CN112644488A - Adaptive cruise system - Google Patents

Adaptive cruise system Download PDF

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CN112644488A
CN112644488A CN202011613586.0A CN202011613586A CN112644488A CN 112644488 A CN112644488 A CN 112644488A CN 202011613586 A CN202011613586 A CN 202011613586A CN 112644488 A CN112644488 A CN 112644488A
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motion
vehicle
planning
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CN112644488B (en
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徐巍
王斌
韩海兰
戴一凡
卢贤票
张晓莉
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Tsinghua University
Suzhou Automotive Research Institute of Tsinghua University
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Tsinghua University
Suzhou Automotive Research Institute of Tsinghua University
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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
    • 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
    • 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
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces

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  • Automation & Control Theory (AREA)
  • Transportation (AREA)
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  • Mathematical Physics (AREA)
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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 a motion plan, the motion planning component is a downstream component of the motion plan, and the perception prediction component is used for determining target data in an interested area of a vehicle; the motion planning component is used for splitting an OSQP solver for secondary planning through an operator according to the target data and planning a motion curve of the vehicle; the motion control assembly 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.

Description

Adaptive cruise system
Technical Field
The embodiment of the application relates to the technical field of intelligent driving, in particular to a self-adaptive cruise system.
Background
With the development of science and technology, the automobile intelligent degree is continuously improved, and the automatic driving automobile gradually becomes mature. In which the driving assistance system has entered a large-scale commercial stage, 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 systems such as constant-speed cruising, self-adaptive cruising, following driving systems and the like can greatly reduce the driving fatigue of the driver and improve the driving comfort and the traffic efficiency. The device can obviously reduce the tension and fatigue of a driver, assist or replace the driver to drive along with a front vehicle under the dangerous condition of the occurrence of the barrier, avoid the collision with the barrier and improve the passing efficiency, thereby reducing the casualties caused by the accidents to the maximum extent.
Adaptive cruise, which may also be referred to as active cruise, is similar to conventional cruise control, and includes a radar sensor, a digital signal processor, and a control module. In an adaptive cruise system, the system uses a low power radar or infrared beam to obtain the exact position of the leading vehicle, and if the leading vehicle is found to slow down or a new target is detected, the system sends an execution signal to the engine or the brake system to reduce the speed of the vehicle, so that the vehicle and the leading vehicle maintain a safe driving distance. When the front road obstacle is cleared, the speed is accelerated to be recovered to the set speed, and the radar system can automatically monitor the next target. The active cruise control system replaces a driver to control the speed of the vehicle, and frequent cancellation and setting of cruise control are avoided.
However, the existing adaptive cruise system directly calculates the speed control amount through the target speed and the target distance without continuous speed planning, which results in unsmooth control and affects the safety, comfort and energy-saving performance of driving.
Disclosure of Invention
The embodiment of the application provides a self-adaptation cruise system, has improved the security, the travelling comfort and the energy-conservation nature of driving.
The application provides a self-adaptation cruise system, includes:
the system comprises a perception prediction component, an exercise planning component and an exercise control component, wherein the perception prediction component is connected with the exercise planning component, and the exercise planning component is connected with the exercise control component;
the perception prediction component is used for determining target data in an area of interest of the vehicle;
the motion planning component is used for splitting a quadratic programming OSQP solver through an operator according to the target data and planning a motion curve of the vehicle;
and the motion control component is used for controlling the motion of the vehicle according to the motion curve and a 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 interested region determining unit is used for predicting the interested region of the vehicle according to the current motion state of the vehicle and the Ackerman steering model, and determining the interested region of the vehicle;
and the data filtering unit is used for filtering the perception data according to the interested region to obtain target data in the interested region.
Optionally, the region of interest determination unit is specifically configured to:
obtaining 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 ackermann steering model based on the motion state parameters;
and generating the region of interest of the vehicle according to the motion path and the width of the vehicle.
Optionally, the motion planning component comprises:
the target parameter determining unit is used for determining the target speed and the 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 speed and the target distance to obtain a motion curve of the vehicle.
Optionally, the target parameter determining unit is specifically configured to:
determining the previous vehicle speed and the actual vehicle following distance at the current moment according to the target data;
determining the target distance of the vehicle according to the current speed of the vehicle and the speed of the vehicle ahead;
and determining the target speed of the vehicle according to the target distance and the actual vehicle following distance.
Optionally, the motion planning unit is specifically configured to:
determining a constraint condition 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 the motion curve of the vehicle.
