CN112305911A - Feedback prediction control method and device under complex environment and vehicle - Google Patents
Feedback prediction control method and device under complex environment and vehicle Download PDFInfo
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Abstract
The invention discloses a feedback prediction control method under a complex environment, which comprises the following steps: step S1, the vehicle starts automatic driving; step S2, predicting the future motion track of the vehicle: predicting a predicted path of the vehicle according to the control algorithm determined in step S1; step S3, environmental information preprocessing: controlling the vehicle to run along the predicted path, acquiring obstacle boundary information or road boundary information around the vehicle in real time, and correspondingly setting safety belts B1 or B2 which are parallel to the obstacle boundary A1 or the road boundary A2 and are spaced by a preset safety distance L on the side, close to the vehicle, of the obstacle boundary A1 or the road boundary A2; step S4, vehicle scrub detection: and judging whether the vehicle has the rubbing risk or not. In addition, the invention also discloses a feedback prediction control device and a vehicle under the complex environment. The invention can lead the vehicle to find that the predicted path has the rubbing risk, and avoid rubbing by adjusting the motion track of the vehicle.
Description
Technical Field
The invention relates to the technical field of unmanned driving, in particular to a feedback prediction control method and device under a complex environment and a vehicle.
Background
Today, the rapid development of the unmanned technology, the unmanned algorithm takes great progress from the height and the effectiveness. In the aspect of unmanned control, the control algorithm ensures the rapidity, accuracy and stability of road tracking, but the control capability and safety of the algorithm can not be ignored.
Currently, the unmanned vehicle uses the existing control algorithm (path tracking algorithm such as pure tracking pure pursuit) to complete accurate tracking of a given path on an upper layer, and uses the same control method to predict a motion track in a future period of time. In addition, whether danger exists or not can be judged according to the predicted motion trail, and then the unmanned vehicle determines whether sudden stop operation is carried out or running is continued.
However, the existing actual control model of the vehicle belongs to a 'grey box' or even a 'black box', and under some extreme conditions, tracking deviation can occur when no vehicle is determined. On the predicted future path, the vehicle will stop as soon as a light collision occurs. The existing control algorithm can not avoid the rubbing and the collision by adjusting under the condition of slight rubbing and the unmanned driving can not be continued.
It should be noted that, in the actual control model of the vehicle, some parameters are unknown, and the "gray box" method is a method for acquiring other parts of the model by a data reconstruction method, wherein only part of the model structure is known. The black box method refers to a method in which the model structure and parameters are unknown and can only be estimated by inputting and outputting data.
Disclosure of Invention
The invention aims to provide a feedback prediction control method under a complex environment, a device thereof and a vehicle, aiming at the technical defects in the prior art.
Therefore, the invention provides a feedback prediction control method under a complex environment, which comprises the following steps:
step S1, the vehicle starts automatic driving: controlling the vehicle to finish speed control operation and path following operation according to a preset control algorithm and a preset speed and a preset driving path, and starting automatic driving of the vehicle;
step S2, predicting the future motion track of the vehicle: predicting a predicted path to be traveled by the vehicle according to the control algorithm determined in the step S1;
step S3, environmental information preprocessing: when the vehicle runs along the predicted path, acquiring obstacle boundary information or road boundary information around the vehicle in real time, and correspondingly setting safety belts B1 or B2 which are parallel to the obstacle boundary A1 or the road boundary A2 and are spaced by a preset safety distance L on one side, close to the vehicle, of the obstacle boundary A1 or the road boundary A2 according to the obstacle boundary information or the road boundary information;
step S4, vehicle scrub detection: whether the vehicle on the predicted path enters a dangerous area between a safety belt B1 or a safety belt B2 and an obstacle boundary A1 or a road boundary A2 is detected, and if the vehicle enters the dangerous area, the vehicle is judged to be at the risk of collision.
Preferably, in step S2, a predicted path on which the vehicle will travel is obtained by performing prediction using a pure tracking pure pursuit algorithm;
in step S2, the pure tracking pure purewait algorithm model is based on vehicle kinematics as follows:
wherein X and Y are the axle center positions of the rear axle of the vehicle;
in the formula, l is the wheel base,
is the vehicle course angle, v is the rear axle axis speed of the vehicle, w is the vehicle yaw rate, δfIs the front wheel slip angle.
