CN112644488B - adaptive cruise system - Google Patents
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units
- B60W30/14—Adaptive cruise control
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
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Abstract
The embodiment of the application provides a self-adaptive cruise system which comprises a perception prediction component, a motion planning component and a motion control component, wherein the perception prediction component is an upstream component of motion planning, the motion planning component is a downstream component of motion planning, and the perception prediction component is used for determining target data in a region of interest of a vehicle; the motion planning component is used for planning a motion curve of the vehicle by splitting a quadratic programming (OSQP) solver through an operator according to the target data; the motion control component is used for controlling the motion of the vehicle according to the motion curve and the proportional integral derivative PID controller, so that smooth control on the motion of the vehicle is realized.
Description
Technical Field
The embodiment of the application relates to the technical field of intelligent driving, in particular to a self-adaptive cruise system.
Background
Along with the development of scientific technology, the intelligent degree of the automobile is continuously improved, and the automatic driving automobile gradually tends to be mature. Where driving assistance systems have entered a large-scale commercial phase, key issues affecting driving assistance mainly include safety, comfort and energy saving, which reflect the level of driving assistance development to a large extent. The advanced driver auxiliary system such as constant-speed cruising, self-adaptive cruising, vehicle following running system and the like can greatly relieve the driving fatigue of a driver and improve the driving comfort and the traffic efficiency. The automobile driver can obviously reduce the tension and fatigue of the driver, and assist or replace the driver to run along with the front automobile under the dangerous condition of the obstacle, so that collision with the obstacle is avoided, the passing efficiency is improved, and the casualties caused by accidents are reduced to the greatest extent.
The adaptive cruise may also be referred to as an active cruise, and the system includes a radar sensor, a digital signal processor, and a control module, similar to conventional constant speed cruise control. In an adaptive cruise system, the system uses a low power radar or infrared beam to obtain the exact position of the lead vehicle, and if the lead vehicle is decelerated or a new target is detected, the system sends an execution signal to the engine or brake system to reduce the speed of the vehicle, thereby maintaining a safe distance between the vehicle and the lead vehicle. After the obstacle on the road in front is cleared, the speed of the vehicle is accelerated to be recovered to the set speed, and the radar system automatically monitors the next target. The active cruise control system replaces the driver to control the vehicle speed, avoiding frequent cancellation and setting of the cruise control.
However, the existing adaptive cruise system directly calculates the speed control quantity through the target vehicle speed and the target distance, and no continuous speed planning exists, so that the control is not smooth, and the driving safety, comfort and energy conservation are affected.
Disclosure of Invention
The embodiment of the application provides a self-adaptive cruise system, which improves the driving safety, comfort and energy conservation.
The present application provides an adaptive cruise system comprising:
the motion planning system comprises a perception prediction component, a motion planning component and a motion control component, wherein the perception prediction component is connected with the motion planning component, and the motion planning component is connected with the motion control component;
the perception prediction component is used for determining target data in a region of interest of the vehicle;
the motion planning component is used for planning a motion curve of the vehicle through an Operator Splitting Quadratic Programming (OSQP) solver according to the target data;
the motion control component is used for controlling the motion of the vehicle according to the motion curve and the proportional-integral-derivative PID controller.
Optionally, the perceptual prediction component comprises:
the data calibration unit is used for correcting the sensor data acquired by the sensor according to the rotation matrix to obtain sensing data;
the interest region determining unit is used for predicting the interest region of the vehicle according to the current motion state of the vehicle and the ackerman steering model and determining the interest region of the vehicle;
and the data filtering unit is used for filtering the perception data according to the region of interest to obtain target data in the region of interest.
Optionally, the region of interest determining unit is specifically configured to:
acquiring motion state parameters of the vehicle at the current moment, wherein the motion state parameters comprise the speed, the acceleration and the position of the vehicle;
predicting a motion path of the vehicle through the ackerman steering model based on the motion state parameters;
and generating a region of interest of the vehicle according to the motion path and the vehicle width.
Optionally, the motion planning assembly comprises:
a target parameter determining unit for determining a target speed and a target distance of the vehicle according to the target data;
and the motion planning unit is used for solving the OSQP solver according to the target vehicle speed and the target distance to obtain a motion curve of the vehicle.