Optionally, the motion planning unit is specifically configured to:
determining the upper speed limit and the lower speed limit of the vehicle at different planning moments according to the target speed, the target distance, the maximum following acceleration of the vehicle and the maximum braking 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:
the error parameter determining unit is used for determining a motion error parameter of the vehicle according to the motion curve and the actual motion state of the vehicle;
a motion parameter determination unit for determining a motion control parameter of the vehicle through 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:
acquiring a planned motion parameter of the vehicle at the next planning moment according to the motion curve;
determining the actual motion parameters of the vehicle at the current planning moment according to the data fed back by the signal chassis;
and determining the 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, a motion compensation parameter for the vehicle based on the motion error parameter;
and determining the motion control parameter of the vehicle at the next planning moment according to the planning motion parameter and the motion compensation parameter.
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 an interested area of a vehicle; the motion planning component is used for splitting the quadratic programming OSQP solver through an operator according to the target data and planning the motion curve of the vehicle; and the motion control assembly 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 improved, and the driving experience of a user is improved.
Drawings
FIG. 1 is a schematic structural diagram of an adaptive cruise system according to an embodiment of the present disclosure;
FIG. 2 is a schematic diagram of a region of interest determined by 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 structural diagram of an exercise planning component 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.
Description of reference numerals:
100-an adaptive cruise system;
110-a perceptual prediction component;
111-a 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 present application will be described in further detail with reference to the following drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the application and are not limiting of the application. It should be further noted that, for the convenience of description, only some of the structures related to the present application are shown in the drawings, not all of the structures.
The main ideas of the technical scheme are as follows:
(1) the existing adaptive cruise system calculates speed control quantity through a target speed and a target distance, continuous speed planning is not available, the target speed and the target distance can be discontinuous, and control is unsmooth.
(2) The existing self-adaptive cruise system is a fixed Time Head Way (THW) strategy or an optional THW strategy, namely, a vehicle always keeps one THW or only one THW can be selected from a plurality of THWs at a preset gear in the driving process, and the THW strategy is greatly different from the driving habits of a user (mainly referring to a driver).
(3) According to the conventional self-adaptive cruise system, a region of interest (ROI) of a vehicle is generated through lane line information obtained by vision, dependence on a vision sensor and a vision perception algorithm is too large, and the lane line region is not consistent with an expected path of the vehicle, so that the generated ROI deviates from an actual motion track of the vehicle.
(4) According to the conventional self-adaptive cruise system, the traditional mechanical calibration (namely manual adjustment) mode cannot effectively correct the mechanical installation deviation of the sensor, the calibration method is complicated and long in calibration time, the pitch angle, the roll angle and the yaw angle in the period can be adjusted according to target data fed back by the sensor through the software online calibration mode so as to finish accurate calibration of the sensor, the operation is convenient and fast, and the influence of the mechanical installation deviation of the sensor on the accuracy of subsequent 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 system comprises a perception prediction component 110, an exercise planning component 120 and an exercise control component 130, wherein the perception prediction component is connected with the exercise planning component, and the exercise planning component is connected with the exercise control component;
a perception prediction component 110 for determining target data within a region of interest of the vehicle and sending the determined target data within the region of interest to a motion planning component 120; the motion planning component 120 is configured to plan a motion curve of the vehicle according to the received target data in the region of interest by using an OSQP solver, and send the determined motion curve to the motion control component 130; a motion control component 130 for controlling the motion of the vehicle according to a motion curve and a proportional-integral-derivative (PID) controller.
The target data is obtained by filtering the sensor data acquired by the sensor based on the determined ROI by the perception prediction component 110, and is a basis for the motion planning component 120 to plan the motion of the vehicle and generate a motion curve, so that the target data is very important for improving the motion planning efficiency and accuracy of the motion planning component 120.
It is understood that the sensor used in the embodiment of the present application may be various types of radar sensors, and optionally, the sensor used in the embodiment is 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 torque matrix in the present embodiment may be expressed by the following formula:
Figure BDA0002873618690000081
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 p, y, r together constitute the mechanical mounting offset of the sensor, and the specific values of p, y, r may be determined in advance according to the mounting conditions of the sensor.
In the step, the sensing data is corrected sensor data, the pitch angle, the yaw angle and the roll angle of the sensor are adjusted by correcting each frame of sensor data collected by the sensor according to the rotation matrix, and the on-line calibration of the mechanical installation deviation of the sensor is realized. In addition, the interested area of the vehicle determined by the perception data obtained in the step can reflect the expected motion track of the vehicle.
