WO2022105361A1 - 车辆控制方法、装置、计算机可读存储介质及电子设备 - Google Patents

车辆控制方法、装置、计算机可读存储介质及电子设备 Download PDF

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WO2022105361A1
WO2022105361A1 PCT/CN2021/115788 CN2021115788W WO2022105361A1 WO 2022105361 A1 WO2022105361 A1 WO 2022105361A1 CN 2021115788 W CN2021115788 W CN 2021115788W WO 2022105361 A1 WO2022105361 A1 WO 2022105361A1
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vehicle
vehicle control
control
control method
road friction
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PCT/CN2021/115788
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English (en)
French (fr)
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边学鹏
张亮亮
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北京京东乾石科技有限公司
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Priority to EP21893512.0A priority Critical patent/EP4166410A1/en
Priority to US18/003,352 priority patent/US20230303085A1/en
Priority to JP2023509829A priority patent/JP2023537990A/ja
Publication of WO2022105361A1 publication Critical patent/WO2022105361A1/zh

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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
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/02—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to ambient conditions
    • B60W40/06—Road conditions
    • B60W40/068—Road friction coefficient
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B60W50/0097—Predicting future conditions
    • 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
    • B60W60/00—Drive control systems specially adapted for autonomous road vehicles
    • B60W60/001—Planning or execution of driving tasks
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B60W2050/0001—Details of the control system
    • B60W2050/0002—Automatic control, details of type of controller or control system architecture
    • B60W2050/0004—In digital systems, e.g. discrete-time systems involving sampling
    • B60W2050/0005—Processor details or data handling, e.g. memory registers or chip architecture
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B60W2050/0001—Details of the control system
    • B60W2050/0019—Control system elements or transfer functions
    • B60W2050/0028—Mathematical models, e.g. for simulation
    • 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
    • B60W2510/00—Input parameters relating to a particular sub-units
    • B60W2510/20—Steering systems
    • 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
    • B60W2520/00—Input parameters relating to overall vehicle dynamics
    • B60W2520/06—Direction of travel
    • 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
    • B60W2520/00—Input parameters relating to overall vehicle dynamics
    • B60W2520/10—Longitudinal speed
    • 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
    • B60W2520/00—Input parameters relating to overall vehicle dynamics
    • B60W2520/12—Lateral speed
    • B60W2520/125—Lateral acceleration
    • 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
    • B60W2520/00—Input parameters relating to overall vehicle dynamics
    • B60W2520/26—Wheel slip
    • 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
    • B60W2530/00—Input parameters relating to vehicle conditions or values, not covered by groups B60W2510/00 or B60W2520/00
    • B60W2530/201—Dimensions of vehicle
    • 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
    • B60W2540/00—Input parameters relating to occupants
    • B60W2540/18—Steering angle
    • 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
    • B60W2552/00—Input parameters relating to infrastructure
    • B60W2552/40—Coefficient of friction
    • 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
    • B60W2710/00—Output or target parameters relating to a particular sub-units
    • B60W2710/20—Steering systems
    • B60W2710/207—Steering angle of wheels
    • 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
    • B60W2720/00—Output or target parameters relating to overall vehicle dynamics
    • B60W2720/10—Longitudinal speed
    • 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
    • B60W2720/00—Output or target parameters relating to overall vehicle dynamics
    • B60W2720/10—Longitudinal speed
    • B60W2720/106—Longitudinal acceleration
    • 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
    • B60W2720/00—Output or target parameters relating to overall vehicle dynamics
    • B60W2720/40—Torque distribution
    • B60W2720/403—Torque distribution between front and rear axle
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/10—Internal combustion engine [ICE] based vehicles
    • Y02T10/40—Engine management systems