Optionally, the target parameter determining unit is specifically configured to:
according to the target data, determining the front vehicle speed and the actual following distance at the current moment;
determining a target distance of the vehicle according to the own vehicle speed at the current moment and the front vehicle speed;
and determining the target speed of the vehicle according to the target distance and the actual following distance.
Optionally, the motion planning unit is specifically configured to:
determining constraint conditions of the OSQP solver according to the target vehicle speed and the target distance;
and solving the OSQP solver based on the constraint condition to obtain a motion curve of the vehicle.
Optionally, the motion planning unit is specifically configured to:
determining an upper speed limit and a lower speed limit of the vehicle at different planning moments according to the target speed, the target distance, the maximum vehicle following acceleration of the vehicle and the maximum brake deceleration of the vehicle;
and taking a speed limit envelope formed by the upper speed limit and the lower speed limit as a constraint condition of the OSQP solver.
Optionally, the motion control assembly comprises:
an error parameter determining unit, configured to determine a motion error parameter of the vehicle according to the motion curve and an actual motion state of the vehicle;
a motion parameter determination unit for determining a motion control parameter of the vehicle by the PID controller based on the motion error parameter;
and the motion control unit is used for controlling the motion of the vehicle according to the motion control parameters.
Optionally, the error parameter determining unit is specifically configured to:
according to the motion curve, acquiring planning motion parameters of the vehicle at the next planning moment;
determining actual motion parameters of the vehicle at the current planning moment according to the data fed back by the signal chassis;
and determining a motion error parameter of the vehicle according to the planning motion parameter and the actual motion parameter.
Optionally, the motion parameter determining unit is specifically configured to:
determining, by the PID controller, motion compensation parameters of the vehicle based on the motion error parameters;
and determining a motion control parameter of the next planning moment of the vehicle according to the planning motion parameter and the motion compensation parameter.
The self-adaptive cruise system provided by the embodiment of the application comprises a perception prediction component, a motion planning component and a motion control component, wherein the perception prediction component is connected with the motion planning component, the motion planning component is connected with the motion control component, and the perception prediction component is used for determining target data in a region of interest of a vehicle; the motion planning component is used for planning a motion curve of the vehicle by splitting a quadratic programming (OSQP) solver through an operator according to the target data; and the motion control component is used for controlling the motion of the vehicle according to the motion curve and the proportional integral derivative PID controller, so that the smooth control of the motion of the vehicle is realized, the control precision is improved, the driving safety, the driving comfort and the driving energy conservation are also improved, and the driving experience of a user is improved.
Drawings
FIG. 1 is a schematic diagram of an adaptive cruise system according to an embodiment of the present application;
FIG. 2 is a schematic diagram of a region of interest determined in accordance with an embodiment of the present application;
FIG. 3 is a schematic structural diagram of a perceptual prediction component according to an embodiment of the present application;
FIG. 4 is a schematic diagram of a motion planning assembly according to an embodiment of the present application;
FIG. 5 is a schematic diagram of a motion profile provided by an embodiment of the present application;
fig. 6 is a schematic structural diagram of a motion control assembly according to an embodiment of the present application.
Reference numerals illustrate:
100-adaptive cruise system;
110-a perceptual prediction component;
a 111-data calibration unit;
112-a region of interest determination unit;
113-a data filtering unit;
120-a motion planning component;
121-target parameter determination unit
122-a motion planning unit;
130-a motion control assembly;
131-an error parameter determination unit;
132-a motion parameter determination unit;
133-motion control unit.
Detailed Description
The application is described in further detail below with reference to the drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the application and are not limiting thereof. It should be further noted that, for convenience of description, only some, but not all of the structures related to the present application are shown in the drawings.
The main idea of the technical scheme of the application is as follows:
(1) The existing self-adaptive cruise system calculates the speed control quantity through the target speed and the target distance, continuous speed planning is not available, the target speed and the target distance are possibly discontinuous, so that control is not smooth.
(2) The existing self-adaptive cruise system is a fixed time head distance (THW) strategy or a selectable THW strategy, namely, a vehicle always keeps one THW or can only select one from a plurality of THWs in a preset gear in the driving process, and the driving habit of a user (mainly referred to as a driver) is greatly different.
