined according to the target control gain coefficient and the preset state feedback matrix, the unmanned vehicle is controlled based on the cooperative control parameters, the control of the steering behavior of the unmanned vehicle by the automatic driving system is realized, the divergence of man-machine decision is reduced while the driving of the unmanned vehicle is led or the driving of a driver is assisted, and the man-machine harmony in the co-driving system is improved.
Drawings
In order to more clearly illustrate the technical solution of the exemplary embodiment of the present invention, a brief introduction will be made to the drawings required for describing the embodiment. It should be clear that the described figures are only views of some of the embodiments of the invention to be described, not all, and that for a person skilled in the art, other figures can be derived from these figures without inventive effort.
Fig. 1 is a schematic flow chart of a steering cooperative control method applied to an unmanned vehicle according to a first embodiment of the present invention;
fig. 2 is a schematic flow chart of a steering cooperative control method applied to an unmanned vehicle according to a second embodiment of the present invention;
FIG. 3 is a diagram of a time-varying parameter operating point (t) according to a second embodiment of the present invention p κ) and actual measurement point (| P) h -P m | ρ) of the images;
FIG. 4 is a schematic diagram of a human-machine cooperative lane keeping platform according to a second embodiment of the present invention;
fig. 5 is a block diagram of a steering cooperative control device applied to an unmanned vehicle according to a third embodiment of the present invention;
fig. 6 is a schematic structural diagram of an electronic device according to a fourth embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some of the structures related to the present invention are shown in the drawings, not all of the structures.
Example one
Fig. 1 is a schematic flow chart of a steering cooperative control method applied to an unmanned vehicle according to an embodiment of the present invention, where the present embodiment is applicable to a situation where a driver and an automatic driving system are in a driving-together mode in the unmanned vehicle, and the method may be executed by a steering cooperative control apparatus applied to the unmanned vehicle, where the apparatus may be implemented in the form of software and/or hardware, and the hardware may be electronic equipment, such as a mobile terminal, a PC terminal, or a server.
As shown in fig. 1, the method specifically includes the following steps:
and S110, acquiring current steering angle information of the unmanned vehicle and a man-machine control state of the unmanned vehicle.
The steering angle information refers to an angle formed by the fact that the front wheel of the unmanned vehicle turns to the left or right to the limit position and the center line when the front wheel does not deflect. In the process of practical application, the steering angle information can be detected by a specific sensor, and can also be obtained by calculating and converting other parameters, for example, the steering angle of the unmanned vehicle at a certain moment is determined based on the rotating speed of the steering wheel and the time information. In the present embodiment, the steering angle is used as a parameter, and at least the current steering state and the steering degree of the unmanned vehicle can be visually reflected.
In this embodiment, the unmanned vehicle may be an unmanned vehicle with an autonomous driving system deployed, and it is understood that the unmanned vehicle may be in at least three states when the driver and the autonomous driving system are in a common driving mode with the assistance of the autonomous driving function. The first state is a driving state completely dominated by a driver, and in the state, the unmanned vehicle is completely controlled by the driver; the second state is a driving state under the complete dominance of the automatic driving system, and in the state, the automatic driving system can realize real-time and continuous control on the unmanned vehicle based on network communication, a computer and an automatic control technology; the third state is the driving state that the driver leads, the automatic driving system assists, and drive the mode altogether promptly, and under this kind of state, the driver can independently control unmanned car, and simultaneously, the automatic driving system can gather and draw driver's behavior characterization parameter, and then assigns specific control command to unmanned car and assists the driver to control unmanned car.
Furthermore, the driver and the automatic driving system can independently issue control instructions to the unmanned vehicle in corresponding modes, so that when the unmanned vehicle is in a common driving mode, various man-machine control states are also correspondingly provided. Specifically, the man-machine control states can be three, the first is a man-machine cooperation state, namely, the direction of the torque generated when the driver operates the unmanned vehicle is consistent with the direction of the torque generated when the automatic driving system assists in operating the unmanned vehicle, and in the man-machine cooperation state, the driver can obtain better unmanned vehicle controllability under the condition of small output; the second is a man-machine conflict state which is dominant by a driver, in the state, the unmanned vehicle is mainly controlled by the driver, and the direction of the moment generated when the driver operates the unmanned vehicle is opposite to the direction of the moment generated when the automatic driving system assists to operate the unmanned vehicle; the third is a man-machine conflict state dominated by the automatic driving system, in the state, the unmanned vehicle is mainly controlled by the automatic driving system, and the direction of the moment generated when the driver operates the unmanned vehicle is opposite to the direction of the moment generated when the automatic driving system assists to operate the unmanned vehicle.
In the practical application process, the human-machine control state can be obtained based on specific parameters, for example, the steering torque applied to the unmanned vehicle by a driver and the steering torque applied to the unmanned vehicle by an automatic driving system are collected, and then the specific calculation change is performed on the steering torque to determine the human-machine control state.
And S120, determining a target control gain coefficient corresponding to the unmanned vehicle according to the current steering angle information and the man-machine control state.
It should be noted that, when the unmanned vehicle is in the common driving mode, the automatic driving system collects the driver and the current environment information, and then issues the cooperative control parameter to the unmanned vehicle, it can be understood that the cooperative control parameter may be a variety of parameters that enable the unmanned vehicle to generate a specific action so as to assist the driver in driving, and if the unmanned vehicle steers under the control of the driver, the cooperative control parameter may be a specific value of the steering torque issued by the automatic driving system to the unmanned vehicle.
