CN118405127A - Vehicle driving auxiliary control method, device, equipment and storage medium - Google Patents
Vehicle driving auxiliary control method, device, equipment and storage medium Download PDFInfo
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- CN118405127A CN118405127A CN202410690910.0A CN202410690910A CN118405127A CN 118405127 A CN118405127 A CN 118405127A CN 202410690910 A CN202410690910 A CN 202410690910A CN 118405127 A CN118405127 A CN 118405127A
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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
- B60W30/08—Active safety systems predicting or avoiding probable or impending collision or attempting to minimise its consequences
- B60W30/095—Predicting travel path or likelihood of collision
- B60W30/0953—Predicting travel path or likelihood of collision the prediction being responsive to vehicle dynamic parameters
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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
- B60W30/08—Active safety systems predicting or avoiding probable or impending collision or attempting to minimise its consequences
- B60W30/095—Predicting travel path or likelihood of collision
- B60W30/0956—Predicting travel path or likelihood of collision the prediction being responsive to traffic or environmental parameters
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B62—LAND VEHICLES FOR TRAVELLING OTHERWISE THAN ON RAILS
- B62D—MOTOR VEHICLES; TRAILERS
- B62D15/00—Steering not otherwise provided for
- B62D15/02—Steering position indicators ; Steering position determination; Steering aids
- B62D15/025—Active steering aids, e.g. helping the driver by actively influencing the steering system after environment evaluation
- B62D15/0265—Automatic obstacle avoidance by steering
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Abstract
The application discloses an auxiliary control method, device, equipment and storage medium for vehicle driving, relating to the technical field of driving auxiliary control, comprising the following steps: acquiring a prediction model, driver steering input and motion uncertainty of nearby vehicles; determining a collision avoidance probability constraint condition based on a preset longitudinal equation and motion uncertainty of the prediction model and nearby vehicles; generating steering control parameters based on the collision avoidance probability constraint conditions and the driver steering input; the vehicle steering is controlled based on the steering control parameter. When the vehicle has no collision risk, the auxiliary driving controller accords with the operation of a driver; when the vehicle is likely to collide, the auxiliary driving controller corrects the driver's operation input so as not to affect the normal operation of the driver while ensuring driving safety.
Description
Technical Field
The present application relates to the technical field of driving assistance control, and in particular, to a method, apparatus, device, and storage medium for assisting control of vehicle driving.
Background
In recent years, automobiles have become one of the most commonly used vehicles in daily life, and in driving of automobiles, misoperation of drivers is a major cause of most traffic accidents. Because the driver lacks accurate estimation to the traffic condition, and has the operation errors of occupying the road, overspeed and the like, road traffic accidents are extremely easy to be caused. With the continuous lifting of vehicles and the wide application of intelligent cabins, driver assistance systems are used for vehicle motion manipulation control such as lane changing, overtaking and the like. The intelligent automobile provides an auxiliary driving control function of the automobile through the intelligent cabin system, assists a driver in driving operation so as to avoid driving fatigue and improve driving safety, and is a key component of the intelligent automobile system. Meanwhile, the auxiliary driving control function of the intelligent cabin can correct dangerous operation of a driver and prevent possible traffic accidents such as collision. Many research institutions, equipment manufacturers and component suppliers in the automotive field are concerned with the development and application of assisted driving control technology. However, road traffic conditions are complex and variable, making it difficult for intelligent vehicles to cooperatively interact with surrounding vehicles, which need to be considered in the design of a driving assistance controller. Meanwhile, the auxiliary driving control method needs to consider the operation behavior of the driver and provide proper control quantity for the intelligent automobile driver. The driver is helped to avoid surrounding vehicles and normal operation of the driver is not disturbed, so that possible collision is avoided, and driving safety is ensured.
Much research effort has been devoted to the field of advanced driving assistance control methods. An adaptive cruise control method is designed that considers upper and lower limits of vehicle system constraints. Nonlinear characteristics of the vehicle dynamics equation are described using the T-S fuzzy approach. Based on the T-S fuzzy method, a sliding mode control strategy is provided in consideration of the bounded sector. Input-output hidden markov models capturing various driving information through system inputs are proposed, mainly for predicting vehicle speed. Incorporating the predicted vehicle speed into the adaptive cruise control may reduce the energy consumption of the intelligent vehicle. A recurrent neural network is employed to predict nearby vehicle trajectories. A cooperative motion control algorithm is developed for an autonomous vehicle using predicted vehicle trajectories. The recurrent neural network predicts nearby vehicle speeds using long and short term memory units, and a series of driving scenarios were designed to demonstrate that the relative distance between an autonomous vehicle and nearby vehicles has a greater impact on driver comfort than the relative speed. A model predictive control method including driver behavior prediction is developed to track a reference trajectory and maintain a safe distance from a cut-in vehicle. In addition to the challenges of cooperating with nearby vehicles, driving assistance controllers face the difficulty of properly providing control forces. The driver's behavior must be taken into account to prevent the auxiliary controller from colliding with the human driver. A driving auxiliary controller is designed by adopting a robust gain scheduling control method, a shared control strategy is established to control man-machine interaction by formalizing a hybrid system under a hybrid automatic scheme, and a combined driver model consisting of a compensation transfer function and a prediction part is provided. Based on adaptive dynamic programming and output adjustment theory, the steering controller and the driver can jointly achieve ideal lane keeping performance. Robust sliding mode control methods that take into account varying parameters are proposed to solve the problem of collisions between the driver and the controller during a typical maneuver. A mixed model based on a hidden Markov model and a support vector machine is developed to identify the optimal control mode, so that the transverse control problem between the automatic controller of the lane change auxiliary system and a driver is solved. In addition, the design of the assist controller typically ignores the uncertainty of the motion of the nearby vehicle, and most existing driver assist control techniques provide additional control inputs based on driver maneuvers such that the actual control amount of the smart car is the sum of the driver input steering angle and the assist control steering angle. However, due to variations in driver steering behavior, the steering angle provided by the assist control may conflict with driver operation, which may in turn result in a collision of the intelligent vehicle with surrounding vehicles. During travel of the intelligent vehicle, the intelligent vehicle may not avoid a collision that may occur due to uncertainty in the location of nearby vehicles.