Preferably, in step S2, the method further includes the following steps:
on the basis of the predicted path, when the vehicle travels along the predicted path, the vehicle is moved forward in such a manner that the rear axis of the vehicle falls on the predicted path and the heading of the vehicle is aligned in the tangential direction of the predicted path.
Preferably, the step S4 specifically includes the following steps:
and placing the frame model of the vehicle on the predicted path point by point in advance to carry out friction risk check, and judging that the vehicle has friction risk if the frame model of the vehicle enters a dangerous area between a safety belt B1 or B2 and a road boundary or an obstacle boundary when the frame model of the vehicle is placed on the predicted path.
Preferably, after step S4, the method further includes the following steps:
step S5: when the preset control algorithm adopted in the step S1 is the pure tracking pure pursuit algorithm, a first control algorithm feedback control mode is adopted to correct the driving path of the vehicle, and the method specifically includes the following steps:
first, setting a step size thetah；
Secondly, sampling in the first step under the current angle of the vehicle calculated by pure tracking pure pursuit algorithm
The method comprises the following steps of providing i sampling angles, wherein i is an odd number, clockwise distributing two sides of a current angle, wherein the two sides are respectively provided with (i-1)/2 sampling angles, and the angle calculated by an algorithm is the (i +1)/2 angle, wherein the adjacent angle difference is the sampling step length;
thirdly, taking the sampling angle of the previous step as the current angle, and sampling the second step
Having i2A sampling angle;
fourthly, continuing to sample for n times and sampling
Having inA sampling angle;
fifthly, sampling for i and n times each time to obtain a plurality of tentacle-shaped motion tracks with different sampling angles respectively;
in the fifth step, evaluating a plurality of motion tracks through three evaluation conditions, wherein the motion tracks meeting the three conditions are optimal motion tracks; these three evaluation conditions are specifically: a. the motion trail obtained by sampling has no collision, the angle change is minimum, and the motion trail is shortest;
sixthly, executing a first angle of the optimal motion track;
seventh, angle _ out is equal to anglepure_pursuit+angleOptimal sampling valueAnd adding the first angle of the optimal motion track to the angle calculated by the pure pursuit algorithm to finish the feedback correction process.
Preferably, after step S4, the method further includes the following steps:
step S5: when the preset control algorithm adopted in the step S1 is the existing PID algorithm, a second control algorithm feedback control manner is adopted to correct the driving path of the vehicle, which specifically includes the following steps:
step one, Err _ dis is max (L-d), and the maximum deviation value in the prediction time domain is selected, namely the input of the pid algorithm is calculated;
in the second step, the first step is that,
namely the calculation result of the pid algorithm;
third, angle _ out is anglepure_pursuit+angleincrease_pid(t)Namely calculating the final output angle;
wherein in the first step Err _ dis is the deviation;
in the second step, increment _ pid (t) is the output value of pid algorithm, which corresponds to proportional element, integral element and differential element in turn;
in the third step, angle _ out is the final output angle, anglepure_pursuitAngle calculated for the original control algorithm employed in step S1increase_pid(t)Is the corrected angle of pid.
Preferably, after the step S4 or S5, the following steps are further included:
step S6, controlling the driving posture of the vehicle: and performing longitudinal speed control on the vehicle, and controlling the vehicle to run at a reduced speed when the vehicle approaches a safety belt B1 or a safety belt B2.
In addition, the invention also provides a feedback prediction control device under a complex environment, which comprises the following modules:
the vehicle starting automatic driving module is used for controlling the vehicle to finish speed control operation and path following operation according to a preset control algorithm and a preset speed planned by the upper layer and a given driving path, and starting automatic driving of the vehicle;
the vehicle future motion track prediction module is connected with the vehicle starting automatic driving module and used for predicting and obtaining a predicted path where the vehicle will run according to a control algorithm determined by the vehicle starting automatic driving module;
the environment information preprocessing module is connected with the vehicle future motion trail predicting module and used for acquiring barrier boundary information or road boundary information around the vehicle in real time when the vehicle runs along a predicted path, and correspondingly setting safety belts B1 or B2 which are parallel to the barrier boundary A1 or the road boundary A2 and are spaced by a preset safety distance L on one side, close to the vehicle, of the barrier boundary A1 or the road boundary A2 according to the barrier boundary information or the road boundary information;
and the vehicle collision detection module is connected with the environmental information preprocessing module and is used for detecting whether the vehicle on the predicted path enters a dangerous area between a safety belt B1 or B2 and an obstacle boundary A1 or a road boundary A2 determined by the environmental information preprocessing module, and if so, judging that the vehicle has the collision risk.
