Vehicle control method, medium, product, equipment and vehicle

Abstract

The invention relates to a vehicle control method, a medium, a product, equipment and a vehicle, and the method comprises the steps: responding to a control instruction of the vehicle, and determining a control parameter in an intelligent driving system based on the control instruction; and performing intelligent driving control on the vehicle based on the control parameters. According to the method, the specific control parameters are determined through the control instruction input by the user, and intelligent driving control is performed on the vehicle according to the specific control parameters, so that the driving safety and the user experience can be remarkably improved, and meanwhile, the man-machine conflict can be avoided.

Description

Technical Field

[0001] The present application relates to the technical field of vehicles, and in particular, to a vehicle control method, medium, product, device, and vehicle. Background Art

[0002] Currently, in the human-machine co-driving mode (i.e., the driver and the autonomous driving system jointly participate in driving), there is an easy control conflict between the driver and the autonomous driving system. For example, the autonomous driving system may control the vehicle according to sensor data and preset rules, while the driver may take different operations to control the vehicle based on their own driving experience and intuition. When there is a conflict between the two, it may lead to instability in vehicle driving and even cause safety accidents. Summary of the Invention

[0003] Embodiments of the present application provide a vehicle control method, medium, product, device, and vehicle to solve the above problems.

[0004] To achieve the above object, according to the first aspect of the present application, a vehicle control method is provided, and the method includes:

[0005] In response to a control instruction of the vehicle, determine control parameters in the intelligent driving system based on the control instruction;

[0006] Perform intelligent driving control on the vehicle based on the control parameters.

[0007] Optionally, the control instruction includes at least one of a steering control instruction, an acceleration control instruction, and a braking control instruction.

[0008] Optionally, the step of in response to a control instruction of the vehicle, determining control parameters in the intelligent driving system based on the control instruction includes:

[0009] In response to the steering control instruction, use at least one of the target motor torque and the target steering angle of the vehicle as the control parameter based on the steering control instruction.

[0010] Optionally, the target steering angle includes the steering angle of the rear wheels, and / or the target motor torque includes at least one of the front axle motor torque, the left rear wheel motor torque, and the right rear wheel motor torque.

[0011] Optionally, the method further includes:

[0012] In response to the acceleration control instruction, use the target motor torque and / or acceleration of the vehicle as the control parameter based on the acceleration control instruction.

[0013] Optionally, the method further includes:

[0014] In response to the braking control instruction, the target braking torque and / or speed of the vehicle are used as the control parameters based on the braking control instruction.

[0015] Optionally, the intelligent driving control of the vehicle based on the control parameters includes:

[0016] Determining the control amount of the control parameters based on the input data corresponding to the control instruction and the vehicle prediction model;

[0017] Performing intelligent driving control on the vehicle based on the control amount of the control parameters.

[0018] Optionally, the determining the control amount of the control parameters based on the input data corresponding to the control instruction and the vehicle prediction model includes:

[0019] Determining the prediction data of the vehicle based on the input data corresponding to the control instruction and the vehicle prediction model;

[0020] Obtaining the control amount based on the deviation between the prediction data and the vehicle state data.

[0021] Optionally, the determining the prediction data of the vehicle based on the input data corresponding to the control instruction and the vehicle prediction model includes:

[0022] Taking the control parameters and the state parameters of the vehicle as variable parameters in the vehicle dynamics model to obtain the vehicle prediction model;

[0023] Processing the input data corresponding to the control instruction based on the vehicle prediction model to obtain the prediction data.

[0024] Optionally, the obtaining the control amount based on the deviation between the prediction data and the vehicle state data includes:

[0025] Establishing an objective function based on the deviation between the prediction data and the vehicle state data;

[0026] Determining the control amount by minimizing the objective function.

[0027] Optionally, the establishing an objective function based on the deviation between the prediction data and the vehicle state data includes:

[0028] Performing weighted processing on a first error term and a second error term based on a first weight and a second weight to obtain the objective function, where

[0029] The first error term indicates the deviation between the vehicle state data and the prediction data;

[0030] The second error term indicates the degree of change of the control parameter.

[0031] Optionally, determining the control quantity by minimizing the objective function includes:

[0032] By minimizing the objective function, determining the control quantity with the numerical value of the control parameter that satisfies the constraint condition.

[0033] Optionally, the constraint condition includes a constraint condition on at least one of the control parameter, the state parameter, and the change parameter.

[0034] Optionally, the constraint condition of the control parameter includes a constraint condition on at least one of the control quantity and the control increment corresponding to the control parameter.

[0035] Optionally, obtaining the control quantity based on the deviation between the prediction data and the vehicle state data includes:

[0036] Based on the deviation, determining the control increment of the control parameter;

[0037] Based on the control increment and the preset control quantity, determining the control quantity of the control parameter.

[0038] Optionally, the prediction data includes a predicted yaw rate,

[0039] The predicted yaw rate is determined by the following method:

[0040] Based on the front wheel angle of the vehicle in the input data and the preset conversion model in the vehicle prediction model, obtaining the predicted yaw rate.

[0041] Optionally, the preset conversion model is used to indicate the relationship between the ratio of the yaw rate of the vehicle to the front wheel angle and the driving speed.

[0042] Optionally, the vehicle state data includes at least one of the longitudinal speed, lateral speed, heading angle, and yaw rate of the vehicle.

[0043] According to the second aspect of the present application, an embodiment of the present application further provides an electronic device, including:

[0044] A memory, on which a computer program is stored;

[0045] A processor, configured to execute the computer program in the memory to implement the steps of any one of the methods provided by the embodiments of the present application.

[0046] According to a third aspect of the present application, embodiments of the present application further provide a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of any one of the methods provided by the embodiments of the present application are implemented.

[0047] According to a fourth aspect of the present application, embodiments of the present application further provide a computer program product, including a computer program or instruction, and when the computer program or instruction is executed by a processor, the steps of any one of the methods provided by the embodiments of the present application are implemented.

[0048] According to a fifth aspect of the present application, embodiments of the present application further provide a vehicle, including the electronic device as described above, or implementing the steps of any one of the methods provided by the embodiments of the present application.