The foregoing is provided merely for the purpose of facilitating understanding of the technical solutions of the present application and is not intended to represent an admission that the foregoing is prior art.
Disclosure of Invention
The application mainly aims to provide an auxiliary control method, device, equipment and storage medium for vehicle driving, and aims to solve the technical problem that collision avoidance is not realized through cooperative cooperation of an intelligent vehicle and surrounding vehicles in the prior art.
In order to achieve the above object, the present application provides an auxiliary control method for driving a vehicle, the auxiliary control method for driving a vehicle comprising:
acquiring a prediction model, driver steering input and motion uncertainty of nearby vehicles;
determining a collision avoidance probability constraint condition based on a preset longitudinal equation and motion uncertainty of the prediction model and the nearby vehicle;
Generating steering control parameters based on the collision avoidance probability constraints and the driver steering inputs;
controlling the steering of the vehicle based on the steering control parameter.
In an embodiment, before the step of obtaining the predictive model, the driver steering input, and the longitudinal equations and the motion uncertainty of the nearby vehicle, the method further comprises:
establishing a track prediction model of a nearby vehicle, and determining a longitudinal equation and a motion uncertainty of the nearby vehicle based on the track prediction model of the nearby vehicle;
Creating a vehicle system model, and determining a prediction model based on the vehicle system model;
a driver model is created, based on which driver steering input is represented.
In one embodiment, the step of establishing a trajectory prediction model of the nearby vehicle, determining a longitudinal equation and a motion uncertainty of the nearby vehicle based on the trajectory prediction model of the nearby vehicle comprises:
Acquiring sampling frequency, longitudinal speed of a nearby vehicle at a preset time step and longitudinal position of the nearby vehicle at the preset time step;
creating a track prediction model of the nearby vehicle based on the sampling frequency, the longitudinal speed of the nearby vehicle at the preset time step and the longitudinal position of the nearby vehicle at the preset time step;
obtaining a longitudinal equation of the nearby vehicle and a longitudinal motion matrix of the nearby vehicle when a preset time step is obtained through the track prediction model of the nearby vehicle;
And predicting the motion trail of the nearby vehicle through the longitudinal motion equation of the nearby vehicle and the longitudinal motion matrix of the nearby vehicle to obtain the motion uncertainty of the nearby vehicle.
In one embodiment, the creating a vehicle system model, determining a predictive model based on the vehicle system model, includes:
Acquiring a vehicle motion schematic diagram, and establishing a motion model of a vehicle relative to an expected path through the vehicle motion schematic diagram;
Establishing a dynamics model of the vehicle through the motion model of the vehicle relative path and a vehicle motion schematic diagram;
Obtaining the global position of the vehicle and the lateral tire force of the vehicle through the dynamic model of the vehicle;
obtaining a transverse motion model of the vehicle through the vehicle motion schematic diagram, a motion model of the vehicle relative to a desired path, a dynamics model, a global position of the vehicle and lateral tire force of the vehicle;
Discretizing the transverse motion model of the vehicle by using an Euler method to obtain a prediction model of the vehicle.
In an embodiment, the step of creating a driver model, representing driver steering input based on the driver model, comprises:
acquiring the operation characteristics of a driver, the lateral position deviation of the vehicle and the steering behavior of the driver;
obtaining a driver steering input based on the lateral position deviation of the vehicle and the driver's steering behavior;
and dispersing the steering input of the driver to obtain a driver model so as to represent the steering input of the driver.
In an embodiment, the step of determining the collision avoidance probability constraint based on the longitudinal equation and the motion uncertainty of the prediction model and the nearby vehicle comprises:
acquiring a position constraint function and a position constraint boundary according to the prediction model;
Obtaining the lateral position collision avoidance constraint of the vehicle based on the position constraint function and the position constraint boundary;
And determining collision avoidance probability constraint conditions based on longitudinal equations and motion uncertainties of the nearby vehicles and lateral position collision avoidance constraints of the vehicles.
In an embodiment, the step of generating steering control parameters based on the collision avoidance probability constraints and the driver steering input comprises:
Establishing a state constraint of a prediction model based on the lateral speed and the yaw speed of the vehicle;
establishing input constraint of a prediction model based on an upper bound and a lower bound of a control quantity, a minimum steering angle, a maximum steering angle, a minimum driving moment and a maximum driving moment of the prediction model;
Establishing an auxiliary controller based on state constraint, input constraint and collision avoidance probability constraint conditions of the prediction model;
And calculating by the auxiliary controller based on the steering input of the driver to obtain steering control parameters.
In addition, to achieve the above object, the present application also provides an auxiliary control device for driving a vehicle, the device comprising:
The acquisition module is used for acquiring the prediction model, the steering input of the driver and the movement uncertainty of the nearby vehicle;
the first calculation module is used for determining collision avoidance probability constraint conditions based on a preset longitudinal equation and motion uncertainty of the prediction model and the nearby vehicle;
The second calculation module is used for generating steering control parameters based on the collision avoidance probability constraint conditions and the steering input of the driver;
and the control module is used for controlling the vehicle to steer based on the steering control parameter.
In addition, in order to achieve the above object, the present application also provides an auxiliary control device for driving a vehicle, the device comprising: a memory, a processor and a computer program stored on the memory and executable on the processor, the computer program being configured to implement the steps of the method of assisting control of vehicle driving as described above.
In addition, in order to achieve the above object, the present application also provides a storage medium, which is a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the vehicle driving assistance control method as described above.
Furthermore, to achieve the above object, the present application provides a computer program product comprising a computer program which, when executed by a processor, implements the steps of the vehicle driving assistance control method as described above.