Preferably, the following modules are also included:
a driving posture module for controlling the vehicle: and performing longitudinal speed control on the vehicle, and controlling the vehicle to run at a reduced speed when the vehicle approaches a safety belt B1 or a safety belt B2.
In addition, the invention also provides a vehicle which comprises the feedback prediction control device under the complex environment.
Compared with the prior art, the feedback prediction control method and device under the complex environment and the vehicle are scientific in design and can enable the vehicle to find that the predicted path has the rubbing risk.
In addition, the invention can avoid friction and collision by adjusting the motion track of the vehicle under the condition of obtaining the feedback of the environmental information, continues the unmanned task, improves the vehicle control capability and has great practical significance.
Drawings
FIG. 1 is a schematic diagram of a basic flow of a feedback prediction control method under a complex environment according to the present invention;
FIG. 2 is a schematic diagram of the predicted path and scrub detection of the present invention;
FIG. 3 is a schematic view of a scene analysis of a predicted path and a road boundary of a vehicle according to the present invention;
FIG. 4 is a schematic view of a scene analysis of a predicted path of a vehicle and an obstacle boundary obtained by the present invention;
FIG. 5 is a schematic diagram of obtaining a plurality of future motion trajectories by using an angular sampling method based on a vehicle kinematic model according to the present invention.
Detailed Description
In order to make the technical means for realizing the invention easier to understand, the following detailed description of the present application is made in conjunction with the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are merely illustrative of the relevant application and are not limiting of the application. It should be noted that, for convenience of description, only the portions related to the present application are shown in the drawings.
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.
It should be noted that in the description of the present application, the terms of direction or positional relationship indicated by the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. are based on the directions or positional relationships shown in the drawings, which are merely for convenience of description, and do not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation, and thus, should not be construed as limiting the present application.
Referring to fig. 1 to 5, the present invention provides a feedback prediction control method under a complex environment, including the following steps:
step S1, the vehicle starts automatic driving: according to the speed planned by an upper layer (for example, an automobile driving computer ECU, namely an electronic controller unit) and a given driving path, controlling the vehicle to complete speed control operation and path following operation according to a preset control algorithm, and starting automatic driving of the vehicle;
step S2, predicting the future motion track of the vehicle: predicting a predicted path to be traveled by the vehicle according to the control algorithm determined at said step S1 (i.e. predicting a predicted path to be traveled by the vehicle, i.e. a future movement locus, by using the same control algorithm as at step S1);
step S3, environmental information preprocessing: when the vehicle travels along the predicted path (specifically, according to the same control algorithm of step S1), acquiring in real time obstacle boundary information or road boundary information (belonging to environment information around the vehicle) around the vehicle, and then according to the obstacle boundary information or road boundary information, correspondingly setting a seat belt B1 or B2 (see fig. 3 and 4) parallel to the obstacle boundary a1 or road boundary a2 and spaced by a preset safety distance L (e.g., 0.5m or 1m) at a position where the obstacle boundary a1 or road boundary a2 is close to the vehicle; step S4, vehicle scrub detection: whether the vehicle on the predicted path enters a dangerous area between a safety belt B1 or a safety belt B2 and an obstacle boundary A1 or a road boundary A2 is detected, and if the vehicle enters the dangerous area, the vehicle is judged to be at the risk of collision.
In the present invention, in step S1, the vehicle starts automatic driving, and the following of speed and path can be completed by the existing control algorithm (or control algorithm module).
In the present invention, in a specific implementation, in step S1, the control algorithm includes two control algorithms, specifically: a lateral (i.e., the left-right direction of the vehicle) control algorithm and a longitudinal (i.e., the front-rear direction of the vehicle) control algorithm. Wherein, with regard to the longitudinal control algorithm, the vehicle is controlled to follow the upper planned speed, in particular by using the PID method; regarding the lateral control algorithm, the main body of the lateral control algorithm controls the vehicle to perform the upper-layer given path following by using the existing pure tracking pure pursuit algorithm (as a path tracking algorithm).
