WO2021227086A1 - 获取车辆滚动阻力系数的方法及装置 - Google Patents

获取车辆滚动阻力系数的方法及装置 Download PDF

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Publication number
WO2021227086A1
WO2021227086A1 PCT/CN2020/090703 CN2020090703W WO2021227086A1 WO 2021227086 A1 WO2021227086 A1 WO 2021227086A1 CN 2020090703 W CN2020090703 W CN 2020090703W WO 2021227086 A1 WO2021227086 A1 WO 2021227086A1
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WIPO (PCT)
Prior art keywords
vehicle
rolling resistance
resistance coefficient
target vehicle
target
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PCT/CN2020/090703
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English (en)
French (fr)
Inventor
周维
陈效华
刘亚林
余瑶
Original Assignee
华为技术有限公司
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Application filed by 华为技术有限公司 filed Critical 华为技术有限公司
Priority to CN202080004974.0A priority Critical patent/CN112689585B/zh
Priority to PCT/CN2020/090703 priority patent/WO2021227086A1/zh
Publication of WO2021227086A1 publication Critical patent/WO2021227086A1/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
    • 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
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
    • G01M17/00—Testing of vehicles

Definitions

  • This application relates to the field of automobile technology, and in particular to a method and device for obtaining the rolling resistance coefficient of a vehicle.
  • the coefficient of rolling resistance of a vehicle is the ratio of the driving force of the vehicle to the load of the vehicle when the wheel is rolling under certain conditions, and it is related to the structural material of the tire, the tire inflation pressure, the type of road (that is, the type of road surface), the road condition, and the speed of the vehicle. , The force of the vehicle is related.
  • the rolling resistance coefficient of a vehicle is an important parameter that affects the economy, power, and safety of the vehicle during driving. That is to say, the more accurate the value of the rolling resistance coefficient of the vehicle, the more economical, dynamic and safe the vehicle is during driving. The higher the sex.
  • offline identification methods mainly include bench test methods and road taxi test methods.
  • the bench test method is based on the longitudinal dynamics of the vehicle, according to the test by placing the tested tire on a rotating drum (National Standard GB/T 18861-2012), or placing the tested vehicle on a dynamometer for testing Wait for the obtained data to determine the rolling resistance coefficient of the vehicle.
  • Road coasting refers to a decelerating movement in which the vehicle is accelerated to a predetermined speed on a level road surface and without wind, then the engine is removed from the gear and the kinetic energy of the vehicle is used to continue driving.
  • the road taxiing test method refers to the calculation of the rolling resistance coefficient of the vehicle based on the longitudinal dynamics of the vehicle, using the taxiing distance and speed of the taxiing vehicle monitored during the road taxiing process.
  • the test scenarios of the bench test method and the road taxi test method are single, which cannot simulate various road surfaces, and the tested vehicle lacks the ability to recognize the operating environment, which may lead to the rolling resistance coefficient of the vehicle in the actual operating environment and the bench test Or the test results obtained from the road taxiing test are quite different, which reduces the identification accuracy of the rolling resistance coefficient of the vehicle, thereby affecting the economy, power and safety of the vehicle during driving.
  • Online identification that is, the identification of the rolling resistance coefficient of the vehicle using the data collected during the actual driving of the vehicle.
  • the online identification method may reduce the accuracy of the sliding resistance of the vehicle and the identification accuracy of the rolling resistance coefficient of the vehicle due to the impact of the accuracy of the road gradient, the change of the vehicle quality, and the accuracy of the vehicle acceleration, thereby affecting the vehicle's driving process.
  • This application provides a method and device for obtaining the rolling resistance coefficient of a vehicle, which are determined based on the actual driving force of the target vehicle, the theoretical driving force of the target vehicle, the road surface image information of the road where the target vehicle is located, and the target vehicle rolling resistance coefficient identification model.
  • the rolling resistance coefficient of the target vehicle is used to improve the identification accuracy of the rolling resistance coefficient of the vehicle, thereby ensuring the power, economy and safety of the target vehicle during its driving.
  • the present application provides a method for obtaining the rolling resistance coefficient of a vehicle, which relates to the technical field of automobiles.
  • the method includes: first, obtaining the theoretical driving force of the target vehicle and the actual driving force of the target vehicle. Then, based on the theoretical driving force of the target vehicle and its actual driving force, the first vehicle rolling resistance coefficient that minimizes the difference between the theoretical driving force and the actual driving force is obtained.
  • the road surface image information of the road on which the target vehicle is located is input into the vehicle rolling resistance coefficient identification model, and the value range of the vehicle rolling resistance coefficient corresponding to the road surface type of the road on which the target vehicle is located is obtained.
  • the vehicle rolling resistance coefficient identification model is trained according to the vehicle rolling resistance coefficient database.
  • the vehicle rolling resistance coefficient database includes the vehicle rolling resistance coefficients of different vehicles on different types of roads, and the values of the vehicle rolling resistance coefficients corresponding to different types of roads. Scope. Finally, the target vehicle rolling resistance coefficient is obtained according to the value range of the vehicle rolling resistance coefficient corresponding to the road surface type of the target vehicle and the first vehicle rolling resistance coefficient.
  • the first vehicle rolling resistance coefficient that minimizes the difference between the theoretical driving force of the target vehicle and its actual driving force is determined, which can reduce the road on which the target vehicle is located.
  • the accuracy of the road gradient, the speed of the target vehicle, the acceleration of the target vehicle and other data will affect the identification of the rolling resistance coefficient of the first vehicle, thereby improving the identification accuracy of the rolling resistance coefficient of the first vehicle.
  • the value range of the vehicle rolling resistance coefficient corresponding to the road surface type of the road on which the vehicle is located is determined, which can improve the road surface of the road on which the target vehicle is located.
  • the target vehicle rolling resistance coefficient can be obtained according to the value range of the vehicle rolling resistance coefficient corresponding to the road surface type of the target vehicle, and the first vehicle rolling resistance coefficient.
  • the vehicle corresponding to the road surface type of the target vehicle can be obtained.
  • the value range of the rolling resistance coefficient constrains the value of the target vehicle's rolling resistance coefficient within the normal range, improves the identification accuracy of the target vehicle's rolling resistance coefficient, and ensures the dynamics, economy and safety of the vehicle during driving.
  • the first vehicle rolling resistance coefficient is not within the value range of the vehicle rolling resistance coefficient corresponding to the road surface type of the road on which the target vehicle is located.
  • the road surface image information of the road on which the target vehicle is located is input into the vehicle rolling resistance coefficient identification model to obtain the value range of the vehicle rolling resistance coefficient corresponding to the road surface type of the road on which the target vehicle is located, including:
  • the road surface image information is input into the vehicle rolling resistance coefficient model, and the value range of the vehicle rolling resistance coefficient corresponding to the road surface type of the road on which the target vehicle is located, and the second vehicle rolling resistance coefficient are obtained.
  • obtaining the target vehicle rolling resistance coefficient includes: using the second vehicle rolling resistance coefficient as the target vehicle rolling resistance coefficient.
  • the application can directly determine the second vehicle rolling resistance coefficient based on the road surface image information of the road where the target vehicle is located and the vehicle rolling resistance coefficient identification model, and reduce the road surface of the road where the target vehicle is located directly based on experience.
  • the road surface type corresponding to the image information affects the identification accuracy of the rolling resistance coefficient of the second vehicle, and improves the identification accuracy of the rolling resistance coefficient of the second vehicle, thereby improving the identification accuracy of the rolling resistance coefficient of the target vehicle.
  • the value range of the rolling resistance coefficient of the target vehicle corresponding to the road surface type of the target vehicle is used to restrict the value of the rolling resistance coefficient of the target vehicle to further improve the identification accuracy of the rolling resistance coefficient of the target vehicle.
  • the first vehicle rolling resistance coefficient is within a value range of the vehicle rolling resistance coefficient corresponding to the road surface type of the road on which the target vehicle is located.
  • the road surface image information of the road on which the target vehicle is located is input into the vehicle rolling resistance coefficient identification model to obtain the value range of the vehicle rolling resistance coefficient corresponding to the road surface type of the road on which the target vehicle is located, including:
  • the road surface image information is input into the vehicle rolling resistance coefficient model, and the value range of the vehicle rolling resistance coefficient corresponding to the road surface type of the road on which the target vehicle is located, and the second vehicle rolling resistance coefficient are obtained.
  • the first vehicle rolling resistance coefficient and the second vehicle rolling resistance coefficient are fused to obtain the target fusion result.
  • obtaining the vehicle rolling resistance coefficient of the target vehicle includes: using the target fusion result as the target vehicle rolling resistance coefficient.
  • the present application can compare the first vehicle rolling resistance coefficient and the second vehicle rolling resistance coefficient when the first vehicle rolling resistance coefficient is within the value range of the vehicle rolling resistance coefficient corresponding to the road surface type of the target vehicle.
  • the target fusion result of the coefficients, as the rolling resistance coefficient of the target vehicle can further improve the identification accuracy of the rolling resistance coefficient of the target vehicle, and ensure the dynamics, economy and safety of the vehicle during driving.
  • fusing the first vehicle rolling resistance coefficient and the second vehicle rolling resistance coefficient to obtain the target fusion result includes: inputting the first vehicle rolling resistance coefficient and the second vehicle rolling resistance coefficient into Karl Mann filter to obtain the target fusion result.
  • the Kalman filter is a centralized Kalman filter or a joint Kalman filter.
  • this application can use the Kalman filter to filter and fuse the first vehicle rolling resistance coefficient and the second rolling resistance coefficient, and determine the obtained target fusion result as the target vehicle rolling resistance coefficient, thereby improving the rolling resistance of the target vehicle.
  • the identification accuracy of the drag coefficient ensures the dynamics, economy and safety of the vehicle during driving.
  • obtaining the theoretical driving force and actual driving force of the target vehicle includes: obtaining the road gradient of the road on which the target vehicle is located, the acceleration of the target vehicle, the speed of the target vehicle, and the actual driving force of the target vehicle. Then, the road gradient of the target vehicle, the acceleration of the target vehicle, and the speed of the target vehicle are input into the longitudinal dynamics model of the target vehicle to obtain the theoretical driving force of the target vehicle.
  • the longitudinal dynamics model of the target vehicle is used to represent The movement law of the longitudinal movement of the target vehicle.
  • this application can determine the first vehicle rolling resistance coefficient according to the actual driving force of the target vehicle and the theoretical driving force of the target vehicle, thereby reducing vehicle load, vehicle acceleration, vehicle speed, and identification of the road slope of the road on which the vehicle is located.
  • the actual driving force of the target vehicle is determined according to the motor torque, accelerator pedal opening, and brake pedal opening of the target vehicle, which can reduce the use of the road gradient of the road where the target vehicle is located, and the target vehicle
  • the measurement accuracy of the road slope of the road on which the target vehicle is located, the acceleration of the target vehicle and the speed of the target vehicle are calculated to calculate the actual driving force of the target vehicle
  • the influence of improves the identification accuracy of the actual driving force of the vehicle, thereby improving the identification accuracy of the rolling resistance coefficient of the first vehicle.
  • obtaining the first vehicle rolling resistance coefficient based on the theoretical driving force and the actual driving includes: determining the first vehicle rolling resistance coefficient based on the least square method based on the theoretical driving force and the actual driving force.
  • the method further includes: based on the least squares method and based on the theoretical driving force and the actual driving force, determining the vehicle load that minimizes the difference between the theoretical driving force and the actual driving force as the target vehicle load.