One or more technical schemes provided by the application have at least the following technical effects:
Acquiring a prediction model, driver steering input and motion uncertainty of nearby vehicles; determining a collision avoidance probability constraint condition based on a preset longitudinal equation and motion uncertainty of the prediction model and nearby vehicles; generating steering control parameters based on the collision avoidance probability constraint conditions and the driver steering input; the vehicle steering is controlled based on the steering control parameter. When the vehicle has no collision risk, the auxiliary driving controller accords with the operation of a driver; when the vehicle is likely to collide, the auxiliary driving controller corrects the driver's operation input so as not to affect the normal operation of the driver while ensuring driving safety.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and together with the description, serve to explain the principles of the application.
In order to more clearly illustrate the embodiments of the application or the technical solutions of the prior art, the drawings which are used in the description of the embodiments or the prior art will be briefly described, and it will be obvious to a person skilled in the art that other drawings can be obtained from these drawings without inventive effort.
FIG. 1 is a flowchart of a method for assisting in controlling driving of a vehicle according to an embodiment of the present application;
FIG. 2 is a schematic diagram of a vehicle motion provided by an embodiment of an auxiliary control method for driving a vehicle;
FIG. 3 is a schematic flow chart of a second embodiment of a method for assisting in controlling driving of a vehicle according to the present application;
FIG. 4 is a schematic flow chart of a third embodiment of an auxiliary control method for driving a vehicle according to the present application;
FIG. 5 is a schematic block diagram of an auxiliary control device for driving a vehicle according to an embodiment of the present application;
fig. 6 is a schematic device structure diagram of a hardware operating environment related to an auxiliary control method for driving a vehicle according to an embodiment of the present application.
The achievement of the objects, functional features and advantages of the present application will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the technical solution of the present application and are not intended to limit the present application.
For a better understanding of the technical solution of the present application, the following detailed description will be given with reference to the drawings and the specific embodiments.
Since most of the existing driver assistance control technologies provide additional control inputs based on driver manipulation, the actual control quantity of the intelligent vehicle is the sum of the driver input steering angle and the assistance control steering angle. However, due to variations in driver steering behavior, the steering angle provided by the assist control may conflict with driver operation, which may in turn result in a collision of the intelligent vehicle with surrounding vehicles.
The present application provides a solution to build a driver steering model and take into account the driver's operating characteristics in the assisted driving control. And (3) integrating the vehicle model and the driver model, and solving steering input of auxiliary driving through a predictive control method. When the vehicle has no collision risk, the auxiliary driving controller accords with the operation of a driver; when the vehicle is likely to collide, the auxiliary driving controller corrects the driver's operation input so as not to affect the normal operation of the driver while ensuring driving safety.
It should be noted that, the execution body of the embodiment may be a computing service device having functions of data processing, network communication and program running, such as a tablet computer, a personal computer, a mobile phone, or an electronic device capable of implementing the above functions, an auxiliary control device for driving a vehicle, and the like. The present embodiment and the following embodiments will be described below with reference to an auxiliary control device for driving a vehicle.
Based on this, an embodiment of the present application provides a vehicle driving assistance control method, and referring to fig. 1, fig. 1 is a schematic flow chart of a first embodiment of a vehicle driving assistance control apparatus method according to the present application.
In this embodiment, the method of the auxiliary control device for driving a vehicle includes steps S10 to S40:
Step S10, obtaining a prediction model, steering input of a driver and uncertainty of movement of a nearby vehicle;
Before step S10, the method further includes:
Establishing a track prediction model of the nearby vehicle, and determining a longitudinal equation and motion uncertainty of the nearby vehicle based on the track prediction model of the nearby vehicle;
Creating a vehicle system model, and determining a prediction model based on the vehicle system model;
A driver model is created, based on which driver steering input is represented.
It should be noted that, the overall framework of the driver assistance system designed by the present strategy is that the human driver and the intelligent vehicle are connected through the driver assistance controller designed by the SMPC method. The steering angle of the driver is not directly controlled to the intelligent vehicle but is transmitted to a controller designed to correct dangerous operations.
Further, acquiring sampling frequency, longitudinal speed of the nearby vehicle at a preset time step and longitudinal position of the nearby vehicle at the preset time step;
Creating a track prediction model of the nearby vehicle based on the sampling frequency, the longitudinal speed of the nearby vehicle at the preset time step and the longitudinal position of the nearby vehicle at the preset time step;
Obtaining a longitudinal equation of the nearby vehicle and a longitudinal motion matrix of the nearby vehicle when a preset time step is obtained through a track prediction model of the nearby vehicle;
And predicting the motion trail of the nearby vehicle through the longitudinal motion equation of the nearby vehicle and the longitudinal motion matrix of the nearby vehicle to obtain the motion uncertainty of the nearby vehicle.
In specific implementation, a track prediction model of a nearby vehicle is designed firstly, and is used for predicting the running track of the nearby vehicle and incorporating the running track into a driving strategy of a vehicle only so as to avoid collision. The present invention assumes that nearby vehicles remain in their respective lanes, considering only the longitudinal movement of nearby vehicles. The vehicle longitudinal motion equation is used for motion prediction. Nominal and uncertain movements of nearby vehicles are considered. Defining a longitudinal motion model of a nearby vehicle as:
Where T s is the sampling frequency, v η,k is the longitudinal speed of the nearby vehicle at time step k, and x η,k is the longitudinal position.
Since the collision avoidance method of the intelligent vehicle considers cooperation with the nearby vehicle within a few seconds ahead, it is assumed that the nearby vehicle maintains a constant speed in the near term. Assuming that the state of the nearby vehicle in the longitudinal direction is η k at time step k, the expression is:
The longitudinal equation of motion of a nearby vehicle can be expressed as:
wherein a η is the longitudinal motion matrix of the nearby vehicle.
The above longitudinal motion equation is used to predict the short-term motion profile of the vehicle. Assuming that both vehicle speed and position are gaussian, the mean and variance of nearby vehicle speeds and positions can be expressed as:
Wherein, And Σ v,k are the average and covariance of the vehicle longitudinal speed at time step k respectively,And Σ x,k are the average and covariance, respectively, of the longitudinal position of the vehicle at time step k.