Definitions

  • the present disclosure relates to the field of vehicle control, and in particular, to a vehicle control method, a vehicle control device, a computer-readable storage medium, and an electronic device.
  • the control of unmanned vehicles is mainly divided into upper and lower layers of controllers.
  • the main function of the upper layer controller is to process the reference trajectory and generate the percentage of throttle and brake according to the reference trajectory and vehicle status and positioning information.
  • the main function of the lower layer controller is to execute the upper layer control.
  • the accelerator and brake commands and front wheel angle commands output by the device drive the vehicle.
  • the calibration table method is usually used.
  • the system obtains vehicle status information in real time, analyzes from multiple dimensions, and then updates the calibration table online, so as to realize the adaptive output of the brake accelerator.
  • the disadvantage of this method is that it needs to collect data offline in advance to generate a calibration table.
  • noise will inevitably be substituted into it, resulting in the model getting worse and worse, thus increasing the size of the model. Risk of uncontrollable vehicle.
  • the purpose of the present disclosure is to provide a vehicle control method, a vehicle control device, a computer-readable storage medium, and an electronic device, aiming at improving the adaptive control accuracy of the vehicle switching on different road surfaces.
  • a vehicle control method comprising: acquiring a slip rate of a current control period of a vehicle; invoking a corresponding calculation strategy according to the slip rate to calculate a road in the current control period of the vehicle friction coefficient; inputting the road friction coefficient into the vehicle control optimization model to obtain a control instruction of the current control cycle of the vehicle, so as to control the vehicle in real time.
  • a vehicle control device comprising: a slip rate module for acquiring the slip rate of the current control cycle of the vehicle; and a friction coefficient module for obtaining the slip rate according to the slip rate Invoke the corresponding calculation strategy to calculate the road friction coefficient of the current control cycle of the vehicle; the control command module is used to input the road friction coefficient into the vehicle control optimization model to obtain the control command of the current control cycle of the vehicle, so as to control the vehicle for real-time control.
  • a computer-readable storage medium on which a computer program is stored, and when the program is executed by a processor, implements the vehicle control method in the above-mentioned embodiment.
  • an electronic device comprising: one or more processors; and a storage device for storing one or more programs, when the one or more programs are When executed by one or more processors, the one or more processors implement the vehicle control method in the above-mentioned embodiment.
  • FIG. 1 schematically shows a flow chart of a vehicle control method in an exemplary embodiment of the present disclosure
  • FIG. 2 schematically shows a flow chart of a method for calculating a road friction coefficient in an exemplary embodiment of the present disclosure
  • FIG. 3 schematically shows a flow chart of a method for generating a vehicle control instruction in an exemplary embodiment of the present disclosure
  • FIG. 4 schematically shows the composition diagram of a vehicle control device in an exemplary embodiment of the present disclosure
  • FIG. 5 schematically shows a schematic diagram of a computer-readable storage medium in an exemplary embodiment of the present disclosure
  • FIG. 6 schematically shows a schematic structural diagram of a computer system of an electronic device in an exemplary embodiment of the present disclosure.
  • Example embodiments will now be described more fully with reference to the accompanying drawings.
  • Example embodiments can be embodied in various forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art.
  • the trajectory tracking controller of the unmanned vehicle plays an important role in the automatic driving software system, which can drive the vehicle to drive on the planned trajectory.
  • the controller can be divided into two layers of controllers: the upper controller is mainly used to process the reference trajectory, and According to the reference trajectory and vehicle status and positioning information, the accelerator and brake percentage is generated.
  • the lower controller includes the wire-controlled system and the vehicle chassis controller. The main function is to execute the accelerator brake command and the front wheel angle command output by the upper controller to drive the vehicle.
  • the technology of the lower-level controller has been very mature, and most of the current mainstream research focuses on the upper-level controller.
  • unmanned vehicles in the field of logistics and distribution has good prospects, such as intelligent unmanned delivery vehicles.
  • the driving conditions of unmanned delivery vehicles are more complicated than that of passenger vehicles.
  • the driving roads are generally sidewalks. Driving on water, snow and ice is a common phenomenon, and the driving speed of the former is also higher than that of the latter. Therefore, how to realize the good driving of unmanned delivery vehicles on these roads with relatively low friction coefficient is a relatively important topic.
  • unmanned vehicles are similar to wheeled robots, their driving conditions are completely different. The former mostly drives outdoors, while the latter mostly drives indoors. The speed of the former is also higher than that of the latter.
  • the calibration table method is usually used.
  • the system obtains vehicle status information in real time, analyzes from multiple dimensions, and then analyzes the state of the vehicle.
  • the calibration table is updated online, so as to realize the adaptive output of the brake throttle.
  • the disadvantage of this method is that it needs to collect data offline in advance to generate a calibration table.
  • noise will inevitably be substituted into it, resulting in the model getting worse and worse, thus increasing the size of the model. Risk of uncontrollable vehicle.
  • the present disclosure provides a vehicle control method, which adopts different calculation strategies to calculate the corresponding longitudinal force by considering the influence of the longitudinal force of the front and rear wheels on the friction coefficient under the condition of different slip rates, whereby, the road friction coefficient with higher precision can be obtained for the adaptive control of the vehicle running on the road surface with different friction coefficient.
  • FIG. 1 schematically shows a flow chart of a vehicle control method in an exemplary embodiment of the present disclosure.
  • the vehicle control method includes steps S1 to S3:
  • a corresponding calculation strategy is invoked according to the slip ratio of the current control period of the vehicle to calculate the road friction coefficient of the current control period of the vehicle, and then the road friction coefficient is input into the vehicle
  • the control optimization model obtains the control instructions of the current control cycle of the vehicle to control the vehicle in real time. It can realize the use of different calculation strategies under different slip rates to obtain the corresponding road friction coefficient, improve the parameter identification accuracy, and then use the high-precision road friction coefficient to build a vehicle control optimization model to predict and obtain vehicle control commands. Improve the adaptive control accuracy of vehicle driving road switching.
  • step S1 the slip ratio of the current control cycle of the vehicle is obtained.
  • control instructions of the vehicle need to be updated within a set control period, so the running state of the vehicle needs to be monitored in real time, and the slip rate of the current control period of the vehicle needs to be obtained.
  • the control period is a set value, which can be set according to requirements, such as 20ms and 50ms. Reasonable settings can also be made according to the road conditions of the vehicle or the external environment. For example, in the same weather environment, different periods can be set for the vehicle to drive on the asphalt road and the cement road, or the vehicle can be set to drive in the rainy and snowy weather in winter.
  • the control period is shorter than the running control period when it is sunny in summer.
  • v represents the longitudinal speed of the vehicle, which can be obtained by the speed sensor
  • v fw represents the wheel speed of the front wheel
  • v rw represents the wheel speed of the rear wheel, which can be obtained by the wheel speed sensor respectively. Therefore, when accelerating, the calculated slip value ranges from [0,1], and when braking and decelerating, the slip value ranges from [-1,0].
  • the average value of the slip ratios of the front and rear wheels may be taken as the slip ratio of the entire vehicle.
  • the slip rate is not only obtained from the front or rear wheels, but is comprehensively considered to make the identified vehicle slip rate closer to the actual operating conditions of the vehicle.
  • step S2 a corresponding calculation strategy is invoked according to the slip ratio to calculate the road friction coefficient of the current control period of the vehicle.
  • FIG. 2 schematically shows a schematic flowchart of calculating a road friction coefficient in an exemplary embodiment of the present disclosure, including:
  • step S201 the identification of the road friction coefficient is performed considering two situations of low slip ratio and high slip ratio, so first, the slip ratio of the vehicle is divided into corresponding sections.
  • the slip rate ⁇ x ⁇ [-1, 1] set an interval parameter value ⁇ ' x , where ⁇ ' x ⁇ [0, 1], set [- ⁇ ' x , ⁇ ' x ] as Set as the first interval, corresponding to low slip rate, and set [-1, - ⁇ ' x ] ⁇ [ ⁇ ' x , 1] as the second interval, corresponding to high slip rate.
  • the interval parameter value ⁇ ' x can be given based on empirical values, such as 0.3, 0.5, etc., or an optimal value can be given after verification of the simulation model, such as building a Simulink simulation, or a Carsim simulation experiment.
  • step 202 if the calculated vehicle slip is in the first interval, that is, in the case of a low slip rate, the corresponding first calculation strategy is invoked.
  • the first calculation strategy includes: obtaining the influence ratio of front and rear wheel friction; calculating the vehicle longitudinal force based on the front and rear wheel friction influence ratio and the coefficient of the slip slope and road friction; The road friction coefficient.
  • the front wheel and the rear wheel are separately considered when calculating the longitudinal force of the entire vehicle, so a front and rear wheel friction influence proportional parameter ⁇ is introduced.
  • unmanned delivery vehicles are generally driven by rear wheels, and the front wheels do not provide driving force when accelerating, that is, ⁇ is 0; when braking, the value of ⁇ is determined by the chassis configuration, and generally takes 0.1 ⁇ 0.3.
  • the vehicle longitudinal force is calculated based on the front and rear wheel friction influence ratio and the coefficient of slip slope and road friction, and according to the relationship between the vehicle longitudinal force and the wheel slip rate:
  • F x1 represents the longitudinal force of the vehicle with the slip rate in the first interval
  • k represents the relationship between the slip slope of the rear wheel of the vehicle and the road friction coefficient
  • ⁇ represents the road friction coefficient
  • ⁇ represents the influence ratio of front and rear wheel friction
  • F zf , F zr represent the normal force of the front wheel and the rear wheel respectively, which can be obtained by the following methods:
  • m represents the vehicle mass
  • L r and L f represent the distance from the center of mass of the vehicle to the front and rear axles, respectively
  • L represents the wheelbase of the vehicle
  • a x represents the longitudinal acceleration of the vehicle
  • h represents the height of the center of mass of the vehicle
  • D represents the Air resistance constant
  • ha represents the height of the front face of the vehicle.
  • the friction effect ratio of the front and rear wheels is introduced when calculating the longitudinal force of the whole vehicle, and the slip of the front and rear wheels is calculated.
  • the calculated longitudinal force of the whole vehicle can be more in line with the actual operation of the vehicle, the accuracy of the longitudinal force value is higher, and the applicability to different unmanned vehicles is also higher.
  • step 202 if the calculated vehicle slip is in the second interval, that is, in the case of a low slip rate, a corresponding second calculation strategy is invoked.
  • the second calculation strategy includes: calculating the normalized vehicle longitudinal force of the front and rear wheels; and calculating the road friction coefficient based on the vehicle longitudinal force.
  • F x2 represents the longitudinal force of the vehicle with the slip rate in the second interval
  • F z represents the normalized normal force of the front and rear wheels
  • ⁇ represents the road friction coefficient
  • F z can be obtained by:
  • a magic formula tire model can also be used to calculate the longitudinal force of the entire vehicle, that is:
  • F x2 represents the longitudinal force of the vehicle with the slip rate in the second interval
  • F zf and F zr represent the normal forces of the front and rear wheels, respectively
  • ⁇ xf and ⁇ xr represent the slippage of the front and rear wheels of the vehicle, respectively.
  • the displacement rate, B and C represent the tire model parameters, respectively, with values of 14 and 1.3.
  • the calculation of the road friction coefficient based on the longitudinal force of the entire vehicle adopts the forgetting factor least squares method, and the specific steps include: determining the input value of the coefficient according to the called calculation strategy; and obtaining the current value of the vehicle.
  • the parameters of the control period are controlled, and the output value is calculated; according to the input value and the output value, the forgetting factor least square method is used to calculate the road friction parameter to be estimated.
  • y(t) represents the vehicle longitudinal force Fx; Indicates the input value, at low slip rate and high slip rate, its values are k( ⁇ F zf ⁇ xf +F zr ⁇ xr ) and F z respectively, if the magic formula tire model is used, the input value is F zf sin [Carctan(B ⁇ xf )]+F zr sin[Carctan(B ⁇ xf )]; ⁇ (t) represents the road friction parameter ⁇ to be estimated; e(t) represents the error between the output value and the estimated value.
  • the forgetting factor least squares method is used for parameter identification.
  • the steps are as follows:
  • ⁇ k-1 is the parameter identification result at time k-1, that is, the road friction parameter of the previous control cycle, which is a known quantity.
  • P is the intermediate variable matrix. Before the algorithm starts, it is necessary to assign an initial value to the P matrix.
  • the calculation method of the P matrix at time k is as follows:
  • I represents the moment of inertia of the vehicle around the z-axis
  • ⁇ represents the forgetting factor, the larger the value, the slower the convergence speed, so the parameter update will be slower, and the parameter update delay will be larger when the vehicle switches between roads with different friction coefficients
  • the ⁇ value is 0.95.
  • ⁇ k is the road friction parameter ⁇ to be identified at the current k moment.
  • a corresponding calculation strategy is designed to obtain road friction parameters, thereby further improving the accuracy of road friction parameters.
  • a verification step may be added to correct the calculation of the parameters, for example, the road friction parameters estimated initially are used as the observed values, and k, ⁇ , ⁇ , etc. correspond to The parameters are used as the extended state to establish the state equation and the observation equation.
  • the extended Kalman filter algorithm is used to establish the standard filtering recursion process, and the signal noise is filtered to calculate the final road friction parameters, which makes the calculation of road friction parameters more accurate.
  • the slip rate of the vehicle is divided into two cases of low slip rate and high slip rate and parameter identification is carried out respectively, which can make the identification
  • the road friction coefficient is more in line with the actual situation of the vehicle.
  • step S3 the road friction coefficient is input into the vehicle control optimization model to obtain a control instruction of the current control cycle of the vehicle, so as to control the vehicle in real time.
  • step S3 further includes step S300, pre-constructing the vehicle control optimization model, including: configuring decision variables of the vehicle control optimization model, including state variables and control variables; establishing a loss function as an objective function, and setting constraints to construct a vehicle control optimization model, wherein the constraints are related to the road friction coefficient.
  • the decision variables of the vehicle control optimization model are determined as Among them, the decision variables include six state variables, which are the vehicle position coordinates x and y in the global coordinate system, the heading angle ⁇ , the longitudinal linear velocity v, the tire slip angle ⁇ and the heading angular acceleration And two control variables, respectively, the vehicle longitudinal force F, the front wheel angle ⁇ .
  • the vehicle control optimization model is established, that is, the optimal control solution is obtained through rolling optimization, and based on constraints, one or some performance indicators can be optimized to achieve the control effect.
  • the general form of the objective function can be expressed as a quadratic function of the state and control input.
  • the control variable it is also added to the objective function, which is expressed as follows:
  • J is the loss function
  • N p , N c are the prediction time domain and the control time domain, respectively
  • w l , ws , w ⁇ , w v , w ⁇ , w F , w ⁇ represent the weight of each optimization objective
  • e l , es , e ⁇ , e v , e ⁇ , e F and e ⁇ represent the error values of each optimization objective, respectively.
  • constraints need to be set, including: dynamic model constraints, starting point constraints, front wheel rotation angle and front wheel rotation angle increment constraints, longitudinal force and longitudinal force increment constraints, speed and Velocity Increment Constraint. The details are as follows:
  • a predicted dynamic model equation of the vehicle's reference trajectory point state variables is configured as the dynamic model constraint.
  • X is the state variable
  • choose U is the control variable
  • A, B, and C are the matrix parameters of the dynamic model.
  • x k+1 x k +v k Tcos( ⁇ + ⁇ )-vTsin( ⁇ + ⁇ ) ⁇ ( ⁇ k + ⁇ k )+vTsin( ⁇ + ⁇ ) ⁇ ( ⁇ + ⁇ ) (20)