[0049] Some embodiments of this specification at least include the following beneficial effects: determining the control parameters of the intelligent driving system based on the user's control instructions, overcoming the problems in the traditional intelligent driving system where the control parameters are fixed and it is difficult to cooperate with the user's operations. By obtaining the user's control instructions in real time, the control parameters of intelligent driving can be dynamically adjusted, so as to better match the user's driving intention and improve the safety and stability of driving.

[0050] Other features and advantages of the present application will be described in detail in the subsequent specific implementation part. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present application, and those skilled in the art can obtain other drawings based on these drawings without creative efforts.

[0052] In order to more fully understand the present application and its beneficial effects, the following will be described in conjunction with the drawings, where the same reference numerals in the following description represent the same parts.

[0053] Figure 1 is a schematic structural diagram of a vehicle control system according to some embodiments of this specification;

[0054] Figure 2 is an exemplary flowchart of a vehicle control method according to some embodiments of this specification;

[0055] Figure 3 is an exemplary schematic diagram of a drive motor of a vehicle according to some embodiments of this specification;

[0056] Figure 4It is an exemplary schematic diagram of the independent rear-wheel steering of a vehicle shown in some embodiments of this specification;

[0057] Figure 5 It is an exemplary schematic diagram of another vehicle control method shown in some embodiments of this specification;

[0058] Figure 6 It is a schematic structural diagram of an electronic device shown in some embodiments of this specification;

[0059] Figure 7 It is an exemplary schematic diagram of a vehicle shown in some embodiments of this specification. Detailed implementation manners

[0060] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative efforts shall fall within the protection scope of the present application.

[0061] To facilitate the understanding of the implementation solutions provided in the embodiments of the present application, the relevant application backgrounds of the vehicle control method provided in the embodiments of the present application will be described first.

[0062] Currently, most model predictive controls only consider single power drive systems and single front-wheel steering systems. The upper limit of the vehicle state change speed restricts the optimization of state quantity tracking control, making the obtained control results difficult to meet actual requirements; large deviations are likely to occur.

[0063] In view of this, some embodiments of this specification provide a vehicle control method. By multiple motor independent drive systems and independent rear-wheel steering systems, the rate of change of vehicle state quantities is increased. At the same time, by combining the reference relationship between the front-wheel angle and the yaw angular velocity, it helps to improve the control effect and reduce understeering and oversteering of vehicle steering.

[0064] Figure 1 It is a schematic structural diagram of a vehicle control system shown in some embodiments of this specification.

[0065] As Figure 1 shown, the vehicle control system 100 may include a first sensor 101, a controller 102, an actuator 103, and a second sensor 104.

[0066] The first sensor 101 is used to collect environmental information around the vehicle.

[0067] In some embodiments, the first sensor 101 inputs the environmental information of the vehicle to the controller 102 in a wired transmission manner. Among them, the environmental information includes road conditions, the operating states of surrounding vehicles, obstacles, traffic signs, etc.

[0068] In some embodiments, the wired transmission manner includes a CAN (Controller Area Network) bus, etc.

[0069] In some embodiments, the second sensor 104 is communicatively connected to the controller 102 and is used to connect to a driving control device for collecting the operation intention of the user on the driving control device.

[0070] In some embodiments, the driving control device includes a steering wheel, an accelerator pedal, a brake pedal, etc.

[0071] In some embodiments, the operation intention may include any parameter information that can characterize the operation intention of the user, such as the rotation angle of the steering wheel, the accelerator pedal signal, the brake pedal signal, etc.

[0072] In some embodiments, the controller 102 may be built-in with an intelligent driving system, which can process the input data to obtain corresponding output results. It can sense and process the received environmental information, plan the intelligent driving route and the actions to be taken, and transmit the vehicle action data to the actuator 103 through wired transmission.

[0073] In some embodiments, the actuator 103 is electrically connected to the controller 102 and is used to connect to a driving control device for controlling the driving control device to complete a predetermined action according to the control amount, and for stopping the control of the driving control device when receiving a parking instruction.

[0074] Among them, the actuator 103 is used to convert the control amount into physical control data, such as the steering wheel angle or the throttle depth, etc. The actuator 103 is also used to control the rotation mechanism of the steering wheel, the pushing mechanisms of the accelerator and the brake, etc. according to the physical control data, and complete the control of the vehicle through the physical actions of these mechanisms.

[0075] In some embodiments, the first sensor 101 includes at least two of a lidar, a millimeter-wave radar, an ultrasonic radar, an inertial measurement unit, an image sensor, and a position sensor. Among them, the lidar is used to obtain point cloud data, the millimeter-wave radar and the ultrasonic radar are used to obtain the position information of the objects around the vehicle, the inertial measurement unit is used to obtain the yaw angle of the vehicle, the image sensor is used to obtain the image data around the vehicle, and the position sensor is used to obtain the positioning coordinates of the vehicle.

[0076] In some embodiments, the second sensor 104 includes at least one of a steering wheel angle sensor, a wheel speed sensor, and an inertial gyroscope. The steering wheel angle sensor is used to obtain the rotation angle information of the steering wheel, the wheel speed sensor is used to obtain the rotation speed information of the wheels, and the inertial gyroscope is used to obtain the angular velocity information of the vehicle in the axial direction.

[0077] In some embodiments, by using the steering wheel angle sensor, the wheel speed sensor, and the inertial gyroscope, the data of the vehicle can be obtained in real time, facilitating the controller 102 to determine whether the vehicle is traveling according to a predetermined action based on the real-time state data of the vehicle.

[0078] In some embodiments, the control quantity may include acceleration information, deceleration information, turning angle information, etc.

[0079] In some embodiments, the actuator 103 is used to convert the control quantity into a physical control quantity and control the driving control device to complete a predetermined action according to the physical control quantity.

[0080] Specifically, the physical control quantity includes the target rotation angle of the steering wheel, the first target depth of the accelerator pedal, and the second target depth of the brake pedal. The transmission module includes a drive shaft, a first control rod, and a second control rod. Among them, the drive shaft is used to drive the steering wheel to rotate by the target rotation angle, the first control rod is used to control the accelerator pedal to the first target depth, and the second control rod is used to control the brake pedal to the second target depth.