In this regard, in the present embodiment, the target control gain factor is a factor for adjusting the cooperative control parameter output by the automatic driving system, and specifically, the target control gain factor is a value amplified or reduced as one control parameter, and is used at least for controlling the lateral running and the longitudinal running of the unmanned vehicle. From the aspect of transverse driving, the steering angle information of the unmanned vehicle can be controlled based on the target control gain coefficient, and the turning angle of the vehicle is further controlled; from the perspective of longitudinal travel, the information such as the speed and acceleration of the unmanned vehicle can be controlled based on the target control gain coefficient. It should be understood by those skilled in the art that, after the target control gain coefficient is introduced into the automatic driving system, whether to trigger the control effect of the target control gain coefficient on the lateral driving and the longitudinal driving of the vehicle may be selected according to actual situations, and the embodiment of the present disclosure is not specifically limited herein.
Taking the steering process of the unmanned vehicle as an example, the target control gain coefficient may be a steering control gain penalty coefficient κ. It can be understood that, when the automatic driving system generates a specific value of the steering torque, the generated steering torque can be made to better conform to the intention of the driver based on the steering control gain penalty coefficient κ, so that the driving assistance effect is achieved when the driver controls the steering of the unmanned vehicle.
In the present embodiment, the target control gain coefficient may be obtained based on the steering angle information and the human-machine control state acquired in the foregoing process. Specifically, based on steering angle information (including steering wheel rotation speed, steering time information and the like) issued by a driver and an automatic driving system to the unmanned vehicle, when the unmanned vehicle is in different human-computer control states, a steering control gain penalty coefficient k corresponding to the human-computer control state can be determined.
For example, when the unmanned vehicle is in the human-computer cooperation state, based on the information in the human-computer cooperation state and the current steering angle information of the unmanned vehicle, a steering control gain penalty coefficient k can be determined 1 Penalty factor k based on steering control gain 1 The steering torque issued by the automatic driving system to the unmanned vehicle at present can be maintained or finely adjusted, and the direction of the issued steering torque cannot be changed; when the unmanned vehicle is in a man-machine conflict state dominated by a driver or dominated by an automatic driving system, a steering control gain penalty coefficient kappa can be determined based on information in the man-machine conflict state and current steering angle information of the unmanned vehicle 2 Penalty factor k based on steering control gain 2 The direction of the steering torque currently applied to the unmanned vehicle by the automatic driving system can be changed. It should be noted that, under different human-computer cooperation states, the steering control gain penalty coefficient k determined by the unmanned vehicle 1 、κ 2 The parameter may be an empirical parameter obtained through a plurality of simulation experiments.
And S130, determining the cooperative control parameters of the unmanned vehicle according to the target control gain coefficient and a preset state feedback matrix so as to control the unmanned vehicle based on the cooperative control parameters.
Wherein, in order to make the automatic driving system accurately obtain the cooperative control parameter, a state feedback matrix can be preset. Those skilled in the art should understand that the state feedback is a way that the state variable of the system is transmitted to the input end for feedback through a proportional link, and the corresponding matrix is a state feedback matrix, and the state feedback matrix can at least fully reflect the internal characteristics of the system, at least is used for improving the performance of the automatic driving system, and can be used for adjusting the cooperative control parameter output by the automatic driving system in the specific steering process of the unmanned vehicle.
In this embodiment, the cooperative control parameter of the unmanned vehicle is obtained based on the target gain coefficient and the state feedback matrix, so as to control the unmanned vehicle. Continuing with the above example, during the steering process of the unmanned vehicle, when the unmanned vehicle is in the human-machine cooperation state, the steering control gain penalty coefficient k is determined based on the current steering angle information and the human-machine control state 1 Penalty factor k based on steering control gain 1 The steering torque currently issued to the unmanned vehicle by the automatic driving system can be maintained or finely adjusted, so that the direction of the steering torque applied to the unmanned vehicle by the automatic driving system is continuously kept to be the same as the direction of the steering torque applied to the unmanned vehicle by the driver, namely, when the driver controls the unmanned vehicle to steer, the automatic driving system is kept to correctly understand the intention of the driver, and the effect of assisting driving is realized; when the unmanned vehicle is in a man-machine conflict state dominated by a driver or dominated by an automatic driving system, a steering control gain penalty coefficient kappa is determined based on the current steering angle information and the man-machine control state 2 Penalty factor k based on steering control gain 2 The direction of the steering torque applied to the unmanned vehicle by the automatic driving system at present can be changed, namely, the direction of the steering torque is the same as the direction of the steering torque applied to the unmanned vehicle by the driver, and therefore the condition of man-machine conflict is relieved.
According to the technical scheme, the current steering angle information and the man-machine control state of the unmanned vehicle are obtained, the target control gain coefficient corresponding to the unmanned vehicle is determined according to the current steering angle information and the man-machine control state, the parameters are quantized, the cooperative control parameters of the unmanned vehicle are determined according to the target control gain coefficient and a preset state feedback matrix, the unmanned vehicle is controlled based on the cooperative control parameters, the steering behavior of the unmanned vehicle is controlled by the automatic driving system, the divergence of man-machine decision is reduced while the unmanned vehicle is driven or a driver is assisted, and the man-machine harmony in the common driving system is improved.
Example two
Fig. 2 is a schematic flow chart of a steering cooperative control method applied to an unmanned vehicle according to a second embodiment of the present invention, and based on the second embodiment, a motion mode of the unmanned vehicle is predefined, so as to facilitate further optimization of an adjustment machine of a steering control gain penalty coefficient κ; the man-machine steering power is introduced and a corresponding quantification method is adopted, so that the control right of the man-machine can be adjusted more timely when man-machine conflict occurs; the differentiation state of the unmanned vehicle is determined through the preset mapping relation, the flexibility and the adaptability of the automatic driving system in the decision process are improved, further, the anti-aliasing filtering processing is carried out on the gain coefficient to be processed and controlled, the flexible scheduling of the control right of the unmanned vehicle is realized, and the problem that the steering guiding auxiliary torque is unsmooth due to the jumping of the steering control gain punishment coefficient kappa is avoided; and adjusting the running speed and the angular speed of the unmanned vehicle and the theoretical steering angle within the preset time length based on the acquired cooperative control parameters, so that the consistency of man-machine decision is ensured. The technical scheme of the embodiment can be referred to for the specific implementation mode. The technical terms that are the same as or corresponding to the above embodiments are not repeated herein.