  • y k+1 y k +v k Tsin( ⁇ + ⁇ )+vTcos( ⁇ + ⁇ ) ⁇ ( ⁇ k + ⁇ k )-vTcos( ⁇ + ⁇ ) ⁇ ( ⁇ + ⁇ ) (21)
  • the subscript k represents the value of the parameter at the current k time
  • k+1 represents the value at the predicted time k+1
  • the parameter without the subscript represents the parameter value at the reference trajectory point
  • T represents the control period
  • C f Represents the cornering stiffness of the vehicle tire
  • a and b represent the distance from the front and rear axles to the center of mass of the vehicle
  • m represents the mass of the vehicle
  • I represents the moment of inertia of the vehicle around the z-axis
  • F drag represents the air resistance
  • F fric represents the road frictional resistance.
  • the air resistance F drag is calculated as follows:
  • D is the air resistance constant
  • the road friction resistance F fric is calculated as follows:
  • ⁇ represents the road friction coefficient, that is, the road friction coefficient obtained based on the above method.
  • an initial value of a state variable at a starting point of the vehicle is configured as the starting point constraint.
  • the starting point constraint means that the vehicle state prediction needs to start from the current state, and the constraints are as follows:
  • x(0), y(0), ⁇ (0), v(0), ⁇ (0) and Respectively represent the initial values of vehicle starting position coordinates, heading angle, speed, slip angle, and heading angular acceleration, x(vehicle), y(vehicle), ⁇ (vehicle), v(vehicle), ⁇ (vehicle) and Respectively represent the vehicle's current position coordinates, heading angle, speed, slip angle, and heading angular acceleration.
  • a front wheel turning angle constraint value range determined based on the maximum lateral acceleration value of the vehicle, the vehicle wheelbase, the longitudinal linear velocity, and the maximum and minimum front wheel turning angles is configured as the front wheel turning angle constraint condition ; configure the front wheel turning angle increment constraint value range determined based on the maximum and minimum front wheel turning angle increments of the vehicle as the front wheel turning angle increment constraint condition.
  • the present disclosure restricts the turning angle. Its constraint range changes with the speed of the vehicle:
  • ⁇ k represents the rotation angle at time k
  • ⁇ min represents the final minimum rotation angle constraint value
  • ⁇ max represents the final maximum corner constraint value
  • ⁇ (max) and ⁇ (min) respectively represent the maximum and minimum turning angles that the vehicle can actually support.
  • ⁇ k-1 represents the rotation angle at time k-1
  • ⁇ min and ⁇ max represent the minimum and maximum rotation angle increment constraints, respectively.
  • a range of longitudinal force constraint values determined based on the maximum and minimum longitudinal forces of the vehicle is configured as the longitudinal force constraint condition;
  • the longitudinal force increment constraint value range is used as the longitudinal force increment constraint condition.
  • the longitudinal force constraint refers to the longitudinal force value that the vehicle can actually support, and the constraint conditions are set as follows:
  • F k represents the vehicle longitudinal force at time k
  • F min and F max represent the minimum and maximum longitudinal force constraint values, respectively.
  • F k-1 represents the longitudinal force of the vehicle at time k-1
  • ⁇ F min and ⁇ F max represent the minimum and maximum longitudinal force increment constraints, respectively.
  • a speed constraint value range determined based on the maximum and minimum longitudinal linear speeds of the vehicle is configured as the speed constraint;
  • the speed constraint increment value range is used as the speed increment constraint condition.
  • the constraint setting is based on the maximum speed supported by the vehicle and the maximum speed supported by the automatic driving system.
  • the constraints are as follows:
  • v k represents the longitudinal linear velocity at time k
  • v min and v max represent the minimum and maximum longitudinal linear velocities, respectively.
  • the velocity increment constraint is obtained from the acceleration, and the velocity increment constraint is set as follows:
  • v k-1 represents the longitudinal linear velocity at time k-1
  • T represents the control period
  • a min and a max represent the maximum deceleration and maximum acceleration supported by the vehicle, respectively.
  • the sequence of steps for establishing the objective function and setting constraints is not limited, and the constraints can also be set first and then the objective function is established.
  • the rolling optimization model design is carried out based on this model, so as to improve the accuracy of model prediction, realize the adaptive control effect when the vehicle is switched from ordinary roads to ice and snow, and improve the vehicle when driving at high speed. control effect.
  • FIG. 3 schematically shows a schematic diagram of generating a vehicle control command in an exemplary embodiment of the present disclosure, including:
  • step S301 the state variable parameter values of the current control cycle are obtained and input into the vehicle control optimization model.
  • the state variables of the current control cycle include vehicle position coordinates x, y, heading angle ⁇ , longitudinal linear velocity v, tire slip angle ⁇ and heading angular acceleration
  • the vehicle position coordinates x and y can be obtained through the GPS of the vehicle
  • the heading angle ⁇ and the heading angular acceleration It can be calculated from the lightning point cloud image of the vehicle
  • the longitudinal linear velocity v can be obtained by the wheel speedometer
  • the tire slip angle ⁇ can be determined by the parameter value of the previous control cycle.
  • Step S302 updating the model based on the road friction coefficient.
  • the obtained road friction coefficient ⁇ is brought into F fric to calculate the road friction resistance, and then the update objective function and constraints in the model are optimized.
  • Step S303 based on the objective function and constraint conditions in the vehicle control optimization model to obtain control variable parameter values.
  • the input of the vehicle control optimization model is the state variable
  • the output is the control variable.
  • the obtained state variable parameter values of the current control cycle are input into the updated optimization model, and the output control variables include the vehicle longitudinal force F and the front wheel angle ⁇ .
  • Step S304 generating a control instruction based on the control variable parameter value.
  • the vehicle longitudinal force F output by the optimized model is converted into torque and input to the wire control system, and the front wheel angle ⁇ is also generated to generate the front wheel angle command to control the vehicle.
  • the vehicle control optimization model By configuring the longitudinal force of the whole vehicle as the output variable, the vehicle control optimization model directly outputs the torque command, thus eliminating the calibration step of converting the acceleration command into the torque in the traditional method, simplifying the system and reducing the delay of the system, enhancing the Real-time control effect.
  • a control command verification link may be added, and after the command verification is passed, the controller is input to control the vehicle.
  • the command operation simulation is performed in advance in the virtual environment. If the simulation passes, the control command is used to carry out If the simulation shows that the vehicle runs abnormally after the running command, the control command is not used to perform emergency braking.
  • control command verification can be selectively turned on according to the driving situation. For example, when the vehicle is driving on a slippery road in rainy and snowy weather, or there is an uneven road surface or a sloping road in the driving route, or when the output control command data is abnormal, select the start command verification link. Verification of the calculated control instructions can further ensure the correctness of the vehicle adaptive instructions and avoid vehicle control errors caused by calculation errors.
  • an obstacle detection device may also be set at the front end or other parts of the unmanned vehicle.
  • the obstacle braking instruction is triggered , the braking command has the highest priority and can cause the vehicle to brake urgently.
  • the vehicle collision or damage caused by the sudden appearance of animals, pedestrians and objects can be avoided, and unnecessary losses can be reduced.
  • FIG. 4 schematically shows a composition diagram of a vehicle control apparatus in an exemplary embodiment of the present disclosure.
  • the vehicle control apparatus may include a slip rate module 401 , a friction coefficient module 402 and a control command module 403 . in:
  • the slip rate module 401 is used to obtain the slip rate of the current control cycle of the vehicle;
  • a friction coefficient module 402 configured to invoke a corresponding calculation strategy according to the slip rate, to calculate the road friction coefficient of the current control cycle of the vehicle;
  • the control instruction module 403 is configured to input the road friction coefficient into the vehicle control optimization model to obtain the control instruction of the current control cycle of the vehicle, so as to control the vehicle in real time.
  • the friction coefficient module 402 includes: a slip rate interval unit and a calculation strategy unit (not shown in the figure), and the slip rate interval unit is used to determine the position in which the slip rate is located. Slip rate interval; wherein, each described slip rate area is correspondingly configured with its own calculation strategy; the calculation strategy unit is used to call the corresponding first calculation strategy if the slip rate is in the first interval; or if the slip rate is in the first interval When the rate is in the second interval, the corresponding second calculation strategy is invoked.
  • the calculation strategy unit includes a first calculation strategy unit, configured to obtain a front and rear wheel friction influence ratio; calculate the entire vehicle based on the front and rear wheel friction influence ratio and a coefficient of slip slope and road friction longitudinal force; the road friction coefficient is calculated based on the longitudinal force of the entire vehicle.
  • the calculation strategy unit further includes a second calculation strategy unit for calculating the vehicle longitudinal force normalized by the front and rear wheels; and calculating the road friction coefficient based on the vehicle longitudinal force.
  • the calculation of the road friction coefficient based on the longitudinal force of the entire vehicle is calculated by a forgetting factor least square method.
  • the vehicle control device further includes a building vehicle control optimization model module (not shown in the figure) for configuring decision variables of the vehicle control optimization model, including state variables and control variables; establishing loss function as an objective function, and set constraints to build a vehicle control optimization model, wherein the constraints are related to the road friction coefficient.
  • a building vehicle control optimization model module (not shown in the figure) for configuring decision variables of the vehicle control optimization model, including state variables and control variables; establishing loss function as an objective function, and set constraints to build a vehicle control optimization model, wherein the constraints are related to the road friction coefficient.
  • the state variables include vehicle position coordinates, heading angle, longitudinal linear velocity, tire slip angle and heading angular acceleration; and the control variables include vehicle longitudinal force and front wheel rotation angle.
  • the constraints include: dynamic model constraints, starting point constraints, front wheel turning angle and front wheel turning angle increment constraints, longitudinal force and longitudinal force increment constraints, velocity and velocity increment constraints .
  • the control instruction module 403 includes: an input unit, an output unit, and a control instruction unit (not shown in the figure), wherein the input unit is used to obtain the state variable parameter value of the current control cycle and input it to the vehicle a control optimization model; and an update model based on the road friction coefficient; an output unit is used for obtaining control variable parameter values based on the objective function and constraint conditions in the vehicle control optimization model; a control instruction unit is used for obtaining control variable parameter values based on the control variable parameter The value generates the control instruction.
  • modules or units of the apparatus for action performance are mentioned in the above detailed description, this division is not mandatory. Indeed, according to embodiments of the present disclosure, the features and functions of two or more modules or units described above may be embodied in one module or unit. Conversely, the features and functions of one module or unit described above may be further divided into multiple modules or units to be embodied.
  • FIG. 5 schematically shows a schematic diagram of a computer-readable storage medium in an exemplary embodiment of the present disclosure.
  • a program product 500 for implementing the above method according to an embodiment of the present disclosure is described, which can A portable compact disc read only memory (CD-ROM) is used and includes program code and can be run on terminal equipment such as a mobile phone.
  • CD-ROM portable compact disc read only memory
  • the program product of the present disclosure is not limited thereto, and in this document, a readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.
  • FIG. 6 schematically shows a schematic structural diagram of a computer system of an electronic device in an exemplary embodiment of the present disclosure.
  • the computer system 600 includes a central processing unit (Central Processing Unit, CPU) 601, which can be loaded into a random device according to a program stored in a read-only memory (Read-Only Memory, ROM) 602 or from a storage part 608 Various appropriate actions and processes are performed by accessing programs in a memory (Random Access Memory, RAM) 603 . In the RAM 603, various programs and data required for system operation are also stored.
  • the CPU 601, the ROM 602, and the RAM 603 are connected to each other through a bus 604.
  • An Input/Output (I/O) interface 605 is also connected to the bus 604 .
  • I/O Input/Output
  • the following components are connected to the I/O interface 605: an input section 606 including a keyboard, a mouse, etc.; an output section 607 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc. ; a storage part 608 including a hard disk and the like; and a communication part 609 including a network interface card such as a LAN (Local Area Network) card, a modem, and the like.
  • the communication section 609 performs communication processing via a network such as the Internet.
  • a drive 610 is also connected to the I/O interface 605 as needed.
  • a removable medium 611 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is mounted on the drive 610 as needed so that a computer program read therefrom is installed into the storage section 608 as needed.
  • embodiments of the present disclosure include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the method illustrated in the flowchart.
  • the computer program may be downloaded and installed from the network via the communication portion 609 and/or installed from the removable medium 611 .
  • CPU central processing unit
  • various functions defined in the system of the present disclosure are executed.
  • the computer-readable medium shown in the embodiments of the present disclosure may be a computer-readable signal medium or a computer-readable storage medium, or any combination of the above two.
  • the computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus or device, or a combination of any of the above.
  • Computer readable storage media may include, but are not limited to, electrical connections with one or more wires, portable computer disks, hard disks, random access memory (RAM), read only memory (ROM), erasable Erasable Programmable Read Only Memory (EPROM), flash memory, optical fiber, portable Compact Disc Read-Only Memory (CD-ROM), optical storage device, magnetic storage device, or any suitable of the above The combination.
  • a computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.
  • a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code therein.
  • Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the foregoing.
  • a computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device .
  • Program code embodied on a computer-readable medium may be transmitted using any suitable medium, including but not limited to wireless, wired, etc., or any suitable combination of the foregoing.
  • each block in the flowchart or block diagrams may represent a module, segment, or portion of code that contains one or more logical functions for implementing the specified functions executable instructions.
  • 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.
  • the units involved in the embodiments of the present disclosure may be implemented in software or hardware, and the described units may also be provided in a processor. Among them, the name of these units does not constitute a limitation of the unit itself under certain circumstances.
  • the present disclosure also provides a computer-readable medium.
  • the computer-readable medium may be included in the electronic device described in the above embodiments; it may also exist alone without being assembled into the electronic device. middle.
  • the above-mentioned computer-readable medium carries one or more programs, and when the above-mentioned one or more programs are executed by an electronic device, enables the electronic device to implement the methods described in the above-mentioned embodiments.
  • modules or units of the apparatus for action performance are mentioned in the above detailed description, this division is not mandatory. Indeed, according to embodiments of the present disclosure, the features and functions of two or more modules or units described above may be embodied in one module or unit. Conversely, the features and functions of one module or unit described above may be further divided into multiple modules or units to be embodied.
  • the exemplary embodiments described herein may be implemented by software, or may be implemented by software combined with necessary hardware. Therefore, the technical solutions according to the embodiments of the present disclosure may be embodied in the form of software products, and the software products may be stored in a non-volatile storage medium (which may be CD-ROM, U disk, mobile hard disk, etc.) or on the network , including several instructions to cause a computing device (which may be a personal computer, a server, a touch terminal, or a network device, etc.) to execute the method according to an embodiment of the present disclosure.
  • a computing device which may be a personal computer, a server, a touch terminal, or a network device, etc.