Thus, the longitudinal movement uncertainty of a nearby vehicle can be expressed as:
Ση,k＝[Σx,k Σν,k]T
Within the prediction horizon, the uncertainty of the movement of the nearby vehicle in the longitudinal direction can be derived from the evaluation of the equation of motion. The propagation of motion uncertainty can be expressed as:
Further, a vehicle motion diagram is obtained, and a motion model of the vehicle relative to a desired path is established through the vehicle motion diagram;
establishing a dynamic model of the vehicle through a motion model of a relative path of the vehicle and a vehicle motion schematic diagram;
Obtaining the global position of the vehicle and the lateral tire force of the vehicle through a dynamic model of the vehicle;
obtaining a transverse motion model of the vehicle through a vehicle motion schematic diagram, a motion model of the vehicle relative to a desired path, a dynamics model, a global position of the vehicle and lateral tire force of the vehicle;
discretizing a transverse motion model of the vehicle by using an Euler method to obtain a prediction model of the vehicle.
In a specific implementation, a planar motion model of the vehicle is built for the driver-assist controller design. In constructing a vehicle model, it is desirable to simplify the model complexity and maintain model accuracy.
As shown in fig. 2, where X and Y represent the longitudinal and transverse positions of the vehicle, respectively, in the global coordinate system. Psi is the vehicle yaw angle, and F xf and F yf are the front wheel longitudinal and lateral tire forces, respectively. F xr and F yr denote the longitudinal tire force and the lateral tire force of the rear wheel, respectively. From a vehicle motion diagram, a motion model of a vehicle relative to a desired path can be expressed as:
Where v x is the vehicle longitudinal speed, v y is the vehicle lateral speed, and y e is the current lateral position deviation of the vehicle. ω z is the yaw rate of the vehicle, ω d is the reference yaw rate of the vehicle, and the vehicle yaw rate error can be calculated as ψ e, which is the difference between the vehicle yaw rate and the reference yaw rate.
On the basis, a dynamics model of the vehicle is established, and the relation between the tire force and the speed change rate of the vehicle is analyzed through the vehicle dynamics model. The vehicle dynamics model can be expressed as:
Wherein m and I z are the mass and moment of inertia of the vehicle, respectively; c f is the cornering stiffness of the front axle and C r is the cornering stiffness of the rear axle. l f is the distance from the center of gravity of the vehicle to the front axle, l r is the distance from the center of gravity of the vehicle to the rear axle, and the vehicle length is the sum of l f and l r. Under the normal running and lane changing situations of the vehicle, the yaw angle error of the vehicle is small. Under a small angle vacation, the error in vehicle yaw rate may be approximated as cos ψ e ≡1 and sin ψ e≈ψe. Thus, the global vehicle position can be reduced to:
under normal running conditions of the vehicle, in order to facilitate the design of the auxiliary controller and ensure the calculation accuracy, the calculation of the lateral tire force of the vehicle by using the linear tire model can be expressed as:
Wherein δ is the steering angle of the front wheel; c f and C r are the cornering stiffness of the front and rear wheels, respectively. Based on the above formula, the lateral motion model of the vehicle can be expressed as:
Where ζ is a system state vector, which may be represented as ζ= [ y e ψe vy ωz]T. A is a system matrix, and B is a control matrix. The control amount u is the front wheel steering angle, i.e., u=δ. d is the vehicle model error, which may be represented as d= [ d y dψ dv dω ], and the corresponding model error matrix may be represented as B d. The system matrix and the control matrix can be expressed as:
The system matrix a contains the vehicle longitudinal speed v x, and since the model prediction time is short, it is assumed that the vehicle speed is unchanged in the model prediction time range so as to reduce the calculation amount. Meanwhile, the Euler method is adopted to discretize the vehicle system model, and then the discretized dynamics model of the vehicle can be expressed as follows:
ξ(k+1)＝(I+TsA)ξ(k)+TsBu(k)+TsBdd(k)
Where T s is the time step and k is an example of the time step. The discrete model described above is used as a predictive model in the SMPC method.
Further, the operation characteristics of the driver, the lateral position deviation of the vehicle, and the steering behavior of the driver are acquired;
obtaining a driver steering input based on the lateral position deviation of the vehicle and the driver's steering behavior;
the driver steering input is discretized to obtain a driver model to represent the driver steering input.
In a specific implementation, in order to consider the steering behavior of the driver when designing the auxiliary controller of the smart car, the steering operation input of the driver is described using a driver model. The steering angle input by the driver can be obtained from the lateral position deviation of the vehicle and the steering behavior of the driver, i.e
Where δ d is the steering angle input when the driver operates. G d and τ d are the driver steering gain and the driver hysteresis coefficient, respectively. s denotes the laplace operator. y p is the lateral deviation of the vehicle from the reference trajectory at the driver pre-aiming point, which can be expressed as:
yp＝ye+vxτpψe
Where y e is the current lateral position deviation of the intelligent vehicle, ψ e is the yaw angle error of the difference between the vehicle yaw angle and the reference yaw angle, v x is the longitudinal speed of the vehicle, τ p is the driver pre-aiming time. The vehicle position deviation of different pretightening points can be solved through pretightening time parameters of the driver so as to describe different driver behaviors.
According to the steering operation characteristic and the pretightening deviation of the driver, the steering angle change rate of the driver can be solved and expressed as follows:
The steering rate of the driver can be derived from this equation. To maintain consistency with the controller design, the steering angle of the driver may be discretized as:
Where y p (k) is the vehicle lateral position deviation of the driver pre-aiming point at time k. The driver model in the above method shows that the driver provides corresponding steering angles according to the parameters such as the transverse position deviation, steering gain and the like of the pre-aiming point vehicle, and tracks the reference track.
Step S20, determining a collision avoidance probability constraint condition based on a preset longitudinal equation and motion uncertainty of the prediction model and the nearby vehicles;
in a specific implementation, based on a vehicle model and a driver model, steering operation input of a driver is considered when an auxiliary driving controller of the intelligent automobile is designed, a model prediction algorithm is adopted to design the controller, and steering auxiliary control is provided for the driver.
