CN111338346A - Automatic driving control method and device, vehicle and storage medium - Google Patents
Automatic driving control method and device, vehicle and storage medium Download PDFInfo
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Abstract
The invention discloses an automatic driving control method, an automatic driving control device, a vehicle and a storage medium. The method comprises the following steps: determining a planned path and a planned speed of automatic driving according to a first objective function under a natural coordinate system; determining an automatic driving target track according to the planned path, the planned speed and a second target function; and solving a control sequence according to the target track, and controlling the automatic driving of the vehicle according to the control sequence. According to the technical scheme, the target track of automatic driving of the vehicle is accurately determined according to the target function on the basis of the planned path and the planned speed, so that the automatic driving of the vehicle is controlled according to the target track, and the reliability of automatic driving is improved.
Description
Technical Field
The embodiment of the invention relates to the technical field of vehicle control, in particular to an automatic driving control method, an automatic driving control device, a vehicle and a storage medium.
Background
The automobile is an indispensable vehicle in life and work of people, has stronger and stronger intelligence, and especially the autopilot function has brought very big facility for people. In the trajectory planning process of automatic driving, parameters in vehicle kinematics and dynamic models have uncertainty, and in a complex random road environment, other motor vehicles, pedestrians and the like exist, and the behavior randomness of the intelligent bodies brings great difficulty and challenge to the autonomous decision making of the vehicles.
At present, some intelligent decision methods are applied to automatic driving, for example, automatic driving planning and control are carried out based on deep learning, and the method can theoretically realize fitting of any function, but needs a large number of artificial labels and samples, and cannot be realized in a short period; for example, planning and control are performed based on reinforcement learning, reward and punishment functions are set based on behavior meanings, and decisions are optimized through continuous trial and error, but some errors are unacceptable and untried in automatic driving decisions. Therefore, the planning and control of the automatic driving still stay in a simpler working condition at present, and a reliable autonomous decision cannot be realized.
Disclosure of Invention
The invention provides an automatic driving control method, an automatic driving control device, a vehicle and a storage medium, and aims to improve the reliability of automatic driving.
In a first aspect, an embodiment of the present invention provides an automatic driving control method, including:
determining a planned path and a planned speed of automatic driving according to a first objective function under a natural coordinate system;
determining an automatic driving target track according to the planned path, the planned speed and a second target function;
and solving a control sequence according to the target track, and controlling the automatic driving of the vehicle according to the control sequence.
Further, the determining the planned path and the planned speed of the automatic driving according to the first objective function in the natural coordinate system includes:
setting candidate path points in the lanes within a set range and generating a path candidate set;
establishing a first objective function under a natural coordinate system based on the path candidate set, wherein the first objective function is respectively associated with the actual speed, the actual acceleration derivative and the actual path of the vehicle;
and solving the actual speed, the actual acceleration derivative and the actual path which enable the first objective function to be minimum, and determining the planned path and the planned speed according to the obtained actual speed, actual acceleration derivative and actual path.
Further, the determining a target trajectory for autonomous driving according to the planned path, the planned speed, and a second objective function includes:
establishing a second objective function under a natural coordinate system based on the path candidate set, wherein the second objective function is respectively associated with an actual state quantity and a state gap quantity of a vehicle, and the state gap quantity comprises a gap quantity between the actual state quantity and the planned path and a gap quantity between the actual state quantity and the planned speed;
and solving the actual state quantity which enables the second objective function to be minimum, and determining the target track of automatic driving according to the actual state quantity.
Further, the method also comprises the following steps: reading the actual state quantity through a sensor;
the actual state quantity includes: actual lateral displacement, actual longitudinal displacement, actual speed, actual acceleration, actual yaw rate, actual body angle, actual heading angle, and actual acceleration;
the actual state quantity is within the range of the speed limit, start-stop limit, and traveling direction limit of the road.
Further, the constraint conditions corresponding to the first objective function and the second objective function include:
the target trajectory is outside an area of an obstacle vehicle shape set as an ellipse having a major axis on a horizontal axis.
Further, the solving a control sequence according to the target trajectory includes:
constructing the first objective function, the second objective function and the Hamilton function corresponding to the constraint condition;
partial differentiation is carried out on the Hamiltonian to obtain a first function;
and solving the first function by adopting a continuity/Generalized minimal residual (C/GMRES) algorithm to obtain the control sequence.
Further, before determining the planned path and the planned speed of the automatic driving according to the first objective function in the natural coordinate system, the method further includes:
and converting the Cartesian coordinate system into a natural coordinate system, wherein the natural coordinate system takes the center line of the road as a horizontal axis and takes the normal of the road as a vertical axis.