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Abstract

一种车辆控制方法，包括：步骤（S1）：获取车辆当前控制周期的滑移率；步骤（S2）根据所述滑移率调用对应的计算策略，以计算所述车辆当前控制周期的道路摩擦系数；步骤（S3）：将所述道路摩擦系数输入车辆控制优化模型获取车辆当前控制周期的控制指令，以对所述车辆进行实时控制。该车辆控制方法能够实现在不同滑移率情况下采用不同的计算策略来获取对应的道路摩擦系数，提高参数辨识精度，进而将高精度的道路摩擦系数用于构建车辆控制优化模型进行预测得到车辆控制指令，以提高车辆行驶路面切换的自适应控制精度。还提供了实现上述控制方法的装置、计算机可读存储介质及电子设备。

Description

车辆控制方法、装置、计算机可读存储介质及电子设备
相关申请的交叉引用
本申请要求于2020年11月19日提交的申请号为202011302765.2、名称为“车辆控制方法、装置、计算机可读存储介质及电子设备”的中国专利申请的优先权，该中国专利申请的全部内容通过引用结合在本申请中。
技术领域
本公开涉及车辆控制领域，具体涉及一种车辆控制方法、一种车辆控制装置、一种计算机可读存储介质和一种电子设备。
背景技术
随着自动驾驶技术的发展，无人车应用越来越广泛。目前对无人车的控制主要分为上下两层控制器，上层控制器主要作用是处理参考轨迹、并根据参考轨迹和车辆状态及定位信息生成油门刹车百分比，下层控制器主要作用是执行上层控制器输出的油门刹车指令、前轮转角指令驱动车辆行驶。
在相关技术中，对下层控制器技术较为成熟，而上层控制器方面还有待研究。通常采用标定表方法，当车辆行驶在不同摩擦系数的道路时，系统实时获取车辆状态信息，从多维度分析然后对标定表进行在线更新，从而实现刹车油门的自适应输出。但该方法的弊端是需要事先离线采集数据生成标定表，同时由于车辆实时反馈的数据信噪比较差，在线更新标定表时未免会将噪声代入其中导致模型越来越差，从而增大了车辆不可控的风险。
需要说明的是，在上述背景技术部分公开的信息仅用于加强对本公开的背景的理解，因此可以包括不构成对本领域普通技术人员已知的相关技术的信息。
发明内容
本公开的目的在于提供一种车辆控制方法、一种车辆控制装置、一种计算机可读存储介质和一种电子设备，旨在实现提高车辆在不同路面切换的自适应控制精度。
根据本公开实施例的一个方面，提供了一种车辆控制方法包括：获取车辆当前控制周期的滑移率；根据所述滑移率调用对应的计算策略，以计算所述车辆当前控制周期的道路摩擦系数；将所述道路摩擦系数输入车辆控制优化模型获取车辆当前控制周期的控制指令，以对所述车辆进行实时控制。
根据本公开实施例的第二个方面，提供了一种车辆控制装置，包括：滑移率模块，用于获取车辆当前控制周期的滑移率；摩擦系数模块，用于根据所述滑移率调用对应的计算策略，以计算所述车辆当前控制周期的道路摩擦系数；控制指令模块，用于将所述道路摩擦系数输入车辆控制优化模型获取车辆当前控制周期的控制指令，以对所述车辆进行实时 控制。
根据本公开实施例的第三个方面，提供了一种计算机可读存储介质，其上存储有计算机程序，所述程序被处理器执行时实现如上述实施例中的车辆控制方法。
根据本公开实施例的第四个方面，提供了一种电子设备，包括：一个或多个处理器；存储装置，用于存储一个或多个程序，当所述一个或多个程序被所述一个或多个处理器执行时，使得所述一个或多个处理器实现如上述实施例中的车辆控制方法。
应当理解的是，以上的一般描述和后文的细节描述仅是示例性和解释性的，并不能限制本公开。
附图说明
此处的附图被并入说明书中并构成本说明书的一部分，示出了符合本公开的实施例，并与说明书一起用于解释本公开的原理。显而易见地，下面描述中的附图仅仅是本公开的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他的附图。在附图中：
图1示意性示出本公开示例性实施例中一种车辆控制方法的流程示意图；
图2示意性示出本公开示例性实施例中一种计算道路摩擦系数方法的流程示意图；
图3示意性示出本公开示例性实施例中一种生成车辆控制指令方法的流程示意图；
图4示意性示出本公开示例性实施例中一种车辆控制装置的组成示意图；
图5示意性示出本公开示例性实施例中一种计算机可读存储介质的示意图；
图6示意性示出本公开示例性实施例中一种电子设备的计算机系统的结构示意图。
在附图中，相同或对应的标号表示相同或对应的部分。
具体实施方式
现在将参考附图更全面地描述示例实施方式。然而，示例实施方式能够以多种形式实施，且不应被理解为限于在此阐述的范例；相反，提供这些实施方式使得本公开将更加全面和完整，并将示例实施方式的构思全面地传达给本领域的技术人员。
此外，所描述的特征、结构或特性可以以任何合适的方式结合在一个或更多实施例中。在下面的描述中，提供许多具体细节从而给出对本公开的实施例的充分理解。然而，本领域技术人员将意识到，可以实践本公开的技术方案而没有特定细节中的一个或更多，或者可以采用其它的方法、组元、装置、步骤等。在其它情况下，不详细示出或描述公知方法、装置、实现或者操作以避免模糊本公开的各方面。
附图中所示的方框图仅仅是功能实体，不一定必须与物理上独立的实体相对应。即，可以采用软件形式来实现这些功能实体，或在一个或多个硬件模块或集成电路中实现这些功能实体，或在不同网络和/或处理器装置和/或微控制器装置中实现这些功能实体。
附图中所示的流程图仅是示例性说明，不是必须包括所有的内容和操作/步骤，也 不是必须按所描述的顺序执行。例如，有的操作/步骤还可以分解，而有的操作/步骤可以合并或部分合并，因此实际执行的顺序有可能根据实际情况改变。
无人车轨迹跟踪控制器在自动驾驶软件系统中具有重要作用，可以驱动车辆在规划的轨迹上行驶，目前控制器可以分为上下两层控制器：上层控制器主要作用是处理参考轨迹、并根据参考轨迹和车辆状态及定位信息生成油门刹车百分比，下层控制器包含线控系统和车辆底盘控制器，主要作用是执行上层控制器输出的油门刹车指令、前轮转角指令驱动车辆行驶。下层控制器其技术已经非常成熟，目前主流研究大部分集中于上层控制器方面。
无人车在物流配送领域应用具有良好的前景，例如智能无人配送车，无人配送车行驶路况相较乘用车行驶路况更加复杂，其行驶道路一般为人行道，在夏季和冬季时，在带积水、雪面、冰面上行驶是比较常见的现象，前者行驶速度相比后者也较高。所以如何实现无人配送车在这些摩擦系数比较低的路面上良好行驶是一个比较重要的课题。另外，无人车虽然与轮式机器人较相似，但行驶工况却完全不同，前者多在室外行驶，后者多在室内行驶，前者行驶速度相比后者也较高。
目前无人车应对冰面、雪面的控制方法还较少，相关技术中通常采用标定表方法，当车辆行驶在不同摩擦系数的道路时，系统实时获取车辆状态信息，从多维度分析然后对标定表进行在线更新，从而实现刹车油门的自适应输出。但该方法的弊端是需要事先离线采集数据生成标定表，同时由于车辆实时反馈的数据信噪比较差，在线更新标定表时未免会将噪声代入其中导致模型越来越差，从而增大了车辆不可控的风险。
鉴于相关技术中存在的问题，本公开提供一种车辆控制方法，通过在不同滑移率情况下考虑前、后轮的纵向力对摩擦系数的影响分别采用不同的计算策略计算对应的纵向力，从而得到精度较高的道路摩擦系数以用于车辆在不同摩擦系数路面上行驶的自适应控制。
以下对本公开实施例的技术方案的实现细节进行详细阐述。
图1示意性示出本公开示例性实施例中一种车辆控制方法的流程示意图。如图1所示，该车辆控制方法包括步骤S1至步骤S3：
S1，获取车辆当前控制周期的滑移率；
S2，根据所述滑移率调用对应的计算策略，以计算所述车辆当前控制周期的道路摩擦系数；
S3，将所述道路摩擦系数输入车辆控制优化模型获取车辆当前控制周期的控制指令，以对所述车辆进行实时控制。
在本公开的实施例所提供的技术方案中，根据车辆当前控制周期的滑移率调用对应的计算策略，以计算所述车辆当前控制周期的道路摩擦系数，再将所述道路摩擦系数输入车辆控制优化模型获取车辆当前控制周期的控制指令，以对所述车辆进行实时控制。能够实现在不同滑移率情况下采用不同的计算策略来获取对应的道路摩擦系数，提高参 数辨识精度，进而将高精度的道路摩擦系数用于构建车辆控制优化模型进行预测得到车辆控制指令，以提高车辆行驶路面切换的自适应控制精度。
下面，将结合附图及实施例对本示例实施方式中的车辆控制方法的各个步骤进行更详细的说明。
在步骤S1中，获取车辆当前控制周期的滑移率。
在本公开的一个实施例中，需要在设定的控制周期内更新车辆的控制指令，因此需要实时监控车辆的运行状态，获取车辆当前控制周期的滑移率。控制周期是一个设定值，可以根据需求设定，例如20ms、50ms都可以。还可以根据车辆行驶的路面情况或外界环境进行合理设置，举例来说，在同一天气环境中，车辆行驶在柏油路面和水泥路面可以设置不同的周期，或者可以设置车辆行驶在冬季雨雪天气的控制周期小于夏季晴朗的行驶控制周期。
用σ xf表示车辆的前轮滑移率，σ xr表示车辆的后轮滑移率，那么计算车辆的滑移率首先分别计算车辆的前轮和后轮的滑移率，可分为两种情况：
1)加速运行时：
Figure PCTCN2021115788-appb-000001
Figure PCTCN2021115788-appb-000002
2)制动减速时
Figure PCTCN2021115788-appb-000003
Figure PCTCN2021115788-appb-000004
其中，v表示车辆纵向速度，可以通过速度传感器获取；v fw表示前轮轮速，v rw表示后轮轮速，可以分别由轮速计传感器观测获取。因此，在加速运行时，计算的滑移率值范围为[0,1]，在制动减速时，滑移率值范围为[-1,0]。
计算整车的滑移率时，可以根据前轮和后轮综合得到，例如取前轮的滑移率、后轮的滑移率、前后轮滑移率的均值或是其他取值方法，本公开在此不做具体限定。在本公开的实施例中，可以取前后轮滑移率的均值作为车辆整体的滑移率。