  • the present application can determine the target vehicle load based on the least squares method, according to the actual driving force of the target vehicle and the theoretical driving force of the target vehicle, so as to improve the vehicle load on the basis of improving the identification accuracy of the vehicle rolling resistance coefficient.
  • the identification accuracy of the vehicle thereby further ensuring the dynamics, economy and safety of the vehicle in the driving process.
  • the method further includes: obtaining the target vehicle load from the road gradient of the road where the target vehicle is located, the acceleration of the target vehicle, the speed of the target vehicle, and the longitudinal dynamics model of the target vehicle.
  • the load is the vehicle load when the difference between the actual driving force of the target vehicle and the theoretical driving force of the target vehicle is the smallest.
  • the present application can determine the target vehicle load based on the obtained target vehicle rolling resistance coefficient and the longitudinal dynamics model of the vehicle, and further improve the identification accuracy of the target vehicle load, thereby ensuring the power of the target vehicle during its driving. Sex, economy and safety.
  • the present application provides a device for obtaining the rolling resistance coefficient of a vehicle, which relates to the technical field of automobiles.
  • the device includes an obtaining unit and a processing unit: the obtaining unit is used to obtain the theoretical driving force of the target vehicle and the actual driving force of the target vehicle . Then, the processing unit is used to obtain the first vehicle rolling resistance coefficient based on the theoretical driving force of the target vehicle and the actual driving force of the target vehicle.
  • the first vehicle rolling resistance coefficient makes the difference between the actual driving force of the target vehicle and the theoretical driving force The smallest.
  • the processing unit is also used to input the road surface image information of the road on which the target vehicle is located into the vehicle rolling resistance coefficient identification model to obtain the value range of the vehicle rolling resistance coefficient corresponding to the road surface type of the road on which the target vehicle is located.
  • the vehicle rolling resistance coefficient identification model is trained according to the vehicle rolling resistance coefficient database, which includes the value ranges of the vehicle rolling resistance coefficients of different vehicles on different types of roads.
  • the processing unit is also used to obtain the target vehicle rolling resistance coefficient according to the value range of the vehicle rolling resistance coefficient corresponding to the road surface type of the target vehicle and the first vehicle rolling resistance coefficient.
  • the first vehicle rolling resistance coefficient is not within the value range of the vehicle rolling resistance coefficient corresponding to the road surface type of the road where the target vehicle is located, and the processing unit is specifically configured to use the road surface image of the road where the target vehicle is located.
  • the information is input to the vehicle rolling resistance coefficient identification model, and the value range of the vehicle rolling resistance coefficient corresponding to the road surface type of the target vehicle and the second vehicle rolling resistance coefficient are obtained.
  • the processing unit is specifically further configured to use the second vehicle rolling resistance coefficient as the target vehicle rolling resistance coefficient.
  • the first vehicle rolling resistance coefficient is within the value range of the vehicle rolling resistance coefficient corresponding to the road surface type of the road on which the target vehicle is located, and the processing unit is specifically configured to combine the first vehicle rolling resistance coefficient and the The second vehicle rolling resistance coefficient is fused to obtain the target fusion result. Then, the processing unit is specifically used to use the target fusion result as the target vehicle rolling resistance coefficient.
  • the processing unit is specifically configured to input the first vehicle rolling resistance coefficient and the second vehicle rolling resistance coefficient into the Kalman filter for filtering and fusion to obtain the target fusion result.
  • the Kalman filter is a centralized Kalman filter or a joint Kalman filter.
  • the acquiring unit specifically acquires the road gradient of the road on which the target vehicle is located, the acceleration of the target vehicle, the speed of the target vehicle, and the actual driving force of the target vehicle. Then, the acquiring unit is specifically used to input the road gradient of the road on which the target vehicle is located, the acceleration of the target vehicle, and the speed of the target vehicle into the longitudinal dynamics model of the target vehicle to obtain the theoretical driving force of the target vehicle.
  • the longitudinal dynamics model of the target vehicle is used to represent the motion law of the longitudinal motion of the target vehicle.
  • the processing unit is specifically further configured to determine the first vehicle rolling resistance coefficient based on the least square method and according to the actual driving force and the theoretical driving force of the target vehicle.
  • the processing unit is specifically further configured to determine the target vehicle load based on the least square method and according to the theoretical driving force and the actual driving force of the target vehicle.
  • the target vehicle load is the vehicle load that minimizes the difference between the actual driving force of the target vehicle and the theoretical driving force.
  • the processing unit is specifically used to input the road gradient of the road where the target vehicle is located, the acceleration of the target vehicle, the speed of the target vehicle, and the rolling resistance coefficient of the target vehicle into the longitudinal dynamics model of the vehicle to obtain the target Vehicle load.
  • the target vehicle load is the vehicle load when the difference between the actual driving force of the target vehicle and the theoretical driving force is the smallest.
  • the present application provides a device for obtaining the rolling resistance coefficient of a vehicle.
  • the device includes a processor and a memory; wherein the memory is used to store computer program instructions, and the processor runs the computer program instructions to obtain the rolling resistance coefficient of the vehicle.
  • the device executes the method for obtaining the rolling resistance coefficient of the vehicle as described in the first aspect.
  • the present application provides a computer-readable storage medium, including computer instructions, when the computer instructions are executed by a processor, cause the device for obtaining the rolling resistance coefficient of a vehicle to execute the obtaining of the rolling resistance coefficient of the vehicle as described in the first aspect Methods.
  • the present application provides a computer program product, which is characterized in that when the computer program product runs on a processor, the device for obtaining the rolling resistance coefficient of a vehicle executes the obtaining of the rolling resistance coefficient of the vehicle as described in the first aspect.
  • FIG. 1 is the value of the vehicle rolling resistance coefficient of the vehicle on different road types according to the embodiments of the application;
  • FIG. 2 is a schematic structural diagram of a vehicle provided by an embodiment of the application.
  • FIG. 3 is a schematic structural diagram of a computer system provided by an embodiment of this application.
  • FIG. 4 is a schematic diagram 1 of the application of a cloud-side command automatic driving vehicle provided by an embodiment of this application;
  • FIG. 5 is a second schematic diagram of the application of a cloud-side command automatic driving vehicle provided by an embodiment of this application;
  • FIG. 6 is a schematic structural diagram of a computer program product provided by an embodiment of the application.
  • FIG. 7 is a schematic flowchart of a method for obtaining a rolling resistance coefficient of a vehicle according to an embodiment of the application.
  • FIG. 8 is a schematic diagram of data stored in a vehicle rolling resistance coefficient database provided by an embodiment of the application.
  • FIG. 9 is a schematic diagram of a model training process provided by an embodiment of the application.
  • FIG. 10 is a schematic diagram of a process for determining the rolling resistance coefficient of a target vehicle according to an embodiment of the application
  • FIG. 11 is a first schematic diagram of filtering and fusing the first vehicle rolling resistance coefficient and the second vehicle rolling resistance coefficient through a Kalman filter according to an embodiment of the application;
  • FIG. 12 is a second schematic diagram of filtering and fusing the first vehicle rolling resistance coefficient and the second vehicle rolling resistance coefficient through a Kalman filter according to an embodiment of the application;
  • FIG. 13 is a schematic structural diagram of a device for obtaining a rolling resistance coefficient of a vehicle provided by an embodiment of the application.
  • the method for obtaining the rolling resistance coefficient of the vehicle provided by the embodiment of the present application is applied to the vehicle, or applied to other devices (such as a cloud server) that can communicate with the vehicle.
  • the vehicle can implement the method for obtaining the rolling resistance coefficient of the vehicle provided by the embodiments of the present application through the components (including hardware and software) it contains, based on the actual driving force of the vehicle, the theoretical driving force of the vehicle, the road surface image information of the road where the vehicle is located, and
  • the vehicle rolling resistance coefficient identification model is used to determine the rolling resistance coefficient of the first vehicle, the rolling coefficient of the second vehicle and the road surface type of the road where the vehicle is located, and then determine the rolling resistance of the target vehicle according to the first rolling resistance coefficient and the second vehicle rolling resistance coefficient Coefficient, that is, the rolling resistance coefficient of the vehicle when the vehicle is on the current road.
  • the method of obtaining the rolling resistance coefficient of the vehicle in the embodiment of the present application through other equipment (such as a server, a mobile phone terminal, etc.) to determine the rolling resistance coefficient of the vehicle when the vehicle is on the current road, and improve the identification accuracy of the rolling resistance coefficient of the vehicle. Ensure the power, economy and safety of the vehicle during driving.
  • other equipment such as a server, a mobile phone terminal, etc.
  • the vehicle may include the following modules:
  • the sensing module 201 is used to obtain vehicle status information through on-board sensors and roadside sensors, such as vehicle speed, road gradient of the road where the vehicle is located, vehicle acceleration, vehicle motor torque, vehicle accelerator pedal opening, and vehicle Brake pedal opening and so on.
  • the sensing module 201 is configured to obtain the state information of the vehicle by calculating the data monitored by the dead reckoning (DR) sensor.
  • the DR sensor is the sensor used for dead reckoning with the collected data.
  • the DR sensor includes, for example, sensors in the sensor system of the vehicle, such as gyroscopes, accelerometers, speed sensors, GPS sensors, etc., or the DR
  • the sensors include the above-mentioned on-board sensors and roadside sensors. DR is a commonly used positioning technology.
  • the perception module 201 is used to obtain image information in the surrounding environment, such as road surface image information of the road where the vehicle is located, through a visual sensor or the like.
  • the sensing module 201 is used to obtain information such as the motor torque of the vehicle, the opening of the accelerator pedal of the vehicle, and the opening of the brake pedal of the vehicle directly from the travel system or control system of the vehicle, or use a sensor (such as an angle sensor) to control the accelerator.
  • Pedal, brake pedal and other monitored data are calculated to determine information such as the motor torque of the vehicle, the opening of the accelerator pedal of the vehicle, and the opening of the brake pedal of the vehicle.
  • the sensing module 201 is also used to calculate the data monitored by the sensor, the speed of the vehicle, the acceleration of the vehicle, the road gradient of the road where the vehicle is located, the motor torque of the vehicle, and the speed of the vehicle determined by calculating the data monitored by the sensor.
  • the accelerator pedal opening degree and the vehicle brake pedal opening degree are sent to the central processing module 202 and the planning control module 203.
  • the central processing module 202 is used to obtain the data monitored by the sensor sent by the sensing module 201, and calculate the speed of the vehicle, the acceleration of the vehicle, the road gradient of the road where the vehicle is located, and the motor of the vehicle by calculating the data monitored by the sensor Information such as torque, accelerator pedal opening of the vehicle, and brake pedal opening of the vehicle.
  • the central processing module 201 is used to determine the driving force of the vehicle, that is, the actual driving force of the vehicle, according to the motor torque of the vehicle, the opening of the accelerator pedal of the vehicle, and the opening of the brake pedal of the vehicle.
  • the central processing module 202 is also used to input the speed of the vehicle, the acceleration of the vehicle, and the slope of the road where the vehicle is located in the information obtained from the perception module 201 into the longitudinal dynamics model of the vehicle to obtain the vehicle
  • the driving force of the vehicle is the theoretical driving force of the vehicle.
  • the theoretical driving force of a vehicle is related to the coefficient of rolling resistance of the vehicle and the load of the vehicle.
  • the coefficient of rolling resistance of the vehicle is the ratio of the driving force required by the vehicle to the vehicle load during driving under certain conditions.