In a second aspect, an embodiment of the present invention provides an automatic driving control apparatus, including:
the behavior planning module is used for determining a planned path and a planned speed of automatic driving according to a first objective function under a natural coordinate system;
the track determining module is used for determining an automatic driving target track according to the planned path, the planned speed and a second target function;
the sequence solving module is used for solving a control sequence according to the target track;
and the control module is used for controlling the automatic driving of the vehicle according to the control sequence.
In a third aspect, an embodiment of the present invention provides a vehicle, including:
one or more processors;
storage means for storing one or more programs;
when the one or more programs are executed by the one or more processors, the one or more processors are caused to implement the automatic driving control method according to the first aspect.
In a fourth aspect, the present invention further provides a computer-readable storage medium, on which a computer program is stored, which when executed by a processor implements the automatic driving control method according to the first aspect.
The embodiment of the invention provides an automatic driving control method, an automatic driving control device, a vehicle and a storage medium, wherein the method comprises the following steps: determining a planned path and a planned speed of automatic driving according to a first objective function under a natural coordinate system; determining an automatic driving target track according to the planned path, the planned speed and a second target function; and solving a control sequence according to the target track, and controlling the automatic driving of the vehicle according to the control sequence. According to the technical scheme, the target track of automatic driving of the vehicle is accurately determined according to the target function on the basis of the planned path and the planned speed, so that the automatic driving of the vehicle is controlled according to the target track, and the reliability of automatic driving is improved.
Drawings
Fig. 1 is a flowchart of an automatic driving control method according to an embodiment of the present invention;
FIG. 2 is a diagram illustrating a natural coordinate system according to a first embodiment of the present invention;
fig. 3 is a flowchart of an automatic driving control method according to a second embodiment of the present invention;
FIG. 4 is a diagram illustrating a path candidate set according to a second embodiment of the present invention;
FIG. 5 is a schematic diagram of a planned path according to a second embodiment of the present invention;
FIG. 6 is a diagram illustrating a planning speed according to a second embodiment of the present invention;
FIG. 7 is a schematic diagram illustrating a method for determining a target trajectory for autonomous driving according to a second embodiment of the present invention;
fig. 8 is a schematic diagram of a constraint condition of a vehicle shape in the second embodiment of the invention;
FIG. 9 is a schematic diagram of a target trajectory according to a second embodiment of the present invention;
fig. 10 is a schematic structural diagram of an automatic driving control device according to a third embodiment of the present invention;
fig. 11 is a schematic hardware structure diagram of a vehicle according to a fourth embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some of the structures related to the present invention are shown in the drawings, not all of the structures.
Example one
Fig. 1 is a flowchart of an automatic driving control method according to an embodiment of the present invention. The present embodiment is applicable to a case where automatic driving of a vehicle is realized by predicting and controlling the trajectory and state of the vehicle. In particular, the autopilot control method may be performed by an autopilot control device, which may be implemented in software and/or hardware and integrated in the vehicle.
As shown in fig. 1, the method specifically includes the following steps:
and S110, determining a planned path and a planned speed of automatic driving according to the first objective function in a natural coordinate system.
Specifically, in path planning, calculation is generally performed based on a cartesian coordinate system, but in the present embodiment, considering that the autonomous driving is in a scene of a structured road, it is difficult to establish an objective function, a constraint condition, a prediction model, or the like using the cartesian coordinate system, and therefore, an autonomous driving trajectory is predicted and planned based on a natural coordinate system. First, a planned path and a planned speed are determined based on a first objective function, for example, road data including lane directions, vehicle lines, obstacle positions, etc. are collected by a camera, a distance sensor, a radar, etc., and a suitable path and a speed of a vehicle traveling on the path are planned accordingly. The process of determining the planned path and the planned speed is substantially a process of optimizing a first objective function, where the first objective function may be a maximized optimization problem or a minimized optimization problem, and the associated variables include speed, acceleration, path (or position), and a solution that maximizes or minimizes the value of the first objective function may be solved in an iterative manner, that is, the planned path and the corresponding planned speed on the path may be obtained. The first objective function is established according to the principle that the speed of the vehicle is stably changed on the basis of meeting the speed limit requirement of the road and selecting a driving path without obstacles. The planned path and the planned speed are used as a preliminary decision in the automatic driving control process and can be used as a hot start initial value and a reference value of subsequent target track planning.
It should be noted that different planned paths have a great influence on the determination of the planned speed, and different lanes in which the vehicle is located have different obstacles, so that the vehicle may take different actions such as passing, following, avoiding, and the like. Before planning, obstacles around the vehicle and surrounding vehicles need to be detected, and for example, a speed chart can be drawn according to the position of the vehicle, the position of the surrounding vehicles and the speed of the surrounding vehicles, and the initial planning of the speed can be performed in the speed chart. The abscissa of the velocity diagram is time and the ordinate is longitudinal (i.e. road direction or vehicle direction of travel) displacement. In the process of planning the speed, a reference speed can be set as a limiting factor of working conditions such as road speed limit, start and stop of traffic lights and the like. On the basis, the first objective function is established by considering factors such as speed, acceleration derivative, continuity and the like, different weights can be given to the factors, and finally a solution which enables the weighted sum value of the factors to be maximum or minimum is obtained, namely the planned path and the planned speed can be obtained.