[0081] More specifically, the first control rod and the second control rod are hydraulic rods or pneumatic rods.

[0082] It should be noted that the above description of the vehicle control system and its modules is only for convenience of description and does not limit this specification to the scope of the examples given. It can be understood that for those skilled in the art, after understanding the principle of the system, they may, without departing from this principle, make any combination of the various modules, or form a subsystem and connect it with other modules. In some embodiments, Figure 1 what is disclosed may be different modules in a system, or a module may implement the functions of two or more of the above-mentioned modules. For example, the various modules may share a storage module, or each module may have its own storage module. Such deformations are all within the protection scope of this specification.

[0083] Figure 2 is an exemplary flowchart of a vehicle control method according to some embodiments of this specification. In some embodiments, the process 200 may be executed based on the controller. As Figure 2 shown, the process 200 includes the following steps.

[0084] Step 210, in response to a control instruction of the vehicle, determine control parameters in the intelligent driving system based on the control instruction.

[0085] The control instruction refers to an instruction issued by the driver through operating the driving control devices of the vehicle (such as the steering wheel, accelerator pedal, brake pedal, etc.), used to indicate the movement direction, speed or other operation requirements of the vehicle.

[0086] The control parameter refers to the control parameter used by the intelligent driving system to adjust the movement state of the vehicle at the current moment.

[0087] In some embodiments, based on the control instruction, through Model Predictive Control (MPC), the control quantities to be predicted may include the front wheel angle or steering wheel angle of the vehicle, as well as possible additional yaw moments, etc.

[0088] Step 220, perform intelligent driving control on the vehicle based on the control parameters.

[0089] In some embodiments, according to the determined control parameters, the intelligent driving system can be controlled to control the actuators of the vehicle (such as the engine, brakes, steering system). For example, longitudinal control such as adjusting the speed and acceleration of the vehicle; and for another example, lateral control such as adjusting the steering angle of the vehicle.

[0090] In some embodiments of this specification, by the control instruction input by the user, specific control parameters are determined, and intelligent driving control is performed on the vehicle accordingly, which can not only significantly improve driving safety and user experience, but also avoid conflicts between humans and machines.

[0091] In some embodiments, the control instruction includes at least one of a steering control instruction, an acceleration control instruction, and a braking control instruction.

[0092] The steering control instruction refers to an operation instruction issued by the driver by turning the steering wheel, indicating the steering angle of the vehicle. In some embodiments, the steering control instruction may include the turning angle of the steering wheel.

[0093] The acceleration control instruction is an instruction issued by the driver by stepping on the accelerator pedal, indicating the acceleration requirement of the vehicle. In some embodiments, the acceleration control instruction may include the depth signal of the accelerator pedal.

[0094] The braking control instruction is an instruction issued by the driver by stepping on the brake pedal, indicating the deceleration requirement of the vehicle. In some embodiments, the braking control instruction may include the depth signal of the brake pedal.

[0095] In some embodiments, the control instructions of the driver can be detected by sensors. For example, the rotation angle of the steering wheel can be detected by a steering wheel angle sensor; the depth signals of the accelerator pedal and the brake pedal can be detected by a pedal position sensor.

[0096] In some embodiments, the control instructions can be converted into electrical signals by sensors and transmitted to the vehicle controller (such as an Electronic Control Unit (ECU)). The controller can process based on the above electrical signals and calculate the control amount corresponding to the control parameters in combination with the current state data of the vehicle (such as speed, yaw rate, etc.).

[0097] In the human-machine co-driving mode, the autonomous driving system needs to fuse the driver's control instructions with the autonomous decisions of the autonomous driving system.

[0098] In some embodiments, the method further includes:

[0099] In response to the vehicle's control instructions, determining the control parameters in the intelligent driving system based on the control instructions, including:

[0100] In response to the steering control instructions, taking at least one of the target motor torque and the target angle of rotation of the vehicle as the control parameter based on the steering control instructions.

[0101] In some embodiments, the user can input an acceleration control instruction by means of the steering wheel angle, voice instruction, remote control, etc.

[0102] The target motor torque refers to the torque that the vehicle power system (such as an electric motor) needs to output to achieve the desired longitudinal acceleration or braking force.

[0103] The target angle of rotation refers to the steering angle that the vehicle front wheels need to reach to achieve the desired yaw rate or steering trajectory.

[0104] In some embodiments, the required longitudinal acceleration or braking force can be calculated based on the speed, desired speed, and path planning of the vehicle at the current moment, and then the target motor torque can be determined.

[0105] The current moment generally refers to the time point when the processor performs data acquisition, processing, and decision-making. For example, the current moment can be the time point when the processor last acquires data, updates the control instructions, or makes a decision.

[0106] In some embodiments, the target angle of rotation can be calculated based on the current state data of the vehicle (such as speed, yaw rate) and the desired trajectory through model predictive control (MPC) or other control algorithms.

[0107] In some embodiments of this specification, by using the target motor torque or the target steering angle as control parameters, the vehicle steering can be controlled more precisely, enabling a quick response to complex road conditions. In the human-machine collaboration mode, the driving authority can be dynamically allocated according to the driver's intention and the system's requirements, reducing the driver's operation burden while ensuring the vehicle's safety in complex environments.

[0108] In some embodiments, the target steering angle includes the steering angle of the rear wheels, and / or the target motor torque includes at least one of the front axle motor torque, the left rear wheel motor torque, and the right rear wheel motor torque.

[0109] In some embodiments, the target steering angle may include the steering angles of the front and rear wheels, depending on the vehicle's steering system design and control strategy. For example, in a four-wheel independent steering (4WIS) vehicle, the target steering angle may include the steering angles of the left front wheel, the right front wheel, the left rear wheel, and the right rear wheel.

[0110] The front axle motor torque refers to the torque that the vehicle's front axle motor needs to output, used to drive the front wheels or achieve the braking force of the front wheels.