  • the central processing module 202 is also used to determine the vehicle rolling resistance coefficient that minimizes the difference between the actual driving force of the vehicle and the theoretical driving force of the vehicle as the first vehicle rolling resistance coefficient.
  • the central processing module 202 is also used to input the image information of the surrounding environment of the vehicle obtained by the sensor, that is, the image information of the road surface of the road where the vehicle is located, into the vehicle rolling resistance coefficient identification model to obtain the corresponding road surface type of the road where the vehicle is located.
  • the central processing module 202 is also used to perform model training according to the vehicle rolling resistance coefficient database and the deep learning algorithm to obtain the vehicle rolling resistance coefficient identification model.
  • the vehicle rolling resistance coefficient database is used to store different vehicles on different types of roads. The vehicle rolling resistance coefficient on the above, and the value range of the vehicle rolling resistance coefficient corresponding to different types of roads.
  • the central processing module 202 is further configured to compare the first vehicle rolling resistance coefficient and the second vehicle rolling resistance coefficient according to whether the first vehicle rolling resistance coefficient is within the value range of the vehicle rolling resistance coefficient corresponding to the road surface type of the road on which the vehicle is located.
  • the target fusion result obtained by the fusion, or the second vehicle rolling resistance coefficient is determined as the target vehicle rolling resistance coefficient, that is, the vehicle rolling resistance coefficient of the vehicle in the current scene obtained by the embodiment of the present application.
  • the central processing module 202 is also used to send the rolling resistance coefficient of the target vehicle to the planning control module 203.
  • the planning control module 203 is used to obtain the sensor-monitored data sent by the sensing module 201, and to calculate and determine the speed of the vehicle, the acceleration of the vehicle, the road gradient of the road where the vehicle is located, and the motor torque of the vehicle based on the data monitored by the sensor. , The accelerator pedal opening of the vehicle, the brake pedal opening of the vehicle and other information.
  • the planning control module 203 is configured to obtain the target vehicle rolling coefficient sent by the central processing module 202.
  • the planning control module 203 is also configured to plan the driving path of the vehicle according to the acquired information, generate a driving strategy, and output an action instruction corresponding to the driving strategy to control the vehicle to perform automatic driving according to the instruction.
  • This module is a traditional control module of autonomous vehicles.
  • the vehicle-mounted communication module 204 (not shown in FIG. 2) is used for information exchange between the own vehicle and other vehicles.
  • the storage component 205 (not shown in FIG. 2) is used to store the executable codes of the above-mentioned various modules, and running these executable codes can realize part or all of the method procedures of the embodiments of the present application.
  • the computer system of the vehicle (the computer system may be located in the vehicle or outside the vehicle) includes a processor 301, which is coupled to the system bus 302
  • the processor 301 may be one or more processors, where each processor may include one or more processor cores.
  • the video adapter 303 can drive the display 324, and the display 324 is coupled to the system bus 302.
  • the system bus 302 is coupled with the input/output (I/O) bus (BUS) 305 through the bus bridge 304, the I/O interface 306 is coupled with the I/O bus 305, and the I/O interface 306 communicates with various I/O devices, For example, an input device 307 (such as a keyboard, a mouse, a touch screen, etc.), a media tray 308 (such as a CD-ROM, a multimedia interface, etc.).
  • the transceiver 309 can send and/or receive radio communication signals
  • the camera 310 can capture static and dynamic digital video images
  • USB universal serial bus
  • the interface connected to the I/O interface 306 may be a USB interface.
  • the processor 301 may be any traditional processor, including a reduced instruction set computer (RISC) processor, a complex instruction set computer (CISC) processor, or a combination of the foregoing.
  • the processor 301 may also be a dedicated device such as an application specific integrated circuit (ASIC).
  • the processor 301 may also be a neural network processor or a combination of a neural network processor and the foregoing traditional processors.
  • the computer system is used for data processing.
  • the computer system may be located far away from the autonomous vehicle and wirelessly communicate with the autonomous vehicle, or located in the autonomous vehicle superior.
  • some of the processes described in this application may be configured to be executed on a processor in an autonomous vehicle, and other processes may be executed by a remote processor, including taking actions required to perform a single manipulation.
  • the computer system may communicate with a software deployment server (deploying server) 313 through a network interface 312.
  • the network interface 312 may be a hardware network interface, such as a network card.
  • the network (network) 314 can be an external network, such as the Internet, or an internal network, such as Ethernet or a virtual private network (VPN).
  • the network 314 can also be a wireless network, such as a WiFi network or a cellular network. Network, etc.
  • the hard disk drive interface 315 and the system bus 302 are coupled.
  • the hard disk drive interface 315 and the hard disk drive 316 are connected.
  • the system memory 317 and the system bus 302 are coupled.
  • the data running in the system memory 317 may include the operating system (OS) 318 and application programs 319 of the computer system.
  • OS operating system
  • the operating system (OS) 318 includes but is not limited to Shell 320 and kernel 321.
  • Shell 320 is an interface between the user and the kernel 321 of the operating system 318.
  • Shell 320 is the outermost layer of operating system 318. The shell manages the interaction between the user and the operating system 318: waiting for the user's input, interpreting the user's input to the operating system 318, and processing various output results of the operating system 318.
  • the kernel 321 is composed of parts of the operating system 318 for managing memory, files, peripherals, and system resources, and directly interacts with the hardware.
  • the kernel 321 of the operating system 318 generally runs processes, provides communication between processes, and provides functions such as central processing unit (CPU) time slice management, interruption, memory management, and IO management.
  • CPU central processing unit
  • Application programs 319 include programs 323 related to autonomous driving, such as programs that manage the interaction between autonomous vehicles and road obstacles, programs that control the driving route or speed of autonomous vehicles, and control interaction between autonomous vehicles and other cars on the road/autonomous vehicles Procedures, etc.
  • the application 319 also exists on the deploying server313 system. In one embodiment, when the application 319 needs to be executed, the computer system can download from
  • the application program 319 may be an application program that controls the vehicle to determine a driving strategy based on the state information of the vehicle, the surrounding environment information of the vehicle, and a traditional control module, such as the planning control module 203.
  • the processor 301 of the computer system calls the application 319 to obtain the driving strategy.
  • the sensor 322 is associated with the computer system, and the sensor 322 is used to detect the environment around the computer system.
  • the sensor 322 can detect animals, cars, obstacles, and/or pedestrian crossings.
  • the sensor 322 can also detect the environment around the aforementioned objects such as animals, cars, obstacles and/or pedestrian crossings.
  • the environment around the animal for example, other animals that appear around the animal, weather conditions, and the brightness of the environment around the animal.
  • the computer system can also be located on a self-driving car.
  • the sensor 322 may be at least one of a camera, an infrared sensor, a chemical detector, and a microphone.
  • the computer system may also receive information from other computer systems or transfer information to other computer systems.
  • the sensor data collected from the sensor system of the vehicle can be transferred to another computer, and the data can be processed by another computer.
  • the data from the computer system can be transmitted to the computer system 410 on the cloud side via the network for further processing.
  • the network and intermediate nodes can include various configurations and protocols, including the Internet, World Wide Web, Intranet, virtual private network, wide area network, local area network, private network using one or more company’s proprietary communication protocols, Ethernet, WiFi and HTTP, And various combinations of the foregoing. This communication can be performed by any device capable of transferring data to and from other computers, such as modems and wireless interfaces.
  • the computer system 410 may include a server with multiple computers, such as a load balancing server group.
  • the server 420 exchanges information with different nodes of the network.
  • the computer system 410 may have a configuration similar to a computer system, and has a processor 430, a memory 440, instructions 450, and data 460.
  • the data 460 of the server 420 may include providing road-related information.
  • the server 420 may receive, monitor, store, update, and transmit various information related to roads in the surrounding environment.
  • the information may include, for example, road conditions and vehicle conditions in the form of reports, radar information, forecasts, etc.
  • the cloud service center can receive information (such as data collected by vehicle sensors or other information) from vehicles 513 and 512 (vehicles 513 and 512 may be autonomous vehicles) in its operating environment 500 via a network 511 such as a wireless communication network .
  • the cloud service center 520 controls the vehicle 513 and the vehicle 512 by running its stored programs related to controlling the automatic driving of the automobile according to the received data.
  • Programs related to controlling auto-driving cars can be: programs that manage the interaction between autonomous vehicles and road obstacles, or programs that control the route or speed of autonomous vehicles, or programs that control interaction between autonomous vehicles and other autonomous vehicles on the road.
  • the cloud service center 520 may provide a part of the map to the vehicle 513 and the vehicle 512 through the network 511.
  • operations can be divided between different locations.
  • multiple cloud service centers can receive, confirm, combine, and/or send information reports.
  • information reports and/or sensor data can also be sent between vehicles.
  • Other configurations are also possible.
  • the cloud service center 520 sends suggested solutions to the autonomous vehicle regarding possible driving situations in the operating environment (eg, inform the obstacle in front and tell how to circumvent it). For example, the cloud service center 520 may assist the vehicle in determining how to proceed when facing a specific obstacle in the environment.
  • the cloud service center 520 sends a response to the autonomous vehicle indicating how the vehicle should travel in a given scene. For example, the cloud service center 520 can confirm the existence of a temporary stop sign in front of the road based on the collected sensor data. For example, based on the “lane closed” sign and the sensor data of construction vehicles, it can be determined that the lane is closed due to construction.
  • the cloud service center 520 sends a suggested operation mode for the vehicle to pass through the obstacle (for example, instructing the vehicle to change lanes on another road).
  • the operation steps used for the autonomous driving vehicle can be added to the driving information map.
  • this information can be sent to other vehicles in the area that may encounter the same obstacle, so as to assist other vehicles to recognize the closed lane and pass smoothly.
  • the disclosed methods may be implemented as computer program instructions in a machine-readable format, encoded on a computer-readable storage medium, or encoded on other non-transitory media or articles.
  • Figure 6 schematically illustrates a conceptual partial view of an example computer program product arranged in accordance with at least some of the embodiments shown herein, the example computer program product including a computer program for executing a computer process on a computing device.
  • the example computer program product 600 is provided using a signal bearing medium 601.
  • the signal bearing medium 601 may include one or more program instructions 602, which, when run by one or more processors, may provide all or part of the functions described above with respect to FIGS. 2 to 5, or may provide descriptions in subsequent embodiments. All or part of the function.
  • one or more features in S701 to S704 may be undertaken by one or more instructions associated with the signal bearing medium 601.
  • the program instructions 602 in FIG. 6 also describe example instructions.
  • the signal-bearing medium 601 may include a computer-readable medium 603, such as, but not limited to, a hard disk drive, compact disk (CD), digital video compact disk (DVD), digital tape, memory, read-only storage memory (read -only memory, ROM) or random access memory (RAM), etc.
  • the signal bearing medium 601 may include a computer recordable medium 604, such as, but not limited to, memory, read/write (R/W) CD, R/W DVD, and so on.
  • the signal-bearing medium 601 may include a communication medium 605, such as, but not limited to, digital and/or analog communication media (e.g., fiber optic cables, waveguides, wired communication links, wireless communication links, etc.).
  • the signal bearing medium 601 may be communicated by a wireless communication medium 605 (for example, a wireless communication medium that complies with the IEEE 802.11 standard or other transmission protocols).
  • the one or more program instructions 602 may be, for example, computer-executable instructions or logic-implemented instructions.