In the present invention, in concrete implementation, in step S2, the predicted path on which the vehicle will travel is obtained by performing prediction using the pure tracking pure pursuit algorithm.
In step S2, the same control algorithm as in step S1 is used to predict the future trajectory of the vehicle.
In step S2, the predictive control algorithm (i.e., pure tracking pure pursuit algorithm) model is based on vehicle kinematics as follows:
wherein X and Y are the axle center positions of the rear axle of the vehicle;
in the formula, l is the wheel base,
is the vehicle course angle, v is the rear axle axis speed of the vehicle, w is the vehicle yaw rate, δfIs the front wheel slip angle.
In step S2, it should be noted that, according to the present invention, a multi-step prediction may be performed by using a pure pursuit algorithm based on a vehicle kinematic model, so as to obtain a predicted path shown by a dotted line in fig. 2.
In a specific implementation of the present invention, in step S2, the method further includes the following steps:
on the basis of the predicted path, when the vehicle travels along the predicted path, the vehicle (specifically, the frame model of the vehicle) is moved forward in such a manner that the rear axis of the vehicle falls on the predicted path and the heading direction (i.e., the forward traveling direction) of the vehicle is aligned with the tangential direction of the predicted path.
In step S3, the present invention can set a seat belt B1 or B2 parallel to the boundary and spaced apart by a certain distance L in correspondence to the acquired information of the obstacle boundary a1 or the road boundary a2 fed back from the outside, as shown in fig. 3 and 4.
With the present invention, the distance d2 between the vehicle and the road boundary d1 or between the vehicle and the obstacle boundary on the predicted path may be calculated based on the obstacle boundary information or the road boundary information.
In the invention, the obstacle boundary is the laser point cloud of the detected obstacle sensed by the existing sensing and predicting equipment (such as laser radar and other equipment).
Regarding road boundaries, wherein the road boundaries which are raised and can be found by the existing perception module can be treated equally when the obstacle boundaries are pits or road boundaries which cannot be perceived and identified, the road boundaries need to be provided by the existing high-precision map.
For the invention, both the boundary of the obstacle and the boundary of the road can be provided by the upper layer module, and are information of a string of points under the vehicle coordinate system.
It should be noted that the obstacle boundary information and the road boundary information are provided by an upper module of a control module as an environment information preprocessing module, the control module as the environment information preprocessing module receives information based on a series of points in vehicle coordinates, and after the information is obtained, the control module as the environment information preprocessing module can define a dangerous area (a safety belt B1 or a safety belt B2), so that the distance values of d1 and d2 can be obtained.
It should be noted that the obstacle boundary information and the road boundary information can be provided by the existing perceptual laser algorithm and the high-precision map. That is, the laser point cloud information of the obstacle or the road is obtained through the existing perceptual laser algorithm and the high-precision map, that is, the obstacle boundary information or the road boundary information (the environmental information belonging to the surroundings of the vehicle) around the vehicle can be obtained.
In a specific implementation of the present invention, step S3 further includes the following steps:
the danger interval distance d1 between the vehicle and the obstacle boundary on the preset path or the danger interval distance d2 between the vehicle and the road boundary on the preset path is obtained according to the predicted path of the vehicle and the obstacle boundary information or the road boundary information.
The specific operation is as follows: for the vehicle on the predicted path, when the vehicle enters a dangerous area between a safety belt B1 or B2 and a road boundary or an obstacle boundary at a plurality of position points on the preset path, defining the position points as dangerous points, and measuring and obtaining the vertical distance between the dangerous points and the road boundary or the obstacle boundary as a dangerous spacing distance d1 or a dangerous spacing distance d 2;
wherein, the position point with the shortest vertical distance is the most dangerous point.
It should be noted that, for the present invention, a section of road can be predicted, the vehicle model is swung upwards, and then the friction risk check is performed, so that the most dangerous position point on the vehicle deep into the dangerous area can be obtained, and the position point-to-straight line calculation is performed, so that the values of d1 and d2 are obtained. The location point is a dangerous point of the vehicle, and a small section of the boundary or the expanded boundary near the dangerous point is approximated as a straight line. The obstacle contour obtained by processing the environment information in step S3 can be used for sampling correction in the following first mode, and the calculated d1 and d2 can be used for feedback correction in the following second mode (PID adjustment mode).