In order to avoid collision with the driver, the auxiliary controller conforms to the steering input of the driver without collision risk; and when the intelligent automobile may collide with surrounding vehicles, the auxiliary controller needs to correct the steering behavior of the driver. Thus, the assisted driving control objective function considering the driver behavior can be expressed as:
From the above equation, in order not to interfere with the driver steering behavior, the difference between the driver steering input δ (t) and the assist steering control input u (t|t) at time step t should be reduced. Meanwhile, in order to avoid collision between the intelligent automobile and surrounding vehicles, a constraint function of the vehicle position needs to be designed, and the intelligent automobile position is limited in a safety range. To cooperatively interact with surrounding vehicles in a road traffic environment, lateral position constraints of the intelligent vehicle are established to prevent possible collisions. In the lateral position, the collision avoidance constraint may be expressed by a linear inequality as follows:
Where g k is a position constraint function and h k is a position constraint boundary.
Depending on the lateral movement state of the vehicle, the lower and upper limits of the lateral position of the vehicle depend on the longitudinal position of the time step k. Therefore, in order to avoid collision, the lower and upper bounds defining the vehicle lateral position constraint are respectivelyAndThe lateral position constraints of the intelligent vehicle may be further expressed as:
The location of nearby vehicles cannot be accurately predicted. By means of motion prediction, the position of a nearby vehicle can be predicted indefinitely. The probability constraints are utilized to account for the prediction uncertainty introduced by nearby vehicle locations. Probability symbols that meet the collision avoidance constraint are converted into occasional constraints of the SMPC algorithm. The probability constraint of avoiding collision is that
Where Pr (·) is a probability symbol and p represents the corresponding probability.
In the design of the vehicle auxiliary controller, it is necessary to satisfy the input constraint and the state constraint of the vehicle system in addition to the collision avoidance constraint. In order to ensure the safety of driving and the comfort of riding, it is necessary to control the vehicle state such as the lateral speed and the yaw rate within a reasonable range as shown in the following formula:
at the same time, considering the input saturation of the actuator, it is necessary to establish the constraint condition of the control input, i.e
umin≤u≤umax
Where u min and u max are the lower and upper bounds of the control quantity. Defining the minimum and maximum steering angles δ min and δ max, respectively, and the minimum and maximum drive torques T min and T max, respectively, the input constraints for the control amounts can be expressed as:
Step S30, generating steering control parameters based on collision avoidance probability constraint conditions and steering input of a driver;
In a specific implementation, the goal of the driver assistance control is to help the driver avoid collisions according to the situation of nearby vehicles without affecting the driver's operation. Therefore, the designed assist controller must not interfere with the steering behavior of the driver while the driver is able to avoid the nearby vehicle, while correcting the driver's possible collision behavior through collision avoidance constraints. The auxiliary controller designed by adopting the model predictive control mode can be expressed as:
so that ζ (t|t) =ζ (t) satisfies:
Where N is the total time step of the model prediction. By solving the optimization problem, a series of auxiliary control amounts and vehicle motion states within the predicted time can be calculated. The auxiliary controller sends the first control quantity to the intelligent vehicle for motion control. Meanwhile, collision avoidance constraints are formulated by utilizing continuous vehicle states, the collision avoidance problem of the intelligent vehicle is expressed as constraint conditions of a model predictive control algorithm, and the corresponding optimization problem is solved.
Step S40, controlling the steering of the vehicle based on the steering control parameters;
it should be noted that, when the vehicle has no collision risk, the auxiliary driving controller accords with the operation of the driver; when the vehicle is likely to collide, the auxiliary driving controller corrects the driver's operation input so as not to affect the normal operation of the driver while ensuring driving safety.
The embodiment provides an auxiliary control method for driving a vehicle so as to realize cooperative coordination of an intelligent vehicle and surrounding vehicles. A driver steering model is built and the operating characteristics of the driver are taken into account in the assisted driving control. And (3) integrating the vehicle model and the driver model, and solving steering input of auxiliary driving through a predictive control method. When the vehicle has no collision risk, the auxiliary driving controller accords with the operation of a driver; when the vehicle is likely to collide, the auxiliary driving controller corrects the driver's operation input so as not to affect the normal operation of the driver while ensuring driving safety.
In the second embodiment of the present application, the same or similar content as in the first embodiment of the present application may be referred to the above description, and will not be repeated. On this basis, referring to fig. 3, in step S20, the auxiliary control method for driving the vehicle further includes steps S201 to S203:
step S201, a position constraint function and a position constraint boundary are obtained according to a prediction model;
in a specific implementation, to cooperatively interact with surrounding vehicles in a road traffic environment, lateral position constraints of the intelligent vehicle are established to prevent possible collisions. In the lateral position, the collision avoidance constraint may be expressed by a linear inequality as follows:
Where g k is a position constraint function and h k is a position constraint boundary.
Step S202, obtaining the lateral position collision avoidance constraint of the vehicle based on the position constraint function and the position constraint boundary;
in a specific implementation, the lower and upper bounds of the lateral position of the vehicle depend on the longitudinal position of the time step k, depending on the lateral movement state of the vehicle. Therefore, in order to avoid collision, the lower and upper bounds defining the vehicle lateral position constraint are respectively AndThe lateral position constraints of the intelligent vehicle may be further expressed as:
Step S203, determining a collision avoidance probability constraint condition based on the longitudinal equation and the motion uncertainty of the nearby vehicle and the lateral position collision avoidance constraint of the vehicle.
In a specific implementation, probability constraints are utilized to account for the prediction uncertainty introduced by nearby vehicle locations. Probability symbols that meet the collision avoidance constraint are converted into occasional constraints of the SMPC algorithm. The probability constraint of avoiding collision is that
Where Pr (·) is a probability symbol and p represents the corresponding probability.