  • a computing device such as that described with respect to FIGS. 2 to 4 may be configured to respond to one or more of the computer readable medium 603, and/or the computer recordable medium 604, and/or the communication medium 605
  • the program instructions 602 communicated to the computing device provide various operations, functions, or actions. It should be understood that the arrangement described here is for illustrative purposes only.
  • this application proposes an acquisition method.
  • the execution subject of the method can be a vehicle (such as an autonomous vehicle) or other equipment other than the vehicle, or a processor on the vehicle or other equipment other than the vehicle, as mentioned in the above content.
  • the method includes steps S701 to S704:
  • the actual driving force of the target vehicle is obtained based on the motor torque of the target vehicle, the accelerator pedal opening of the target vehicle, and the brake pedal opening of the target vehicle. If the brake pedal opening of the target vehicle is 0, the target vehicle is not braking at the current moment. If the brake pedal opening of the target vehicle is greater than 0, the target vehicle is in the braking state at the current moment. Similarly, if the accelerator pedal opening of the target vehicle is 0, the target vehicle is not accelerating at the current moment. If the accelerator pedal opening of the target vehicle is greater than 0, the target vehicle is accelerating at the current moment.
  • the status information of the target vehicle includes the road gradient of the road where the target vehicle is located, and the target vehicle The speed of the target vehicle, the acceleration of the target vehicle, the motor torque of the target vehicle, the accelerator pedal opening of the target vehicle, and the brake pedal opening of the target vehicle. Then, the actual driving force of the target vehicle is obtained according to the motor torque of the target vehicle, the accelerator pedal opening of the target vehicle, and the brake pedal opening of the target vehicle.
  • the motor torque of the target vehicle, the accelerator pedal opening of the target vehicle, and the brake pedal opening of the target vehicle can be obtained by calculating data monitored by sensors such as angle sensors, or It is obtained directly from the traveling system or control system of the target vehicle.
  • the aforementioned DR sensor includes a gyroscope, an accelerometer, a speed sensor, an angle sensor, a vision sensor, and the like.
  • the data monitored by the sensor at the same time are used for calculation to obtain the road gradient of the road on which the target vehicle is located, the speed of the target vehicle, and the acceleration of the target vehicle, as well as the motor torque of the target vehicle and the target Information such as the accelerator pedal opening degree of the vehicle and the brake pedal opening degree of the target vehicle has an association relationship between these information, such as a temporal association relationship. Therefore, the data obtained by the sensor and the above-mentioned information calculated from the data can be filtered according to the brake pedal opening of the target vehicle, and then the filtered data and information can be used to obtain the theoretical drive of the target vehicle through this step S701. The actual driving force of the target vehicle is obtained by using information such as the motor torque of the target vehicle, the accelerator pedal opening of the target vehicle, and the brake pedal opening of the target vehicle.
  • the brake pedal opening of the target vehicle may be changed It is 0, that is, the target vehicle is not braking as the filter condition. If the brake pedal opening of the target vehicle is 0, the road gradient of the road on which the target vehicle is located can be obtained at the same time as the brake pedal opening of the target vehicle, the target vehicle speed, the target vehicle acceleration, and the motor of the target vehicle Information such as torque and accelerator pedal opening of the target vehicle, as well as data obtained by the sensors used to determine this information, are filtered.
  • the road gradient of the road on which the target vehicle is located, the acceleration of the target vehicle, and the speed of the target vehicle are input into the longitudinal dynamics model of the target vehicle to obtain the theoretical driving force of the target vehicle.
  • the longitudinal dynamics model of the target vehicle is used to express the motion law of the longitudinal motion of the target vehicle.
  • the theoretical driving force of the target vehicle is related to the rolling resistance coefficient of the target vehicle and the vehicle load, that is, the theoretical driving force of the target vehicle obtained in this step S701 is an expression about the rolling resistance coefficient of the vehicle and the vehicle load.
  • the coefficient of rolling resistance of a vehicle is the ratio of the driving force of the vehicle to the load of the vehicle when the wheels of the vehicle are rolling under certain conditions.
  • the speed of the vehicle, the force of the vehicle, etc. are related.
  • the certain conditions mentioned above may be that the air resistance is 0, the road on which the target vehicle is currently located is a flat road, and the target vehicle is traveling at a constant speed, that is, the road on which the target vehicle is currently located has no slope. And the acceleration and air resistance of the target vehicle are not considered.
  • A represents the windward area of the target vehicle
  • v represents the speed of the target vehicle
  • Av 2 represents the air resistance
  • ⁇ represents the moment of inertia coefficient
  • a represents the acceleration of the target vehicle
  • ⁇ ma represents the acceleration resistance
  • the first vehicle rolling resistance coefficient is the vehicle rolling resistance coefficient that minimizes the difference between the actual driving force of the target vehicle and the theoretical driving force of the target vehicle.
  • the actual driving force of the target vehicle and the theoretical driving force of the target vehicle obtained in step S701 are used to obtain the minimum difference between the actual driving force of the target vehicle and the theoretical driving force of the target vehicle.
  • the rolling resistance coefficient of the vehicle is the first rolling resistance coefficient of the vehicle.
  • the vehicle load when the difference between the actual driving force of the target vehicle and the theoretical driving force of the target vehicle is minimized can also be obtained as the target vehicle load.
  • Exemplary and that actual target vehicle drive force is F 't (k)
  • the difference between the actual driving force of the target vehicle and the theoretical driving force of the target vehicle is
  • the difference between the actual driving force of the target vehicle and the theoretical driving force of the target vehicle is the smallest, that is, the value of L is the smallest.
  • the vehicle rolling resistance coefficient at this time is the first vehicle rolling resistance coefficient ⁇ 1 .
  • the vehicle load at this time is the target vehicle load m.
  • the actual target vehicle drive force is F 't (k)
  • the difference between the actual driving force of the target vehicle and the theoretical driving force of the target vehicle is
  • the difference between the actual driving force of the target vehicle and the theoretical driving force of the target vehicle is the smallest, that is, the value of L is the smallest.
  • ⁇ 1 is the vehicle rolling resistance coefficient that minimizes the difference between the actual driving force F't (k) of the target vehicle and the theoretical driving force F t (k) of the target vehicle.
  • the present application can obtain the first vehicle rolling resistance coefficient and the target vehicle load according to the actual driving force of the target vehicle and the theoretical driving force of the target vehicle according to the least square method, so as to improve the rolling of the vehicle.
  • the identification accuracy of the drag coefficient the identification accuracy of the vehicle load is improved, so as to further ensure the power, economy and safety of the vehicle in the driving process.
  • S703 Input the road surface image information of the road on which the target vehicle is located into the vehicle rolling resistance coefficient identification model to obtain the value range of the vehicle rolling resistance coefficient corresponding to the road surface type of the road on which the vehicle is located.
  • the vehicle rolling resistance coefficient identification model is obtained by training according to the vehicle rolling resistance coefficient database. More specifically, the vehicle rolling resistance coefficient identification model is obtained by model training according to the vehicle rolling resistance coefficient database and a deep learning algorithm.
  • the vehicle rolling resistance coefficient identification model is used to identify the road surface image information of the road where the vehicle is located, and determine the value range of the vehicle rolling resistance coefficient corresponding to the road surface type of the road where the vehicle is located.
  • the vehicle rolling resistance coefficient database is used to store the vehicle rolling resistance coefficients of different vehicles on different types of roads, and the value ranges of the vehicle rolling resistance coefficients corresponding to different types of roads.
  • the vehicle rolling resistance coefficient database may number the road image information stored in it according to the road surface type, and store the image information of the road surface type corresponding to the number and the vehicle corresponding to the road surface type for each number.
  • the vehicle rolling resistance coefficient database may also store vehicle rolling resistance coefficients and the like when vehicles with different tire types are running on different types of roads.
  • the vehicle rolling resistance coefficient database may store the data shown in FIG. 8.
  • the road types corresponding to numbers 1-10 are dry dirt roads, icy asphalt roads, dry gravel roads, dirt roads, rainy cement roads, dry cement roads, dry stone roads, rainy asphalt roads, and On sunny asphalt roads
  • the rolling resistance coefficients of the vehicles corresponding to these 10 road types are [a1, a2], [a3, a4], [a5, a6], [a7, a8], [a9, a10], [a11, a12], [a13, a14], [a15, a16] and [a17, a18]
  • the vehicle rolling resistance coefficient database also stores image information for each road surface type, and the road surface image information corresponding to each road surface type Contain at least one image, such as b1-b18 shown in Figure 8.
  • the process of model training according to the vehicle rolling resistance coefficient database and the deep learning algorithm can be shown in Figure 9.
  • the road image information in the vehicle rolling resistance coefficient database Enter the deep learning model to obtain the road surface type and the vehicle rolling resistance coefficient ⁇ 2 under the current conditions of the vehicle. That is, the value range [ ⁇ min , ⁇ max ] of the vehicle rolling resistance coefficient corresponding to the road surface type input to the road image information in the depth model and the vehicle rolling resistance coefficient ⁇ 2 of the vehicle under the current condition can be obtained.
  • the deep learning model includes a convolutional layer 1, a pooling layer 1, a convolutional layer 2, a pooling layer 2, and a fully-connected layer.
  • the road surface image information of the road on which the target vehicle is located is input to the vehicle rolling resistance coefficient identification model, and the vehicle rolling resistance coefficient identification model recognizes the input road image information, and outputs the road surface type corresponding to the road image information , Which is the pavement type of the road where the target vehicle is located. Then, query in the vehicle rolling resistance coefficient database according to the road surface type to determine the value range of the vehicle rolling resistance coefficient corresponding to the road surface type.
  • the road surface image information may be image information of the road surface of the road where the target vehicle is located, which is acquired by a visual sensor or the like.
  • the present application can reduce the limitation of a single environment, obtain the vehicle rolling resistance coefficient of the target vehicle in various environments, so as to improve the identification accuracy of the vehicle rolling resistance coefficient, and ensure the economy and safety of the target vehicle during its driving. Sexuality and motivation.
  • S704 Acquire the target vehicle rolling resistance coefficient according to the value range of the vehicle rolling resistance coefficient corresponding to the road surface type of the road on which the target vehicle is located and the first vehicle rolling resistance coefficient.
  • the vehicle rolling resistance coefficient corresponding to the road surface type of the road on which the target vehicle is located can be obtained, and the first 2.
  • the second vehicle rolling resistance coefficient is obtained by inputting the road surface image information of the road where the target vehicle is located into the vehicle rolling resistance coefficient identification model, and is estimated according to the vehicle rolling resistance coefficient identification model. The coefficient of rolling resistance of the vehicle when it is on the road.
  • the second vehicle rolling resistance coefficient and the value range of the vehicle rolling resistance coefficient corresponding to the road surface type of the target vehicle are all based on the vehicle rolling resistance coefficient identification model to identify the road surface image information of the road where the target vehicle is located It is obtained that the second vehicle rolling resistance coefficient is greater than the minimum value of the value range corresponding to the road surface type of the road on which the vehicle is located, and the second vehicle rolling resistance coefficient is less than the maximum value of the value range. That is, the value of the second vehicle rolling resistance coefficient is within the value range of the vehicle rolling resistance coefficient corresponding to the road type of the road on which the target vehicle is located.