As shown in fig. 2, the method specifically includes the following steps:
s210, collecting current steering angle information of the unmanned vehicle based on a preset steering angle collecting sensor; and acquiring the man-machine control state of the unmanned vehicle based on a preset control state sensor.
In this embodiment, in order to obtain the steering angle information of the unmanned vehicle, a steering angle collector may be preset on the unmanned vehicle. The steering angle acquisition sensor is a device for positioning a steering wheel or a front wheel of the unmanned vehicle so as to determine the angular position of a specific shaft in the unmanned vehicle, and comprises a coil set, a coil support, a coupler element and the like. It can be understood that after the steering angle collecting sensor determines the current steering angle information of the unmanned vehicle, the current steering angle information can be sent to a vehicle machine system or an automatic driving system, so that the system executes a subsequent processing process based on the parameter.
In the practical application process, when the steering angle information is obtained by calculating the rotating speed of the steering wheel and the time information, the steering angle acquisition sensor can also be a sensor for acquiring the rotating speed of the steering wheel, and after the sensor acquires the rotating speed of the steering wheel and transmits the rotating speed to a vehicle machine system or an automatic driving system, the sensor can obtain the corresponding steering angle information in the derivative form after processing
As will be appreciated by those skilled in the art,
that is, the differential form of the steering angle information, and meanwhile, the specific deployment and installation manner of the steering angle acquisition sensor should be selected according to the actual situation, which is not described herein again in the embodiments of the present disclosure.
It should be noted that the steering angle collecting sensor obtains the current steering angle information of the unmanned vehicle
And then, the current motion mode of the unmanned vehicle can be determined. In practical application, two motion modes can be predefined and expressed by M T To show how, in particular,
as can be understood by the formula, when M T When the steering mode is more than 0, the direction of the unmanned vehicle in the turning process is not reached to the target direction, and when M is greater than 0 T Less than 0 in the return mode indicates that the direction of the unmanned vehicle during the turn has approached or reached the target direction.
The unmanned vehicle movement mode is predefined, and further optimization of the adjusting machine of the steering control gain penalty coefficient kappa in the technical scheme provided by the embodiment of the disclosure is facilitated.
In this embodiment, the control state sensor may be a sensor that collects relevant information of a control state of the unmanned vehicle, and it should be understood by those skilled in the art that the sensor may at least determine the relevant information after collecting the relevant information, so as to determine whether the unmanned vehicle is in a human-computer cooperation state at the current moment, a human-computer conflict state dominated by a driver, or a human-computer conflict state dominated by an automatic driving system.
And S220, determining a gain coefficient to be processed of the unmanned vehicle according to the man-machine control state and the current steering angle information.
In this embodiment, after the various acquisition sensors acquire and acquire the human-machine control state and the steering angle information of the unmanned vehicle at the current time, a corresponding to-be-processed control gain coefficient may be directly obtained, and in the subsequent data processing process, the vehicle-mounted device system or the automatic driving system may process the to-be-processed control gain coefficient based on a specific algorithm (such as a steering control gain penalty coefficient adjustment mechanism), so as to obtain a target to-be-processed control gain coefficient.
Optionally, determining a steering parameter corresponding to the human-machine system according to the current steering angle information and a preset time window; and determining a gain coefficient to be processed according to the steering power of the target person or the steering power of the automatic driving system in the steering parameters and the man-machine control state.
Wherein the steering parameters comprise target person steering power and automatic driving system steering power. The steering power refers to the power applied to the unmanned vehicle by a steering torque in the process of controlling the steering of the unmanned vehicle by a driver or an automatic driving system, and specifically, the steering power of a target person can be P h To indicate that the autopilot system steering power may be in P m To indicate.
In this embodiment, after the steering angle acquisition sensor acquires the current steering angle information of the unmanned vehicle, the steering power of the target person and the steering power of the automatic driving system can be determined by combining a preset time window. In particular, a time window parameter T is predefined w During this time, the yaw pseudo-work (pseu) applied to the unmanned vehicle by the driver and the autonomous driving system, respectively, can be obtained by calculationdo raw energy, PYE), namely the product of steering torque of both human and machine parties and lateral movement of the unmanned vehicle, at least reflecting the contribution degree of a driver and an automatic driving system to the lateral movement of the unmanned vehicle, and quantitatively describing the human-machine conflict degree and the dominant weight of both to the unmanned vehicle. The formulas adopted in the calculation process are respectively
And
further, after the yaw pseudo-work corresponding to the driver and the automatic driving system is determined, the ratio of the yaw pseudo-work to the time information is taken, and then the steering power P of the target person can be obtained h And the steering power P of the automatic driving system m 。
In the practical application process, when the steering angle information is acquired
Steering power P of the target person, obtained on the basis of the steering wheel speed h And steering power P of automatic driving system m It can also be obtained directly on the basis of the product of the steering torque applied by the human machine to the unmanned vehicle and the steering wheel rotation speed. The target person steering power calculation formula corresponding to the driver may be
The steering power calculation formula of the automatic driving system corresponding to the automatic driving system can be
Because the human-computer Steering Power (Steering Power) is used for judging that the lag time of human-computer conflict is less than the response from the driver torque to the lateral movement of the whole vehicle, the human-computer Steering Power is used for describing the magnitude of the human-computer conflict degree in the practical application process, and the quantization method can adjust the human-computer control right in time when the human-computer conflict occurs while reflecting the dominant weight of the human-computer parties on the control of the unmanned vehicle.