The embodiment provides an auxiliary control method for driving a vehicle, which comprises the steps of obtaining a position constraint function and a position constraint boundary according to a prediction model; obtaining a lateral position collision prevention constraint of the vehicle based on the position constraint function and the position constraint boundary; the collision avoidance probability constraint conditions are determined based on longitudinal equations and motion uncertainties of nearby vehicles and lateral position collision avoidance constraints of the vehicles, the auxiliary controller comprises the motion uncertainties of the nearby vehicles and driver behaviors of the intelligent vehicles, the nearby vehicles are predicted and taken into the auxiliary controller to avoid collision, and the SMPC algorithm is used for considering accidental constraints generated by the motion uncertainties of the nearby vehicles.
In the third embodiment of the present application, the same or similar contents as those of the above-described embodiments can be referred to the above description, and the description thereof will be omitted. On this basis, referring to fig. 4, in step S30, the auxiliary control method for driving the vehicle further includes steps S301 to S304:
step S301, establishing state constraint of a prediction model based on the lateral speed and the yaw speed of the vehicle;
in a specific implementation, in the design of the vehicle auxiliary controller, in addition to meeting the collision avoidance constraint, the input constraint and the state constraint of the vehicle system need to be met. In order to ensure the safety of driving and the comfort of riding, it is necessary to control the vehicle state such as the lateral speed and the yaw rate within a reasonable range as shown in the following formula:
Step S302, establishing input constraint of a prediction model based on an upper bound and a lower bound of a control quantity, a minimum steering angle, a maximum steering angle, a minimum driving moment and a maximum driving moment of the prediction model;
In a specific implementation, at the same time, considering the input saturation of the actuator, it is necessary to establish constraints of the control input, i.e.
umin≤u≤umax
Where u min and u max are the lower and upper bounds of the control quantity. Defining the minimum and maximum steering angles δ min and δ max, respectively, and the minimum and maximum drive torques T min and T max, respectively, the input constraints for the control amounts can be expressed as:
Step S303, establishing an auxiliary controller based on state constraint, input constraint and collision avoidance probability constraint conditions of the prediction model;
In a specific implementation, the goal of the driver assistance control is to help the driver avoid collisions according to the situation of nearby vehicles without affecting the driver's operation. Therefore, the designed assist controller must not interfere with the steering behavior of the driver while the driver is able to avoid the nearby vehicle, while correcting the driver's possible collision behavior through collision avoidance constraints. The auxiliary controller designed by adopting the model predictive control mode can be expressed as:
so that ζ (t|t) =ζ (t) satisfies:
where N is the total time step of the model prediction. By solving the optimization problem, a series of auxiliary control amounts and vehicle motion states within the predicted time can be calculated.
Step S304, calculating through the auxiliary controller based on the steering input of the driver to obtain steering control parameters.
The auxiliary controller sends the first control amount to the intelligent vehicle for motion control. Meanwhile, collision avoidance constraints are formulated by utilizing continuous vehicle states, the collision avoidance problem of the intelligent vehicle is expressed as constraint conditions of a model predictive control algorithm, and the corresponding optimization problem is solved.
Further, in complex driving scenarios, the intelligent vehicle must cooperate with nearby vehicles to avoid collisions. Nearby vehicles are considered moving obstacles. By predicting the longitudinal position of a nearby vehicle, including the nominal position and the uncertainty position. Unlike control input constraints and state constraints, which are performed as hard constraints, accidental constraints avoiding collisions should satisfy the probability p.
The vehicle system is broken down into a nominal part and an uncertainty part, taking into account the uncertainty of the nearby vehicle motion. For the longitudinal movement of a nearby vehicle, its longitudinal position x η,k can be resolved into:
Wherein, And e η,k are the nominal longitudinal position and the uncertainty position, respectively, of the nearby vehicle at time step k. The nominal position is derived from the average value and the uncertainty position is calculated from the covariance value. The upper limit of the longitudinal position of the nearby vehicle must satisfy the following condition:
The symbol Pr (·) represents the probability that the inequality is satisfied, i.e., the probability that the nearby vehicle position is less than the upper limit value is p. Since the nearby vehicle position is generally less than the maximum value, the probability of satisfying the inequality is high. Therefore, the probability value p is tentatively 0.8.ltoreq.p.ltoreq.0.9. This probability constraint complicates the SMPC algorithm and is difficult to solve. To facilitate controller design, random constraints are converted into deterministic forms. This random constraint can be rewritten as:
Where x ηmax,k is the maximum of the obstacle longitudinal position, γ x,k is the safety factor, defined as:
Pr(eη,k≤γx,k)＝p
since the motion uncertainty is assumed to be a gaussian distribution, e η,k～N(0,Ση,k), the safety margin is calculated as:
Where Φ -1 is the inverse of the gaussian distribution. Likewise, the lower limit of the longitudinal position of the nearby vehicle is also calculated with the same probability p, expressed as:
Where x ηmin,k is the minimum of the longitudinal positions of the nearby vehicles.
According to the position range of the nearby vehicle in the formula, the anti-collision constraint condition of the intelligent vehicle can be formulated. In order to avoid nearby vehicles, the lateral deviation of the intelligent vehicle should be limited to a safe range, taking into account the uncertainty of the longitudinal position of the nearby vehicle. The constraint is rewritable as:
Due to AndNon-linearly with the vehicle longitudinal position x, so the constraint is generally non-convex and non-differential.
The control force can be calculated by solving the fade-out optimization problem. The cost function includes the steering behavior of the driver. The vehicle system model, vehicle state limits, and actuator saturation are all contained in constraints. The optimization problem can then be expressed as
subj.toξ(t|t)＝ξ(t)
ξ(t+k+1|t)＝Aξ(t+k|t)+Bu(t+k|t)+Ddω(t+k|t)
umin≤u<umax
for k＝0,2,3,&,N-1
Where u (t+k|t) represents the control force of the time step (t+k) calculated at time step t.