  • the first vehicle rolling resistance coefficient is less than the smallest value in the range of the vehicle rolling resistance coefficient corresponding to the road surface type of the target vehicle, or the first vehicle rolling resistance coefficient is greater than The maximum value in the value range, that is, the first vehicle rolling resistance coefficient is not within the value range of the vehicle rolling resistance coefficient corresponding to the road surface type of the target vehicle, it can be regarded as the first vehicle
  • the difference between the rolling resistance coefficient and the actual vehicle rolling resistance coefficient is large. Therefore, in the process of obtaining the target vehicle rolling resistance coefficient, the first vehicle rolling resistance coefficient is not considered, but the second vehicle rolling resistance coefficient is directly used as the target vehicle. Rolling resistance coefficient to ensure the accuracy of the obtained target vehicle rolling resistance coefficient.
  • the rolling resistance coefficient of the first vehicle is greater than or equal to the minimum value in the value range, and less than or equal to the maximum value in the value range, that is to say, the rolling resistance coefficient of the first vehicle is on the road where the target vehicle is located.
  • the value range of the vehicle rolling resistance coefficient corresponding to the road surface type it can be considered that the difference between the first vehicle rolling resistance coefficient and the actual vehicle rolling resistance coefficient is small.
  • the first vehicle rolling resistance coefficient and the second vehicle rolling resistance coefficient are fused to obtain the target fusion result, and the target fusion result is used as the target vehicle rolling resistance coefficient, so as to comprehensively consider the first vehicle rolling resistance coefficient and the first vehicle rolling resistance coefficient. 2.
  • the effect of the rolling resistance coefficient of the vehicle is to improve the identification accuracy of determining the rolling resistance coefficient of the target vehicle, and to ensure the power, safety, and economy of the target vehicle during its driving.
  • this application can determine the value range of the vehicle rolling resistance coefficient of the target vehicle on the current road, the first vehicle rolling resistance coefficient, and the second vehicle rolling resistance coefficient obtained by using the vehicle rolling resistance coefficient identification model. , Use the second vehicle rolling resistance coefficient or the target fusion result of the first vehicle rolling resistance coefficient and the second vehicle rolling resistance coefficient as the target vehicle rolling resistance coefficient to improve the identification accuracy of the vehicle rolling resistance coefficient and ensure that the vehicle is in the driving process The power, economy and safety of the company.
  • the present application can determine the actual driving force of the target vehicle according to the motor torque of the target vehicle, the accelerator pedal opening of the target vehicle, and the brake pedal opening of the target vehicle, so as to reduce the use of the target vehicle.
  • the acceleration of the target vehicle and the speed of the target vehicle to determine the actual driving force of the target vehicle, the measurement accuracy of the road gradient of the road where the target vehicle is located, the acceleration of the target vehicle, and the speed of the target vehicle The influence of the calculation of the driving force of the vehicle in the driving process, thereby improving the identification accuracy of the actual driving force of the target vehicle.
  • the theoretical driving force of the first vehicle is determined to determine the rolling resistance coefficient of the first vehicle to reduce the influence of the road slope of the road where the target vehicle is located, the acceleration of the target vehicle and the speed of the target vehicle on the identification accuracy of the first vehicle rolling resistance coefficient, and improve the first vehicle Identification accuracy of rolling resistance coefficient.
  • the road surface image information and the vehicle rolling resistance coefficient identification model to determine the second vehicle rolling resistance coefficient and the road surface type of the road on which the vehicle is located, it can reduce the limitation of a single vehicle driving environment and determine the vehicle rolling in a complex driving environment
  • the drag coefficient improves the identification accuracy of the rolling resistance coefficient of the vehicle.
  • the target rolling resistance coefficient is determined to further improve the rolling resistance of the vehicle
  • the identification accuracy of the coefficients ensures the power, economy and safety of the target vehicle in the driving process.
  • the first vehicle rolling resistance coefficient and the second vehicle rolling resistance coefficient are input to the Kalman filter for filtering and fusion to obtain the target fusion result, and the target fusion result is determined as the target vehicle rolling resistance coefficient .
  • the Kalman filter is a centralized Kalman filter or a joint Kalman filter.
  • Kalman filter is based on the state quantity x k-1 at time k-1, and predicts the state quantity at time k to obtain x k/k-1. Observe the value to obtain z k , and then analyze between the predicted x k/k-1 and the observed z k , or use the observed z k to correct the predicted x k/k-1 , So as to get the process of estimating the state quantity x k at time k.
  • x(k) is the system state at time k
  • x(k-1) is the system state at time k-1
  • u(k) is the control amount of the system at time k.
  • a and B are system parameters
  • H is a parameter of the measurement system.
  • A, B, and H can be matrices.
  • w(k) and v(k) represent process and measurement noise respectively, which can be assumed to be Gaussian white noise, and the covariances of w(k) and v(k) are Q and R respectively.
  • the settings P and Q do not change with the change of the system state.
  • x(k/k-1) is the predicted value obtained by predicting the system state at time k based on the system state at time k-1
  • x(k-1/k-1) is the estimation of the system state at time k-1 value.
  • the first vehicle rolling resistance coefficient ⁇ 1 (k) and the second vehicle rolling resistance coefficient ⁇ 2 (k) The difference (predicted value) of is input to the Kalman filter, that is, ⁇ 1 (k)- ⁇ 2 (k) shown in the figure is input to the Kalman filter, and ⁇ 1 (k) output by the Kalman filter is obtained - ⁇ 2 (k) is the estimated value ⁇ 3 (k). Then the difference ⁇ (k) between ⁇ 3 (k) and ⁇ 1 (k) is output as the target fusion result, or ⁇ (k) can also be the sum of ⁇ 3 (k) and ⁇ 1 (k) as the target Fusion result output. That is to say, the target fusion result ⁇ (k) can be used as the target vehicle rolling resistance coefficient obtained in the embodiment of the present application.
  • the first Kalman filter and the second Kalman filter constitute a joint Kalman filter as an example, where the first Kalman filter and the second Kalman filter All are the vehicle rolling resistance coefficient Kalman filter, that is, the filter used for filtering and fusing the vehicle rolling resistance coefficient.
  • the first vehicle rolling resistance coefficient ⁇ 1 (k) and the second vehicle rolling resistance coefficient ⁇ 2 (k) are respectively input into the first Kalman filter and the second Kalman filter as predicted values, and obtain the ⁇ 3 (k) and ⁇ 4 (k).
  • the distribution coefficient can also be understood as a weight, and the weighted summation of ⁇ 3 (k) and ⁇ 4 (k) is performed to obtain the target fusion result ⁇ (k), which can be used as the target vehicle rolling OK.
  • the target fusion result may be fed back to the first Kalman filter and the second Kalman filter with a certain distribution coefficient, and the sum of the distribution coefficients is 1.
  • the target fusion result may be fed back to the first Kalman filter and the second Kalman filter with a certain distribution coefficient, and the sum of the distribution coefficients is 1.
  • this application can use the Kalman filter to filter and fuse the first vehicle rolling resistance coefficient and the second rolling resistance coefficient, and determine the obtained filtering result as the target vehicle rolling resistance coefficient. Improve the identification accuracy of the vehicle rolling resistance coefficient, and ensure the power, economy and safety of the target vehicle in the driving process.
  • the above-mentioned Kalman filter can also be replaced with other filters such as a particle filter, or the Kalman filter can be replaced with an algorithm such as deep learning.
  • the deep learning algorithm is used to fuse the first vehicle rolling resistance coefficient and the second vehicle rolling resistance coefficient to obtain the target fusion result, and determine the fusion result as the target vehicle rolling resistance coefficient, thereby improving the identification accuracy of the vehicle rolling resistance coefficient. Ensure the power, economy and safety of the vehicle during driving.
  • the road gradient of the road on which the target vehicle is located, the acceleration of the target vehicle, the speed of the target vehicle, and the rolling resistance coefficient of the target vehicle obtained in step S704 obtained in step S701 are input into the target vehicle's rolling resistance coefficient.
  • the longitudinal dynamics model output the vehicle load that minimizes the difference between the first vehicle rolling resistance coefficient and the second vehicle rolling resistance coefficient, and determine the vehicle load as the target vehicle load.
  • this application can determine the target vehicle load based on the acquired target vehicle rolling resistance coefficient and the longitudinal dynamics model of the vehicle, so as to further improve the identification accuracy of the target vehicle load, thereby ensuring the dynamic performance of the vehicle during driving. , Economy and safety.
  • the embodiment of the present application can divide the device for obtaining the rolling resistance coefficient of the vehicle according to the above-mentioned method into functional modules.
  • FIG. 13 shows the obtaining of the vehicle involved in the above-mentioned embodiment.
  • a possible structure diagram of the rolling resistance coefficient device As shown in FIG. 13, the device for obtaining the rolling resistance coefficient of a vehicle includes an obtaining unit 1301 and a processing unit 1302.
  • the device for obtaining the coefficient of rolling resistance of the vehicle may also include other modules, or the device for obtaining the coefficient of rolling resistance of the vehicle may include fewer modules.
  • the obtaining unit 1301 is used to obtain the theoretical driving force of the target vehicle and the actual driving force of the target vehicle.
  • the acquiring unit 1301 is used to acquire the road gradient of the road on which the target vehicle is located, the acceleration of the target vehicle, the speed of the target vehicle, and the actual driving force of the target vehicle. Then, the road gradient of the road where the target vehicle is located, the acceleration of the target vehicle, and the speed of the target vehicle are input into the longitudinal dynamics model of the target vehicle to obtain the theoretical driving force of the target vehicle.
  • the longitudinal dynamics model of the target vehicle is used to represent the motion law of the longitudinal motion of the target vehicle.
  • the processing unit 1302 is configured to obtain the first vehicle rolling resistance coefficient based on the theoretical driving force of the target vehicle and the actual driving force of the target vehicle. Wherein, the first vehicle rolling resistance coefficient minimizes the difference between the actual driving force and the theoretical driving force of the target vehicle.
  • the processing unit 1302 is specifically configured to obtain the first vehicle rolling resistance coefficient according to the actual driving force and the theoretical driving force of the target vehicle based on the least square method.
  • the processing unit 1302 is specifically further configured to obtain the target vehicle load based on the least square method and according to the theoretical driving force and the actual driving force of the target vehicle.
  • the target vehicle load is the vehicle load that minimizes the difference between the actual driving force of the target vehicle and the theoretical driving force.
  • the processing unit 1302 is also used to input the road surface image information of the road on which the target vehicle is located into the vehicle rolling resistance coefficient identification model to obtain the value range of the vehicle rolling resistance coefficient corresponding to the road surface type on the road on which the target vehicle is located.
  • the vehicle rolling resistance coefficient identification model is trained based on the vehicle rolling resistance coefficient database.
  • the vehicle rolling resistance coefficient database includes the vehicle rolling resistance coefficients of different vehicles on different types of roads, and the value ranges of the vehicle rolling resistance coefficients corresponding to different types of roads. .
  • the processing unit 1302 is also used to obtain the target vehicle rolling resistance coefficient according to the value range of the vehicle rolling resistance coefficient corresponding to the road surface type of the target vehicle and the first vehicle rolling resistance coefficient.
  • the first vehicle rolling resistance coefficient is not within the value range of the vehicle rolling resistance coefficient corresponding to the road surface type of the road where the target vehicle is located, and the processing unit 1302 is specifically configured to determine the road surface of the road where the target vehicle is located.
  • the image information is input to the vehicle rolling resistance coefficient identification model, and the value range of the vehicle rolling resistance coefficient corresponding to the road surface type of the road on which the target vehicle is located, and the second vehicle rolling resistance coefficient are obtained.