(2) And predicting the interested area of the vehicle according to the current motion state of the vehicle and the Ackerman steering model, and determining the interested area of the vehicle.
In the step, the region of interest of the vehicle is predicted based on the current motion state of the vehicle and the ackermann steering model, wherein the current motion state of the vehicle can be described by the 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 met; the region of interest is a region through which the vehicle is likely to travel. The predicted interesting region is closely related to the current motion state of the vehicle and combines the characteristics of the Ackerman steering model, so that the reliability is higher.
In a possible implementation manner, in this step, by obtaining motion state parameters of the vehicle at the current time, the motion state parameters include a speed, an acceleration and a position of the vehicle; predicting a motion path of the vehicle through an Ackerman steering model based on the motion state parameters; and generating the region of interest of the vehicle according to the motion path and the width of the vehicle.
In this embodiment, the speed and acceleration information in the motion state parameters of the vehicle at the current time may be acquired from an information chassis of the vehicle, and the position information may be acquired from a positioning device of the vehicle.
Fig. 2 is a schematic diagram of a region of interest determined by an embodiment of the present application, as shown in fig. 2, a region formed by two arcs parallel to a motion path and two straight lines perpendicular to the vehicle is a region of interest of a finally determined vehicle, where R is a turning radius of the vehicle, δ is a turning angle of the vehicle ahead relative to the vehicle, and w represents a vehicle width, the arcs with arrows from the vehicle ahead are motion paths of the vehicle (i.e., the vehicle) predicted by an ackermann steering model.
(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 perception data obtained in step (1) is filtered, that is, data outside the region of interest is filtered, and only data in the region of interest is retained to obtain target data in the region of interest, so that interference of irrelevant data on motion planning performed by the motion planning component 120 is avoided, and the motion planning efficiency of the motion planning component 120 is improved.
Optionally, fig. 3 is a schematic structural diagram of a perceptual prediction component provided in an embodiment of the present application, and as shown in fig. 3, the perceptual prediction component 110 includes: a data calibration unit 111, a region of interest determination unit 112 and a data filtering unit 113, the data calibration unit 111, the region of interest determination unit 112 and the data filtering unit 113 being connected, respectively.
The data calibration unit 111 is configured to perform the step (1), modify the sensor data to obtain sensing data, and send the sensing data to the data filtering unit 113; the region-of-interest determining unit 112 is configured to perform the step (2), predict 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), filter the sensing data, and send the target data in the region of interest obtained by filtering 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, and accuracy and efficiency of performing motion planning on the vehicle by the motion planning component 120 are ensured.
The process of the motion planning component 120 for motion planning 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 constructed OSQP solver 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, the OSQP solver constructed by the embodiment of the present application is shown in equations (2) - (9).
The standard form of the OSQP optimization problem is as follows:
Figure BDA0002873618690000101
subject to l≤Ax≤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 for embodiments of the present application) is represented as:
x＝[v a j] (4)
where v is velocity, a is acceleration, and j is jerk.
The weight matrix is represented as:
P＝[P1 P2 P3] (5)
wherein, P1Is the weight of v, P2Is the weight of a, P3Is the weight of j, P1、P2And P3The setting can be carried out according to the actual situation and the solving requirement.
The gradient matrix is represented as:
q＝[q1 q2 q3]＝(6)
wherein q is1Bias parameter of v, q2Bias parameter of a, q3A bias parameter of j, q1、q2And q is3The setting can be carried out according to the actual situation and the solving requirement.
The constraint vector is represented as:
Figure BDA0002873618690000111
Figure BDA0002873618690000112
wherein 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 (an interval between two adjacent planning times is a 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 (a time when planning starts) to the kth planning time (a 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 represented as:
Ax＝[v0 a0 j0 … vk ak jk]T (9)
as shown in formula (9), AxIncluding the state vectors from the 0 th planning time to the kth planning time.
Exemplarily, fig. 4 is a schematic structural diagram of an exercise planning component provided in an embodiment of the present application, and as shown in fig. 4, the exercise planning component 120 of the present embodiment includes: a target parameter determination unit 121 and an exercise planning unit 122, the target parameter determination unit 121 being connected to the exercise planning unit 122.
A target parameter determining unit 121, configured to determine a target vehicle speed and a target distance of the vehicle according to the target data in the region of interest; and the motion planning unit 122 is configured to solve the OSQP solver according to the target vehicle speed and the target distance to obtain a motion curve of the vehicle.