[0111] The left rear wheel motor torque refers to the torque that the vehicle's left rear wheel motor needs to output. The right rear wheel motor torque refers to the torque that the vehicle's right rear wheel motor needs to output. In a rear-wheel independent drive vehicle, the rear wheel motor torque can be independently adjusted to achieve differential drive or improve the vehicle's stability and flexibility.

[0112] In some embodiments of this specification, by calculating the value of the target steering angle through a vehicle dynamics model, a steer-by-wire system, or an optimization algorithm, precise and stable steering control can be achieved; by accurately determining the value of the target motor torque, smooth acceleration, deceleration, and emergency obstacle avoidance of the vehicle can be realized, improving the vehicle's driving safety and comfort.

[0113] In some embodiments, the method further includes:

[0114] In response to an acceleration control instruction, using the target motor torque and / or acceleration of the vehicle as control parameters based on the acceleration control instruction.

[0115] In some embodiments, the user can input an acceleration control instruction through the accelerator pedal, voice command, or remote control, etc.

[0116] In some embodiments, based on the acceleration control instruction, the control parameters that the vehicle's power system (such as a motor or an engine) needs to output can be determined, such as the target motor torque, the throttle opening, or the fuel injection amount, etc.

[0117] In some embodiments of this specification, by determining the value of the target motor torque and / or acceleration, the acceleration process of the vehicle can be made smoother, improving the user's driving experience.

[0118] In some embodiments, the method further includes:

[0119] In response to a braking control instruction, the target braking torque and / or speed of the vehicle are used as control parameters based on the braking control instruction.

[0120] In some embodiments, the user can input a braking control instruction by means of a brake pedal, a voice command, remote control, etc.

[0121] In some embodiments of this specification, by means of the target braking torque, the braking force of each wheel can be precisely controlled to ensure the stability and safety of the vehicle under different road conditions; by determining the specific value of the speed, the required deceleration can be calculated, and the vehicle speed can be precisely controlled through the braking torque; it helps to respond quickly to emergencies and reduce the braking distance.

[0122] In some embodiments, intelligent driving control of the vehicle is performed based on the control parameters, including:

[0123] Based on the input data corresponding to the control instruction and the vehicle prediction model, determine the control amount of the control parameter;

[0124] Perform intelligent driving control of the vehicle based on the control amount of the control parameter.

[0125] The control parameter refers to the type of parameter variable used to control the vehicle. The control amount refers to the specific value of the control parameter. For example, the control amount can include the specific value of the target motor torque, the specific value of the target rotation angle, etc.

[0126] The input data corresponding to the control instruction refers to the instructions issued by the user through various interaction methods to achieve operations such as accelerating, braking, and steering of the vehicle. For example, the input data can include the displacement or pressure of the accelerator pedal, the displacement or pressure of the brake pedal, the angle of the steering wheel, the rotational speed or torque, etc.

[0127] In some embodiments, determining the control amount of the control parameter based on the input data corresponding to the control instruction and the vehicle prediction model includes:

[0128] Based on the input data corresponding to the control instruction and the vehicle prediction model, determine the prediction data of the vehicle;

[0129] Based on the deviation between the prediction data and the vehicle state data, obtain the control amount.

[0130] A vehicle prediction model is a model that predicts the motion state of a vehicle over a period of time in the future. The vehicle prediction model can predict prediction data such as the position, speed, acceleration, and yaw rate of the vehicle by combining the vehicle's dynamics model, control inputs (such as throttle, brake, steering wheel angle, etc.), and environmental information (such as road conditions, traffic conditions, etc.).

[0131] Prediction data refers to the state data of the predicted vehicle over a future time period.

[0132] Vehicle state data includes information such as the vehicle's speed, acceleration, yaw rate, steering wheel angle, throttle opening, and brake force.

[0133] In some embodiments, vehicle state data can be obtained by real-time acquisition through the vehicle's sensors (such as inertial measurement units, wheel speed sensors, cameras, etc.). In some embodiments, vehicle state data can be the desired state data. For example, vehicle state data can include data such as the driving path or motion state to be tracked. For example, vehicle state data can include information such as reference position, reference speed, reference acceleration, and predicted yaw rate.

[0134] In some embodiments, the deviation between vehicle state data and prediction data can be calculated. The deviation can include any one or a combination of position error, speed error, yaw rate error, etc.

[0135] Position error refers to the difference between the reference position and the predicted position.

[0136] Speed error refers to the difference between the reference speed and the predicted speed.

[0137] Yaw rate error refers to the difference between the predicted yaw rate and the predicted yaw rate.

[0138] In some embodiments, the optimal control quantity can be calculated through optimization algorithms (such as model predictive control MPC, PID control, etc.) to minimize the error.

[0139] In some embodiments of this specification, by determining the deviation between the prediction data and the vehicle state data, the vehicle can more accurately track a preset path (such as a lane center line or a target trajectory), which helps to control the vehicle to maintain stable driving in a complex traffic environment. For example, in lateral control, minimizing the deviation can reduce the deviation of the vehicle's lateral position, ensure the vehicle drives smoothly within the lane, and avoid unnecessary lane departure.

[0140] In some embodiments, based on the input data corresponding to the control instruction and the vehicle prediction model, the prediction data of the vehicle is determined, including:

[0141] Taking the control parameters and the state parameters of the vehicle as variable parameters in the vehicle dynamics model, a vehicle prediction model is obtained;

[0142] Based on the vehicle prediction model, the input data corresponding to the control instruction is processed to obtain prediction data.

[0143] The state parameters of the vehicle are the parameter types used to describe the motion state of the vehicle.

[0144] The vehicle dynamics model is a mathematical model that describes the motion state and force conditions of the vehicle during driving. For example, through experimental tests and data fitting, a mathematical model describing the motion and force of the vehicle can be established. Exemplarily, the side slip characteristic curve of the tire can be used to describe the relationship between the lateral force and the side slip angle.

[0145] Exemplarily, a non-linear vehicle dynamics model is established.