(3) In the existing self-adaptive cruise system, the lane line information obtained by vision is used for generating the interested region (region of interest, ROI) of the vehicle, the dependence on the visual sensor and the visual perception algorithm is too large, and the lane line region is inconsistent with the expected path of the vehicle, so that the generated ROI deviates from the actual motion track of the vehicle.
(4) The traditional mechanical calibration (namely manual adjustment) mode cannot effectively correct the mechanical installation deviation of the sensor, the calibration method is complex and long in calibration time, and the pitch angle, the roll angle and the yaw angle of the sensor can be adjusted according to the target data fed back by the sensor in the online calibration mode of software, so that the accurate calibration of the sensor is completed, the operation is convenient, and the influence of the mechanical installation deviation of the sensor on the accuracy of the follow-up vehicle motion planning is reduced.
Fig. 1 is a schematic structural diagram of an adaptive cruise system according to an embodiment of the present application, and as shown in fig. 1, an adaptive cruise system 100 according to the present embodiment includes:
the motion planning system comprises a perception prediction component 110, a motion planning component 120 and a motion control component 130, wherein the perception prediction component is connected with the motion planning component, and the motion planning component is connected with the motion control component;
a perception prediction component 110 for determining target data within a region of interest of the vehicle and transmitting the determined target data within the region of interest to a motion planning component 120; a motion planning component 120 for planning a motion curve of the vehicle through an OSQP solver based on the received target data within the region of interest, and transmitting the determined motion curve to a motion control component 130; a motion control assembly 130 for controlling vehicle motion in accordance with a motion profile and a proportional-integral-derivative (proportion integration differentiation, PID) controller.
The target data, which is obtained by filtering the sensor data collected by the sensor by the perception prediction component 110 based on the determined ROI, is the basis for the motion planning component 120 to perform motion planning on the vehicle and generate a motion curve, so that the target data is important to improve the motion planning efficiency and accuracy of the motion planning component 120.
It will be appreciated that the sensor used in the embodiments of the present application may be any type of radar sensor, alternatively the sensor used in the embodiments may be a millimeter wave radar sensor.
In one possible implementation, the perception prediction component 110 determines target data within a region of interest of a vehicle by:
(1) And correcting the sensor data acquired by the sensor according to the rotation matrix to obtain sensing data.
Alternatively, the rotation matrix in this embodiment may be expressed by the following formula:
where p is a pitch angle correction amount, y is a yaw angle correction amount, and r is a roll angle correction amount.
It will be appreciated that the p, y, r together form the mechanical mounting offset of the sensor, and that the specific values of p, y, r may be determined in advance based on the mounting of the sensor.
In the step, the sensing data is corrected sensor data, and the sensor data of each frame acquired by the sensor are corrected according to the rotation matrix, so that the purposes of adjusting the pitch angle, the yaw angle and the roll angle of the sensor are achieved, the mechanical installation deviation of the sensor is calibrated on line, and compared with the prior art, the method for correcting the mechanical installation deviation of the sensor through the rotation matrix is more convenient to operate and better in correction effect. And the interested region of the vehicle determined by the perception data obtained by the step can reflect the expected motion trail of the vehicle.
(2) And predicting the region of interest of the vehicle according to the current motion state of the vehicle and the Ackerman steering model, and determining the region of interest of the vehicle.
In the step, a region of interest of the vehicle is predicted based on the current motion state of the vehicle and the ackerman steering model, wherein the current motion state of the vehicle can be described by current motion state parameters of the vehicle, such as speed, position and the like; the Ackerman steering model is a kinematic model for planning the track of the vehicle, so that the planned vehicle motion track is more practical, and the kinematic geometric constraint in the driving process is satisfied; the region of interest is the region through which the vehicle is likely to travel. The predicted region of interest is closely related to the current motion state of the vehicle, and the characteristics of the ackerman steering model are combined, so that the reliability is higher.
In one possible embodiment, in this step, the motion state parameters of the vehicle at the current moment are obtained, where the motion state parameters include the speed, acceleration and position of the vehicle; predicting a motion path of the vehicle through an ackerman steering model based on the motion state parameters; based on the motion path and the vehicle width, a region of interest of the vehicle is generated.