And S120, determining a target track of automatic driving according to the planned path, the planned speed and a second target function.
Specifically, on the basis of determining the planned path and the planned speed, the target track of automatic driving is further determined according to the second objective function, namely the planned path and the planned speed are optimized, so that the form track and the speed of the vehicle are more consistent with the condition of continuity, and the driving state of the vehicle is conveniently tracked in real time. The second objective function considers the closeness (or difference) of the actual state of the vehicle to the planned result in step S110, so that the actual trajectory coincides with the planned path, and also considers the speed, acceleration, yaw rate, magnitude of the acceleration derivative, and the like of the vehicle, so that the actual speed coincides with the planned speed, and further, restricts the heading angle of the vehicle in the final state on the current path to be parallel to the lane line, ensuring that the traveling direction does not deviate. The process of determining the target trajectory is substantially a process of performing optimization solution on a second objective function, and the second objective function may be a maximized optimization problem in a form, or may be a minimized optimization problem.
And S130, solving a control sequence according to the target track.
Specifically, a control sequence for the vehicle is solved according to a target trajectory, where the control sequence includes various parameters for automatic driving control of the vehicle to achieve the target trajectory, for example, the target trajectory is a planned route that is traveled at a planned speed (including when a lane change is made, and a specific trajectory and speed of the lane change process are determined), and it is about 5 minutes to travel the route, and then the control sequence, specifically, a real-time gear, a steering wheel angle, a pressure amplitude of an accelerator pedal or a brake pedal, an engine speed, a switch of a steering lamp, and the like of the vehicle within 5 minutes, may be reversely solved according to a dynamic model and a kinematic model of the vehicle, so that the vehicle may be controlled to accurately and automatically drive according to the target trajectory.
And S140, controlling the automatic driving of the vehicle according to the control sequence.
Specifically, the vehicle is controlled according to the control sequence, so that the vehicle can be automatically driven according to the target track accurately by controlling the corresponding speed, acceleration, yaw rate, course angle and the like in the automatic driving process.
Further, before determining the planned path and the planned speed of the automatic driving according to the first objective function in the natural coordinate system, the method further includes: and converting the Cartesian coordinate system into a natural coordinate system, wherein the natural coordinate system takes the center line of the road as a horizontal axis and takes the normal of the road as a vertical axis.
Fig. 2 is a schematic diagram of establishing a natural coordinate system according to a first embodiment of the present invention. As shown in fig. 2, road data such as a lane direction, a vehicle line, an obstacle position, and the like are collected by a camera, a distance sensor, a radar, and the like, and a cartesian coordinate system is converted into a natural coordinate system when a road shape is known. The natural coordinate system takes the road center line as a reference line as a horizontal axis and the normal line of the road curve as a vertical axis, the curvature of the road curve can be ignored after conversion, and planning and prediction calculation are simplified.
According to the automatic driving control method provided by the embodiment of the invention, the planned path and the planned speed are determined according to the first objective function under the natural coordinate system, the target track of automatic driving is determined according to the second objective function on the basis, the control sequence is solved according to the target track, and the automatic driving of the vehicle is controlled, so that the target track is consistent with the planned path and the planned speed, the automatic driving of the vehicle is accurately controlled, and the reliability of automatic driving is improved.
Example two
Fig. 3 is a flowchart of an automatic driving control method according to a second embodiment of the present invention, where the second embodiment is optimized based on the first embodiment, and a process of solving each objective function and a control sequence is specifically described. It should be noted that technical details that are not described in detail in the present embodiment may be referred to any of the above embodiments.
Specifically, as shown in fig. 3, the method specifically includes the following steps:
s210, converting the Cartesian coordinate system into a natural coordinate system, wherein the natural coordinate system takes the center line of the road as a horizontal axis and takes the normal of the road as a vertical axis.
And S220, setting candidate route points in the lane within the set range and generating a route candidate set.
Specifically, a path candidate set is generated by integrating the conditions of each lane in a certain range. Fig. 4 is a schematic diagram of a path candidate set according to a second embodiment of the present invention. As shown in fig. 4, candidate route points are set in segments on each adjacent lane within a certain distance (S ═ 100 m) ahead of the vehicle, a route candidate set is generated by traversing all the connection manners, and the planned route obtained by solving the first objective function is an optimal route in the route candidate set as a reference for the planned route.