In a specific implementation of the present invention, the step S4 specifically includes the following steps:
and (3) checking the vehicle model: and when the frame model of the vehicle (such as the frame of the vehicle) enters a dangerous area between a safety belt B1 or a safety belt B2 and a road boundary or an obstacle boundary, judging that the vehicle has the collision risk.
It should be noted that, in terms of specific implementation of the present invention, a dangerous area (i.e., a safety belt) may be defined around an obstacle or a road, where the dangerous area (i.e., the safety belt) is different from an outline of the obstacle, and it may be considered that the obstacle is subjected to an expansion process, and the expanded area is a dangerous area, and when a vehicle model is checked on a predicted path, the vehicle model enters the defined dangerous area (i.e., the safety belt), and it is considered that there is a collision risk. The danger zone is a seat belt, and a vehicle entering the danger zone (i.e., the zone between seat belt B1 or B2 and the road boundary or obstacle boundary) needs to start correcting the angle, or otherwise may hit the road boundary or obstacle.
In a specific implementation of the present invention, after step S4, the method further includes the following steps:
step S5: when the preset control algorithm adopted in the step S1 is the pure tracking pure pursuit algorithm, a first control algorithm feedback control mode is adopted to correct the driving path of the vehicle, and the method specifically includes the following steps based on a vehicle kinematics model:
first, setting a step size thetah(step size, i.e., the distance traveled by the vehicle per unit time, e.g., 1 second);
secondly, sampling in the first step under the current angle of the vehicle calculated by pure tracking pure pursuit algorithm
The method has i sampling angles, i is an odd number, two sides of the current angle are distributed clockwise, each of the two sides has (i-1)/2 sampling angles, the algorithm calculates the angle as the (i +1)/2 angle, wherein the adjacent angle difference is a sampling step length, for example:
thirdly, taking the sampling angle of the previous step as the current angle, and sampling the second step
Having i2A sampling angle;
fourthly, continuing to sample for n times and sampling
Having inA sampling angle;
and fifthly, sampling for i and n times at a time, and obtaining a plurality of whisker-shaped motion tracks with different sampling angles respectively, namely a plurality of future motion tracks of the vehicle, as shown in fig. 5. It should be noted that sampling correction is a way to avoid danger, the tentacle-shaped path is a driving track that may appear at the sampling angle, and the curve drawn in fig. 5 is convenient to understand, and if the tentacle-shaped movement track has a collision problem, it is directly deleted.
In the fifth step, evaluating a plurality of motion tracks through three evaluation conditions, wherein the motion tracks meeting the three conditions are optimal motion tracks; these three evaluation conditions are specifically: a. the motion trail obtained by sampling has no collision, the angle change is minimum (the included angle between the motion trail and the current path of the vehicle) b, and the motion trail is shortest c.
In the present invention, the whisker-like trajectory is derived at the sampling angle for optimal angle selection, and the first condition is collision detection, which is used to derive the optimal current sampling angle.
In particular, an evaluation function may be set, the evaluation function includes the three conditions, and the calculation of the evaluation function is minimum. The evaluation function determines which condition is more inclined by setting different weights.
It should be noted that, for the present invention, in the previous step S2, only one path is predicted, and then feedback correction is performed in two ways. One way is pid correction, one is sampling correction, and the input of pid and sampling is the dangerous obstacle boundary and road boundary of the upper layer output for feedback.
And sixthly, executing the first angle of the optimal motion track.
Seventh, angle _ out is equal to anglepure_pursuit+angleOptimal sampling valueNamely: and sampling the correction angle to obtain an optimal first angle, and adding the first angle of the optimal motion track to the angle calculated by the pure pursuit algorithm to finish the feedback correction process.
In a specific implementation of the present invention, after step S4, the method further includes the following steps:
step S5: when the preset control algorithm adopted in step S1 is the existing PID algorithm, a second control algorithm feedback control mode is adopted to correct the driving path of the vehicle, specifically: based on the input instruction of the upper PID controller, the feedback correction is realized by applying the existing PID method and adopting a PID adjusting mode.
The second control algorithm feedback control mode specifically comprises the following steps:
step one, Err _ dis is max (L-d), and the maximum deviation value in the prediction time domain is selected, namely the input of the pid algorithm is calculated;
in the present invention, the deviation value obtained from L-d is the input of pid algorithm, L is the preset safety distance, and d is d1 or d2 (as described above).