In this embodiment, the speed and acceleration information in the motion state parameters of the vehicle at the current time may be obtained from the information chassis of the vehicle, and the position information may be obtained from the positioning device of the vehicle.
Fig. 2 is a schematic diagram of a region of interest determined according to an embodiment of the present application, and as shown in fig. 2, an arc with an arrow from a vehicle to the front is a motion path of a vehicle (i.e., a vehicle itself) predicted by an ackerman steering model, and a region formed by two arcs parallel to the motion path and two straight lines perpendicular to the vehicle is a region of interest of the finally determined vehicle, where R is a turning radius of the vehicle itself, δ is a corner of a front vehicle relative to the vehicle itself, and w represents a vehicle width.
(3) And filtering the perception data according to the region of interest to obtain target data in the region of interest.
In this step, according to the region of interest determined in step (2), the perceived data obtained in step (1) is filtered, that is, the data outside the region of interest is filtered, and only the data in the region of interest is retained, so as to obtain the target data in the region of interest, thereby avoiding the interference of the irrelevant data on the motion planning of the motion planning component 120 and improving the motion planning efficiency of the motion planning component 120.
Optionally, fig. 3 is a schematic structural diagram of a perceptual prediction component provided in an embodiment of the present application, as shown in fig. 3, the perceptual prediction component 110 includes: the data calibration unit 111, the region of interest determination unit 112 and the data filtering unit 113, the data calibration unit 111, the region of interest determination unit 112 and the data filtering unit 113 are respectively connected.
A data calibration unit 111, configured to perform the step (1) to implement correction of the sensor data, obtain the sensing data, and send the sensing data to the data filtering unit 113; a region of interest determining unit 112 configured to perform the step (2) to implement prediction of the region of interest of the vehicle, and send the predicted region of interest to the data filtering unit 113; the data filtering unit 113 is configured to perform the step (3) to filter the perceived data, and send the filtered target data in the region of interest to the motion planning component 120, so that the motion planning component 120 performs motion planning on the vehicle according to the target data in the region of interest, thereby ensuring accuracy and efficiency of the motion planning component 120 in performing motion planning on the vehicle.
The process of motion planning by the motion planning component 120 will be described in detail below.
Because the motion planning component 120 performs motion planning based on the OSQP solver, in this embodiment, the OSQP solver needs to be constructed first, and the OSQP solver obtained by the construction is stored in the motion planning component 120, and when the motion planning component 120 performs motion planning, the OSQP solver is directly called from a corresponding position. Illustratively, an OSQP solver constructed in accordance with an embodiment of the present application is shown in equations (2) - (9).
The standard form of the OSQP optimization problem is as follows:
subject to l≤A x ≤u (3)
wherein P is a weight matrix, x is a state vector, q is a gradient vector, l and u are constraint vectors, and Ax is a constraint matrix.
The state vector (decision variable of an embodiment of the present application) is expressed as:
x＝[v a j] (4)
where v is velocity, a is acceleration, and j is jerk.
The weight matrix is expressed as:
P＝[P 1 P 2 P 3 ] (5)
wherein P is 1 Weight of v, P 2 Weight of a, P 3 Is the weight of j, P 1 、P 2 And P 3 The method can be set according to actual conditions and solving requirements.
The gradient matrix is expressed as:
q＝[q 1 q 2 q 3 ]＝(6)
wherein q 1 Bias parameter of v, q 2 A is the bias parameter of a, q 3 Is the bias parameter of j, q 1 、q 2 And q 3 The method can be set according to actual conditions and solving requirements.
The constraint vector is expressed as:
where l is a constraint lower limit vector of the state vector, u is a constraint upper limit vector of the state vector, k is a planning time (the interval between two adjacent planning times is one prediction step), and as can be seen from formulas (7) and (8), l includes a lower limit of the state vector from the 0 th planning time (the time when planning starts) to the kth planning time (the time when planning ends), and u includes an upper limit of the state vector from the 0 th planning time to the kth planning time.
The constraint matrix is expressed as:
A x ＝[v 0 a 0 j 0 … v k a k j k ] T (9)
as can be seen from equation (9), A x Including the state vector from the 0 th planning time to the kth planning time.