In this embodiment, the target vehicle speed and the target distance are targets for planning the movement of the vehicle, the target vehicle speed is a speed at which the vehicle is expected to maintain the movement, and the target distance is a distance at which the vehicle is expected to keep from a preceding vehicle.
In a possible implementation, the target parameter determining unit 121 is specifically configured to: determining the front vehicle speed and the actual vehicle following distance at the current moment according to the target data in the region of interest; determining the target distance of the vehicle according to the speed of the vehicle at the current moment and the speed of the vehicle ahead; and determining the target speed of the vehicle according to the target distance and the actual vehicle following distance.
The current moment is the 0 th planning moment, and the actual following distance refers to the actual distance between the vehicle and the previous vehicle. In this embodiment, the current preceding vehicle speed may be determined according to the preceding vehicle speed information in the target data in the area of interest of the vehicle at the current time determined by the perception prediction component 110, and the actual following vehicle distance may be determined according to the position information of the preceding vehicle in the target data in the area of interest of the vehicle at the current time determined by the perception prediction component 110 and the position information of the vehicle determined by the own vehicle. Meanwhile, the speed of the bicycle at the current moment can be determined according to data fed back by the signal chassis.
Optionally, in the present embodiment, the target distance of the vehicle is determined by the following formula:
starget＝thw×vprec+ssafe (10)
stargetrepresenting target distance, thw time distance, vprecIndicating the speed, s, of the vehicle aheadsafeFor the safe distance, thw can be determined by the following formula:
thw＝crvego+Tmin (11)
vegoas the speed of the bicycle, crFor adjusting the parameters, TminIs the most importantSmall time, crAnd TminAll are calibration quantities, which are determined by calibration before the vehicle leaves the factory.
ssafeCan be determined by the following equation (12):
ssafe＝csafevego+smin (12)
csafeto a safety factor, sminAt a minimum safe distance, csafeAnd sminAnd also is a calibration amount, which is determined by calibration before the vehicle leaves the factory.
As can be seen from the above equations (10) to (12), thw and s are obtained by substituting the vehicle speed at the current time into equations (11) and (12), respectivelysafeThen, the speed of the front vehicle and the solution are used to obtain thw and ssafeAnd substituting the obtained result into the formula (10) to obtain a specific value of the target distance.
Exemplarily, the determination of the target distance is performed by the following formula in the present embodiment:
vtarget＝vprec+cssdiff (13)
sdiff＝sact-starget (14)
wherein v istargetRepresenting target speed, csIndicating a distance adjustment parameter (a calibration quantity determined by calibration before delivery of the vehicle), sactRepresenting the actual distance between the vehicle and the preceding vehicle, i.e. the actual following distance, sdiffRepresenting the difference between the actual following distance and the target distance.
In a possible implementation manner, in this embodiment, the motion planning unit 122 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 is to be understood that, in the present embodiment, the determination of the constraint is to determine the values of l and u in equations (7) to (8).
Optionally, in the present embodiment, the upper speed limit and the lower speed limit of the vehicle at different planning times are determined by the following formulas:
vmax＝amax×t1 (15)
vup＝vtarget+OFFSET (16)
vdmax＝decmax×t2 (17)
vmin＝amin×t3 (18)
vdown＝vtarget-OFFSET (19)
vdmin＝decmin×t4 (20)
Figure BDA0002873618690000141
wherein v is represented by the formula (15)maxRepresents t0(0 th planning time) to tmUpper speed limit of the vehicle between (mth planning moment), amaxIndicating a set maximum following acceleration, t, of the vehicle1Represents t0To tmAny planning time distance t between0Time (e.g., the time from the mth planning time to the 0 th planning time is m × ω, and ω is the prediction step size).
In the formula (16) vupRepresents tm(mth planning time) to tn(nth planned time) the upper limit of the vehicle speed, OFFSET represents the speed OFFSET, and v is given by equation (16)upThe determination may be made by calculation from the target speed and the speed offset.
In formula (17), vdmaxRepresents tn(nth planning time) to tkUpper speed limit, dec of the vehicle between (kth planning moment)maxIndicating the set maximum braking deceleration, t, of the vehicle2Represents tnTo tkAny planning time distance t betweenkFor example, the time from the nth planning time to the kth planning time is (k-n) × ω, where ω is the prediction step size).
The upper speed limit from the 0 th planning time to the k-th planning time can be determined through the above-mentioned (15) - (17).