In the second step, the first step is that,
namely the calculation result of the pid algorithm;
third, angle _ out is anglepure_pursuit+angleincrease_pid(t)Namely calculating the final output angle;
by adjusting three parameters of kp, ki and kd, a better avoidance effect can be obtained.
It should be noted that, in the present invention, as in the first control algorithm feedback control method, the second control algorithm feedback control method of the present invention is also to add a feedback correction angle on the basis of the angle calculated by the control algorithm, and only the feedback correction method of the second control algorithm feedback control method is the pid method.
In the first step, Err _ dis is a deviation, L is a preset safe distance L from the obstacle boundary a1 or the road boundary a2, and is a distance of a safe line (the side far from the obstacle is a safe area, and the side near the obstacle is a dangerous area);
in the second step, increment _ pid (t) is the output value of pid algorithm, which corresponds to proportional element, integral element and differential element in the formula.
In the third step, angle _ out is the final output angle, anglepure_pursuitAngle calculated for the original control algorithm (i.e., the preset control algorithm employed in step S1)increase_pid(t)Is the corrected angle of pid.
In the invention, three parameters of kp, ki and Kd are adjustable parameters of the algorithm, wherein Kd is a proportional parameter, ki is an integral parameter and Kd is a differential parameter.
It should be noted that, for the present invention, Pid correction is given by a correction angle in reverse according to the degree of possible penetration of the vehicle into the dangerous area in the future, and the deeper the penetration into the dangerous area (the larger L-d), the larger the value of Pid correction angle.
In a specific implementation manner, after the step S4 or the step S5, the method further includes the following steps:
step S6, controlling the driving posture of the vehicle: the vehicle is subjected to speed control in the longitudinal direction (i.e., the front-rear direction of the vehicle), and when the vehicle approaches the seat belt B1 or B2 (for example, the vehicle is at a preset distance, such as 1 meter, from the seat belt B1 or B2), the vehicle is controlled to run at a reduced speed.
In the present invention, the vehicle is subjected to longitudinal speed control, and the vehicle is decelerated near the seat belt so that the driver can stop the vehicle in an emergency when the lateral collision avoidance wiping is ineffective.
In the present invention, the lateral (i.e., the left-right direction of the vehicle) travel path control of the vehicle may be based on an existing PID method or an existing sampling method (e.g., an existing pure tracking pure pursuit algorithm).
For the vehicle transverse control output, the sampling algorithm (method) adopted can be as follows: angle _ out ═ anglepure_pursuit+angleOptimal sampling valueExisting algorithms;
for the vehicle lateral control output, the adopted PID algorithm (method) can be as follows: angle _ out ═ anglepure_pursuit+ increment _ pid (t), which is an existing algorithm.
In addition, based on the feedback prediction control method under the complex environment provided by the invention, in order to execute the feedback prediction control method under the complex environment, the invention also provides a feedback prediction control device under the complex environment, which comprises the following modules:
the vehicle starting automatic driving module is used for controlling the vehicle to complete speed control operation and path following operation according to a preset control algorithm and starting automatic driving of the vehicle according to the speed planned by an upper layer (such as an automobile driving computer ECU, namely an electronic controller unit) and a given driving path;
the vehicle future motion trail prediction module is connected with the vehicle starting automatic driving module and used for predicting and obtaining a predicted path to be traveled by the vehicle according to a control algorithm determined by the vehicle starting automatic driving module (namely, the predicted path to be traveled by the vehicle, namely, a future motion trail is predicted and obtained by using the same control algorithm used by the vehicle starting automatic driving module);
the environment information preprocessing module is connected with the vehicle future motion trajectory predicting module and is used for acquiring obstacle boundary information or road boundary information (belonging to environment information around the vehicle) around the vehicle in real time when the vehicle runs along a predicted path (according to a control algorithm determined by the vehicle starting automatic driving module), and correspondingly setting safety belts B1 or B2 (shown in figures 3 and 4) which are parallel to the obstacle boundary A1 or the road boundary A2 and are spaced by a preset safety distance L (such as 0.5m or 1m) at the position, close to the vehicle, of the obstacle boundary A1 or the road boundary A2 according to the obstacle boundary information or the road boundary information;
and the vehicle collision detection module is connected with the environmental information preprocessing module and is used for detecting whether the vehicle on the predicted path enters a dangerous area between a safety belt B1 or B2 and an obstacle boundary A1 or a road boundary A2 determined by the environmental information preprocessing module, and if so, judging that the vehicle has the collision risk.