Figure PCTCN2021115788-appb-000005
由于车辆采用的驱动方式可能存在不同，因此在计算滑移率时并不仅仅根据前轮或后轮获得，而是综合考虑，使其识别的车辆滑移率更贴近车辆实际运行情况。
在步骤S2中，根据所述滑移率调用对应的计算策略，以计算所述车辆当前控制周期的道路摩擦系数。
图2示意性示出本公开示例性实施例中一种计算道路摩擦系数的流程示意图，包括：
S201，确定所述滑移率所处的滑移率区间；其中，各所述滑移率区域对应配置有各 自的计算策略；
S202，若滑移率处于第一区间时，则调用对应的第一计算策略；或者若滑移率处于第二区间时，则调用对应的第二计算策略。
在步骤S201中，考虑低滑移率和高滑移率两种情况进行道路摩擦系数的辨识，因此首先将车辆的滑移率分成对应的区间。
由上述方法可知滑移率σ x∈[-1，1]，设定一个区间参数值σ' x，其中σ' x∈[0，1]，将[-σ' x，σ' x]设定为第一区间，对应于低滑移率，将[-1，-σ' x]∪[σ' x，1]设定为第二区间，对应于高滑移率。
其中，区间参数值σ' x可以根据经验值给定，例如0.3、0.5等，也可以通过仿真模型验证后给定一个最优值，例如构建Simulink仿真，或是Carsim仿真实验。
在步骤202中，若计算的车辆滑移处于第一区间，也就是低滑移率的情况，调用对应的第一计算策略。
具体地，所述第一计算策略包括：获取前后轮摩擦影响比例；基于所述前后轮摩擦影响比例以及滑移斜率与道路摩擦的系数计算整车纵向力；基于所述整车纵向力计算所述道路摩擦系数。
在本公开的一个实施例中，在低滑移率时，计算整车纵向力时将前轮和后轮分开考虑，因此引入一个前后轮摩擦影响比例参数ρ。以无人配送车为例，无人配送车一般均为后轮驱动，其加速时前轮不提供驱动力，即ρ为0；在制动时，ρ值由底盘配置所决定，一般取0.1～0.3。
然后，基于所述前后轮摩擦影响比例以及滑移斜率与道路摩擦的系数计算整车纵向力，根据整车纵向力与车轮滑移率的关系：
F x1＝kμ(ρF zfσ xf+F zrσ xr)          (6)
其中，F x1表示滑移率处于第一区间的整车纵向力，k表示车辆后轮的滑移斜率与道路摩擦系数的关系，μ表示道路摩擦系数，ρ表示前后轮摩擦影响比例，F zf、F zr分别表示前轮、后轮的法向力，可以通过以下方法获得：
Figure PCTCN2021115788-appb-000006
Figure PCTCN2021115788-appb-000007
其中，m表示车辆质量，g重力加速度，L r、L f分别表示车辆质心到前轴、后轴的距离，L表示车辆轴距，a x表示车辆纵向加速度，h表示车辆质心高度，D表示空气阻力常数，h a表示车辆前脸高度。
在低滑移率的情况下，考虑到无人车在不同驱动方式下，前后轮可能存在不同的行驶情况，因此在计算整车纵向力时引入前后轮摩擦影响比例，将前后轮的滑移率对整车纵向力的影响区分考虑，可以使得计算得到的整车纵向力更符合车辆的实际运行情况，纵向力值精确度更高，对于不同的无人车适用性也较高。
在步骤202中，若计算的车辆滑移处于第二区间，也就是低滑移率的情况，调用对应的第二计算策略。
具体地，所述第二计算策略包括：计算前后轮归一化的整车纵向力；基于所述整车纵向力计算所述道路摩擦系数。
在高滑移率下，或者是紧急制动情况下，滑移斜率值已经与道路摩擦系数μ呈非线性关系，所以低滑移率情况下的方法将无法应用。因此通过整车纵向力与车轮滑移率的关系：
Figure PCTCN2021115788-appb-000008
其中，F x2表示滑移率处于第二区间的整车纵向力，F z表示前后轮归一化的法向力，μ表示道路摩擦系数。
F z可以通过以下方法获得：
Figure PCTCN2021115788-appb-000009
在本公开的一个实施例中，计算整车纵向力时还可以采用魔术公式轮胎模型计算整车纵向力，即：
F x2＝(F zfsin[Carctan(Bσ xf)]+F zrsin[Carctan(Bσ xf)]) Tμ      (11)
其中，F x2表示滑移率处于第二区间的整车纵向力，F zf、F zr分别表示前轮、后轮的法向力，σ xf、σ xr分别表示车辆前轮、后轮的滑移率，B、C分别表示轮胎模型参数，分别取值为14和1.3。
将两种计算策略下的最终应用标准的函数表示如下：
Figure PCTCN2021115788-appb-000010
在本公开的一个实施例中，所述基于所述整车纵向力计算所述道路摩擦系数采用遗忘因子最小二乘法计算，具体步骤包括：根据调用的计算策略确定系数输入值；以及获取车辆当前控制周期的参数，计算输出值；根据所述输入值和输出值采用遗忘因子最小二乘法计算待估计的道路摩擦参数。
将式(12)表示为参数识别标准形式：
Figure PCTCN2021115788-appb-000011
其中，y(t)表示整车纵向力Fx；
Figure PCTCN2021115788-appb-000012
表示输入值，在低滑移率和高滑移率下，其值分别为k(ρF zfσ xf+F zrσ xr)和F z，若采用魔术公式轮胎模型，则输入值是F zfsin[Carctan(Bσ xf)]+F zrsin[Carctan(Bσ xf)]；θ(t)表示待估计的道路摩擦参数μ；e(t)表示为输出值和估计值的误差。
然后应用遗忘因子最小二乘法进行参数辨识，步骤如下：
1)获取车辆当前控制周期的参数，得到k时刻的整车纵向力F x作为输出值y(k)，并基于上述方法计算输入值
Figure PCTCN2021115788-appb-000013
2)计算输出值和估计值的误差e(k)：
Figure PCTCN2021115788-appb-000014
其中，θ k-1为k-1时刻的参数辨识结果，即上一控制周期的道路摩擦参数，为已知量。
3)计算增益矩阵K：
Figure PCTCN2021115788-appb-000015
其中，P是中间变量矩阵，在算法启动之前，需要给P矩阵赋初值。k时刻P矩阵计算方法如下：
Figure PCTCN2021115788-appb-000016
其中，I表示车辆绕z轴的转动惯量；λ表示遗忘因子，其值越大，收敛速度越慢，所以会导致参数更新较慢，导致车辆在不同摩擦系数道路切换时参数更新延迟较大；其值越小，收敛速度越快，但是参数抗干扰能力会变弱，导致噪声数据可能会耦合其中进而导致参数更新不准确。所以需要在快速响应和参数稳定性两者之间取得平衡。在本公开的一个实施例中，λ值取0.95。
4)计算待估计的道路摩擦参数θ(t)：
Figure PCTCN2021115788-appb-000017
其中，θ k为当前k时刻待辨识的道路摩擦参数μ。
在本公开的一个实施例中，滑移率区间还可以是多个区间，可例如增加第三个区间，第三个区间包含第一区间和第二区间的临界值，比如设定σ' x＝0.3，则第一区间为[-0.3,0.3]，第二区间为[-1,-0.3]∪[0.3,1]，第三区间为[-0.4,-0.2]∪[0.2,0.4]，当滑移率处于第三区间时，可以取第一计算策略和第二计算策略计算出的较小值作为道路摩擦参数。根据滑移率的不同值对应的车辆运行性质，同时考虑到高低滑移率的中间区间，设计对应的计算策略以获取道路摩擦参数，进而进一步提高道路摩擦参数的精度。
在本公开的一个实施例中，估计完道路摩擦参数后，还可以添加校验步骤对参数的计算进行修正，例如将初步估计出的道路摩擦参数作为观测量，k、μ、ρ等对应的参数作为扩充状态建立状态方程和观测方程，利用扩展卡尔曼滤波算法建立标准滤波递推过程，过滤信号噪声计算最终的道路摩擦参数，使得道路摩擦参数计算更加精准。
由于无人车会在不同路面上行走，例如普通道路、冰面、雪面等，将车辆的滑移率分为低滑移率和高滑移率两种情况分别进行参数辨识，可以使得辨识的道路摩擦系数更符合车辆行驶的实际情况。
在步骤S3中，将所述道路摩擦系数输入车辆控制优化模型获取车辆当前控制周期的控制指令，以对所述车辆进行实时控制。
在本公开的一个实施例中，步骤S3还包括步骤S300，预先构建所述车辆控制优化模型，包括：配置车辆控制优化模型的决策变量，包括状态变量和控制变量；建立损失 函数作为目标函数，以及设定约束条件以构建车辆控制优化模型，其中，所述约束条件与所述道路摩擦系数相关。
首先确定车辆控制优化模型的决策变量为
Figure PCTCN2021115788-appb-000018
其中，决策变量包括六个状态变量，分别为全局坐标系下的车辆位置坐标x、y，航向角θ，纵向线速度v，轮胎滑移角α和航向角加速度
Figure PCTCN2021115788-appb-000019
以及两个控制变量，分别为整车纵向力F，前轮转角δ。
然后建立车辆控制优化模型,也就是通过滚动优化求取最优控制解,基于约束，使某一或某些性能指标达到最优实现控制作用。
建立优化模型的目标函数，目标函数的一般形式可表示为状态和控制输入的二次函数，此处为了对控制变量进行有效的抑制，也将其加入了目标函数，表示如下：
Figure PCTCN2021115788-appb-000020
其中，J为损失函数，N p、N c分别为预测时域和控制时域，w l、w s、w θ、w v、w α、
Figure PCTCN2021115788-appb-000021
w F、w δ分别表示各个优化目标的权重，e l、e s、e θ、e v、e α、
Figure PCTCN2021115788-appb-000022
e F、e δ分别表示各个优化目标误差值。
构建损失函数为目标函数后，还需要设定约束条件，约束条件包括：动力学模型约束、起始点约束、前轮转角及前轮转角增量约束、纵向力及纵向力增量约束、速度及速度增量约束。具体内容如下：
1)动力学模型约束：
在本公开的一个实施例中，配置预测的所述车辆的参考轨迹点状态变量的动力学模型方程作为所述动力学模型约束条件。
建立动力学模型简化模型如下：
Figure PCTCN2021115788-appb-000023
其中，X为状态变量，选取