[0146] Figure 3 It is an exemplary schematic diagram of the drive motor of the vehicle shown in some embodiments of the present specification;

[0147] Figure 4 It is an exemplary schematic diagram of the independent rear-wheel steering of the vehicle shown in some embodiments of the present specification;

[0148] Such as Figure 3 , Figure 4 As shown, the drive system of the controlled vehicle is independently driven by three drive motors, with a single motor in the front and two motors in the rear, and the steering system includes front-wheel steering and independent rear-wheel steering. The following non-linear longitudinal and lateral vehicle dynamics model is established:

[0149]

[0150] Wherein:

[0151]

[0152] Wherein, X and Y are the horizontal and vertical coordinates in the ground coordinate system; v x and v y are the longitudinal and lateral speeds of the vehicle respectively; φ is the body heading angle; ω is the yaw angular velocity; m is the vehicle mass, X ij (i can take f and r, representing front and rear respectively, and j can take l and r, representing left and right respectively) is the longitudinal component force received by the wheel in the ground coordinate system; Y ij is the lateral component force received by the wheel in the ground coordinate system; I z is the vehicle moment of inertia; a is the distance from the center of mass to the front axle, b is the distance from the center of mass to the rear axle; t f is the front wheel track, t r is the rear wheel track; δ ij is the wheel angle, Fxij is the longitudinal force exerted on the tire in the tire coordinate system, F yij is the lateral force exerted on the tire in the tire coordinate system.

[0153] Exemplarily, the control parameters are determined according to the relationship between the driver control and the autonomous driving system control as: the front axle motor torque T f , the left rear motor torque T rl , the right rear motor torque T rr , the left rear wheel angle δ rl , the right rear wheel angle δ rr , that is:

[0154] u = [T f , T rl , T rr , δ rl , δ rr T ;

[0155] Then the front wheel longitudinal force R f is the front wheel equivalent radius; the left rear wheel longitudinal force the right rear wheel longitudinal force R r is the rear wheel equivalent radius; the conversion relationship between the front wheel angle and the front wheel equivalent angle δ f is established according to the vehicle design; the conversion relationship between the tire lateral force and the sideslip angle is established through the equivalent tire model. The dynamic model of the vehicle is expressed as where the state parameter x = [δ f , φ, v x , v y , ω] T . The dynamic model of the vehicle is linearized, discretized, and the control quantity is converted into the form of control increment, then the vehicle prediction model can be obtained:

[0156]

[0157] In the formula; ξ = [δ f , φ, v x , v y , ω, T f , T rl , T rr , δ rl , δ rr T is the system state quantity; is the coefficient matrix, is the constant matrix; Δu = [ΔT f , ΔT rl , ΔT rr , Δδ​​rl , Δδ rr T is the control increment.

[0158] In some embodiments, it includes prediction data at multiple moments.

[0159] Based on the vehicle prediction model, the input data corresponding to the control instruction is processed to obtain prediction data, including:

[0160] The prediction data at the current moment and the control quantity at the previous moment are input into the vehicle prediction model to obtain the prediction data at the next moment and the control quantity at the current moment.

[0161] The preset data at multiple moments refers to the future moments corresponding to each time step within a future time period.

[0162] In some embodiments, for multiple prediction data and vehicle state data, an optimal control input sequence is solved through an optimization algorithm (such as quadratic programming).

[0163] At the k-th time step, y(k) is the initial value of the control system prediction, denoted as y(k|k) = y(k). The controlled vehicle is updated according to the operation signal (such as the steering angle of the front wheels, etc.) input at the current time step and the vehicle state data, and then the first term of the obtained optimal control sequence is used as the input for the next time step, and optimization is solved in combination with the output of the controlled vehicle at the next time step. Repeating continuously realizes the rolling optimization of the optimal control sequence and obtains the state at future moments.

[0164] In some embodiments, at each time step, only the first control input in the optimal control input sequence is executed. The time is advanced by one time step, and the prediction and optimization of the state data are performed again.

[0165] In some embodiments of this specification, through the discretized vehicle prediction model, the motion state of the vehicle within several future time steps can be predicted more accurately, thereby achieving more precise trajectory tracking.

[0166] In some embodiments, by minimizing the objective function, the control quantity is determined, and determining the control quantity includes:

[0167] Based on the deviation between the prediction data and the vehicle state data, an objective function is established;

[0168] By minimizing the objective function, the control quantity is determined.

[0169] The objective function is a quantifiable function of system performance (such as error, energy consumption, smoothness, etc.).

[0170] In some embodiments, in model predictive control, the optimized objective function is a quadratic function. ​

[0171] In some embodiments of the present specification, the optimization objective function can minimize (or maximize) performance metrics, including error terms, control input terms, and combinations thereof; find the optimal control input sequence to make the actual state of the system as close as possible to the desired trajectory.

[0172] In some embodiments, based on the deviation between the predicted data and the vehicle state data, an objective function is established, including:

[0173] Based on the first weight and the second weight, the first error term and the second error term are weighted to obtain the objective function, where

[0174] The first error term indicates the deviation between the vehicle state data and the predicted data;

[0175] The second error term indicates the degree of change of the control parameter.

[0176] The first weight and the second weight can be system preset values, system default values, or determined according to the actual situation.

[0177] In some embodiments, based on the sensors configured on the controlled vehicle (mainly including IMU, vehicle-mounted camera, millimeter-wave radar, ultrasonic radar, lidar), through the multi-sensor information fusion method and after passing through the vehicle state estimation module (such as the Kalman filter model, etc.), the vehicle state data in the vehicle prediction model is obtained.

[0178] In some embodiments, the objective function includes trajectory tracking accuracy and control effort cost:

[0179]

[0180] In the formula, N p is the prediction step, Q and R are weight coefficients, ξ(k) is the predicted data, ξ r (k) is the vehicle state data, and Δu(k) is the control increment of the control parameter.

[0181] In some embodiments of the present specification, according to the longitudinal and lateral vehicle dynamics model including four-wheel steering angles and four-wheel longitudinal and lateral forces; input five control quantities of the front motor, left rear motor, right rear motor torques, and left and right rear wheel steering angles; convert the control quantities into variables of the dynamics model to obtain the vehicle prediction model; according to this vehicle prediction model, trajectory tracking can be performed, and at the same time, the vehicle can be tracked and controlled according to the relationship between the front wheel steering and the steering wheel input, which helps to improve the control effect.