In a specific implementation, the feedback prediction control apparatus in a complex environment further includes the following modules:
a driving posture module for controlling the vehicle: the vehicle is subjected to speed control in the longitudinal direction (i.e., the front-rear direction of the vehicle), and when the vehicle approaches the seat belt B1 or B2 (for example, the vehicle is at a preset distance, such as 1 meter, from the seat belt B1 or B2), the vehicle is controlled to run at a reduced speed.
In the present invention, the vehicle is subjected to longitudinal speed control, and the vehicle is decelerated near the seat belt so that the driver can stop the vehicle in an emergency when the lateral collision avoidance wiping is ineffective.
In addition, the invention also provides a vehicle which comprises the feedback prediction control device under the complex environment.
Based on the technical scheme, compared with the prior art, the unmanned vehicle control method and the unmanned vehicle control system have the advantages that the prediction function of the control module is enriched, the unmanned control capability of the vehicle can be improved, and the safety of the unmanned vehicle is improved.
According to the invention, a mode for effectively improving the vehicle control capability based on environmental information feedback and control prediction is provided.
In summary, compared with the prior art, the feedback prediction control method and device under the complex environment and the vehicle provided by the invention have the advantages that the design is scientific, and the vehicle can find that the predicted path has the rubbing risk.
In addition, the invention can avoid friction and collision by adjusting the motion track of the vehicle under the condition of obtaining the feedback of the environmental information, continues the unmanned task, improves the vehicle control capability and has great practical significance.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.
Claims (10)
1. A feedback prediction control method under a complex environment is characterized by comprising the following steps:
step S1, the vehicle starts automatic driving: controlling the vehicle to finish speed control operation and path following operation according to a preset control algorithm and a preset speed and a preset driving path, and starting automatic driving of the vehicle;
step S2, predicting the future motion track of the vehicle: predicting a predicted path to be traveled by the vehicle according to the control algorithm determined in the step S1;
step S3, environmental information preprocessing: when the vehicle runs along the predicted path, acquiring obstacle boundary information or road boundary information around the vehicle in real time, and correspondingly setting safety belts B1 or B2 which are parallel to the obstacle boundary A1 or the road boundary A2 and are spaced by a preset safety distance L on one side, close to the vehicle, of the obstacle boundary A1 or the road boundary A2 according to the obstacle boundary information or the road boundary information;
step S4, vehicle scrub detection: whether the vehicle on the predicted path enters a dangerous area between a safety belt B1 or a safety belt B2 and an obstacle boundary A1 or a road boundary A2 is detected, and if the vehicle enters the dangerous area, the vehicle is judged to be at the risk of collision.
2. The feedback predictive control method under a complex environment according to claim 1, wherein in step S2, a predicted path on which the vehicle will travel is obtained by performing prediction using a pure tracking pure pursuit algorithm;
in step S2, the pure tracking pure purewait algorithm model is based on vehicle kinematics as follows:
wherein X and Y are the axle center positions of the rear axle of the vehicle;
in the formula, l is the wheel base,
is the vehicle course angle, v is the rear axle axis speed of the vehicle, w is the vehicle yaw rate, δfIs the front wheel slip angle.
3. The feedback predictive control method under a complex environment according to claim 1, further comprising, in step S2, the steps of:
on the basis of the predicted path, when the vehicle travels along the predicted path, the vehicle is moved forward in such a manner that the rear axis of the vehicle falls on the predicted path and the heading of the vehicle is aligned in the tangential direction of the predicted path.
4. The feedback predictive control method under the complex environment according to claim 1, wherein the step S4 specifically includes the following steps:
and placing the frame model of the vehicle on the predicted path point by point in advance to carry out friction risk check, and judging that the vehicle has friction risk if the frame model of the vehicle enters a dangerous area between a safety belt B1 or B2 and a road boundary or an obstacle boundary when the frame model of the vehicle is placed on the predicted path.