Fig. 4 is a schematic structural diagram of a motion planning assembly according to an embodiment of the present application, and as shown in fig. 4, a motion planning assembly 120 of the present embodiment includes: a target parameter determination unit 121 and a motion planning unit 122, the target parameter determination unit 121 being connected to the motion planning unit 122.
A target parameter determining unit 121 for determining a target speed and a target distance of the vehicle according to target data in the region of interest; the motion planning unit 122 is configured to solve the OSQP solver according to the target vehicle speed and the target distance, so as to obtain a motion curve of the vehicle.
In this embodiment, the target vehicle speed and the target distance are targets for motion planning of the vehicle, the target vehicle speed is a speed at which the vehicle is expected to maintain motion, the target distance is a distance between the vehicle and the front vehicle, and by setting the target vehicle speed and the target distance, it is ensured that a safe driving distance can be maintained with the front vehicle when the vehicle in front of the vehicle is found.
In one possible implementation, the target parameter determination unit 121 is specifically configured to: according to target data in the interested area, determining the front vehicle speed and the actual following distance at the current moment; determining a target distance of the vehicle according to the own vehicle speed and the front vehicle speed at the current moment; and determining the target speed of the vehicle according to the target distance and the actual following distance.
The current time is the 0 th planning time, and the actual following distance refers to the actual distance between the vehicle and the front vehicle. In this embodiment, the speed of the front vehicle at the current time may be determined according to the speed information of the front vehicle in the target data in the region of interest of the vehicle at the current time determined by the sensing prediction component 110, and the actual following distance may be determined according to the position information of the front vehicle in the target data in the region of interest of the vehicle at the current time determined by the sensing prediction component 110 and the position information of the vehicle determined by the own vehicle. Meanwhile, the vehicle speed at the current moment can be determined according to the data fed back by the signal chassis.
Alternatively, the target distance of the vehicle is determined by the following formula in the present embodiment:
s target ＝thw×v prec +s safe (10)
s target represents the target distance, thw represents the time distance, v prec Indicating the speed of the front vehicle s safe Is a safe distance, wherein thw can be determined by the following formula:
thw＝c r v ego +T min (11)
v ego c is the speed of the vehicle r For speed regulation parameters, T min For minimum time, c r And T min The calibration amounts are determined by calibration before the vehicle leaves the factory.
s safe Can be determined by the following formula (12):
s safe ＝c safe v ego +s min (12)
c safe as a safety factor s min For minimum safety distance c safe Sum s min The calibration quantity is also determined by calibration before the vehicle leaves the factory.
As can be seen from the above formulas (10) - (12), the own vehicle speed at the present time is substituted into formulas (11) and (12), respectively, to obtain thw and s safe And then willFront vehicle speed and solving to obtain thw and s safe Substituting the target distance into the formula (10) to obtain a specific value of the target distance.
Illustratively, the determination of the target distance is performed in the present embodiment by the following formula:
v target ＝v prec +c s s diff (13)
s diff ＝s act -s target (14)
wherein v is target Representing the target speed, c s Representing distance adjustment parameters (which are the calibration amounts determined by calibration before the delivery of the vehicle), s act Representing the actual distance between the vehicle and the preceding vehicle, i.e. the actual following distance, s diff Representing the difference between the actual following distance and the target distance.
In one possible implementation, the motion planning unit 122 in this embodiment is specifically configured to:
and determining constraint conditions of the OSQP solver according to the target speed and the target distance, and solving the OSQP solver based on the constraint conditions to obtain a motion curve of the vehicle.
It will be appreciated that in this embodiment, determining the constraint is determining the values of l and u in equations (7) - (8).
Optionally, in this embodiment, the upper speed limit and the lower speed limit of the vehicle at different planning moments are determined by the following formulas:
v max ＝a max ×t 1 (15)
v up ＝v target +OFFSET (16)
v dmax ＝dec max ×t 2 (17)
v min ＝a min ×t 3 (18)
v down ＝v target -OFFSET (19)
v dmin ＝dec min ×t 4 (20)
wherein v in formula (15) max Representing t 0 (0 th planning time) to t m Upper speed limit of vehicle between (mth planning moment), a max Indicating a set maximum following acceleration of the vehicle, t 1 Representing t 0 To t m Distance t between any planning moments 0 For example, the time from the mth planning time to the 0 th planning time is m x ω, ω being the prediction step size).