Figure PCTCN2021115788-appb-000024
U为控制变量，选取U＝[F δ] T，A、B、C为动力学模型的矩阵参数。
对式(19)在任意参考轨迹点处采用泰勒级数公式展开得到离散化后的动力学模型如下：
x k+1＝x k+v kTcos(θ+α)-vTsin(θ+α)·(θ k+α k)+vTsin(θ+α)·(θ+α)    (20)
y k+1＝y k+v kTsin(θ+α)+vTcos(θ+α)·(θ k+α k)-vTcos(θ+α)·(θ+α)    (21)
Figure PCTCN2021115788-appb-000025
Figure PCTCN2021115788-appb-000026
Figure PCTCN2021115788-appb-000027
Figure PCTCN2021115788-appb-000028
其中，下标k表示参数在当前k时刻的取值，k+1表示在预测的k+1时刻的取值，无下标的参数表示参考轨迹点处的参数值，T表示控制周期，C f表示车辆轮胎侧偏刚度，a、b分别表示前后轴到车辆质心的距离，m表示车辆质量，I表示车辆绕z轴的转动惯量，F drag表示空气阻力，F fric表示道路摩擦阻力。
空气阻力F drag的计算方式如下：
F drag＝Dv 2                                                            (26)
其中，D为空气阻力常数；
道路摩擦阻力F fric的计算方式如下：
F fric＝μmg                                                   (27)
其中，μ表示道路摩擦系数，也就是基于上述方法获取的道路摩擦系数。
2)起始点约束：
在本公开的一个实施例中，配置所述车辆的起始点处的状态变量初始值作为所述起始点约束条件。
起始点约束表示在进行车辆状态预测时需从当前状态开始，约束条件如下：
x(0)＝x(vehicle)                                                   (28)
y(0)＝y(vehicle)                                                   (29)
θ(0)＝θ(vehicle)                                                   (30)
v(0)＝v(vehicle)                                                   (31)
α(0)＝α(vehicle)                                                   (32)
Figure PCTCN2021115788-appb-000029
x(0)、y(0)、θ(0)、v(0)、α(0)和
Figure PCTCN2021115788-appb-000030
分别表示车辆起始位置坐标、航向角、速度、滑移角、航向角加速度的初始值，x(vehicle)、y(vehicle)、θ(vehicle)、v(vehicle)、α(vehicle)和
Figure PCTCN2021115788-appb-000031
分别表示车辆当前位置坐标、航向角、速度、滑移角、航向角加速度。
3)前轮转角及前轮转角增量约束：
在本公开的一个实施例中，配置基于所述车辆的最大横向加速度值、车辆轴距、纵向线速度和最大、最小前轮转角确定的前轮转角约束值范围作为所述前轮转角约束条件；配置基于所述车辆的最大、最小前轮转角增量确定的前轮转角增量约束值范围作为所述前轮转角增量约束条件。
为了防止车辆高速绕行或拐弯时发生侧翻，本公开对转角进行了约束。其约束范围随车速变化而改变：
Figure PCTCN2021115788-appb-000032
设置前轮转角约束如下：
δ min＜δ k＜δ max                (35)
其中，δ k表示k时刻的转角，δ min表示最终最小转角约束值，
Figure PCTCN2021115788-appb-000033
δ max表示最终最大转角约束值，
Figure PCTCN2021115788-appb-000034
δ(max)、δ(min)分别表示车辆实际可支持的最大和最小转角。
同时，设置前轮转角增量约束如下：
Δδ min＜δ k-δ k-1＜Δδ max               (36)
其中，δ k-1表示k-1时刻的转角，Δδ min、Δδ max分别表示最小、最大转角增量约束值。
4)纵向力及纵向力增量约束：
在本公开的一个实施例中，配置基于所述车辆的最大、最小纵向力确定的纵向力约束值范围作为所述纵向力约束条件；配置基于所述车辆的最大、最小纵向力增量确定的纵向力增量约束值范围作为所述纵向力增量约束条件。
纵向力约束参考车辆实际可支持的纵向力值，设置约束条件如下：
F min＜F k＜F max            (37)
其中，F k表示k时刻的整车纵向力，F min、F max分别表示最小、最大纵向力约束值。
同时，设置纵向力增量约束如下：
ΔF min＜F k-F k-1＜ΔF max         (38)
其中，F k-1表示k-1时刻的整车纵向力，ΔF min、ΔF max分别表示最小、最大纵向力增量约束值。
5)速度及速度增量约束：
在本公开的一个实施例中，配置基于所述车辆的最大、最小纵向线速度确定的速度约束值范围作为所述速度约束条件；配置基于所述车辆的最大、最小纵向线速度增量确定的速度约束增量值范围作为所述速度增量约束条件。
为了防止车辆超速，有必要对车速进行约束，该约束设置基于车辆最大支持速度和自动驾驶系统支持的最大速度，约束条件如下：
v min＜v k＜v max            (39)
其中，v k表示k时刻的纵向线速度，v min、v max分别表示最小、最大纵向线速度。
速度增量约束由加速度进行获取，设置速度增量约束如下：
a min·T＜v k-v k-1＜a max·T         (40)
其中，v k-1表示k-1时刻的纵向线速度，T表示控制周期，a min、a max分别表示车辆支持的最大减速度和最大加速度。
在构建车辆控制优化模型时，对建立目标函数与设定约束条件的步骤顺序不做限定，也可以先设定约束条件再建立目标函数。
由于无人车在不同摩擦系数的路面上切换行驶，轮胎滑移角以及纵向滑移率的实时变化会严重影响其行驶效果。因此，对于轮胎滑移角，考虑将其作为车辆控制优化模型输入的状态变量轮胎滑移角α，对于纵向滑移率，采用不同纵向滑移率下的不同计算策略计算道路摩擦系数，以用于更新动力学模型进行预测，进而基于该模型进行滚动优化模型设计，从而提高模型预测的精度，实现车辆由普通公路切换到冰面、雪面时的自适应控制效果，改善车辆在高速行驶时的控制效果。
同时为了提高车辆纵向行驶的平稳性，添加了纵向力及纵向力增量约束、速度及速度增量约束等；为了增强车辆的横向稳定性，添加了前轮转角约束。
在设计车辆控制优化模型时，选取必要的状态变量，例如车辆坐标、航向角等，以及添加对行驶效果影响度较高的轮胎滑移角，可以保证在满足控制精度需求的同时避免了过多参数造成的计算过程冗杂和速度较慢。
图3示意性示出本公开示例性实施例中一种生成车辆控制指令的示意图，包括：
步骤S301，获取当前控制周期的状态变量参数值输入所述车辆控制优化模型。
当前控制周期的状态变量包括车辆位置坐标x、y，航向角θ，纵向线速度v，轮胎滑移角α和航向角加速度
Figure PCTCN2021115788-appb-000035
其中车辆位置坐标x、y可以通过车辆的GPS获取；航向角θ和航向角加速度
Figure PCTCN2021115788-appb-000036
可以通过车辆的雷电点云图计算得到；纵向线速度v可以通过轮速计获取；轮胎滑移角α可以通过上一控制周期的参数取值确定。
步骤S302，基于所述道路摩擦系数更新模型。
基于上述方法，将获取的道路摩擦系数μ带入至F fric计算道路摩擦阻力，进而优化模型中的更新目标函数和约束条件。
步骤S303，基于所述车辆控制优化模型中的目标函数和约束条件求解得到控制变量参数值。
具体而言，车辆控制优化模型的输入为状态变量，输出为控制变量。将获取的当前控制周期的状态变量参数值输入更新后的优化模型，输出控制变量包括整车纵向力F和前轮转角δ。
步骤S304，基于所述控制变量参数值生成控制指令。
将优化模型输出的整车纵向力F转换为力矩输入到线控系统，同时还将前轮转角δ生成前轮转角指令进行车辆控制。
通过配置整车纵向力为输出变量，使得车辆控制优化模型直接输出力矩指令，从而省去了传统方法由加速度指令转换为力矩的标定步骤，简化了系统的同时也减小了系统的延 迟，增强了控制效果的实时性。
在本公开的一个实施例中，计算得到控制指令后，可以增加一个控制指令验证的环节，当指令验证通过后再输入控制器对车辆进行控制。事先采集车辆的行驶环境信息以生成车辆运行的虚拟环境，用来对车辆行驶状况进行实时监控，生成控制指令之后，在虚拟环境中提前进行指令运行仿真，若仿真通过，则使用该控制指令进行控制，若仿真显示车辆在运行指令后出现行驶异常，则不使用该控制指令，进行紧急制动。
其中，控制指令验证可以根据行驶情况选择性开启。例如车辆行驶在雨雪天气的湿滑路面，或者行驶路线中存在不平整路面或有坡度的行驶路况，又或者是输出的控制指令数据异常时，再选择开启指令验证环节。对计算得到的控制指令进行验证，可以进一步确保车辆自适应指令的正确性，避免因计算失误造成的车辆控制错误。
在本公开的一个实施例中，还可以在无人车的前端或者其他部位设置一个障碍物检测装置，当检测到障碍物与车辆之间的距离小于预设距离，则触发障碍物制动指令，该制动指令具有最高级优先权，可以使车辆紧急制动。通过设置障碍物制动指令的自动触发，可以避免因动物、行人、物品突然出现的突发情况而导致的车辆冲撞或损毁，减少不必要的损失。应当注意，尽管在附图中以特定顺序描述了本公开中方法的各个步骤，但是，这并非要求或者暗示必须按照该特定顺序来执行这些步骤，或是必须执行全部所示的步骤才能实现期望的结果。附加的或备选的，可以省略某些步骤，将多个步骤合并为一个步骤执行，以及/或者将一个步骤分解为多个步骤执行等。