5. The feedback predictive control method under a complex environment according to claim 1, further comprising, after step S4, the steps of:
step S5: when the preset control algorithm adopted in the step S1 is the pure tracking pure pursuit algorithm, a first control algorithm feedback control mode is adopted to correct the driving path of the vehicle, and the method specifically includes the following steps:
first, setting a step size thetah；
Secondly, sampling in the first step under the current angle of the vehicle calculated by pure tracking pure pursuit algorithm
The method comprises the following steps of providing i sampling angles, wherein i is an odd number, clockwise distributing two sides of a current angle, wherein the two sides are respectively provided with (i-1)/2 sampling angles, and the angle calculated by an algorithm is the (i +1)/2 angle, wherein the adjacent angle difference is the sampling step length;
thirdly, taking the sampling angle of the previous step as the current angle, and sampling the second step
Having i2A sampling angle;
fourthly, continuing to sample for n times and sampling
Having inA sampling angle;
fifthly, sampling for i and n times each time to obtain a plurality of tentacle-shaped motion tracks with different sampling angles respectively;
in the fifth step, evaluating a plurality of motion tracks through three evaluation conditions, wherein the motion tracks meeting the three conditions are optimal motion tracks; these three evaluation conditions are specifically: a. the motion trail obtained by sampling has no collision, the angle change is minimum, and the motion trail is shortest;
sixthly, executing a first angle of the optimal motion track;
seventh, angle _ out is equal to anglepure_pursuit+angleOptimal sampling valueAnd adding the first angle of the optimal motion track to the angle calculated by the pure pursuit algorithm to finish the feedback correction process.
6. The feedback predictive control method under a complex environment according to claim 1, further comprising, after step S4, the steps of:
step S5: when the preset control algorithm adopted in the step S1 is the existing PID algorithm, a second control algorithm feedback control manner is adopted to correct the driving path of the vehicle, which specifically includes the following steps:
step one, Err _ dis is max (L-d), and the maximum deviation value in the prediction time domain is selected, namely the input of the pid algorithm is calculated;
in the second step, the first step is that,
namely the calculation result of the pid algorithm;
third, angle _ out is anglepure_pursuit+angleincrease_pid(t)Namely calculating the final output angle;
wherein in the first step Err _ dis is the deviation;
in the second step, increment _ pid (t) is the output value of pid algorithm, which corresponds to proportional element, integral element and differential element in turn;
in the third step, angle _ out is the final output angle, anglepure_pursuitAngle calculated for the original control algorithm employed in step S1increase_pid(t)Is the corrected angle of pid.
7. The feedback predictive control method under a complex environment according to claim 1, further comprising, after the step S4 or S5, the steps of:
step S6, controlling the driving posture of the vehicle: and performing longitudinal speed control on the vehicle, and controlling the vehicle to run at a reduced speed when the vehicle approaches a safety belt B1 or a safety belt B2.
8. The feedback prediction control device under the complex environment is characterized by comprising the following modules:
the vehicle starting automatic driving module is used for controlling the vehicle to finish speed control operation and path following operation according to a preset control algorithm and a preset speed planned by the upper layer and a given driving path, and starting automatic driving of the vehicle;
the vehicle future motion track prediction module is connected with the vehicle starting automatic driving module and used for predicting and obtaining a predicted path where the vehicle will run according to a control algorithm determined by the vehicle starting automatic driving module;
the environment information preprocessing module is connected with the vehicle future motion trail predicting module and used for acquiring barrier boundary information or road boundary information around the vehicle in real time when the vehicle runs along a predicted path, and correspondingly setting safety belts B1 or B2 which are parallel to the barrier boundary A1 or the road boundary A2 and are spaced by a preset safety distance L on one side, close to the vehicle, of the barrier boundary A1 or the road boundary A2 according to the barrier boundary information or the road boundary information;
and the vehicle collision detection module is connected with the environmental information preprocessing module and is used for detecting whether the vehicle on the predicted path enters a dangerous area between a safety belt B1 or B2 and an obstacle boundary A1 or a road boundary A2 determined by the environmental information preprocessing module, and if so, judging that the vehicle has the collision risk.
9. The feedback predictive control apparatus under a complex environment as set forth in claim 8, further comprising the following modules:
a driving posture module for controlling the vehicle: and performing longitudinal speed control on the vehicle, and controlling the vehicle to run at a reduced speed when the vehicle approaches a safety belt B1 or a safety belt B2.
10. A vehicle characterized by comprising the feedback predictive control apparatus under a complex environment according to claim 8 or 9.
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