In formula (16) v up Representing t m (mth planning time) to t n The upper speed limit of the vehicle between (n-th planning time) and OFFSET represents the speed OFFSET, and v is known from the formula (16) up The calculation determination may be made by the target speed and the speed offset.
In formula (17), v dmax Representing t n (nth planning time) to t k Upper speed limit, dec, of the vehicle between (k-th planning instant) max Indicating a set maximum braking deceleration, t 2 Representing t n To t k Distance t between any planning moments k For example, (k-n) ×ω, ω being the prediction step size) from the nth planning time to the kth planning time.
The upper speed limit from the 0 th planning time to the kth planning time can be determined by the above-described (15) to (17).
The above t m V is max Value of (v) and v up The planning times t, of equal values n V is dmax And v up Is equal to the value of the corresponding programming time. It will be appreciated that a in the above formula max And dec max As well as OFFSET, are known amounts.
In formula (18) v min Representing t 0 (0 th planning time) to t target (planned time when the speed of the vehicle reaches the target speed) lower speed limit of the vehicle, a min Representing the minimum acceleration of the vehicle, t 3 Representing t 0 To t target Distance t between any planning moments 0 Is a time of (a) to be used.
V in formula (19) down At t target (the planned time when the speed of the vehicle reaches the target speed) the lower speed limit of the vehicle is determined by the formula (19), v down The calculation determination may be made by the target speed and the speed offset.
In formula (20), v dmin Representing t target (planned time when the speed of the vehicle reaches the target speed) to t k Lower speed limit, dec, of the vehicle between (k-th planning instant) min Representing minimum braking deceleration of vehicle, t 4 Representing t target To t k Distance t between any planning moments k Is a time of (a) to be used.
By substituting equations (18) - (20) into equation (21), v can be solved min 、v dmin 、 t target 、t k The specific values of the four quantities are taken, and then v obtained by solving min 、v dmin 、t target 、t k The lower speed limit from the 0 th planning time to the k th planning time can be solved by reversely substituting the values into the formulas (18) - (20). Then according to t k 、t m And t n Solving equations (15) - (17) determines the upper speed limit from the 0 th planning time to the k-th planning time.
The upper limits of acceleration at different planning moments can be obtained by deriving the upper limits of the speed at different planning moments, and the upper limits of jerk at different planning moments can be obtained by deriving the upper limits of the acceleration at different planning moments. Correspondingly, the lower acceleration limits at different planning moments can be obtained by deriving the lower speed limits at different planning moments, and further the lower jerk limits at different planning moments can be obtained by deriving the lower acceleration limits at different planning moments.
Further, the constraint upper limit vector of the OSQP solver can be determined according to the upper speed limit, the upper acceleration limit and the upper jerk limit, and the constraint lower limit vector of the OSQP solver can be determined according to the lower speed limit, the lower acceleration limit and the lower jerk limit.
And substituting the constraint upper limit vector and the constraint lower limit vector into the OSQP solver as constraint conditions of the OSQP solver to obtain motion planning parameters of the vehicle at different planning moments, wherein the motion planning parameters comprise speed v (t), acceleration a (t) and jerk j (t), and t represents the planning moment.
Further, by integrating the speeds of the vehicles at different planning times, the distance s (t) of the vehicles at the different planning times can be obtained (assuming that the 0 th planning time is taken as the origin), and the formula for determining s (t) is as follows:
finally, a curve including time, distance, speed and acceleration, i.e., a motion curve, is derived, which is expressed as formula (23).
speed(v,s,a,t) (23)
Where t represents the planning instant, v is the velocity with respect to t, a is the acceleration with respect to t, and s is the distance with respect to t. Fig. 5 is a schematic diagram of a motion curve according to an embodiment of the present application.
The process of motion control by the motion control assembly 130 will be described in detail below.
According to the embodiment of the application, a cascade PID controller is constructed, a prediction step length is calculated, the motion error parameters of each planning moment are obtained, the motion compensation parameters are further determined, and finally the motion control parameters are given by combining the motion compensation parameters and the planning motion parameters, so that the drive-by-wire system controls the motion of the vehicle according to the motion control parameters.