图4示意性示出本公开示例性实施例中一种车辆控制装置的组成示意图，如图4所示，该车辆控制装置可以包括滑移率模块401、摩擦系数模块402和控制指令模块403。其中：
滑移率模块401，用于获取车辆当前控制周期的滑移率；
摩擦系数模块402，用于根据所述滑移率调用对应的计算策略，以计算所述车辆当前控制周期的道路摩擦系数；
控制指令模块403，用于将所述道路摩擦系数输入车辆控制优化模型获取车辆当前控制周期的控制指令，以对所述车辆进行实时控制。
根据本公开的示例性实施例，所述摩擦系数模块402包括：滑移率区间单元和计算策略单元(图中未示出)，滑移率区间单元用于确定所述滑移率所处的滑移率区间；其中，各所述滑移率区域对应配置有各自的计算策略；计算策略单元用于若滑移率处于第一区间时，则调用对应的第一计算策略；或者若滑移率处于第二区间时，则调用对应的第二计算策略。
根据本公开的示例性实施例，所述计算策略单元包括第一计算策略单元，用于获取前后轮摩擦影响比例；基于所述前后轮摩擦影响比例以及滑移斜率与道路摩擦的系数计算整车纵向力；基于所述整车纵向力计算所述道路摩擦系数。
根据本公开的示例性实施例，所述计算策略单元还包括第二计算策略单元，用于计算前后轮归一化的整车纵向力；基于所述整车纵向力计算所述道路摩擦系数。
根据本公开的示例性实施例，所述基于所述整车纵向力计算所述道路摩擦系数采用遗忘因子最小二乘法计算。
根据本公开的示例性实施例，所述车辆控制装置还包括构建车辆控制优化模型模块(图中未示出)，用于配置车辆控制优化模型的决策变量，包括状态变量和控制变量；建立损失函数作为目标函数，以及设定约束条件以构建车辆控制优化模型，其中，所述约束条件与所述道路摩擦系数相关。
根据本公开的示例性实施例，所述状态变量包括车辆位置坐标、航向角、纵向线速度、轮胎滑移角和航向角加速度；所述控制变量包括整车纵向力和前轮转角。
根据本公开的示例性实施例，所述约束条件包括：动力学模型约束、起始点约束、前轮转角及前轮转角增量约束、纵向力及纵向力增量约束、速度及速度增量约束。
根据本公开的示例性实施例，控制指令模块403包括：输入单元、输出单元、控制指令单元(图中未示出)，其中输入单元用于获取当前控制周期的状态变量参数值输入所述车辆控制优化模型；以及基于所述道路摩擦系数更新模型；输出单元用于基于所述车辆控制优化模型中的目标函数和约束条件求解得到控制变量参数值；控制指令单元用于基于所述控制变量参数值生成控制指令。
上述的车辆控制装置400中各模块的具体细节已经在对应的车辆控制方法中进行了详细的描述，因此此处不再赘述。
应当注意，尽管在上文详细描述中提及了用于动作执行的设备的若干模块或者单元，但是这种划分并非强制性的。实际上，根据本公开的实施方式，上文描述的两个或更多模块或者单元的特征和功能可以在一个模块或者单元中具体化。反之，上文描述的一个模块或者单元的特征和功能可以进一步划分为由多个模块或者单元来具体化。
在本公开的示例性实施例中，还提供了一种能够实现上述方法的存储介质。图5示意性示出本公开示例性实施例中一种计算机可读存储介质的示意图，如图5所示，描述了根据本公开的实施方式的用于实现上述方法的程序产品500，其可以采用便携式紧凑盘只读存储器(CD-ROM)并包括程序代码，并可以在终端设备，例如手机上运行。然而，本公开的程序产品不限于此，在本文件中，可读存储介质可以是任何包含或存储程序的有形介质，该程序可以被指令执行系统、装置或者器件使用或者与其结合使用。
在本公开的示例性实施例中，还提供了一种能够实现上述方法的电子设备。图6示意性示出本公开示例性实施例中一种电子设备的计算机系统的结构示意图。
需要说明的是，图6示出的电子设备的计算机系统600仅是一个示例，不应对本公开实施例的功能和使用范围带来任何限制。
如图6所示，计算机系统600包括中央处理单元(Central Processing Unit，CPU)601，其可以根据存储在只读存储器(Read-Only Memory，ROM)602中的程序或者从存储部分608加载到随机访问存储器(Random Access Memory，RAM)603中的程序而执行各种适当的动作和处理。在RAM 603中，还存储有系统操作所需的各种程序和数据。CPU 601、 ROM 602以及RAM 603通过总线604彼此相连。输入/输出(Input/Output，I/O)接口605也连接至总线604。
以下部件连接至I/O接口605：包括键盘、鼠标等的输入部分606；包括诸如阴极射线管(Cathode Ray Tube，CRT)、液晶显示器(Liquid Crystal Display，LCD)等以及扬声器等的输出部分607；包括硬盘等的存储部分608；以及包括诸如LAN(Local Area Network，局域网)卡、调制解调器等的网络接口卡的通信部分609。通信部分609经由诸如因特网的网络执行通信处理。驱动器610也根据需要连接至I/O接口605。可拆卸介质611，诸如磁盘、光盘、磁光盘、半导体存储器等等，根据需要安装在驱动器610上，以便于从其上读出的计算机程序根据需要被安装入存储部分608。
特别地，根据本公开的实施例，下文参考流程图描述的过程可以被实现为计算机软件程序。例如，本公开的实施例包括一种计算机程序产品，其包括承载在计算机可读介质上的计算机程序，该计算机程序包含用于执行流程图所示的方法的程序代码。在这样的实施例中，该计算机程序可以通过通信部分609从网络上被下载和安装，和/或从可拆卸介质611被安装。在该计算机程序被中央处理单元(CPU)601执行时，执行本公开的系统中限定的各种功能。
需要说明的是，本公开实施例所示的计算机可读介质可以是计算机可读信号介质或者计算机可读存储介质或者是上述两者的任意组合。计算机可读存储介质例如可以是——但不限于——电、磁、光、电磁、红外线、或半导体的系统、装置或器件，或者任意以上的组合。计算机可读存储介质的更具体的例子可以包括但不限于：具有一个或多个导线的电连接、便携式计算机磁盘、硬盘、随机访问存储器(RAM)、只读存储器(ROM)、可擦式可编程只读存储器(Erasable Programmable Read Only Memory，EPROM)、闪存、光纤、便携式紧凑磁盘只读存储器(Compact Disc Read-Only Memory，CD-ROM)、光存储器件、磁存储器件、或者上述的任意合适的组合。在本公开中，计算机可读存储介质可以是任何包含或存储程序的有形介质，该程序可以被指令执行系统、装置或者器件使用或者与其结合使用。而在本公开中，计算机可读的信号介质可以包括在基带中或者作为载波一部分传播的数据信号，其中承载了计算机可读的程序代码。这种传播的数据信号可以采用多种形式，包括但不限于电磁信号、光信号或上述的任意合适的组合。计算机可读的信号介质还可以是计算机可读存储介质以外的任何计算机可读介质，该计算机可读介质可以发送、传播或者传输用于由指令执行系统、装置或者器件使用或者与其结合使用的程序。计算机可读介质上包含的程序代码可以用任何适当的介质传输，包括但不限于：无线、有线等等，或者上述的任意合适的组合。
附图中的流程图和框图，图示了按照本公开各种实施例的系统、方法和计算机程序产品的可能实现的体系架构、功能和操作。在这点上，流程图或框图中的每个方框可以代表一个模块、程序段、或代码的一部分，上述模块、程序段、或代码的一部分包含一个或多个用于实现规定的逻辑功能的可执行指令。也应当注意，在有些作为替换的实现中，方框 中所标注的功能也可以以不同于附图中所标注的顺序发生。例如，两个接连地表示的方框实际上可以基本并行地执行，它们有时也可以按相反的顺序执行，这依所涉及的功能而定。也要注意的是，框图或流程图中的每个方框、以及框图或流程图中的方框的组合，可以用执行规定的功能或操作的专用的基于硬件的系统来实现，或者可以用专用硬件与计算机指令的组合来实现。
描述于本公开实施例中所涉及到的单元可以通过软件的方式实现，也可以通过硬件的方式来实现，所描述的单元也可以设置在处理器中。其中，这些单元的名称在某种情况下并不构成对该单元本身的限定。
作为另一方面，本公开还提供了一种计算机可读介质，该计算机可读介质可以是上述实施例中描述的电子设备中所包含的；也可以是单独存在，而未装配入该电子设备中。上述计算机可读介质承载有一个或者多个程序，当上述一个或者多个程序被一个该电子设备执行时，使得该电子设备实现上述实施例中所述的方法。
应当注意，尽管在上文详细描述中提及了用于动作执行的设备的若干模块或者单元，但是这种划分并非强制性的。实际上，根据本公开的实施方式，上文描述的两个或更多模块或者单元的特征和功能可以在一个模块或者单元中具体化。反之，上文描述的一个模块或者单元的特征和功能可以进一步划分为由多个模块或者单元来具体化。
通过以上的实施方式的描述，本领域的技术人员易于理解，这里描述的示例实施方式可以通过软件实现，也可以通过软件结合必要的硬件的方式来实现。因此，根据本公开实施方式的技术方案可以以软件产品的形式体现出来，该软件产品可以存储在一个非易失性存储介质(可以是CD-ROM，U盘，移动硬盘等)中或网络上，包括若干指令以使得一台计算设备(可以是个人计算机、服务器、触控终端、或者网络设备等)执行根据本公开实施方式的方法。
本领域技术人员在考虑说明书及实践这里公开的发明后，将容易想到本公开的其它实施方案。本公开旨在涵盖本公开的任何变型、用途或者适应性变化，这些变型、用途或者适应性变化遵循本公开的一般性原理并包括本公开未公开的本技术领域中的公知常识或惯用技术手段。
应当理解的是，本公开并不局限于上面已经描述并在附图中示出的精确结构，并且可以在不脱离其范围进行各种修改和改变。本公开的范围仅由所附的权利要求来限制。