And S230, establishing a first objective function under a natural coordinate system based on the path candidate set.
In the present embodiment, the first objective function is associated with the actual speed, the actual acceleration derivative, and the actual path of the vehicle, respectively.
Specifically, a reference speed is set as a limiting factor of working conditions such as a road speed limit, a traffic light start-stop and the like, factors such as an actual speed, an actual acceleration derivative and continuity are considered in the establishment of the first objective function, different weights are given to the factors, and a solution enabling the weighted sum value of the factors to be minimum is finally obtained, so that a planned path and a planned speed can be obtained. Taking the minimized optimization objective as an example, the first objective function may be: f. ofspeed＝wrfr+wafa+wjfj+wcfcWherein w isr、wa、wj、wcIs a weight (each weight value is greater than or equal to 0); f. ofr＝|vlimit-vikI represents that the difference value between the actual speed of the vehicle and the reference speed is controlled within a certain range, and the actual speed cannot be too large or too small;
the speed change of the vehicle at the current moment and the previous moment is within a certain range, and the vehicle speed is controlled to be stable;
indicating that the actual acceleration is within a certain range;
indicating that the actual acceleration derivative is within a certain range; wherein, (i, k), (i-1, j), (i-2, j) represent different candidate route point serial numbers set on adjacent lanes within a certain distance in front, refer to the candidate route point labels shown in fig. 4, select different route points to connect and generate different routes, if lane change occurs, j ≠ k; t denotes different moments in time.
S240, solving the actual speed, the actual acceleration derivative and the actual path which enable the first objective function to be minimum, and determining a planned path and a planned speed according to the obtained actual speed, actual acceleration derivative and actual path.
Specifically, the first objective function f is found in the candidate path setspeedThe path with the minimum value is the planned path, meanwhile, the corresponding planned speed can be solved by using an intelligent optimization algorithm, an iterative algorithm and the like, and the speed, the acceleration and the like at each moment can be determined. Different planned paths correspond to different planned speeds, the track distances of the different planned paths are different, the speed plans corresponding to each candidate path are different, and the driving time length is shortened as the planned speed is faster. The optimal solution can be obtained by comparing all possible paths (each path in the path candidate set) with the corresponding speed. By constructing the first objective function in combination with factors such as the travel time, the travel distance, the acceleration derivative and the like, the optimal planned path and the optimal planned speed can be determined.
Fig. 5 is a schematic diagram of a planned path in the second embodiment of the present invention. As shown in fig. 5, for the L coordinate, -2 to 2 correspond to the first lane, 2 to 6 correspond to the second lane, and the S coordinate means the longitudinal distance. Fig. 6 is a schematic diagram of a programming speed in the second embodiment of the present invention. As shown in FIG. 6, the planned vehicle speed and the planned path of the part S ranging from 0 to 40 correspond to a first lane, and the planned vehicle speed and the planned path of the part S ranging from 40 to 80 correspond to a second lane. It should be noted that the lane change trajectory can be planned after the obstacle in front of the lane is monitored by collecting the road data, and the planning speed is solved according to the first objective function.
And S250, establishing a second objective function under a natural coordinate system based on the path candidate set.
In this embodiment, the second objective function is respectively associated with an actual state quantity and a state gap quantity of the vehicle, where the state gap quantity includes a gap quantity between the actual state quantity and the planned path and a gap quantity between the actual state quantity and the planned speed.
Specifically, the optimization goal of the second objective function is to keep the actual state quantity of the vehicle consistent with the planned path and the planned speed, so that the vehicle automatically drives according to the planned path and the planned speed. The actual state quantity needs to be more consistent and continuous on the premise of approaching to the planned path and the planned speedAnd (4) condition, which is convenient for the vehicle to track. Specifically, taking the minimized optimization objective as an example, the second objective function is, for example:
wherein, w1、w2、w3、w4、w5、w6、w7Is a weight (each weight value is greater than or equal to 0); (x, y) is the actual displacement of the vehicle, (x)ref，yref) Is the planned position (x-x) of the automatic driving of the vehicle according to the planned path and the planned speedref)2、(y-yref)2Controlling the difference between the actual position coordinate and the planning position coordinate within a certain range; (v-v)ref)2Controlling the difference between the actual speed and the planned speed within a certain range; in addition, in order to ensure that the target track is smooth enough and the vehicle can be better followed and the riding comfort and the riding stability are improved, the actual acceleration a of the vehicle is controlled within a certain range, and the derivative of the actual acceleration is controlled
Controlling the actual yaw velocity w within a certain range; in addition, in order to ensure that the target track finally falls to the road center as much as possible, the actual course angle of the vehicle needs to be kept as close as possible to the road center, thetaNShould tend towards 0.