Fig. 6 is a schematic structural diagram of a motion control assembly according to an embodiment of the present application, as shown in fig. 6, a motion planning assembly 130 in this embodiment includes: error parameter determining section 131, motion parameter determining section 132, and motion control section 133. Error parameter determining section 131 is connected to motion parameter determining section 132, and motion parameter determining section 132 is connected to motion control section 133.
An error parameter determining unit 131 for determining a motion error parameter of the vehicle according to the motion curve and an actual motion state of the vehicle; a motion parameter determination unit 132 for determining motion control parameters of the vehicle by the PID controller based on the motion error parameters; a motion control unit 133 for controlling the motion of the vehicle according to the motion control parameter.
In one possible implementation, the error parameter determining unit 131 is specifically configured to: determining the current planning moment, and acquiring planning motion parameters (such as planning distance, planning speed and planning acceleration) of the vehicle at the next planning moment from the motion curve; according to the data fed back by the signal chassis, determining actual motion parameters (such as actual distance and actual speed) of the vehicle at the current planning moment; and determining the motion error parameter of the vehicle according to the planned motion parameter and the actual motion parameter.
Let s be planning 、v planning 、a planning Respectively representing the planning distance, the planning speed and the planning acceleration at the next planning moment, and using s actual 、v actual Representing the actual distance and the actual speed at the current planning moment respectively, determining a motion error parameter (such as a distance error parameter and a speed error parameter) of the vehicle by the following formula:
e s ＝|s planning -s actual | (24)
e v ＝|v planning -v actual | (25)
wherein e s Representing distance error parameters e v Representing a speed error parameter.
In one possible implementation, the motion parameter determining unit 132 is specifically configured to:
determining, by the PID controller, motion compensation parameters of the vehicle based on the motion error parameters; and determining the motion control parameters of the next planning moment of the vehicle according to the planning motion parameters and the motion compensation parameters.
Alternatively, the motion compensation parameter of the vehicle is determined by the following formula in the present embodiment:
v c ＝pidController s (e s ) (26)
a c ＝pidController v (e v ) (27)
wherein picontroller () is the control function of the PID controller, v c Representing a speed compensation parameter, a c Is an acceleration compensation parameter.
a target ＝a planning +a c (28)
After the acceleration compensation parameter is determined according to formulas (26) - (27), it is substituted into formula (28) for the target acceleration a target I.e. the motion control parameter of the next planning moment of the vehicle, and to target the acceleration a target To the motion control unit 133 to make the motion control unit 133 to respond to the target acceleration a target Controlling the movement of the vehicle.
It can be appreciated that, due to the physical relationship among the acceleration, the speed and the distance, only one of the three is required to be determined as a motion control parameter in the embodiment, so that the motion control of the vehicle can be realized.
In this embodiment, through the speed control based on the cascade PID, corresponding speed compensation and acceleration compensation are given according to the planned position and the feedback position, the planned speed and the feedback speed, so that a more accurate target acceleration can be output, thereby being beneficial to improving the accuracy of control.
In this embodiment, further, the motion control unit 133 controls the motion control unit according to the target acceleration a target The torque and the braking pressure of the vehicle are controlled so as to control the actual acceleration of the vehicle to be as close to the target acceleration a as possible target And realizing the motion control of the vehicle at the next planning moment. And by analogy, the same execution strategy is adopted at the next planning moment and more planning moments, so that the continuous control of the vehicle from the 0 th planning moment to the k th planning moment can be realized according to the motion curve, the smooth control of the vehicle is realized, and the driving safety, comfort and energy conservation are improved.
Alternatively, in the present embodiment, the motion control unit 133 is a drive-by-wire system of the vehicle.
In the embodiment, the adaptive cruise system comprises a perception prediction component, a motion planning component and a motion control component, wherein the perception prediction component is connected with the motion planning component, the motion planning component is connected with the motion control component, and the perception prediction component is used for determining target data in a region of interest of a vehicle; the motion planning component is used for planning a motion curve of the vehicle by splitting a quadratic programming (OSQP) solver through an operator according to the target data; and the motion control component is used for controlling the motion of the vehicle according to the motion curve and the proportional integral derivative PID controller, so that the smooth control of the motion of the vehicle is realized, the control precision is improved, the driving safety, the driving comfort and the driving energy conservation are also improved, and the driving experience of a user is improved.