Optionally, according to a mapping relationship established in advance, an objective function corresponding to the steering parameter and the human-machine control state is determined, and a control gain coefficient to be processed is determined based on the objective function.
The mapping relation comprises functions of different steering parameters and the determined control gain coefficients corresponding to the man-machine control states, and in the practical application process, the mapping relation is pre-established for the parameters, namely the process of setting an adjusting mechanism for the steering control gain of the automatic driving system. The mapping relationship established in advance for determining the objective function is explained in detail below.
(1) Defining the positive direction of the steering torque, namely tau when the man-machine steering torque is the same ha And τ m When all are greater than 0 or all are less than 0, it can be defined that the unmanned vehicle and motor cooperation state is in the state A;
(2) Determining the current steering angle information of the unmanned vehicle
Then, it is judged
Whether the difference is greater than 0 or less than 0, and two different cases are further divided by using 0 as a boundary point.
(3) On the basis of the above, for
Greater than 0, when the steering torque tau is applied to the unmanned vehicle by the driver ha Greater than 0, and steering torque tau applied to the unmanned vehicle by the autopilot system m When the current time is less than 0, the situation that the unmanned vehicle is in a state B in a motor-generator cooperation state can be defined; steering moment tau when applied to an unmanned vehicle by a driver ha Less than 0, and steering torque tau applied to the unmanned vehicle by the autopilot system m If greater than 0, it may be defined that the unmanned vehicle and motor cooperation state is in state C.
(4) On the basis of the above, for
Less than 0, when the steering torque tau applied to the unmanned vehicle by the driver ha Less than 0, and steering torque tau applied to the unmanned vehicle by the autopilot system m When the value is more than 0, the cooperation state of the unmanned vehicle and the locomotive can be defined to be in a state B; steering moment tau when applied to an unmanned vehicle by a driver ha Greater than 0, and steering torque tau applied to the unmanned vehicle by the autopilot system m And when the current time is less than 0, the unmanned vehicle and motor cooperation state can be defined to be in the state C.
Furthermore, in the mapping relation constructed in advance, the contents represented by the state A, the state B and the state C are different, and the functions corresponding to the states for determining the control gain coefficient are different, so that when the unmanned vehicle is judged to be in different states, the function corresponding to the current state can be selected through the mapping relation, information collected by the collecting sensor is used as input, and the corresponding control gain coefficient to be processed is obtained through calculation based on the function.
Specifically, when the human-computer cooperation state is in the state A, which indicates that the unmanned vehicle is in the human-computer cooperation state, the directions of the human-computer steering torques are consistent, and the steering control gain penalty coefficient k is kept at the minimum value so as to fully exert the auxiliary effect of the automatic driving system, in the state, k = k 1 (ii) a When the human-machine cooperation state is in the state B, which indicates that the unmanned vehicle is in a human-machine conflict state dominated by the driver, the human-machine steering torque direction is opposite (the steering power of the driver is positive), the steering control gain penalty coefficient k is increased to reduce the interference on the steering control of the driver, and in the state, k = k (P = k) (the steering power of the driver is positive) m )。
Since the steering mode and the return mode are predefined for the steering system of the unmanned vehicle, when the human-computer cooperation state is in the state C, the value of κ is different for different modes. When the man-machine cooperation state is in the state C, the man-machine steering torque is opposite (the steering power of the automatic steering system is positive). Further, when the steering system of the unmanned vehicle is in a steering mode, the control authority of the system is kept unchanged to realize the guiding steering dominated by the automatic driving system, and the guiding steering dominated by the automatic driving system is realized in the steering modeIn state, κ = κ 1 (ii) a When the steering system of the unmanned vehicle is in a return-to-normal mode, the steering control gain penalty coefficient kappa is increased to reduce the man-machine conflict in the return-to-normal process of the steering wheel, and the return-to-normal overshoot condition is avoided.
It should be noted that, in the embodiment of the present disclosure, the steering control gain penalty law is defined by a hyperbolic tangent activation function, and an independent variable of the function is the target person steering power P h And steering power P of automatic driving system m . The specific functional formula may be:
wherein the content of the first and second substances,
the return-to-positive mode corresponding to states B and C, and κ (P) h ,P m )＝κ 1 For the other cases, let κ be assumed 1 ＝5，κ 2 ＝60， D 0 ＝1.5，X 0 =3, which can be substituted into the above equation to get the corresponding result.
And S230, performing anti-aliasing filtering processing on the control gain coefficient to be processed to obtain a target control gain coefficient.
In this embodiment, in order to implement flexible scheduling of the control right and avoid the problem that the steering assist torque provided by the automatic driving system is not smooth due to the jump of the steering control gain penalty coefficient κ based on the steering power, the steering control gain penalty coefficient κ serving as the control gain coefficient to be processed needs to be output through a 4-step Butterworth (Butterworth) anti-mixing filter with a cutoff frequency ω c =120rad/s, parameter f 1 ＝2.6131，f 2 ＝3.4142，f 3 =2.6131, the specific calculation formula is
It is understood that the steering control gain penalty coefficient κ subjected to the anti-aliasing filtering process is the target control gain coefficient.
S240, determining the preview duration according to the acquired curvature and a mapping relation established in advance; and determining the cooperative control parameters of the unmanned vehicle according to the preview duration, the target control gain coefficient and the state feedback matrix so as to control the unmanned vehicle based on the cooperative control parameters.