Note that, the above-mentioned tmIs v ismaxValue of (a) and vupAt planning instants t at which the values of (c) are equalnIs v isdmaxAnd vupAre equal. It will be understood that a in the above formulamaxAnd decmaxAnd OFFSET are known quantities.
In the formula (18) vminRepresents t0(0 th planning time) to ttarget(scheduled time when the speed of the vehicle reaches the target speed) to a lower speed limit of the vehicle, aminRepresenting the minimum acceleration, t, of the vehicle3Represents t0To ttargetAny planning time distance t between0Time of (d).
In the formula (19), vdownIs ttarget(scheduled time when the speed of the vehicle reaches the target speed) lower limit of the speed of the vehicle, as can be seen from equation (19), vdownThe determination may be made by calculation from the target speed and the speed offset.
In the formula (20), vdminRepresents ttarget(scheduled time at which speed of vehicle reaches target speed) to tk(kth planning moment) between the lower speed limit, dec of the vehicleminIndicating minimum braking deceleration of the vehicle, t4Represents ttargetTo tkAny planning time distance t betweenkTime of (d).
By substituting equations (18) - (20) into equation (21), v can be solvedmin、vdmin、 ttarget、tkSpecific values of four quantities, and solving the obtained vmin、vdmin、ttarget、tkBy substituting equations (18) - (20) in reverse, the lower speed limit from the 0 th planning time to the k th planning time can be solved. According to tk、tmAnd tnSolving equations (15) - (17) determines the upper speed limit from the 0 th planning time to the k-th planning time.
The upper limits of the acceleration at different planning moments can be obtained by differentiating the upper limits of the speed at different planning moments, and the upper limits of the jerk at different planning moments can be obtained by differentiating 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 the lower jerks 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 an 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 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:
Figure BDA0002873618690000161
finally, a curve including time, distance, speed, and acceleration, i.e., a motion curve, is derived, which is expressed as equation (23).
speed(v,s,a,t) (23)
Where t denotes the planning time, 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. Illustratively, fig. 5 is a schematic diagram of a motion curve provided by 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, the motion error parameters of each planning moment are obtained by calculating a prediction step length, 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 a drive-by-wire system controls the motion of a vehicle according to the motion control parameters.
Exemplarily, fig. 6 is a schematic structural diagram of a motion control assembly provided in an embodiment of the present application, and as shown in fig. 6, the motion planning assembly 130 in the embodiment includes: the motion control device comprises an error parameter determining unit 131, a motion parameter determining unit 132 and a motion control unit 133, wherein the error parameter determining unit 131 is connected with the motion parameter determining unit 132, and the motion parameter determining unit 132 is connected with the motion control unit 133.
An error parameter determination unit 131, 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 132 for determining a motion control parameter of the vehicle through the PID controller based on the motion error parameter; a motion control unit 133 for controlling the motion of the vehicle according to the motion control parameter.
In a possible implementation, the error parameter determining unit 131 is specifically configured to: determining the current planning moment, and acquiring the planned motion parameters (such as planned distance, planned speed and planned acceleration) of the vehicle at the next planning moment from the motion curve; determining actual motion parameters (such as actual distance and actual speed) of the vehicle at the current planning moment according to data fed back by the signal chassis; and determining the motion error parameters of the vehicle according to the planned motion parameters and the actual motion parameters.
Suppose using splanning、vplanning、aplanningRespectively representing the planned distance, planned speed and planned acceleration at the next planned time, using sactual、vactualRespectively representing the actual distance and the actual speed at the current planning moment, determining a motion error parameter (such as a distance error parameter and a speed error parameter) of the vehicle by the following formula:
es＝|splanning-sactual| (24)
ev＝|vplanning-vactual| (25)
wherein e issRepresents a distance error parameter, evRepresenting a speed error parameter.
In a possible implementation, the motion parameter determining unit 132 is specifically configured to:
determining a motion compensation parameter of the vehicle through a PID controller based on the motion error parameter; and determining the motion control parameter of the vehicle at the next planning moment according to the planning motion parameter and the motion compensation parameter.
Optionally, in the present embodiment, the motion compensation parameter of the vehicle is determined by the following formula:
vc＝pidControllers(es) (26)
ac＝pidControllerv(ev) (27)
where pidController () is the control function of the PID controller, vcDenotes a speed compensation parameter, acIs an acceleration compensation parameter.
atarget＝aplanning+ac (28)
After the acceleration compensation parameters are determined according to equations (26) to (27), they are substituted into equation (28) to obtain the target acceleration atargetI.e. the motion control parameter at the next planned moment of the vehicle, and the target acceleration atargetOutput to the motion control unit 133 so that the motion control unit 133 can control the motion according to the target acceleration atargetControlling the motion of the vehicle.