Note that the above is only a preferred embodiment of the present application and the technical principle applied. It will be understood by those skilled in the art that the present application is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the application. Therefore, while the application has been described in connection with the above embodiments, the application is not limited to the embodiments, but may be embodied in many other equivalent forms without departing from the spirit or scope of the application, which is set forth in the following claims.
Claims (8)
1. An adaptive cruise system, comprising:
the motion planning system comprises a perception prediction component, a motion planning component and a motion control component, wherein the perception prediction component is connected with the motion planning component, and the motion planning component is connected with the motion control component;
the perception prediction component is used for determining target data in a region of interest of the vehicle;
the motion planning component is used for planning a motion curve of the vehicle through an Operator Splitting Quadratic Programming (OSQP) solver according to the target data;
the motion control component is used for controlling the vehicle to move according to the motion curve and a proportional-integral-derivative (PID) controller;
wherein the perceptual prediction component comprises:
the data calibration unit is used for correcting the sensor data acquired by the sensor according to the rotation matrix to obtain sensing data;
the interest region determining unit is used for predicting the interest region of the vehicle according to the current motion state of the vehicle and the ackerman steering model and determining the interest region of the vehicle;
the data filtering unit is used for filtering the perception data according to the region of interest to obtain target data in the region of interest;
the region of interest determination unit is specifically configured to: acquiring motion state parameters of the vehicle at the current moment, wherein the motion state parameters comprise the speed, the acceleration and the position of the vehicle; predicting a motion path of the vehicle through the ackerman steering model based on the motion state parameters; and generating a region of interest of the vehicle according to the motion path and the vehicle width.
2. The system of claim 1, wherein the motion planning component comprises:
a target parameter determining unit for determining a target speed and a target distance of the vehicle according to the target data;
and the motion planning unit is used for solving the OSQP solver according to the target vehicle speed and the target distance to obtain a motion curve of the vehicle.
3. The system according to claim 2, wherein the target parameter determination unit is specifically configured to:
according to the target data, determining the front vehicle speed and the actual following distance at the current moment;
determining a target distance of the vehicle according to the own vehicle speed at the current moment and the front vehicle speed;
and determining the target speed of the vehicle according to the target distance and the actual following distance.
4. The system according to claim 2, wherein the motion planning unit is specifically configured to:
determining constraint conditions of the OSQP solver according to the target vehicle speed and the target distance;
and solving the OSQP solver based on the constraint condition to obtain a motion curve of the vehicle.
5. The system according to claim 4, wherein the motion planning unit is specifically configured to:
determining an upper speed limit and a lower speed limit of the vehicle at different planning moments according to the target vehicle speed, the target distance, the maximum vehicle following acceleration of the vehicle and the maximum brake deceleration of the vehicle;
and taking a speed limit envelope formed by the upper speed limit and the lower speed limit as a constraint condition of the OSQP solver.
6. The system of claim 1, wherein the motion control assembly comprises:
an error parameter determining unit, configured to determine a motion error parameter of the vehicle according to the motion curve and an actual motion state of the vehicle;
a motion parameter determination unit for determining a motion control parameter of the vehicle by the PID controller based on the motion error parameter;
and the motion control unit is used for controlling the motion of the vehicle according to the motion control parameters.
7. The system according to claim 6, wherein the error parameter determination unit is specifically configured to:
according to the motion curve, acquiring planning motion parameters of the vehicle at the next planning moment;
determining actual motion parameters of the vehicle at the current planning moment according to the data fed back by the signal chassis;
and determining a motion error parameter of the vehicle according to the planning motion parameter and the actual motion parameter.
8. The system according to claim 7, wherein the motion parameter determination unit is specifically configured to:
determining, by the PID controller, motion compensation parameters of the vehicle based on the motion error parameters;
and determining a motion control parameter of the next planning moment of the vehicle according to the planning motion parameter and the motion compensation parameter.
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| CN113561976B (en) * | 2021-08-19 | 2022-04-19 | 湖南大学无锡智能控制研究院 | Vehicle energy-saving prediction adaptive cruise control method and device based on feedback optimization |
| CN114071013B (en) * | 2021-10-13 | 2023-06-20 | 浙江大华技术股份有限公司 | Target snapshot and tracking method and device for vehicle-mounted camera |
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