And S260, solving the actual state quantity which enables the second objective function to be minimum, and determining the target track of the automatic driving according to the actual state quantity.
Fig. 7 is a schematic diagram illustrating the principle of determining the target trajectory of the automatic driving in the second embodiment of the present invention. As shown in fig. 7, x (t) is the actual state quantity measured by the sensors of the vehicle at time t,
and u' (t) is the optimal control solution at the time t, and y (t) is the output of the model prediction system at the time t. The controller part adopts a model prediction method and is periodically based on the current actual stateAnd solving a finite time open loop optimization problem on line, acting the control sequence corresponding to the obtained actual state quantity on the vehicle, and adjusting the actual state quantity of the vehicle so as to accurately drive according to the target track.
Further, in the process of determining the target trajectory, the method further includes: reading the actual state quantity through a sensor; the actual state quantities include: actual lateral displacement, actual longitudinal displacement, actual speed, actual acceleration, actual yaw rate, actual body angle, actual heading angle, and actual acceleration; the actual state quantity is within the range of the speed limit, start-stop limit, and traveling direction limit of the road.
Specifically, in each control cycle, data is read from the sensors and the actual state quantity of the vehicle is obtained: such as the actual displacement (x, y) of the vehicle, the actual speed v, the actual body angle ψ, the actual heading angle δ, the actual acceleration a, the actual yaw rate ω, and the like. An optimal control sequence of the controlled autonomous vehicle is obtained by solving an open loop optimization problem.
Further, the constraint conditions corresponding to the first objective function and the second objective function include: the target trajectory is outside the area of the obstacle vehicle shape set as an ellipse with the horizontal axis as the major axis.
In particular, the ability to explicitly handle constraints from its model-based prediction of the future dynamic behavior of the system, i.e., adding constraints to the input, output or state variables at the next time, is added in the computation of the target trajectory by the second objective function. Constraints can be explicitly expressed in a quadratic or non-linear programming problem that is solved online. In this embodiment, in addition to the second objective function, the model prediction control may perform hard constraint on the actual state quantity to be solved, so as to avoid collision between the target trajectory and the obstacle vehicle.
Fig. 8 is a schematic diagram of the constraint condition of the vehicle shape in the second embodiment of the present invention. As shown in fig. 8, to avoid the planned trajectory colliding with the obstacle vehicle, a hard constraint needs to be added. Because the longitudinal speed of the vehicle running on the road is often far greater than that of the vehicle running in the lateral directionTherefore, the shape of the obstacle vehicle is assumed to be an ellipse with the major axis of the horizontal axis, so that the vehicle and the obstacle are kept at a certain distance, and safe driving is guaranteed. For example, the constraints are:
wherein (x)obs,yobs) As coordinates of the center point of the obstacle vehicle, rx、ryRespectively the minor and major axes of the ellipse; or converting the constraint condition into an equality constraint and introducing the virtual input quantity udThen the above equation is converted to:
s270, constructing a Hamilton function comprising an objective function and a model of the model prediction system and an equality constraint condition.
Specifically, considering that the vehicle model is a nonlinear model, the present embodiment adopts an algorithm of nonlinear model predictive control, and selects a nonlinear programming method to solve, that is, adopts a C/GMRES algorithm to solve. The C/GMRES algorithm updates the differential equations of the control sequence by using a continuous method and efficiently solves and calculates linear equations by using a generalized minimum residual method. The C/GMRES algorithm is obtained by using the increment of the control sequence at each sampling moment, and the nonlinear optimization problem does not need to be solved by using an iteration method, so that the method has the advantages of high calculation speed and high efficiency. When the C/GMRES algorithm is used for iterative solution, a Hamilton function is constructed according to a first objective function, a second objective function, a prediction model and an equality constraint condition, and partial differentiation is carried out on the Hamilton function, so that a covariate, namely a Hamilton multiplier, is obtained. The Hamiltonian is of the form: h (X, λ, u, μ) ═ L (X, u) + λTf(X,u)+μTC (X, u), where X represents a selected vehicle state measurement vector in a vehicle model state space equation, λ represents a covariate, u represents a selected vehicle control input vector in the vehicle model state space equation, μ represents a Hamilton multiplier associated with an equality constraint, L (X, u) represents an objective function of the vehicle system, f (X, u) represents a non-linear state vector model of the vehicle system, C (X, u), whereAnd u) represents the equality constraints of the vehicle system.
S280, partial differentiation is carried out on the Hamiltonian to obtain a first function.
Specifically, by partially differentiating the input variable with the hamiltonian, the first function F can be jointly constructed as 0 in combination with the equation constraint, and the form of the first function is as follows:
U(t)＝[u0(t),μ0(t),…,uN-1(t),μN-1(t)]，u(t)＝[u0(t),…,uN-1(t)]the input vector is controlled for the vehicle system to be solved.