The embodiment provides an auxiliary control method for vehicle driving, which can calculate a series of auxiliary control quantity and vehicle motion state in prediction time by solving an optimization problem. The auxiliary controller sends the first control quantity to the intelligent vehicle for motion control. Meanwhile, collision avoidance constraints are formulated by utilizing continuous vehicle states, the collision avoidance problem of the intelligent vehicle is expressed as constraint conditions of a model predictive control algorithm, and the corresponding optimization problem is solved.
It should be noted that the above examples are only for understanding the present application, and do not limit the method of assisting control of driving of the vehicle of the present application, and more forms of simple changes based on this technical idea are all within the scope of the present application.
The present application also provides an auxiliary control device for driving a vehicle, referring to fig. 5, the auxiliary control device for driving a vehicle includes:
An acquisition module 10 for acquiring a predictive model, driver steering input and movement uncertainty of nearby vehicles;
A first calculation module 20, configured to determine a collision avoidance probability constraint condition based on a prediction model and a preset longitudinal equation and a motion uncertainty of a nearby vehicle;
A second calculation module 30 for generating steering control parameters based on the collision avoidance probability constraints and the driver steering input;
the control module 40 is used for controlling the steering of the vehicle based on the steering control parameters.
The auxiliary control device for vehicle driving provided by the application can solve the technical problem that collision prevention is not realized through cooperative cooperation of the intelligent vehicle and surrounding vehicles in the prior art by adopting the auxiliary control method for vehicle driving in the embodiment. Compared with the prior art, the beneficial effects of the auxiliary control device for vehicle driving provided by the application are the same as those of the auxiliary control method for vehicle driving provided by the embodiment, and other technical features of the auxiliary control device for vehicle driving are the same as those disclosed by the method of the embodiment, so that the description is omitted herein.
The application provides an auxiliary control device for vehicle driving, comprising: at least one processor; and a memory communicatively coupled to the at least one processor; the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor, so that the at least one processor can execute the auxiliary control method for driving a vehicle in the first embodiment.
As shown in fig. 6, the assistance control apparatus for vehicle driving may include a processing device 1001 (e.g., a central processor, a graphic processor, etc.) that may perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 1002 or a program loaded from a storage device 1003 into a random access Memory (RAM: random Access Memory) 1004. In the RAM1004, various programs and data required for the operation of the assist control device for vehicle driving are also stored. The processing device 1001, the ROM1002, and the RAM1004 are connected to each other by a bus 1005. An input/output (I/O) interface 1006 is also connected to the bus. In general, the following systems may be connected to the I/O interface 1006: input devices 1007 including, for example, a touch screen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, and the like; an output device 1008 including, for example, a Liquid crystal display (LCD: liquid CRYSTAL DISPLAY), a speaker, a vibrator, and the like; storage device 1003 including, for example, a magnetic tape, a hard disk, and the like; and communication means 1009. The communication means 1009 may allow the auxiliary control device for vehicle driving to communicate with other devices wirelessly or by wire to exchange data. While the figures illustrate an auxiliary control device for vehicle driving with various systems, it should be understood that not all illustrated systems are required to be implemented or provided. More or fewer systems may alternatively be implemented or provided.
In particular, according to embodiments of the present disclosure, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method shown in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through a communication device, or installed from the storage device 1003, or installed from the ROM 1002. The above-described functions defined in the method of the disclosed embodiment of the application are performed when the computer program is executed by the processing device 1001.
The auxiliary control device for vehicle driving provided by the application adopts the auxiliary control method for vehicle driving in the embodiment, and can solve the technical problem that collision prevention is not realized through cooperative cooperation of the intelligent vehicle and surrounding vehicles in the prior art. Compared with the prior art, the beneficial effects of the auxiliary control device for vehicle driving provided by the application are the same as those of the auxiliary control device for vehicle driving provided by the embodiment, and other technical features of the auxiliary control device for vehicle driving are the same as those disclosed by the method of the previous embodiment, so that the description is omitted herein.
It is to be understood that portions of the present disclosure may be implemented in hardware, software, firmware, or a combination thereof. In the description of the above embodiments, particular features, structures, materials, or characteristics may be combined in any suitable manner in any one or more embodiments or examples.
The foregoing is merely illustrative of the present application, and the present application is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
The present application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon for performing the vehicle driving assistance control method in the above-described embodiments.
The computer readable storage medium provided by the present application may be, for example, a U disk, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access Memory (RAM: random Access Memory), a Read-Only Memory (ROM: read Only Memory), an erasable programmable Read-Only Memory (EPROM: erasable Programmable Read Only Memory or flash Memory), an optical fiber, a portable compact disc Read-Only Memory (CD-ROM: CD-Read Only Memory), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In this embodiment, a computer-readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, or device. Program code embodied on a computer readable storage medium may be transmitted using any appropriate medium, including but not limited to: wire, fiber optic cable, RF (Radio Frequency), and the like, or any suitable combination of the foregoing.
The above-described computer-readable storage medium may be contained in an assist control device for vehicle driving; or may be present alone without being fitted into an auxiliary control device for vehicle driving.
Computer program code for carrying out operations of the present application may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of remote computers, the remote computer may be connected to the user's computer through any kind of network, including a local area network (LAN: local Area Network) or a wide area network (WAN: wide Area Network), or may be connected to an external computer (for example, through the Internet using an Internet service provider).
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The modules involved in the embodiments of the present application may be implemented in software or in hardware. Wherein the name of the module does not constitute a limitation of the unit itself in some cases.
The readable storage medium provided by the application is a computer readable storage medium, and the computer readable storage medium stores computer readable program instructions (namely computer programs) for executing the auxiliary control method for driving the vehicle, so that the technical problem that collision avoidance is not realized through cooperation of an intelligent vehicle and surrounding vehicles in the prior art can be solved. Compared with the prior art, the beneficial effects of the computer readable storage medium provided by the application are the same as those of the auxiliary control method for vehicle driving provided by the above embodiment, and are not described herein.
The application also provides a computer program product comprising a computer program which, when executed by a processor, implements the steps of a method for assisting in controlling driving of a vehicle as described above.