In this embodiment, the road curvature ρ and the preview time t may also be pre-established p In particular, the mapping relationship can be obtained by the curvature ρ of the road in the fourth quadrant of fig. 3 and the preview time t p Is shown by the mapping relation curve of (2). On the basis, after the curvature radius of the road is collected by using a sensor, the reciprocal of the curvature radius is taken to determine the curvature corresponding to the current road, and the preview time length can be obtained based on the established mapping relation.
The curvature radius and the curvature are in reciprocal relation, and the curvature is the rotation rate of a tangent direction angle to an arc length of a certain point on a curve. The preview duration is duration corresponding to a preview process of the unmanned vehicle, and can be understood as that the preview duration is used for representing duration used for obtaining the curvature, a specific numerical value of the preview duration corresponds to the determined curvature of the road, and when the determined curvature of the road changes, preview time corresponding to the curvature of the road also changes adaptively.
In this embodiment, the image in the fourth quadrant of fig. 3 represents a mapping relationship between the preview time and the curvature of the road, and it can be determined based on the image that when the curvature of the road is within a certain range, a minimum preview time period can be obtained, that is, when the curvature of the road is a specific value within the range, the time required by the automatic driving system to determine the specific value is the shortest.
The technical scheme includes that the unmanned vehicle is subjected to forward aiming mechanism, namely, an automatic driving system detects forward by means of a specific device in the driving process of the unmanned vehicle so as to simulate the real driving effect of a driver, and the method mainly comprises the step of calculating the error between the current position of the unmanned vehicle and a pre-aiming point through a state matrix in an LQR algorithm so as to issue a control command to enable the unmanned vehicle to make an action in advance.
Further, after the preview duration is determined based on the acquired curvature radius and the mapping relation, the cooperative control parameters of the unmanned vehicle can be determined according to the preview duration, the target control gain coefficient and the state feedback matrix. In practical applications, in order to implement the above process, a robust control strategy of linear variable parameters can be deployed for the unmanned vehicle.
In the present embodiment, the autopilot system is based on the preview duration t as a time-varying parameter p And a steering control gain penalty coefficient kappa, which can simulate the steering behavior of a driver and avoid continuous man-machine steering torque conflict through dynamic scheduling of control right. To ensure robust stability of the system within the full-parameter control, the disclosed embodiments employ a state feedback matrix F (t) of linear time-varying parameters p κ) to design an autopilot steering control law, i.e. based on a state feedback matrix F (t) p And kappa) deploying a robust control strategy of linear variable parameters for the unmanned vehicle to obtain specific cooperative control parameters
And
in this embodiment, the mapping relationship further includes a mapping relationship diagram based on actually acquired road curvature, target person steering power, steering power of the automatic driving system, and preview time. With continued reference to FIG. 3, except for the representation of the road curvature ρ and the preview time t p Outside the fourth quadrant of the mapping relation, the first quadrant is a time-varying parameter space MNOP of a human-vehicle-road dynamics system, and it can be understood that when any point is determined from the time-varying parameter space, the preview time length and the steering control gain coefficient corresponding to the point can be obtained; the second quadrant represents the man-machine steering power difference | P h -P m The mapping relation between the I and the steering control gain coefficient kappa can be determined through images when the man-machine steering power difference is in a certain rangeWhen the inside of the vehicle is gradually increased, the corresponding steering control gain coefficient is also continuously increased, so that the control degree of the automatic driving system on the transverse driving and the longitudinal driving of the unmanned vehicle is enhanced, and the steering control gain coefficient stops increasing and tends to be stable only when the man-machine steering power difference exceeds a preset threshold (such as a value corresponding to a critical point of steering torque which can be output by the automatic driving system); the third quadrant represents the road curvature rho measurable by the sensor and the man-machine steering power difference | P h -P m The point where the point is located can be understood, after the road curvature, the man-machine steering power difference, the mapping relation between the man-machine steering power difference and the steering control gain coefficient and the mapping relation between the road curvature and the pre-aiming time are determined, a point which is determined based on the road curvature and the man-machine steering power difference in a certain range can be determined to correspond to the point in the time-varying parameter space of the man-vehicle-road dynamics system.
Optionally, determining at least one target adjustment parameter corresponding to the cooperative control system according to the state feedback matrix; and processing the target control gain coefficient and the preview duration according to at least one target adjustment parameter to obtain the cooperative control parameter of the unmanned vehicle.
In the present embodiment, the state feedback matrix F (t) is implemented using linear time-varying parameters p Kappa) in designing the steering control law of the automatic driving system, specifically, the man-machine double closed-loop generalized system sigma (t) of the unmanned vehicle can be defined p And κ) is
In the formula, A cl (t p ,κ)＝A(t p )+B 12 (κ)F(t p ,κ)，C cl (t p .κ)＝C 1 +D 12 F(t p ,κ)。D 11 、D 12 May be a preset parameter and at the same time, is based on the preview time t p And a steering control gain penalty factor, κ, a (t) may be determined p )、B 11 (t p )、B 12 (κ)、F(t p κ), and then a is obtained by calculation cl (t p Kappa) and C cl (t p κ). A obtained by calculation cl (t p Kappa) and C cl (t p Kappa) into the man-machine double closed-loop generalized system sigma (t) p Kappa) corresponding formula, and obtaining specific cooperative control parameters
And
the cooperative control parameters are used for adjusting the running speed and the angular speed of the unmanned vehicle and the theoretical steering angle within the preset duration. Those skilled in the art will appreciate that the unmanned vehicle based two-man closed-loop generalized system Σ (t) p Kappa) obtaining specific cooperative control parameters
And
then, the automatic driving system can issue corresponding control instructions to the unmanned vehicle according to the running speed, the angular speed and the theoretical steering angle within the preset time length based on the parameters, so that related components in the unmanned vehicle generate corresponding control torque, and the unmanned vehicle can realize automatic driving, or assist a driver to control the unmanned vehicle based on the control torque.