And S290, solving the first function by adopting a generalized minimum residual C/GMRES algorithm based on continuity to obtain the control sequence.
Specifically, after the first function F is derived to be 0, F is approximated to 0 using the C/GMRES algorithm, and the input variable and the derivative value of the hamilton multiplier are iteratively obtained. After the derivative value of the input variable u (t) is obtained, the value of u (t) in the actual time domain, that is, the control sequence, can be obtained by the C/GMRES algorithm. Through the algorithm, the nonlinear model predictive control can be quickly solved, so that the whole track planning process can be quickly iterated in real time, finally, a control sequence U (t) corresponding to the target track is output, and a processor in the vehicle controls the automatic driving of the vehicle according to the control sequence U (t), and performs real-time tracking control on the actual state quantity of the vehicle.
Fig. 9 is a schematic diagram of a target track in the second embodiment of the present invention. And controlling the vehicle according to the control sequence obtained by solving the method, so that automatic driving according to the optimized target track can be realized. As shown in fig. 9, a solid line represents a vehicle track corresponding to a control sequence solved according to the C/GMRES algorithm, a dotted line represents a planned path determined in an initial planning process, a rectangular frame represents an obstacle vehicle that needs to be avoided, and the vehicle changes lanes in an automatic driving process.
The automatic driving control method provided by the second embodiment of the invention is optimized on the basis of the second embodiment, based on a layered target track optimization algorithm framework, a coordinate system is transformed, a candidate path set is generated, a first objective function is established, a planned path and a planned speed are determined, a second objective function is established, a target track is determined, and then a control sequence corresponding to the target track is reversely solved by adopting a C/GMRES algorithm. In the process of establishing the target function, weights are given to various influence factors, so that different actual requirements can be met, and the planning of a target track is more flexible; by adding constraint conditions, the safety of the automatic driving track is ensured, and the automatic driving track is more in line with changeable actual road environments. In addition, the control method of the embodiment selects various key vehicle dynamic state quantities influencing driving decision, hierarchically proposes different optimal objective functions for solving the optimization problem from the selection of the path candidate set, the planning of the path and the speed and the optimization of the target track, realizes the decoupling of the complex path planning problem, enhances the stability and the robustness of the system, and a new high-efficiency numerical algorithm is adopted based on the generalized minimum residual algorithm C/GMRES of a continuous method, updating a differential equation of a control sequence by using a continuous method, solving an open-loop optimal solution of a target track model predictive control problem, obtaining a C/GMRES algorithm sequence by using the increment of the control sequence at each sampling moment, and the traditional iterative method is not used for solving the nonlinear optimization problem, so that the required time is short, the efficiency is higher, and the calculated amount is smaller.
EXAMPLE III
Fig. 10 is a schematic structural diagram of an automatic driving control device according to a third embodiment of the present invention. The present embodiment provides an automatic driving control apparatus including:
a behavior planning module 310, configured to determine a planned path and a planned speed of the autonomous driving according to the first objective function in a natural coordinate system;
a trajectory determination module 320, configured to determine a target trajectory for autonomous driving according to the planned path, the planned speed, and a second objective function;
a sequence solving module 330, configured to solve a control sequence according to the target trajectory;
and a control module 340 for controlling the vehicle to drive automatically according to the control sequence.
According to the automatic driving control device provided by the third embodiment of the invention, the planned path and the planned speed are determined according to the first objective function under the natural coordinate system, the target track of automatic driving is determined according to the second objective function on the basis, the control sequence is solved according to the target track, and the automatic driving of the vehicle is controlled, so that the target track is consistent with the planned path and the planned speed, the automatic driving of the vehicle is accurately controlled, and the reliability of automatic driving is improved.
On the basis of the above embodiment, the behavior planning module 310 includes:
the candidate set setting unit is used for setting candidate route points in the lanes in the setting range and generating a route candidate set;
a first function establishing unit, configured to establish a first objective function in a natural coordinate system based on the path candidate set, where the first objective function is associated with an actual speed, an actual acceleration derivative, and an actual path of the vehicle, respectively;
and the first function solving unit is used for solving the actual speed, the actual acceleration derivative and the actual path which enable the first objective function to be minimum, and determining the planned path and the planned speed according to the obtained actual speed, actual acceleration derivative and actual path.
On the basis of the above embodiment, the trajectory determination module 320 includes:
a second function establishing unit, configured to establish a second objective function in a natural coordinate system based on the path candidate set, where the second objective function is associated with an actual state quantity of a vehicle and a state gap quantity respectively, where the state gap quantity includes a gap quantity between the actual state quantity and the planned path and a gap quantity between the actual state quantity and the planned speed;
and the second function solving unit is used for solving the actual state quantity which enables the second objective function to be minimum and determining the target track of automatic driving according to the actual state quantity.