  • the processing unit 1302 is further configured to use the second vehicle rolling resistance coefficient as the target vehicle rolling resistance coefficient.
  • the first vehicle rolling resistance coefficient is within the value range of the vehicle rolling resistance coefficient corresponding to the road surface type of the road on which the target vehicle is located, and the processing unit 1302 is specifically further configured to calculate the first vehicle rolling resistance coefficient Fuse with the rolling resistance coefficient of the second vehicle to obtain the target fusion result. Then, the processing unit 1302 specifically uses the aforementioned target fusion result as the target vehicle rolling resistance coefficient.
  • the processing unit 1302 is further configured to input the first vehicle rolling resistance coefficient and the second vehicle rolling resistance coefficient into the Kalman filter for filtering and fusion to obtain the target fusion result.
  • the Kalman filter is a centralized Kalman filter or a joint Kalman filter.
  • the processing unit 1302 is specifically used to input the road gradient of the road where the target vehicle is located, the acceleration of the target vehicle, the speed of the target vehicle, and the rolling resistance coefficient of the target vehicle into the longitudinal dynamics model of the vehicle to obtain Target vehicle load.
  • the target vehicle load is the vehicle load when the difference between the actual driving force of the target vehicle and the theoretical driving force is the smallest.
  • the embodiment of the present application provides a computer-readable storage medium storing one or more programs.
  • the one or more programs include instructions that, when executed by a computer, cause the computer to execute steps S701-S704 of the foregoing embodiment.
  • the embodiment of the present application also provides a computer program product containing instructions, which when the instructions are run on a computer, cause the computer to execute the method for obtaining the rolling resistance coefficient of a vehicle as described in steps S701-S704 of the foregoing embodiment.
  • the above-mentioned embodiments may be implemented in whole or in part by software, hardware, firmware or any combination thereof.
  • the above-mentioned embodiments may appear in the form of a computer program product in whole or in part, and the computer program product includes one or more computer instructions.
  • the computer program instructions When the computer program instructions are loaded and executed on the computer, the processes or functions according to the embodiments of the present application are generated in whole or in part.
  • the computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices.
  • Computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium.
  • Computer instructions may be transmitted from a website, computer, server, or data center through a cable (such as Coaxial cable, optical fiber, digital subscriber line (digital subscriber line, DSL) or wireless (such as infrared, wireless, microwave, etc.) transmission to another website site, computer, server or data center.
  • the computer-readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server or a data center integrated with one or more available media.
  • the usable medium may be a magnetic medium (for example, a floppy disk, a hard disk, and a magnetic tape), an optical medium (for example, a DVD), or a semiconductor medium (for example, a solid state disk (SSD)).
  • the disclosed device and method may be implemented in other ways.
  • the device embodiments described above are merely illustrative.
  • the division of the modules or units is only a logical function division. In actual implementation, there may be other division methods, for example, multiple units or components may be divided. It can be combined or integrated into another device, or some features can be omitted or not implemented.
  • the displayed or discussed mutual coupling or direct coupling or communication connection may be indirect coupling or communication connection through some interfaces, devices or units, and may be in electrical, mechanical or other forms.
  • the units described as separate components may be physically separated or not physically separated.
  • the components displayed as units may be one physical unit or multiple physical units, that is, they may be located in one place, or they may be distributed. To many different places. In the application process, some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
  • the functional units in the various embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units may be integrated into one unit.
  • the above-mentioned integrated unit can be implemented in the form of hardware or software functional unit.
  • the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer readable storage medium.
  • the technical solutions of the embodiments of the present application are essentially or the part that contributes to the prior art or the part of the technical solutions can be embodied in the form of a software product, and the computer software product is stored in a storage medium.
  • Including several instructions to make a device (which may be a personal computer, a server, a network device, a single-chip microcomputer, or a chip, etc.) or a processor execute all or part of the steps of the methods described in the various embodiments of the present application.
  • the aforementioned storage media include: U disk, mobile hard disk, read-only memory (read-only memory, ROM), random access memory (random access memory, RAM), magnetic disks or optical disks and other media that can store program codes. .