Claims (18)

  1. 一种车辆控制方法，其中，包括：
    获取车辆当前控制周期的滑移率；
    根据所述滑移率调用对应的计算策略，以计算所述车辆当前控制周期的道路摩擦系数；
    将所述道路摩擦系数输入车辆控制优化模型获取车辆当前控制周期的控制指令，以对所述车辆进行实时控制。
  2. 根据权利要求1所述的车辆控制方法，其中，所述根据所述滑移率调用对应的计算策略包括：
    确定所述滑移率所处的滑移率区间；其中，各所述滑移率区域对应配置有各自的计算策略；
    若滑移率处于第一区间时，则调用对应的第一计算策略，包括：获取前后轮摩擦影响比例；基于所述前后轮摩擦影响比例以及滑移斜率与道路摩擦的系数计算整车纵向力；基于所述整车纵向力计算所述道路摩擦系数；或者
    若滑移率处于第二区间时，则调用对应的第二计算策略，包括：计算前后轮归一化的整车纵向力；基于所述整车纵向力计算所述道路摩擦系数。
  3. 根据权利要求2所述的车辆控制方法，其中，所述基于所述整车纵向力计算所述道路摩擦系数，包括：
    根据调用的计算策略确定系数输入值；以及
    获取车辆当前控制周期的参数，计算输出值；
    根据所述输入值和输出值采用遗忘因子最小二乘法计算待估计的道路摩擦参数。
  4. 根据权利要求1所述的车辆控制方法，其中，所述方法还包括预先构建所述车辆控制优化模型，包括：
    配置车辆控制优化模型的决策变量，包括状态变量和控制变量；
    建立损失函数作为目标函数，以及设定约束条件，以构建车辆控制优化模型，其中，所述约束条件与所述道路摩擦系数相关。
  5. 根据权利要求4所述的车辆控制方法，其中，所述状态变量包括位置坐标、航向角、纵向线速度、轮胎滑移角和航向角加速度；所述控制变量包括整车纵向力和前轮转角。
  6. 根据权利要求4所述的车辆控制方法，其中，所述约束条件包括：动力学模型约束条件、起始点约束条件、前轮转角约束条件、前轮转角增量约束条件、纵向力约束条件、纵向力增量约束条件、速度约束条件、速度增量约束条件。
  7. 根据权利要求6所述的车辆控制方法，其中，配置预测的所述车辆的参考轨迹点状态变量的动力学模型方程作为所述动力学模型约束条件。
  8. 根据权利要求6所述的车辆控制方法，其中，配置所述车辆的起始点处的状态变量初始值作为所述起始点约束条件。
  9. 根据权利要求6所述的车辆控制方法，其中，配置基于所述车辆的最大横向加速度值、车辆轴距、纵向线速度和最大、最小前轮转角确定的前轮转角约束值范围作为所述前轮转角约束条件。
  10. 根据权利要求6所述的车辆控制方法，其中，配置基于所述车辆的最大、最小前轮转角增量确定的前轮转角增量约束值范围作为所述前轮转角增量约束条件。
  11. 根据权利要求6所述的车辆控制方法，其中，配置基于所述车辆的最大、最小纵向力确定的纵向力约束值范围作为所述纵向力约束条件。
  12. 根据权利要求6所述的车辆控制方法，其中，配置基于所述车辆的最大、最小纵向力增量确定的纵向力增量约束值范围作为所述纵向力增量约束条件。
  13. 根据权利要求6所述的车辆控制方法，其中，配置基于所述车辆的最大、最小纵向线速度确定的速度约束值范围作为所述速度约束条件。
  14. 根据权利要求6所述的车辆控制方法，其中，配置基于所述车辆的最大、最小纵向线速度增量确定的速度约束增量值范围作为所述速度增量约束条件。
  15. 根据权利要求1所述的车辆控制方法，其中，所述将所述道路摩擦系数输入车辆控制优化模型获取车辆当前控制周期的控制指令，包括：
    获取当前控制周期的状态变量参数值输入所述车辆控制优化模型；以及基于所述道路摩擦系数更新模型；
    基于所述车辆控制优化模型中的目标函数和约束条件求解得到控制变量参数值；
    基于所述控制变量参数值生成控制指令。
  16. 一种车辆控制装置，其中，包括：
    滑移率模块，用于获取车辆当前控制周期的滑移率；
    摩擦系数模块，用于根据所述滑移率调用对应的计算策略，以计算所述车辆当前控制周期的道路摩擦系数；
    控制指令模块，用于将所述道路摩擦系数输入车辆控制优化模型获取车辆当前控制周期的控制指令，以对所述车辆进行实时控制。
  17. 一种计算机可读存储介质，其上存储有计算机程序，所述程序被处理器执行时实现如权利要求1至15任一项所述的车辆控制方法。
  18. 一种电子设备，其中，包括：
    一个或多个处理器；
    存储装置，用于存储一个或多个程序，当所述一个或多个程序被所述一个或多个处理器执行时，使得所述一个或多个处理器实现如权利要求1至15任一项所述的车辆控制方法。
PCT/CN2021/115788 2020-11-19 2021-08-31 车辆控制方法、装置、计算机可读存储介质及电子设备 WO2022105361A1 (zh)

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Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105083292A (zh) * 2015-09-10 2015-11-25 吉林大学 一种预估未知路面摩擦系数的方法
US20170151955A1 (en) * 2015-11-27 2017-06-01 Robert Bosch Gmbh Method and device for operating a motor vehicle
JP2017178236A (ja) * 2016-03-31 2017-10-05 本田技研工業株式会社 車両駆動システム
CN107664551A (zh) * 2017-06-19 2018-02-06 河海大学 一种车辆与路面摩擦力的实时计算方法
CN109383510A (zh) * 2017-08-11 2019-02-26 福特全球技术公司 基于摩擦系数估计的牵引力控制
CN111746546A (zh) * 2019-03-27 2020-10-09 爱信精机株式会社 路面状态推断装置

Family Cites Families (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP3111401B2 (ja) * 1994-12-16 2000-11-20 本田技研工業株式会社 車両のトータルグリップ力推定装置及び車両のスリップ制御装置
JP2001133391A (ja) * 1999-11-08 2001-05-18 Nissan Motor Co Ltd 路面摩擦係数推定装置
JP3515040B2 (ja) * 2000-03-09 2004-04-05 住友ゴム工業株式会社 路面摩擦係数判定方法
CN110399664B (zh) * 2019-07-15 2023-11-21 北京天时行智能科技有限公司 一种驾驶速度的确定方法及装置
CN111369887A (zh) * 2020-03-31 2020-07-03 戴姆勒股份公司 湿路面摩擦系数地图生成系统和方法

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105083292A (zh) * 2015-09-10 2015-11-25 吉林大学 一种预估未知路面摩擦系数的方法
US20170151955A1 (en) * 2015-11-27 2017-06-01 Robert Bosch Gmbh Method and device for operating a motor vehicle
JP2017178236A (ja) * 2016-03-31 2017-10-05 本田技研工業株式会社 車両駆動システム
CN107664551A (zh) * 2017-06-19 2018-02-06 河海大学 一种车辆与路面摩擦力的实时计算方法
CN109383510A (zh) * 2017-08-11 2019-02-26 福特全球技术公司 基于摩擦系数估计的牵引力控制
CN111746546A (zh) * 2019-03-27 2020-10-09 爱信精机株式会社 路面状态推断装置

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