[0182] In some embodiments, by minimizing the objective function, the control quantity is determined, including:

[0183] By minimizing the objective function, the numerical values of the control parameters that satisfy the constraint conditions are determined as the control quantity.

[0184] Constraints refer to the restrictive conditions that must be satisfied during the optimization process. Constraints can ensure that the optimal control input is physically feasible, safe, and meets the actual operation requirements.

[0185] Constraints can include physical constraints, safety constraints, operation constraints, etc.

[0186] Physical constraints refer to the restrictive conditions determined by the physical characteristics of the vehicle or physical laws.

[0187] In some embodiments, the constraints include constraints on at least one of control parameters, state parameters, and change parameters.

[0188] In some embodiments, the constraints on control parameters include constraints on at least one of the control quantity and control increment corresponding to the control parameter.

[0189] The control increment is the change amount of the control input.

[0190] The constraints on the control increment refer to the restrictive conditions imposed on the change amount of the control input (i.e., the increment of the control quantity) during the optimization process.

[0191] In some embodiments, the constraints on control parameters can include: the throttle opening is between 0% and 100%, the braking force is between 0 and the maximum braking force, the steering wheel angle is within the maximum angle range, etc. The constraints on state parameters can include the vehicle speed is within the safe speed range, the yaw rate is within the physical limit range of the vehicle, the lateral acceleration is within the range allowed by the friction coefficient between the tire and the ground, etc.

[0192] Safety constraints are related to the dynamic behavior of the vehicle, the surrounding environment, and traffic rules.

[0193] Safety constraints can include the safety distance from the vehicle in front, etc.

[0194] Operation constraints are used to ensure that the control input of the vehicle is smooth and conforms to driving habits.

[0195] Operation constraints can include control input change rate constraints, such as the change rate of the throttle opening is within a specific range to avoid sudden acceleration; the change rate of the steering wheel angle is within a certain range to avoid sudden steering.

[0196] In some embodiments, the constraints include: constraints on system state variables and constraints on control increments.

[0197] The constraints on system state variables include stability constraints and control quantity limit constraints: ξ min ≤ξ(k)≤ξ max, where ξ min and ξ max are the minimum vector and the maximum vector of each state variable respectively.

[0198] The constraint condition of the control increment includes the limit constraint of the control increment: ΔU min ≤ΔU(k)≤ΔU max , where ΔU min and ΔU max are the minimum vector and the maximum vector of each control increment respectively.

[0199] In some embodiments of this specification, in model predictive control (MPC), by setting constraint conditions, it can be ensured that the control input and state variables of the vehicle meet the actual requirements.

[0200] The system inside the vehicle may not be able to withstand too large a change rate of the control input, and it is necessary to constrain the increment of the control quantity; by reasonably setting the constraint conditions of the control increment, the control strategy can be optimized to reduce energy consumption while meeting the dynamic requirements of the system.

[0201] In an autonomous vehicle, the constraint conditions of the control increment can be used to limit the change rates of the steering wheel angle, throttle opening, and braking force, etc.

[0202] In some embodiments, based on the deviation, the control increment of the control parameter is determined;

[0203] Based on the control increment and the preset control quantity, the control quantity of the control parameter is determined.

[0204] In some embodiments, based on the optimal quadratic programming, the optimal target control increment that satisfies the constraint conditions and minimizes the objective function is determined, and the target control increment is superimposed on the current control quantity for controlling the vehicle to obtain the optimal control quantity.

[0205] In some embodiments, the control quantity can be transmitted to the chassis by - wire execution system, and the chassis by - wire execution system transmits the execution information to each actuator to control the vehicle.

[0206] In some embodiments of this specification, by restricting the constraint conditions and minimizing the objective function, it helps to achieve the stability and trajectory tracking accuracy of the vehicle, while avoiding drastic changes in the control input, thereby improving the stability and ride comfort of the system.

[0207] In some embodiments, the prediction data includes the predicted yaw rate,

[0208] The predicted yaw rate is determined by the following method:

[0209] Based on the front wheel angle of the vehicle in the input data and the preset conversion model in the vehicle prediction model, a predicted yaw rate is obtained.

[0210] In some embodiments, the front wheel angle of the vehicle can be determined based on the rotation angle of the steering wheel in the steering control instruction through a preset mapping relationship or the like.

[0211] The preset conversion model refers to the mathematical relationship established between the front wheel angle and the yaw rate of the vehicle.

[0212] In some embodiments, the preset conversion model can be determined based on prior knowledge or historical data.

[0213] In some embodiments, the preset conversion model is used to indicate the relationship between the ratio of the yaw rate to the front wheel angle and the driving speed.

[0214] In some embodiments, in the steady-state response of the vehicle, the ratio of the steady-state yaw rate to the front wheel angle is called the steady-state yaw rate gain, also known as the steering sensitivity, denoted by the symbol and is represented as.

[0215] Only consider the scenario where the driver manipulates the steering wheel during the steering process, that is, a step input of the front wheel angle. Considering that the yaw rate gain of neutral steering in the steady-state response under this input is linearly related to the vehicle speed, that is where v is the driving speed of the vehicle and L is the distance from the front axle to the rear axle of the vehicle. The reference relationship between the front wheel angle and the yaw rate is constructed as: K i is the yaw rate gain coefficient, which is determined according to different optimization levels. Exemplarily, the drive system of the controlled vehicle is a three-motor independent drive with a single motor drive on the front axle, a left rear wheel body motor drive, and a right rear wheel motor drive. The steering system includes front-wheel steering and rear-wheel independent steering. Then, the intelligent driving system controls the torques of the three motors and the steering angle of the rear wheels to make the yaw rate gain of the vehicle satisfy the reference relationship.

[0216] In some embodiments of this specification, by determining the predicted yaw rate, the input data of the user can be optimized, and the violent movements of the vehicle during the steering process can be reduced, thereby improving the comfort of the passengers.