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Abstract

一种获取车辆滚动阻力系数的方法及装置，涉及汽车技术领域，用于根据目标车辆（512，513）的实际驱动力、目标车辆（512，513）的理论驱动力以及路面图像信息，来确定目标车辆（512，513）的滚动阻力系数，以提高车辆滚动阻力系数的辨识精度，可以应用在新能源汽车、智能汽车、网联汽车上。该方法包括：获取目标车辆（512，513）的理论驱动力和目标车辆（512，513）的实际驱动力（S701）；基于理论驱动力和实际驱动力，获取第一车辆滚动阻力系数（S702）；将目标车辆（512，513）所处道路的路面图像信息输入车辆滚动阻力系数辨识模型，得到目标车辆（512，513）所处道路的路面类型对应的车辆滚动阻力系数的取值范围（S703）；根据目标车辆（512，513）所处道路的路面类型对应的车辆滚动阻力系数的取值范围和第一车辆滚动阻力系数，获取目标车辆（512，513）的滚动阻力系数（S704）。

Description

获取车辆滚动阻力系数的方法及装置 技术领域
本申请涉及汽车技术领域，尤其涉及一种获取车辆滚动阻力系数的方法及装置。
背景技术
车辆滚动阻力系数是车轮在一定条件下滚动时所需的车辆驱动力与车辆载荷的比值，与轮胎的结构材料、轮胎充气气压、道路种类(也就是路面类型)、道路状况、车辆的行驶速度、车辆的受力情况等有关。以车辆的行驶状态为低速行驶为例，该车辆在不同路面类型下的车辆滚动阻力系数的取值可以如图1所示。车辆滚动阻力系数是影响车辆行驶过程中的经济性、动力性、安全性的重要参数，也就是说，车辆滚动阻力系数的取值越精确，则车辆行驶过程中的经济性、动力性和安全性越高。
在现有技术中，辨识车辆滚动阻力系数的方法可以大致分为两类，即在线辨识和离线辨识。其中，离线辨识的方法主要包括台架测试方法和道路滑行试验方法等。一般的，台架测试方法是指基于车辆纵向动力学，根据将被测轮胎放在转鼓上进行测试(国家标准GB/T 18861-2012)，或者将被测车辆放在测功机上进行测试等得到的数据，确定车辆滚动阻力系数。道路滑行是指将车辆在水平路面且无风的条件下加速至某预定速度后，摘挡脱开发动机，利用汽车的动能继续行驶的减速运动。道路滑行试验方法，是指基于车辆纵向动力学，利用在道路滑行过程中监测到的滑行距离、滑行车速等，求得车辆滚动阻力系数。但是，台架测试方法与道路滑行试验方法的试验场景单一，不能模拟各种路面，且被测车辆缺乏对运行环境识别的能力，可能会导致实际运行环境中的车辆滚动阻力系数与台架测试或道路滑行试验所得的测试结果相差较大，降低车辆滚动阻力系数的辨识精度，从而影响车辆行驶过程中的经济性、动力性和安全性。在线辨识，即利用车辆实际行驶过程中收集到的数据进行车辆滚动阻力系数的辨识。但是，在线辨识方法中，可能会由于道路坡度的精度、车辆质量的变化、车辆加速度的精度的影响，降低车辆行驶滑动阻力的精度以及车辆滚动阻力系数的辨识精度，从而影响车辆行驶过程中的经济性、动力性和安全性。
发明内容
本申请提供一种获取车辆滚动阻力系数的方法及装置，基于目标车辆的实际驱动力、目标车辆的理论驱动力、目标车辆所处道路的路面图像信息以及目标车辆滚动阻力系数辨识模型，来确定目标车辆滚动阻力系数，以提高车辆滚动阻力系数的辨识精度，进而保证目标车辆在其行驶过程中的动力性、经济性和安全性。
为达到上述目的，本申请采用如下技术方案：
第一方面，本申请提供一种获取车辆滚动阻力系数的方法，涉及汽车技术领域，该方法包括：首先，获取目标车辆的理论驱动力和该目标车辆的实际驱动力。然后，基于目标车辆的理论驱动力及其实际驱动力，获取使得该理论驱动力和该实际驱动力的差值最小的第一车辆滚动阻力系数。并将目标车辆所处道路的路面图像信息输入到车辆滚动阻力系数辨识模型，得到目标车辆所处道路的路面类型对应的车辆滚动阻力系数的取值范围。其中，车辆滚动阻力系数辨识模型根据车辆滚动阻力系数数据库训 练得到，该车辆滚动阻力系数数据库包括不同车辆在不同类型路面上的车辆滚动阻力系数，以及不同类型路面对应的车辆滚动阻力系数的取值范围。最后，根据目标车辆所处道路的路面类型对应的车辆滚动阻力系数的取值范围和第一车辆滚动阻力系数，获取目标车辆滚动阻力系数。
通过上述过程，根据目标车辆的理论驱动力和其实际驱动力，来确定使目标车辆的理论驱动力和其实际驱动力的差值最小的第一车辆滚动阻力系数，可以减少目标车辆所处道路的道路坡度、目标车辆的速度、目标车辆的加速度等数据的精度，对第一车辆滚动阻力系数的辨识的影响，从而提高第一车辆滚动阻力系数的辨识精度。其次，根据目标车辆所处道路的路面图像信息和车辆滚动阻力系数辨识模型，来确定车辆所处道路的路面类型对应的车辆滚动阻力系数的取值范围，可以提高对目标车辆所处道路的路面类型的辨识精度。最后，根据目标车辆所处道路的路面类型对应的车辆滚动阻力系数的取值范围、第一车辆滚动阻力系数，来获取目标车辆滚动阻力系数，可以通过目标车辆所处道路的路面类型对应的车辆滚动阻力系数的取值范围，将目标车辆滚动阻力系数的取值约束在正常范围内，提高目标车辆滚动阻力系数的辨识精度，保证车辆在行驶过程中的动力性、经济性和安全性。
在一种可能的实现方式中，第一车辆滚动阻力系数不在目标车辆所处道路的路面类型对应的车辆滚动阻力系数的取值范围内。此时，将目标车辆所处道路的路面图像信息输入车辆滚动阻力系数辨识模型，得到目标车辆所处道路的路面类型对应的车辆滚动阻力系数的取值范围，包括：将目标车辆所处道路的路面图像信息输入车辆滚动阻力系数模型，得到目标车辆所处道路的路面类型对应的车辆滚动阻力系数的取值范围，以及第二车辆滚动阻力系数。根据目标车辆所处道路的路面类型对应的车辆滚动阻力系数的取值范围和第一车辆滚动阻力系数，获取目标车辆滚动阻力系数，包括：将第二车辆滚动阻力系数作为目标车辆滚动阻力系数。
通过上述过程，本申请可以根据目标车辆所处道路的路面图像信息和车辆滚动阻力系数辨识模型，来直接确定第二车辆滚动阻力系数，降低利用直接根据经验所得到的目标车辆所处道路的路面图像信息对应的路面类型，对第二车辆滚动阻力系数的辨识精度的影响，提高第二车辆滚动阻力系数的辨识精度，从而提高目标车辆滚动阻力系数的辨识精度。并利用目标车辆所处道路的路面类型对应的车辆滚动阻力系数的取值范围，对目标车辆滚动阻力系数的取值进行约束，以进一步提高目标车辆滚动阻力系数的辨识精度。
在一种可能的实现方式中，第一车辆滚动阻力系数在目标车辆所处道路的路面类型对应的车辆滚动阻力系数的取值范围内。此时，将目标车辆所处道路的路面图像信息输入车辆滚动阻力系数辨识模型，得到目标车辆所处道路的路面类型对应的车辆滚动阻力系数的取值范围，包括：将目标车辆所处道路的路面图像信息输入车辆滚动阻力系数模型，得到目标车辆所处道路的路面类型对应的车辆滚动阻力系数的取值范围，以及第二车辆滚动阻力系数。将第一车辆滚动阻力系数与第二车辆滚动阻力系数进行融合，得到目标融合结果。根据目标车辆所处道路的路面类型对应的车辆滚动阻力系数的取值范围和第一车辆滚动阻力系数，获取目标车辆的车辆滚动阻力系数，包括：将该目标融合结果作为目标车辆滚动阻力系数。
通过上述过程，本申请可以在第一车辆滚动阻力系数在目标车辆所处道路的路面类型对应的车辆滚动阻力系数的取值范围内时，通过将第一车辆滚动阻力系数和第二车辆滚动阻力系数的目标融合结果，作为目标车辆滚动阻力系数，可以进一步提高目标车辆滚动阻力系数的辨识精度，保证车辆在行驶过程中的动力性、经济性和安全性。
在一种可能的实现方式中，将第一车辆滚动阻力系数以及第二车辆滚动阻力系数进行融合，得到目标融合结果，包括：将第一车辆滚动阻力系数以及第二车辆滚动阻力系数，输入卡尔曼滤波器，得到目标融合结果。其中，卡尔曼滤波器为集中式卡尔曼滤波器或联合式卡尔曼滤波器。
通过上述过程，本申请可以利用卡尔曼滤波器，对第一车辆滚动阻力系数和第二滚动阻力系数进行滤波融合，并将得到的目标融合结果确定为目标车辆滚动阻力系数，从而提高目标车辆滚动阻力系数的辨识精度，保证车辆在行驶过程中的动力性、经济性和安全性。
在一种可能的实现方式中，获取目标车辆的理论驱动力和实际驱动力，包括：获取目标车辆所处道路的道路坡度、目标车辆的加速度、目标车辆的速度以及目标车辆的实际驱动力。然后，将目标车辆所处道路的道路坡度、目标车辆的加速度、目标车辆的速度，输入目标车辆的纵向动力学模型，得到目标车辆的理论驱动力，该目标车辆的纵向动力学模型用于表示目标车辆的纵向运动的运动规律。
通过上述过程，本申请可以根据目标车辆的实际驱动力和目标车辆的理论驱动力来确定第一车辆滚动阻力系数，从而减少车辆载荷、车辆加速度、车辆速度以及车辆所处道路的道路坡度的辨识精度对确定第一车辆滚动阻力系数的影响，提高第一车辆滚动阻力系数的辨识精度，以保证目标车辆在其行驶过程中的动力性、经济性和安全性。
在一种可能的实现方式中，根据目标车辆的电机扭矩、油门踏板开度以及制动踏板开度，来确定目标车辆的实际驱动力，可以减少利用目标车辆所处道路的道路坡度、目标车辆的加速度以及目标车辆的速度进行计算，来获取目标车辆的实际驱动力时，目标车辆所处道路的道路坡度、目标车辆的加速度以及目标车辆的速度的测量精度对目标车辆的实际驱动力的计算的影响，提高车辆实际驱动力的辨识精度，从而提高第一车辆滚动阻力系数的辨识精度。
在一种可能的实现方式中，基于理论驱动力和实际驱动，获取第一车辆滚动阻力系数，包括：基于最小二乘法，根据理论驱动力和实际驱动力，确定第一车辆滚动阻力系数。
在一种可能的实现方式中，该方法还包括：基于最小二乘法，根据理论驱动力和实际驱动力，确定使得理论驱动力和实际驱动力的差值最小的车辆载荷为目标车辆载荷。
通过上述过程，本申请可以基于最小二乘法，根据目标车辆的实际驱动力和目标车辆的理论驱动力，来确定目标车辆载荷，以在提高车辆滚动阻力系数的辨识精度的基础上，提高车辆载荷的辨识精度，从而进一步保证车辆在行驶过程中的动力性、经济性和安全性。
在一种可能的实现方式中，该方法还包括：将目标车辆所处道路的道路坡度、目 标车辆的加速度、目标车辆的速度以及目标车辆的纵向动力学模型，得到目标车辆载荷，该目标车辆载荷为目标车辆的实际驱动力和目标车辆的理论驱动力的差值最小时的车辆载荷。
通过上述过程，本申请可以根据已得到的目标车辆滚动阻力系数和车辆的纵向动力学模型，来确定目标车辆载荷，进一步提高目标车辆荷载的辨识精度，从而保证目标车辆在其行驶过程中的动力性、经济性和安全性。
第二方面，本申请提供一种获取车辆滚动阻力系数的装置，涉及汽车技术领域，该装置包括获取单元和处理单元：获取单元用于获取目标车辆的理论驱动力和该目标车辆的实际驱动力。然后，处理单元用于基于目标车辆的理论驱动力和目标车辆的实际驱动力，获取第一车辆滚动阻力系数，该第一车辆滚动阻力系数使得目标车辆的实际驱动力和理论驱动力的差值最小。再然后，处理单元还用于将目标车辆所处道路的路面图像信息输入车辆滚动阻力系数辨识模型，来获取目标车辆所处道路的路面类型对应的车辆滚动阻力系数的取值范围。其中，车辆滚动阻力系数辨识模型根据车辆滚动阻力系数数据库训练得到，车辆滚动阻力系数数据库包括不同车辆在不同类型路面上的车辆滚动阻力系数的取值范围。最后，处理单元还用于根据目标车辆所处道路的路面类型对应的车辆滚动阻力系数的取值范围和第一车辆滚动阻力系数，获取目标车辆滚动阻力系数。
在一种可能的实现方式中，第一车辆滚动阻力系数不在目标车辆所处道路的路面类型对应的车辆滚动阻力系数的取值范围内，处理单元具体用于将目标车辆所处道路的路面图像信息输入车辆滚动阻力系数辨识模型，得到目标车辆所处道路的路面类型对应的车辆滚动阻力系数的取值范围和第二车辆滚动阻力系数。然后，处理单元具体还用于将第二车辆滚动阻力系数作为目标车辆滚动阻力系数。
在一种可能的实现方式中，第一车辆滚动阻力系数在目标车辆所处道路的路面类型对应的车辆滚动阻力系数的取值范围内，则处理单元具体用于将第一车辆滚动阻力系数和第二车辆滚动阻力系数进行融合，得到目标融合结果。然后，该处理单元具体还用于将目标融合结果作为目标车辆滚动阻力系数。
在一种可能的实现方式中，处理单元具体用于将第一车辆滚动阻力系数以及第二车辆滚动阻力系数，输入卡尔曼滤波器进行滤波融合，得到目标融合结果。其中，卡尔曼滤波器为集中式卡尔曼滤波器或者联合式卡尔曼滤波器。
在一种可能的实现方式中，获取单元具体获取目标车辆所处道路的道路坡度、目标车辆的加速度、目标车辆的速度以及目标车辆的实际驱动力。然后，获取单元具体还用于将目标车辆所处道路的道路坡度、目标车辆的加速度、以及目标车辆的速度输入目标车辆的纵向动力学模型，以得到目标车辆的理论驱动力。其中，该目标车辆的纵向动力学模型用于表示目标车辆的纵向运动的运动规律。
在一种可能的实现方式中，处理单元具体还用于基于最小二乘法，根据目标车辆的实际驱动力与理论驱动力，确定第一车辆滚动阻力系数。
在一种可能的实现方式中，处理单元具体还用于基于最小二乘法，根据目标车辆的理论驱动力和实际驱动力，确定目标车辆载荷。其中，该目标车辆载荷为使得目标车辆的实际驱动力与其理论驱动力的差值最小的车辆载荷。
在一种可能的实现方式中，处理单元具体还用于将目标车辆所处道路的道路坡度、目标车辆的加速度、目标车辆的速度和目标车辆滚动阻力系数输入车辆的纵向动力学模型，得到目标车辆载荷。其中，该目标车辆载荷为目标车辆的实际驱动力与其理论驱动力的差值最小时的车辆载荷。
第三方面，本申请提供一种获取车辆滚动阻力系数的装置，该装置包括：处理器和存储器；其中，存储器用于存储计算机程序指令，处理器运行计算机程序指令以使该获取车辆滚动阻力系数的装置执行第一方面所述的获取车辆滚动阻力系数的方法。
第四方面，本申请提供一种计算机可读存储介质，包括计算机指令，当该计算机指令被处理器运行时，使得获取车辆滚动阻力系数的装置执行如第一方面所述的获取车辆滚动阻力系数的方法。