In order to ensure the progressive stability of the closed-loop system and suppress the disturbance of the steering resistance torque and the road curvature to the system, the constant parameter t is set p ＝t p0 And κ = κ 0 Under the condition of (2), enabling the steady generalized system sigma (t) to be in a stable state by designing a state feedback matrix p0 ,κ 0 ) All poles have negative real parts, and the amplitude-frequency characteristic of interference to the controlled output has a definite limit.
The technical solution of the present embodiment may be applied to various scenarios, for example, to a human-computer cooperative lane keeping platform solution, and the application scenario is described below with reference to fig. 4.
In the scheme of the man-machine cooperative lane keeping platform, in order to enable a driver and an unmanned vehicle to finish a lane keeping task together, NI-PXI can be used as a vehicle-road system simulator, a CarSim real-time simulation environment is embedded in the simulator, and meanwhile, vehicle environment information can be simulated and edited based on a 27-freedom-degree vehicle dynamics model. After a driver is introduced into the platform, the driver can generate steering torque tau according to visual feedback of real-time driving scene information and road feel feedback of a steering resistance torque simulator h (ii) a When the MicroAutoBox is used as a steering controller of the automatic driving system, on one hand, the time-varying parameters (t) can be adjusted in real time based on the state information of the vehicle road fed back by the CarSim and the information such as the steering angle and the steering torque of the driver measured by the steering system p κ) working point location; on the other hand, the robust control strategy of the real-time operation linear variable parameter calculates the target steering torque tau m And the motor is controlled to output steering torque through the independently developed driving plate. In the process of controlling the transverse driving and the longitudinal driving of the unmanned vehicle based on the target control gain coefficient, the automatic driving system can continuously determine a cooperative control parameter based on the input, and then adjust the driving speed and the angular speed of the unmanned vehicle and a theoretical steering angle parameter within a preset time period, and further, the automatic driving system can apply a steering torque to an actuator associated with the automatic driving system based on the output. Therefore, it can be understood that in the lane keeping platform, the driver and the automatic driving system can simultaneously apply the steering torque to the human-computer interaction interface (namely, the steering actuator), and the lane keeping task can be completed through cooperative cooperation.
According to the technical scheme of the embodiment, the unmanned vehicle motion mode is predefined, so that the adjustment machine of the steering control gain penalty coefficient kappa can be further optimized; the man-machine steering power is introduced, and the control right of the man machine can be adjusted more timely when man-machine conflict occurs by adopting a corresponding quantification method; the differentiation state of the unmanned vehicle is determined through the preset mapping relation, the flexibility and the adaptability of the automatic driving system in the decision process are improved, further, the anti-aliasing filtering processing is carried out on the gain coefficient to be processed and controlled, the flexible scheduling of the control right of the unmanned vehicle is realized, and the problem that the steering guiding auxiliary torque is not smooth due to the jumping of the gain punishment coefficient kappa of the steering control is avoided; and adjusting the running speed and the angular speed of the unmanned vehicle and the theoretical steering angle within the preset time length based on the acquired cooperative control parameters, so that the consistency of man-machine decision is ensured.
EXAMPLE III
Fig. 5 is a block diagram of a steering cooperative control apparatus applied to an unmanned vehicle according to a third embodiment of the present invention, which is capable of executing a steering cooperative control method applied to an unmanned vehicle according to any embodiment of the present invention, and has functional modules and beneficial effects corresponding to the execution method. As shown in fig. 5, the apparatus specifically includes: an information acquisition module 310, a gain factor determination module 320, and a coordination control module 330.
The information obtaining module 310 is configured to obtain current steering angle information of the unmanned vehicle and a human-machine control state of the unmanned vehicle.
A gain coefficient determining module 320, configured to determine a target control gain coefficient corresponding to the unmanned vehicle according to the current steering angle information and the human-machine control state; wherein the target control gain coefficient is used for controlling the transverse driving and the longitudinal driving of the unmanned vehicle.
And the cooperative control module 330 is configured to determine a cooperative control parameter of the unmanned vehicle according to the target control gain coefficient and a preset state feedback matrix, so as to control the unmanned vehicle based on the cooperative control parameter.
On the basis of the above technical solutions, the information obtaining module 310 includes a current steering angle information collecting unit and a human-machine control state collecting unit.
And the current steering angle information acquisition unit is used for acquiring the current steering angle information of the unmanned vehicle based on a preset steering angle acquisition sensor.
The human-machine control state acquisition unit is used for acquiring the human-machine control state of the unmanned vehicle based on a preset control state sensor; wherein the human-machine control state comprises at least one of a human-machine cooperation state, a driver-dominated human-machine conflict state, and an unmanned system-dominated human-machine conflict state.
On the basis of the above technical solutions, the gain coefficient determining module 320 includes a to-be-processed control gain coefficient determining unit and a target control gain coefficient determining unit.
And the to-be-processed control gain coefficient determining unit is used for determining the to-be-processed control gain coefficient of the unmanned vehicle according to the man-machine control state and the current steering angle information.
And the target control gain coefficient determining unit is used for obtaining the target control gain coefficient by performing anti-aliasing filtering processing on the control gain coefficient to be processed.
Optionally, the to-be-processed control gain coefficient determining unit is further configured to determine a steering parameter corresponding to the human-machine system according to the current steering angle information and a preset time window; wherein the steering parameter comprises a target person steering power or an automatic driving system steering power; and determining the gain coefficient to be processed according to the steering power of the target person or the steering power of an automatic driving system in the steering parameters and the man-machine control state.