It can be understood that, due to the physical relationship among the acceleration, the speed and the distance, only one of the three needs to be determined as the motion control parameter in the embodiment, so that the motion control of the vehicle can be realized.
In this embodiment, through the velocity control based on the cascade PID, according to the planned position and the feedback position, the planned velocity and the feedback velocity, the corresponding velocity compensation and acceleration compensation are given, and a more accurate target acceleration can be output, thereby facilitating the improvement of the accuracy of the control.
In this embodiment mode, further, the motion control unit 133 controls the motion according to the target acceleration atargetTorque and brake pressure to the vehiclePerforming control so that the actual acceleration of the vehicle is controlled as close as possible to the target acceleration atargetAnd realizing the motion control of the vehicle at the next planning moment. By analogy, the same execution strategy is adopted at the next planning moment and at more subsequent planning moments, so that the continuous control of the vehicle from the 0 th planning moment to the k th planning moment according to the motion curve can be realized, the smooth control of the vehicle is realized, and the driving safety, the driving comfort and the energy saving performance 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 an area of interest of the vehicle; the motion planning component is used for splitting an OSQP solver for secondary planning through an operator according to the target data and planning a motion curve of the vehicle; and the motion control assembly 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 improved, and the driving experience of a user is improved.
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present application and the technical principles employed. 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, although the present application has been described in more detail with reference to the above embodiments, the present application is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present application, and the scope of the present application is determined by the scope of the appended claims.

Claims (10)

1. An adaptive cruise system, comprising:
the system comprises a perception prediction component, an exercise planning component and an exercise control component, wherein the perception prediction component is connected with the exercise planning component, and the exercise planning component is connected with the exercise control component;
the perception prediction component is used for determining target data in an area of interest of the vehicle;
the motion planning component is used for splitting a quadratic programming OSQP solver through an operator according to the target data and planning a motion curve of the vehicle;
and the motion control component is used for controlling the motion of the vehicle according to the motion curve and a proportional-integral-derivative (PID) controller.
2. The system of claim 1, 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 interested region determining unit is used for predicting the interested region of the vehicle according to the current motion state of the vehicle and the Ackerman steering model, and determining the interested region of the vehicle;
and the data filtering unit is used for filtering the perception data according to the interested region to obtain target data in the interested region.
3. The system according to claim 2, characterized in that the region of interest determination unit is in particular configured to:
obtaining 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 ackermann steering model based on the motion state parameters;
and generating the region of interest of the vehicle according to the motion path and the width of the vehicle.
4. The system of claim 1, wherein the motion planning component comprises:
the target parameter determining unit is used for determining the target speed and the 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 speed and the target distance to obtain a motion curve of the vehicle.
5. The system according to claim 4, wherein the target parameter determination unit is specifically configured to:
determining the previous vehicle speed and the actual vehicle following distance at the current moment according to the target data;
determining the target distance of the vehicle according to the current speed of the vehicle and the speed of the vehicle ahead;
and determining the target speed of the vehicle according to the target distance and the actual vehicle following distance.
6. The system according to claim 4, wherein the motion planning unit is specifically configured to:
determining a constraint condition 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 the motion curve of the vehicle.
7. The system according to claim 6, wherein the motion planning unit is specifically configured to:
determining the upper speed limit and the lower speed limit of the vehicle at different planning moments according to the target speed, the target distance, the maximum following acceleration of the vehicle and the maximum braking 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.
8. The system of claim 1, wherein the motion control assembly comprises:
the error parameter determining unit is used for determining a motion error parameter of the vehicle according to the motion curve and the actual motion state of the vehicle;
a motion parameter determination unit for determining a motion control parameter of the vehicle through 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.
9. The system according to claim 8, wherein the error parameter determination unit is specifically configured to:
acquiring a planned motion parameter of the vehicle at the next planning moment according to the motion curve;
determining the actual motion parameters of the vehicle at the current planning moment according to the data fed back by the signal chassis;
and determining the motion error parameter of the vehicle according to the planning motion parameter and the actual motion parameter.
10. The system according to claim 9, wherein the motion parameter determination unit is specifically configured to:
determining, by the PID controller, a motion compensation parameter for the vehicle based on the motion error parameter;
and determining the motion control parameter of the vehicle at the next planning moment according to the planning motion parameter and the motion compensation parameter.
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