Further, the apparatus further comprises:
a state quantity reading unit for reading an actual state quantity by a sensor;
the actual state quantity includes: actual lateral displacement, actual longitudinal displacement, actual speed, actual acceleration, actual yaw rate, actual body angle, actual heading angle, and actual acceleration;
the actual state quantity is within the range of the speed limit, start-stop limit, and traveling direction limit of the road.
Further, the constraint conditions corresponding to the first objective function and the second objective function include:
the target trajectory is outside an area of an obstacle vehicle shape set as an ellipse having a major axis on a horizontal axis.
Further, the sequence solving module 330 includes:
a function constructing unit, configured to construct the first objective function, the second objective function, and a hamiltonian corresponding to the constraint condition;
the partial differentiation unit is used for performing partial differentiation on the Hamiltonian to obtain a first function;
and the solving unit is used for solving the first function by adopting a continuity-based generalized minimum residual C/GMRES algorithm to obtain the control sequence.
Further, the apparatus further comprises:
and the coordinate system conversion module is used for converting the Cartesian coordinate system into a natural coordinate system before determining the planned path and the planned speed of the automatic driving according to the first objective function under the natural coordinate system, wherein the natural coordinate system takes the center line of the road as a horizontal axis and takes the normal of the road as a vertical axis.
The automatic driving control device provided by the third embodiment of the invention can be used for executing the automatic driving control method provided by any embodiment, and has corresponding functions and beneficial effects.
Example four
Fig. 11 is a schematic hardware structure diagram of a vehicle according to a fourth embodiment of the present invention. The terminal includes but is not limited to: desktop computers, notebook computers, smart phones, tablet computers and other intelligent terminals. Further, the server includes, but is not limited to: the system comprises an industrial integration server, a system background server and a cloud server. As shown in fig. 11, the present embodiment provides a vehicle including: a processor 410 and a storage 420. The number of the processors in the vehicle may be one or more, fig. 11 illustrates one processor 410, the processor 410 and the storage device 420 in the vehicle may be connected by a bus or in other manners, and fig. 11 illustrates the connection by the bus.
The one or more programs are executed by the one or more processors 410, causing the one or more processors to implement the autopilot control method of any of the embodiments described above.
The storage device 420 in the vehicle, as a computer-readable storage medium, may be used to store one or more programs, which may be software programs, computer-executable programs, and modules, such as program instructions/modules corresponding to the automatic driving control method in the embodiment of the present invention (for example, the modules in the automatic driving control device shown in fig. 10, including the behavior planning module 310, the trajectory determination module 320, the sequence solving module 330, and the control module 340). The processor 410 executes various functional applications of the vehicle and data processing, i.e., implements the automatic driving control method in the above-described method embodiments, by executing software programs, instructions, and modules stored in the storage device 420.
The storage device 420 mainly includes a storage program area and a storage data area, wherein the storage program area can store an operating system and an application program required by at least one function; the storage data area may store data created according to the use of the vehicle, etc. (planned path and planned speed, etc. as in the above-described embodiments). Further, the storage 420 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid state storage device. In some examples, the storage device 420 may further include memory located remotely from the processor 410, which may be connected to the vehicle over a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
And, when one or more programs included in the above-mentioned vehicle are executed by the one or more processors 410, the following operations are performed: determining a planned path and a planned speed of automatic driving according to a first objective function under a natural coordinate system; determining an automatic driving target track according to the planned path, the planned speed and a second target function; and solving a control sequence according to the target track, and controlling the automatic driving of the vehicle according to the control sequence.
The vehicle proposed by the present embodiment belongs to the same inventive concept as the automatic driving control method proposed by the above embodiments, and technical details that are not described in detail in the present embodiment can be referred to any of the above embodiments, and the present embodiment has the same advantageous effects as the execution of the automatic driving control method.
On the basis of the above-described embodiments, the present embodiment also provides a computer-readable storage medium having stored thereon a computer program that, when executed by an automatic driving control apparatus, implements an automatic driving control method in any of the above-described embodiments of the present invention, the method including: determining a planned path and a planned speed of automatic driving according to a first objective function under a natural coordinate system; determining an automatic driving target track according to the planned path, the planned speed and a second target function; and solving a control sequence according to the target track, and controlling the automatic driving of the vehicle according to the control sequence.
Of course, the storage medium containing the computer-executable instructions provided by the embodiments of the present invention is not limited to the above-described operation of the automatic driving control method, and may also perform related operations in the automatic driving control method provided by any embodiments of the present invention, and has corresponding functions and advantages.