Optionally, the to-be-processed control gain coefficient determining unit is further configured to determine, according to a mapping relationship established in advance, an objective function corresponding to the steering parameter and the human-machine control state, and determine the to-be-processed control gain coefficient based on the objective function; wherein the mapping relation comprises functions for determining control gain coefficients corresponding to different steering parameters and human-computer control states.
On the basis of the above technical solutions, the cooperative control module 330 includes a preview duration determination unit and a cooperative control parameter determination unit.
The preview duration determining unit is used for determining the preview duration according to the acquired curvature and a mapping relation established in advance; the preview duration is used for representing the duration used for acquiring the curvature.
The cooperative control parameter determining unit is used for determining the cooperative control parameters of the unmanned vehicle according to the preview duration, the target control gain coefficient and the state feedback matrix; the mapping relation further comprises a mapping relation graph based on the actually acquired road curvature, the steering power of the target person, the steering power of the automatic driving system and the pre-aiming time.
Optionally, the cooperative control parameter determining unit is further configured to determine at least one target adjustment parameter corresponding to the cooperative control system according to the state feedback matrix; processing the target control gain coefficient and the preview time length according to the at least one target adjustment parameter to obtain a cooperative control parameter of the unmanned vehicle; and the cooperative control parameters are used for adjusting the running speed and the angular speed of the unmanned vehicle and the theoretical steering angle within the preset time length.
According to the technical scheme provided by the embodiment, the current steering angle information of the unmanned vehicle and the man-machine control state of the unmanned vehicle are firstly obtained, then the target control gain coefficient corresponding to the unmanned vehicle is determined according to the current steering angle information and the man-machine control state, the parameters are quantized, the cooperative control parameters of the unmanned vehicle are determined according to the target control gain coefficient and the preset state feedback matrix, the unmanned vehicle is controlled based on the cooperative control parameters, the control of the steering behavior of the unmanned vehicle by the automatic driving system is realized, the divergence of man-machine decision is reduced while the driving of the unmanned vehicle is guided or the driving of a driver is assisted, and the man-machine harmony in the co-driving system is improved.
The steering cooperative control device applied to the unmanned vehicle provided by the embodiment of the invention can execute the steering cooperative control method applied to the unmanned vehicle provided by any embodiment of the invention, and has corresponding functional modules and beneficial effects of the execution method.
It should be noted that, the units and modules included in the apparatus are merely divided according to functional logic, but are not limited to the above division as long as the corresponding functions can be implemented; in addition, specific names of the functional units are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the embodiment of the invention.
Example four
Fig. 6 is a schematic structural diagram of an electronic device according to a fourth embodiment of the present invention. FIG. 6 illustrates a block diagram of an exemplary electronic device 40 suitable for use in implementing embodiments of the present invention. The electronic device 40 shown in fig. 6 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiment of the present invention.
As shown in fig. 6, electronic device 40 is embodied in the form of a general purpose computing device. The components of electronic device 40 may include, but are not limited to: one or more processors or processing units 401, a system memory 402, and a bus 403 that couples the various system components (including the system memory 402 and the processing unit 401).
The system memory 402 may include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM) 404 and/or cache memory 405. The electronic device 40 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 406 may be used to read from and write to non-removable, nonvolatile magnetic media (not shown in FIG. 6, commonly referred to as a "hard drive"). Although not shown in FIG. 6, a magnetic disk drive for reading from and writing to a removable, nonvolatile magnetic disk (e.g., a "floppy disk") and an optical disk drive for reading from or writing to a removable, nonvolatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to the bus 403 by one or more data media interfaces. Memory 402 may include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of embodiments of the invention.
A program/utility 408 having a set (at least one) of program modules 407 may be stored, for example, in the memory 402, such program modules 407 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which or some combination of which may comprise an implementation of a network environment. Program modules 407 generally perform the functions and/or methods of the described embodiments of the invention.
The electronic device 40 may also communicate with one or more external devices 409 (e.g., keyboard, pointing device, display 410, etc.), with one or more devices that enable a user to interact with the electronic device 40, and/or with any devices (e.g., network card, modem, etc.) that enable the electronic device 40 to communicate with one or more other computing devices. Such communication may be through input/output (I/O) interface 411. Also, the electronic device 40 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the internet) via the network adapter 412. As shown, the network adapter 412 communicates with the other modules of the electronic device 40 over the bus 403. It should be appreciated that although not shown in FIG. 6, other hardware and/or software modules may be used in conjunction with electronic device 40, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.
The processing unit 401 executes various functional applications and data processing by running the program stored in the system memory 402, for example, implementing the steering cooperative control method applied to the unmanned vehicle provided by the embodiment of the present invention.
EXAMPLE five
The fifth embodiment of the present invention further provides a storage medium containing computer-executable instructions, which are used to execute a steering cooperative control method applied to an unmanned vehicle when the computer-executable instructions are executed by a computer processor.
The method comprises the following steps:
acquiring current steering angle information of an unmanned vehicle and a man-machine control state of the unmanned vehicle;
determining a target control gain coefficient corresponding to the unmanned vehicle according to the current steering angle information and the man-machine control state; the target control gain coefficient is used for controlling the transverse driving and the longitudinal driving of the unmanned vehicle;
and determining the cooperative control parameters of the unmanned vehicle according to the target control gain coefficient and a preset state feedback matrix so as to control the unmanned vehicle based on the cooperative control parameters.
Computer storage media for embodiments of the invention may employ any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
A computer readable signal medium may include a propagated data signal with computer readable item code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
The item code embodied on the computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Computer project code for carrying out operations for embodiments of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The project code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
It is to be noted that the foregoing description is only exemplary of the invention and that the principles of the technology may be employed. Those skilled in the art will appreciate that the present invention is not limited to the particular embodiments described herein, and that various obvious changes, rearrangements and substitutions will now be apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.