The computer program product provided by the application can solve the technical problem that collision avoidance is not realized through cooperative cooperation of the intelligent vehicle and surrounding vehicles in the prior art. Compared with the prior art, the beneficial effects of the computer program product provided by the application are the same as those of the auxiliary control method for vehicle driving provided by the above embodiment, and are not described herein.
The foregoing description is only a partial embodiment of the present application, and is not intended to limit the scope of the present application, and all the equivalent structural changes made by the description and the accompanying drawings under the technical concept of the present application, or the direct/indirect application in other related technical fields are included in the scope of the present application.
Claims (10)
1. An assist control method for driving a vehicle, characterized by comprising:
acquiring a prediction model, driver steering input and motion uncertainty of nearby vehicles;
determining a collision avoidance probability constraint condition based on a preset longitudinal equation and motion uncertainty of the prediction model and the nearby vehicle;
Generating steering control parameters based on the collision avoidance probability constraints and the driver steering inputs;
controlling the steering of the vehicle based on the steering control parameter.
2. The method for assisting control of vehicle driving according to claim 1, wherein the step of acquiring the predictive model, the driver steering input, and the longitudinal equation and the motion uncertainty of the nearby vehicle is preceded by the step of:
establishing a track prediction model of a nearby vehicle, and determining a longitudinal equation and a motion uncertainty of the nearby vehicle based on the track prediction model of the nearby vehicle;
Creating a vehicle system model, and determining a prediction model based on the vehicle system model;
a driver model is created, based on which driver steering input is represented.
3. The assist control method for vehicle driving according to claim 2, characterized in that the step of establishing a trajectory prediction model of the nearby vehicle, determining a longitudinal equation and a motion uncertainty of the nearby vehicle based on the trajectory prediction model of the nearby vehicle, comprises:
Acquiring sampling frequency, longitudinal speed of a nearby vehicle at a preset time step and longitudinal position of the nearby vehicle at the preset time step;
creating a track prediction model of the nearby vehicle based on the sampling frequency, the longitudinal speed of the nearby vehicle at the preset time step and the longitudinal position of the nearby vehicle at the preset time step;
obtaining a longitudinal equation of the nearby vehicle and a longitudinal motion matrix of the nearby vehicle when a preset time step is obtained through the track prediction model of the nearby vehicle;
And predicting the motion trail of the nearby vehicle through the longitudinal motion equation of the nearby vehicle and the longitudinal motion matrix of the nearby vehicle to obtain the motion uncertainty of the nearby vehicle.
4. The assist control method for vehicle driving according to claim 2, characterized in that the step of creating a vehicle system model, determining a predictive model based on the vehicle system model, includes:
Acquiring a vehicle motion schematic diagram, and establishing a motion model of a vehicle relative to an expected path through the vehicle motion schematic diagram;
Establishing a dynamics model of the vehicle through the motion model of the vehicle relative path and a vehicle motion schematic diagram;
Obtaining the global position of the vehicle and the lateral tire force of the vehicle through the dynamic model of the vehicle;
obtaining a transverse motion model of the vehicle through the vehicle motion schematic diagram, a motion model of the vehicle relative to a desired path, a dynamics model, a global position of the vehicle and lateral tire force of the vehicle;
Discretizing the transverse motion model of the vehicle by using an Euler method to obtain a prediction model of the vehicle.
5. The assist control method for vehicle driving according to claim 2, characterized in that the step of creating a driver model based on which a driver steering input is represented, includes:
acquiring the operation characteristics of a driver, the lateral position deviation of the vehicle and the steering behavior of the driver;
obtaining a driver steering input based on the lateral position deviation of the vehicle and the driver's steering behavior;
and dispersing the steering input of the driver to obtain a driver model so as to represent the steering input of the driver.
6. The assist control method for vehicle driving according to claim 1, characterized in that the step of determining collision avoidance probability constraint conditions based on a longitudinal equation of the prediction model and the nearby vehicle and a motion uncertainty includes:
acquiring a position constraint function and a position constraint boundary according to the prediction model;
Obtaining the lateral position collision avoidance constraint of the vehicle based on the position constraint function and the position constraint boundary;
And determining collision avoidance probability constraint conditions based on longitudinal equations and motion uncertainties of the nearby vehicles and lateral position collision avoidance constraints of the vehicles.
7. The assist control method for vehicle driving according to claim 1, characterized in that the step of generating a steering control parameter based on the collision avoidance probability constraint condition and the driver steering input includes:
Establishing a state constraint of a prediction model based on the lateral speed and the yaw speed of the vehicle;
establishing input constraint of a prediction model based on an upper bound and a lower bound of a control quantity, a minimum steering angle, a maximum steering angle, a minimum driving moment and a maximum driving moment of the prediction model;
Establishing an auxiliary controller based on state constraint, input constraint and collision avoidance probability constraint conditions of the prediction model;
And calculating by the auxiliary controller based on the steering input of the driver to obtain steering control parameters.
8. An assist control device for driving a vehicle, the device comprising:
The acquisition module is used for acquiring the prediction model, the steering input of the driver and the movement uncertainty of the nearby vehicle;
the first calculation module is used for determining collision avoidance probability constraint conditions based on a preset longitudinal equation and motion uncertainty of the prediction model and the nearby vehicle;
The second calculation module is used for generating steering control parameters based on the collision avoidance probability constraint conditions and the steering input of the driver;
and the control module is used for controlling the vehicle to steer based on the steering control parameter.
9. An assist control apparatus for driving a vehicle, characterized by comprising: a memory, a processor and a computer program stored on the memory and executable on the processor, the computer program being configured to implement the steps of the vehicle driving assistance control method according to any one of claims 1 to 7.
10. A storage medium, characterized in that the storage medium is a computer-readable storage medium, on which a computer program is stored, which computer program, when being executed by a processor, realizes the steps of the auxiliary control method for vehicle driving according to any one of claims 1 to 7.
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| CN119502947B (en) * | 2024-12-02 | 2026-01-20 | 西北工业大学 | Collision Avoidance Method for Autonomous Vehicles Based on Fuzzy Model Predictive Control |
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