From the above description of the embodiments, it is obvious for those skilled in the art that the present invention can be implemented by software and necessary general hardware, and certainly, can also be implemented by hardware, but the former is a better embodiment in many cases. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, and the computer software product may be stored in a computer-readable storage medium, such as a floppy disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a FLASH Memory (FLASH), a hard disk or an optical disk of a computer, and includes several instructions to enable a computer device (which may be a personal computer, a server, or a network device) to execute the automatic driving control method according to the embodiments of the present invention.
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention 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 invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.
Claims (10)
1. An automatic driving control method characterized by comprising:
determining a planned path and a planned speed of automatic driving according to a first objective function under a natural coordinate system;
determining an automatic driving target track according to the planned path, the planned speed and a second target function;
and solving a control sequence according to the target track, and controlling the automatic driving of the vehicle according to the control sequence.
2. The method of claim 1, wherein determining the planned path and the planned speed for autonomous driving according to the first objective function in the natural coordinate system comprises:
setting candidate path points in the lanes within a set range and generating a path candidate set;
establishing a first objective function under a natural coordinate system based on the path candidate set, wherein the first objective function is respectively associated with the actual speed, the actual acceleration derivative and the actual path of the vehicle;
and solving the actual speed, the actual acceleration derivative and the actual path which enable the first objective function to be minimum, and determining the planned path and the planned speed according to the obtained actual speed, actual acceleration derivative and actual path.
3. The method of claim 2, wherein determining a target trajectory for autonomous driving based on the planned path, the planned speed, and a second objective function comprises:
establishing a second objective function under a natural coordinate system based on the path candidate set, wherein the second objective function is respectively associated with an actual state quantity and a state gap quantity of a vehicle, and the state gap quantity comprises a gap quantity between the actual state quantity and the planned path and a gap quantity between the actual state quantity and the planned speed;
and solving the actual state quantity which enables the second objective function to be minimum, and determining the target track of automatic driving according to the actual state quantity.
4. The method of claim 3, further comprising: reading the actual state quantity through a sensor;
the actual state quantity includes: actual lateral displacement, actual longitudinal displacement, actual speed, actual acceleration, actual yaw rate, actual body angle, actual heading angle, and actual acceleration;
the actual state quantity is within the range of the speed limit, start-stop limit, and traveling direction limit of the road.
5. The method of claim 1, wherein the constraints for the first objective function and the second objective function comprise:
the target trajectory is outside an area of an obstacle vehicle shape set as an ellipse having a major axis on a horizontal axis.
6. The method of claim 5, wherein said solving a control sequence from said target trajectory comprises:
constructing the first objective function, the second objective function and the Hamilton function corresponding to the constraint condition;
partial differentiation is carried out on the Hamiltonian to obtain a first function;
and solving the first function by adopting a generalized minimum residual C/GMRES algorithm based on continuity to obtain the control sequence.
7. The method according to any one of claims 1-6, further comprising, prior to said determining the planned path and the planned speed for autonomous driving according to the first objective function in the natural coordinate system:
and converting the Cartesian coordinate system into a natural coordinate system, wherein the natural coordinate system takes the center line of the road as a horizontal axis and takes the normal of the road as a vertical axis.
8. An automatic driving control method characterized by comprising:
the behavior planning module is used for determining a planned path and a planned speed of automatic driving according to a first objective function under a natural coordinate system;
the track determining module is used for determining an automatic driving target track according to the planned path, the planned speed and a second target function;
the sequence solving module is used for solving a control sequence according to the target track;
and the control module is used for controlling the automatic driving of the vehicle according to the control sequence.
9. A vehicle, characterized by comprising:
one or more processors;
storage means for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement the autopilot control method of any of claims 1-7.
10. A computer-readable storage medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements an autopilot control method according to one of claims 1 to 7.
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| CN112947492A (en) * | 2021-04-14 | 2021-06-11 | 北京车和家信息技术有限公司 | Vehicle control method, device, storage medium, electronic device and vehicle |
| CN113110489A (en) * | 2021-04-30 | 2021-07-13 | 清华大学 | Trajectory planning method and device, electronic equipment and storage medium |
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| CN114030480A (en) * | 2021-11-03 | 2022-02-11 | 重庆理工大学 | Unmanned vehicle self-adaptive head-adaptive control algorithm based on obstacle avoidance path planning |
| CN114030480B (en) * | 2021-11-03 | 2023-09-22 | 重庆理工大学 | Unmanned vehicle self-adaptive turning control algorithm based on obstacle avoidance path planning |
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| CN114815825A (en) * | 2022-04-21 | 2022-07-29 | 合众新能源汽车有限公司 | Method and device for determining optimal driving track of vehicle |
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| WO2021175313A1 (en) | 2021-09-10 |
| CN111338346B (en) | 2022-10-21 |
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