Claims (8)
1. A steering cooperative control method applied to an unmanned vehicle is characterized by comprising the following steps:
acquiring current steering angle information of an unmanned vehicle and a man-machine control state of the unmanned vehicle;
determining a target control gain coefficient corresponding to the unmanned vehicle according to the current steering angle information and the man-machine control state; the target control gain coefficient is used for controlling the transverse driving and the longitudinal driving of the unmanned vehicle;
determining cooperative control parameters of the unmanned vehicle according to the target control gain coefficient and a preset state feedback matrix so as to control the unmanned vehicle based on the cooperative control parameters;
the target control gain coefficient is a coefficient used for adjusting a cooperative control parameter output by an automatic driving system, and the cooperative control parameter output by the automatic driving system is a parameter which enables an unmanned vehicle to generate a specific action so as to assist a driver in driving;
wherein, the determining a target control gain coefficient corresponding to the unmanned vehicle according to the current steering angle information and the human-machine control state comprises:
determining steering parameters corresponding to a human-computer system according to the current steering angle information and a preset time window; wherein the steering parameters comprise target person steering power or automatic driving system steering power;
determining a control gain coefficient to be processed according to the steering power of a target person or the steering power of an automatic driving system in the steering parameters and the man-machine control state;
and performing anti-aliasing filtering processing on the control gain coefficient to be processed to obtain the target control gain coefficient.
2. The method of claim 1, wherein the obtaining current steering angle information of the unmanned vehicle and the ergonomic state of the unmanned vehicle comprises:
acquiring current steering angle information of the unmanned vehicle based on a preset steering angle acquisition sensor;
acquiring a human-machine control state of the unmanned vehicle based on a preset control state sensor;
wherein the human-machine control state comprises at least one of a human-machine cooperation state, a driver-dominated human-machine conflict state, and an automatic driving system-dominated human-machine conflict state.
3. The method of claim 1, wherein determining the control gain factor to be processed according to the target person steering power or the automatic driving system steering power in the steering parameters and the human-machine control state comprises:
determining a target function corresponding to the steering parameter and the man-machine control state according to a pre-established mapping relation, and determining the control gain coefficient to be processed based on the target function;
wherein the mapping relation comprises functions for determining control gain coefficients corresponding to different steering parameters and human-computer control states.
4. The method of claim 1, wherein determining the cooperative control parameters of the unmanned vehicle according to the target control gain coefficient and a preset state feedback matrix comprises:
determining the preview duration according to the acquired curvature and a mapping relation established in advance; the preview duration is used for representing the duration used for acquiring the curvature;
determining a cooperative control parameter of the unmanned vehicle according to the preview duration, the target control gain coefficient and the state feedback matrix;
the mapping relation further comprises a mapping relation graph based on actually acquired road curvature, target person steering power, automatic driving system steering power and preview time.
5. The method of claim 4, wherein determining cooperative control parameters of the unmanned vehicle based on the preview time duration, a target control gain factor, and the state feedback matrix comprises:
determining at least one target adjustment parameter corresponding to the cooperative control system according to the state feedback matrix;
processing the target control gain coefficient and the preview time length according to the at least one target adjustment parameter to obtain a cooperative control parameter of the unmanned vehicle;
and the cooperative control parameters are used for adjusting the running speed and the angular speed of the unmanned vehicle and the theoretical steering angle within the preset time length.
6. The utility model provides a turn to cooperative control device for unmanned car which characterized in that includes:
the information acquisition module is used for acquiring the current steering angle information of the unmanned vehicle and the man-machine control state of the unmanned vehicle;
the gain coefficient determining module is used for determining a target control gain coefficient corresponding to the unmanned vehicle according to the current steering angle information and the man-machine control state; the target control gain coefficient is used for controlling the transverse driving and the longitudinal driving of the unmanned vehicle;
the cooperative control module is used for determining cooperative control parameters of the unmanned vehicle according to the target control gain coefficient and a preset state feedback matrix so as to control the unmanned vehicle based on the cooperative control parameters;
the target control gain coefficient is a coefficient used for adjusting a cooperative control parameter output by an automatic driving system, and the cooperative control parameter output by the automatic driving system is a parameter which enables an unmanned vehicle to generate a specific action so as to assist a driver in driving;
the gain coefficient determining module comprises a to-be-processed control gain coefficient determining unit and a target control gain coefficient determining unit;
the control gain coefficient to be processed determining unit is used for determining steering parameters corresponding to a human-computer system according to the current steering angle information and a preset time window; wherein the steering parameter comprises a target person steering power or an automatic driving system steering power; determining a control gain coefficient to be processed according to the steering power of a target person or the steering power of an automatic driving system in the steering parameters and the man-machine control state;
and the target control gain coefficient determining unit is used for obtaining the target control gain coefficient by performing anti-aliasing filtering processing on the control gain coefficient to be processed.
7. An electronic device, characterized in that the electronic device comprises:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement a steering coordination control method for an unmanned vehicle as claimed in any of claims 1-5.
8. A storage medium containing computer-executable instructions for performing the steering cooperative control method applied to an unmanned vehicle as recited in any one of claims 1 to 5 when executed by a computer processor.
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| CN112721943A (en) * | 2021-01-20 | 2021-04-30 | 吉林大学 | Man-machine co-driving transverse control method with conflict resolution function |
| CN113619563A (en) * | 2021-09-06 | 2021-11-09 | 厦门大学 | Intelligent electric vehicle transverse control system and method based on man-machine sharing |
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