[0217] In some embodiments, the vehicle state data includes at least one of the longitudinal speed, lateral speed, heading angle, and yaw rate of the vehicle.

[0218] In some embodiments, the vehicle state data can include at least one or a combination of the reference lateral position, reference longitudinal position, reference speed, reference heading angle, etc. in a future time period.

[0219] In some embodiments, a future time period can be divided into a number of discrete future time points, with each time step being a time interval. For example, the future time period is divided into N time steps, and the multiple time steps within the future time period can be expressed as: k, k + 1, k + 2, …, k + N - 1, where: k is the current time step. k + N - 1 is the last time step of the future time period.

[0220] In some embodiments, a reference trajectory can be generated in real time based on the steering wheel rotation angle, throttle pedal depth signal, and brake pedal depth signal in a control instruction. For example, vehicle state data can be dynamically adjusted based on the driver's intention. For example, vehicle state data can be calculated in real time through a path planning algorithm (such as the A* algorithm, Dijkstra algorithm, etc.). Also, for example, a reference speed can be calculated through the driver's throttle pedal opening, and a reference path can be calculated through the steering wheel angle.

[0221] In some embodiments, based on prediction data and a reference trajectory, the control quantity of a control parameter can be determined through model predictive control.

[0222] It should be noted that the above description of the process is only for illustration and example, and does not limit the scope of application of this specification. For those skilled in the art, various modifications and changes can be made to the process under the guidance of this specification. However, these modifications and changes are still within the scope of this specification.

[0223] Figure 5 is an exemplary schematic diagram of another vehicle control method shown according to some embodiments of this specification.

[0224] In some embodiments, as Figure 5 shown, a user (such as a driver) can input a control instruction to a controlled vehicle through a throttle pedal, steering wheel, voice command, or remote control, etc., and the vehicle can determine input data corresponding to the control instruction based on the control instruction.

[0225] In some embodiments, a vehicle dynamics model or kinematics model is established, which can usually be simplified into a linear or nonlinear state - space equation as a vehicle prediction model. For example, for lateral control, a two - degree - of - freedom bicycle model can be used.

[0226] In some embodiments, based on the vehicle state data and input data at the current moment, a vehicle prediction model can be used to predict the system state data within a future period of time. The prediction range is determined by the prediction horizon.

[0227] In some embodiments, the objective function can be optimized to minimize the objective function within the prediction range. The objective function can include the weighted sum of the error of the state data and the control increment. For example, within the prediction range, the optimal control input u is solved by an optimization algorithm (such as quadratic programming) to minimize the objective function. According to the optimization result, the target control increment at the current moment is output to obtain the target control amount, and prediction and optimization are re-performed based on the new vehicle state data at the next sampling moment.

[0228] In some embodiments, based on the target control amount, the chassis-by-wire execution system can be controlled to control the front motor, the left rear wheel motor, the right rear wheel motor, and the steering angles of the left rear wheel and the right rear wheel of the vehicle.

[0229] For the specific implementation of each of the above operations, reference can be made to the previous embodiments and will not be elaborated here.

[0230] Figure 6 is a schematic structural diagram of an electronic device according to some embodiments of the present specification. As Figure 6 shown, the electronic device 600 may include: a processor 601, a memory 602. The electronic device 600 may further include one or more of a multimedia component 603, an input / output (I / O) component 604, and a communication component 605. In this embodiment, the electronic device 600 may be a device for implementing the vehicle control method provided in this embodiment.

[0231] Among them, the processor 601 is used to control the overall operation of the electronic device 600 to complete all or part of the steps in the above vehicle control method. The memory 602 is used to store various types of data to support the operation of the electronic device 600. These data may include, for example, instructions for any application or method operating on the electronic device 600, as well as application-related data, such as contact data, received and sent messages, pictures, audio, video, and so on. The memory 602 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Read Only Memory (PROM), Read Only Memory (ROM), magnetic memory, flash memory, a magnetic disk, or an optical disc. The multimedia component 603 may include a screen and an audio component. The screen can be, for example, a touch screen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signal can be further stored in the memory 602 or sent through the communication component 605. The audio component also includes at least one speaker for outputting audio signals. The I / O component 604 provides an interface between the processor 601 and other interface modules, and the above other interface modules can be a keyboard, a mouse, buttons, etc. These buttons can be virtual buttons or physical buttons. The communication component 605 is used for wired or wireless communication between the electronic device 600 and other devices. Wireless communication, such as Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, 4G, Narrow Band Internet of Things (NB-IoT), Enhanced Machine Type Communication (eMTC), or other 5G, etc., or a combination of one or more of them, is not limited here. Therefore, the corresponding communication component 605 may include: a Wi-Fi module, a Bluetooth module, an NFC module, and so on.

[0232] In an exemplary embodiment, the electronic device 600 may be implemented by one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components, and is used to execute the vehicle control method described above.

[0233] In another exemplary embodiment, a computer-readable storage medium is further provided, on which a computer program is stored. When the program instructions are executed by a processor, the steps of the vehicle control method described above are implemented. For example, the computer-readable storage medium may be the memory 602 including the program instructions described above, and the program instructions may be executed by the processor 601 of the electronic device 600 to implement or execute the various methods, steps, and logic block diagrams disclosed in the embodiments of the present application;

[0234] Or, when the instructions are executed by a computer, the various methods, steps, and logic block diagrams disclosed in the embodiments of the present application are implemented or executed.

[0235] In another exemplary embodiment, a computer program product is further provided, including a computer program or instructions. When the computer program or instructions are executed by a processor, the steps of the vehicle control method described above are implemented. For example, the computer program product may be the memory 602 including the computer program described above, and the computer program may be executed by the processor 601 of the electronic device 600 to implement or execute the various methods, steps, and logic block diagrams disclosed in the embodiments of the present application;

[0236] Or, when the instructions are executed by a computer, the various methods, steps, and logic block diagrams disclosed in the embodiments of the present application are implemented or executed.

[0237] Figure 7 is an exemplary schematic diagram of a vehicle according to some embodiments of the present specification.

[0238] As Figure 7 shown, the present application further provides a vehicle, on which the electronic device provided in any of the above embodiments is provided, and the electronic device is used to execute the vehicle control method provided in any of the above embodiments. Among them, the vehicle may be a fuel vehicle, a plug-in hybrid vehicle, a new energy vehicle, etc., and the present specification does not make specific limitations on this.

[0239] In one embodiment, a vehicle may be configured to operate in a fully or partially autonomous driving mode. For example, while in the autonomous driving mode, the vehicle may control itself, and may determine the current state of the vehicle and its surrounding environment through manual operation, determine the possible behavior of at least one other vehicle in the surrounding environment, and determine the confidence level corresponding to the likelihood of the other vehicle performing the possible behavior, and control the vehicle based on the determined information. When the vehicle is in the autonomous driving mode, the vehicle may be set to operate without human interaction.

[0240] In the description of the present application, the terms "first" and "second" are only used for descriptive purposes and cannot be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more features. In the description of the present application, "a plurality of" means two or more, unless otherwise specifically defined.

[0241] The embodiments, implementation manners and related technical features of the present application may be combined and replaced with each other without conflict.

[0242] The above are only the preferred embodiments of the present application and do not impose any form of limitation on the present application. Although in the embodiments of the present application, the descriptions of the various embodiments have their own emphases, for the parts not detailed in a certain embodiment, reference may be made to the relevant embodiments of other embodiments. However, any simple modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present application without departing from the content of the technical solution of the present application still fall within the scope of the technical solution of the present application.

Claims

1. A vehicle control method, characterized in that: The method comprises: In response to a control instruction of the vehicle, determining a control parameter in the intelligent driving system based on the control instruction; Intelligent driving control is performed on the vehicle based on the control parameters.

2. The method according to claim 1, characterized in that The control command includes at least one of a steering control command, an acceleration control command, and a braking control command.

3. The method according to claim 2, characterized in that The step of responding to a control instruction of the vehicle and determining a control parameter in the intelligent driving system based on the control instruction comprises: In response to the steering control command, at least one of a target motor torque and a target steering angle of the vehicle is used as the control parameter based on the steering control command.

4. The method according to claim 3, characterized in that The target turning angle includes a steering angle of the rear wheels, and / or the target motor torque includes at least one of a front axle motor torque, a left rear wheel motor torque, and a right rear wheel motor torque.

5. The method according to claim 2, characterized in that: The method further comprises: In response to the acceleration control command, a target motor torque and / or acceleration of the vehicle is used as the control parameter based on the acceleration control command.

6. The method according to claim 2, characterized in that The method further comprises: In response to the brake control command, a target brake torque and / or speed of the vehicle is used as the control parameter based on the brake control command.

7. The method according to claim 1, characterized in that The intelligent driving control of the vehicle based on the control parameters includes: Determining a control amount of the control parameter based on input data corresponding to the control instruction and a vehicle prediction model; Intelligent driving control is performed on the vehicle based on the control amount of the control parameter.

8. The method according to claim 7, characterized in that The step of determining the control amount of the control parameter based on the input data corresponding to the control instruction and the vehicle prediction model comprises: Determining prediction data of the vehicle based on input data corresponding to the control instruction and the vehicle prediction model; The control amount is obtained based on the deviation between the prediction data and the vehicle state data.

9. The method according to claim 8, characterized in that The determining the prediction data of the vehicle based on the input data corresponding to the control instruction and the vehicle prediction model includes: Using the control parameters and the state parameters of the vehicle as the variable parameters in the vehicle dynamics model to obtain the vehicle prediction model; The input data corresponding to the control instruction is processed based on the vehicle prediction model to obtain the prediction data.

10. The method according to claim 8, characterized in that The step of obtaining the control amount based on the deviation between the prediction data and the vehicle state data comprises: Establishing an objective function based on the deviation between the predicted data and the vehicle state data; The control amount is determined by minimizing the objective function.

11. The method according to claim 10, characterized in that The establishing of an objective function based on the deviation between the prediction data and the vehicle state data comprises: Based on the first weight and the second weight, the first error term and the second error term are weighted to obtain the objective function, wherein, The first error term indicates a deviation of the vehicle state data from the predicted data; The second error term indicates a degree of change of the control parameter.

12. The method according to claim 10, characterized in that The step of determining the control amount by minimizing the objective function includes: The control amount is determined by minimizing the objective function and taking the value of the control parameter that satisfies the constraint condition.

13. The method according to claim 12, characterized in that The constraint condition includes a constraint condition on at least one of the control parameter, the state parameter, and the change parameter.

14. The method according to claim 13, characterized in that The constraint condition of the control parameter includes a constraint condition on at least one of a control amount and a control increment corresponding to the control parameter.

15. The method according to claim 8, characterized in that The step of obtaining the control amount based on the deviation between the prediction data and the vehicle state data comprises: determining a control increment of the control parameter based on the deviation; Based on the control increment and the preset control amount, the control amount of the control parameter is determined.

16. The method according to claim 8, characterized in that The predicted data includes a predicted yaw rate, The predicted yaw rate is determined by: The predicted yaw rate is obtained based on the front wheel steering angle of the vehicle in the input data and a preset conversion model in the vehicle prediction model.

17. The method according to claim 16, characterized in that The preset conversion model is used to indicate the relationship between the ratio of the yaw rate to the front wheel turning angle of the vehicle and the driving speed.

18. The method according to any one of claims 8 to 17, characterized in that: The vehicle state data includes at least one of a longitudinal speed, a lateral speed, a heading angle, and a yaw rate of the vehicle.

19. An electronic device, characterized in that: include: a memory having a computer program stored thereon; A processor, configured to execute the computer program in the memory to implement the steps of the method according to any one of claims 1 to 18.

20. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 18 are implemented.

21. A computer program product, characterized in that The method comprises a computer program or instructions, which implement the steps of the method according to any one of claims 1 to 18 when executed by a processor.

22. A vehicle, characterized in that: The electronic device comprises the electronic device as claimed in claim 19, or performs the steps of the method as claimed in any one of claims 1 to 18.

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