第五方面，本申请提供一种计算机程序产品，其特征在于，当该计算机程序产品在处理器上运行时，使得获取车辆滚动阻力系数的装置执行如第一方面所述的获取车辆滚动阻力系数的方法。
附图说明
图1为本申请实施例提供的车辆在不同路面类型下的车辆滚动阻力系数的取值；
图2为本申请实施例提供的一种车辆的结构示意图；
图3为本申请实施例提供的一种计算机***的结构示意图；
图4为本申请实施例提供的一种云侧指令自动驾驶车辆的应用示意图一；
图5为本申请实施例提供的一种云侧指令自动驾驶车辆的应用示意图二；
图6为本申请实施例提供的一种计算机程序产品的结构示意图；
图7为本申请实施例提供的一种获取车辆滚动阻力系数的方法的流程示意图；
图8为本申请实施例提供的一种车辆滚动阻力系数数据库存储的数据的示意图；
图9为本申请实施例提供的一种模型训练的过程的示意图；
图10为本申请实施例提供的一种确定目标车辆滚动阻力系数的流程示意图；
图11为本申请实施例提供的一种通过卡尔曼滤波器对第一车辆滚动阻力系数与第二车辆滚动阻力系数进行滤波融合的示意图一；
图12为本申请实施例提供的一种通过卡尔曼滤波器对第一车辆滚动阻力系数与第二车辆滚动阻力系数进行滤波融合的示意图二；
图13为本申请实施例提供的一种获取车辆滚动阻力系数的装置的结构示意图。
具体实施方式
本申请实施例提供的获取车辆滚动阻力系数的方法应用在车辆中，或者应用于可与车辆进行通信的其他设备(比如云端服务器)中。车辆可通过其包含的组件(包括硬件和软件)实施本申请实施例提供的获取车辆滚动阻力系数的方法，根据车辆的实际驱动力、车辆的理论驱动力、车辆所处道路的路面图像信息以及车辆滚动阻力系数辨识模型，来确定第一车辆滚动阻力系数、第二车辆滚动系数和车辆所处道路的路面类型，进而根据该第一滚动阻力系数和第二车辆滚动阻力系数确定目标车辆滚动阻力系数，即车辆在当前道路上时的车辆滚动阻力系数。或者，通过其他设备(比如服务器、手机终端等)实施本申请实施例的获取车辆滚动阻力系数的方法，来确定车辆在当前道路上时的车辆滚动阻力系数，提高车辆滚动阻力系数的辨识精度，保证车辆行 驶过程中的动力性、经济性和安全性。
参见图2，示例性的，车辆中可以包括以下模块：
感知模块201，用于通过车载传感器以及路侧传感器获取车辆的状态信息，例如车辆的速度、车辆所处道路的道路坡度、车辆的加速度、车辆的电机扭矩、车辆的油门踏板开度、车辆的制动踏板开度等等。感知模块201，用于通过对航位推算(dead reckoning，DR)传感器监测到的数据进行计算，来获取车辆的状态信息。其中，DR传感器即所采集到的数据用于航位推算的传感器，该DR传感器中包括例如车辆的传感器***中的传感器，例如陀螺仪、加速度计、速度传感器、GPS传感器等等，或者该DR传感器中包括上述提到的车载传感器以及路侧传感器。DR是一种常用的定位技术，其基本原理是利用方向传感器和速度传感器等传感器采集到的数据来推算车辆的瞬时位置，以实现车辆的连续自主式定位。感知模块201，用于通过视觉传感器等获取周围环境中的图像信息，例如车辆所处道路的路面图像信息。感知模块201，用于对直接从车辆的行进***或控制***等获取车辆的电机扭矩、车辆的油门踏板开度、车辆的制动踏板开度等信息，或利用传感器(例如角度传感器)对油门踏板、制动踏板等进行监测到的数据进行计算，来确定车辆的电机扭矩、车辆的油门踏板开度、车辆的制动踏板开度等信息。感知模块201，还用于将传感器监测到的数据，以及通过对传感器监测到的数据进行计算所确定的车辆的速度、车辆的加速度、车辆所处道路的道路坡度、车辆的电机扭矩、车辆的油门踏板开度、车辆的制动踏板开度等信息，发送给中央处理模块202以及规划控制模块203。
中央处理模块202，用于获取感知模块201发送的传感器监测到的数据，以及对感器监测到的数据进行计算确定的车辆的速度、车辆的加速度、车辆所处道路的道路坡度、车辆的电机扭矩、车辆的油门踏板开度、车辆的制动踏板开度等信息。中央处理模块201用于根据车辆的电机扭矩、车辆的油门踏板开度以及车辆的制动踏板开度等，确定车辆的驱动力，即车辆的实际驱动力。中央处理模块202，还用于将其从感知模块201获取到的信息中的车辆的速度、车辆的加速度、车辆所处道路的道路坡度等，输入到车辆的纵向动力学模型中，以得到车辆的驱动力即车辆的理论驱动力。其中，车辆的理论驱动力与车辆滚动阻力系数和车辆载荷相关联，车辆滚动阻力系数为车辆在一定条件下的行驶过程中所需的驱动力与车辆载荷的比值。中央处理模块202，还用于确定使得车辆的实际驱动力和车辆的理论驱动力的差值最小的车辆滚动阻力系数为第一车辆滚动阻力系数。中央处理模块202，还用于将传感器获取到的车辆周围环境中的图像信息，即车辆所处道路的路面图像信息，输入到车辆滚动阻力系数辨识模型中，得到车辆所处道路的路面类型对应的车辆滚动阻力系数的取值范围，以及车辆的滚动阻力系数即第二车辆滚动阻力系数。可选的，中央处理模块202，还用于根据车辆滚动阻力系数数据库以及深度学习算法进行模型训练，得到车辆滚动阻力系数辨识模型，其中，车辆滚动阻力系数数据库用于存储不同车辆在不同类型路面上的车辆滚动阻力系数，以及不同类型路面对应的车辆滚动阻力系数的取值范围。中央处理模块202，还用于根据第一车辆滚动阻力系数是否在车辆所处道路的路面类型对应的车辆滚动阻力系数的取值范围内，将第一车辆滚动阻力系数和第二车辆滚动阻力系数进行融合得到的目标融合结果，或者第二车辆滚动阻力系数，确定为目标车辆滚动 阻力系数，即本申请实施例所求的车辆在当前场景下的车辆滚动阻力系数。中央处理模块202，还用于将目标车辆滚动阻力系数发送给规划控制模块203。
规划控制模块203，用于获取感知模块201发送的传感器监测到的数据，以及对传感器监测到的数据进行计算确定的车辆的速度、车辆的加速度、车辆所处道路的道路坡度、车辆的电机扭矩、车辆的油门踏板开度、车辆的制动踏板开度等信息。规划控制模块203，用于获取中央处理模块202发送的目标车辆滚动系数。规划控制模块203，还用于根据获取到的信息，对车辆的行驶路径进行规划，生成驾驶策略，并输出与该驾驶策略对应的动作指令，以根据该指令控制车辆进行自动驾驶。该模块是自动驾驶车辆所具备的传统控制模块。
车载通信模块204(图2中未示出)，用于自车和其他车辆之间的信息交互。
存储组件205(图2未示出)，用于存储上述各个模块的可执行代码，运行这些可执行代码可实现本申请实施例的部分或全部方法流程。
在本申请实施例的一种可能的实现方式中，如图3所示，车辆的计算机***(该计算机***位于车辆内或车辆外均可)包括处理器301，处理器301和***总线302耦合，处理器301可以是一个或者多个处理器，其中每个处理器都可以包括一个或多个处理器核。显示适配器(video adapter)303可以驱动显示器324，显示器324和***总线302耦合。***总线302通过总线桥304和输入输出(I/O)总线(BUS)305耦合，I/O接口306和I/O总线305耦合，I/O接口306和多种I/O设备进行通信，比如输入设备307(如：键盘，鼠标，触摸屏等)，多媒体盘(media tray)308(例如，CD-ROM，多媒体接口等)。收发器309(可以发送和/或接收无线电通信信号)，摄像头310(可以捕捉静态和动态数字视频图像)和外部通用串行总线(universal serial bus，USB)端口311。其中，可选地，和I/O接口306相连接的接口可以是USB接口。
其中，处理器301可以是任何传统处理器，包括精简指令集计算(reduced instruction set computer，RISC)处理器、复杂指令集计算(complex instruction set computer，CISC)处理器或上述的组合。可选地，处理器301还可以是诸如专用集成电路(ASIC)的专用装置。可选地，处理器301还可以是神经网络处理器或者是神经网络处理器和上述传统处理器的组合。
可选地，在本申请所述的各种实施例中，如上，计算机***用于数据处理，该计算机***可位于远离自动驾驶车辆的地方，且与自动驾驶车辆无线通信，或者位于自动驾驶车辆上。在其它方面，本申请所述的一些过程可设置在自动驾驶车辆内的处理器上执行，其它一些过程由远程处理器执行，包括采取执行单个操纵所需的动作。
计算机***可以通过网络接口312和软件部署服务器(deploying server)313通信。可选的，网络接口312可以是硬件网络接口，比如网卡。网络(network)314可以是外部网络，比如因特网，也可以是内部网络，比如以太网或者虚拟私人网络(virtual private network，VPN)，可选地，network314还可以为无线网络，比如WiFi网络、蜂窝网络等。
硬盘驱动器接口315和***总线302耦合。硬盘驱动器接口315和硬盘驱动器316相连接。***内存317和***总线302耦合。运行在***内存317的数据可以包括计 算机***的操作***(OS)318和应用程序319。
操作***(OS)318包括但不限于Shell 320和内核(kernel)321。Shell 320是介于使用者和操作***318的kernel 321间的一个接口。Shell 320是操作***318最外面的一层。shell管理使用者与操作***318之间的交互：等待使用者的输入，向操作***318解释使用者的输入，并且处理各种各样的操作***318的输出结果。
内核321由操作***318中用于管理存储器、文件、外设和***资源的部分组成，直接与硬件交互。操作***318的内核321通常运行进程，并提供进程间的通信，提供中央处理器(central processing unit，CPU)时间片管理、中断、内存管理、IO管理等功能。
应用程序319包括自动驾驶相关的程序323，比如，管理自动驾驶汽车和路上障碍物交互的程序，控制自动驾驶汽车的行驶路线或者速度的程序，控制自动驾驶汽车和路上其他汽车/自动驾驶汽车交互的程序等。应用程序319也存在于deploying server313的***上。在一个实施例中，在需要执行应用程序319时，计算机***可以从
deploying server 313下载应用程序319。
又比如，应用程序319可以是控制车辆根据上述车辆的状态信息、车辆的周围环境信息以及传统控制模块，例如上述规划控制模块203，确定驾驶策略的应用程序。计算机***的处理器301调用该应用程序319，得到驾驶策略。
传感器322和计算机***关联，则传感器322用于探测计算机***周围的环境。举例来说，传感器322可以探测动物，汽车，障碍物和/或人行横道等。进一步传感器322还可以探测上述动物，汽车，障碍物和/或人行横道等物体周围的环境。比如：动物周围的环境，例如，动物周围出现的其他动物，天气条件，动物周围环境的光亮度等。可选地，计算机***还可位于自动驾驶的汽车上。可选的，传感器322可以是摄像头，红外线感应器，化学检测器，麦克风等器件中的至少一项。
在本申请的另一些实施例中，计算机***还可以从其它计算机***接收信息或转移信息到其它计算机***。或者，从车辆的传感器***收集的传感器数据可以被转移到另一个计算机，由另一计算机对此数据进行处理。如图4所示，来自计算机***的数据可以经由网络被传送到云侧的计算机***410用于进一步的处理。网络以及中间节点可以包括各种配置和协议，包括因特网、万维网、内联网、虚拟专用网络、广域网、局域网、使用一个或多个公司的专有通信协议的专用网络、以太网、WiFi和HTTP、以及前述的各种组合。这种通信可以由能够传送数据到其它计算机和从其它计算机传送数据的任何设备执行，诸如调制解调器和无线接口。
在一个示例中，计算机***410可以包括具有多个计算机的服务器，例如负载均衡服务器群。为了从计算机***接收、处理并传送数据，服务器420与网络的不同节点交换信息。该计算机***410可以具有类似于计算机***的配置，并具有处理器430、存储器440、指令450、和数据460。
在一个示例中，服务器420的数据460可以包括提供道路相关的信息。例如，服务器420可以接收、监视、存储、更新、以及传送与周边环境中的道路相关的各种信息。该信息可以包括例如以报告形式、雷达信息形式、预报形式等的路面状况、以及车辆状况。
参见图5，为自主驾驶车辆和云服务中心(云服务器)交互的示例。云服务中心可以经诸如无线通信网络的网络511，从其操作环境500内的车辆513、车辆512(车辆513和车辆512可以为自动驾驶车辆)接收信息(诸如车辆传感器收集到数据或者其它信息)。
云服务中心520根据接收到的数据，运行其存储的控制汽车自动驾驶相关的程序对车辆513、车辆512进行控制。控制汽车自动驾驶相关的程序可以为：管理自动驾驶汽车和路上障碍物交互的程序，或者控制自动驾驶汽车路线或者速度的程序，或者控制自动驾驶汽车和路上其他自动驾驶汽车交互的程序。
示例性的，云服务中心520通过网络511可将地图的部分提供给车辆513、车辆512。在其它示例中，可以在不同位置之间划分操作。例如，多个云服务中心可以接收、证实、组合和/或发送信息报告。在一些示例中还可以在车辆之间发送信息报告和/或传感器数据。其它配置也是可能的。
在一些示例中，云服务中心520向自动驾驶车辆发送关于操作环境内可能的驾驶情况所建议的解决方案(如，告知前方障碍物，并告知如何绕开它)。例如，云服务中心520可以辅助车辆确定当面对环境内的特定障碍时如何行进。云服务中心520向自动驾驶车辆发送指示该车辆应当在给定场景中如何行进的响应。例如，云服务中心520基于收集到的传感器数据，可以确认道路前方具有临时停车标志的存在，又比如，基于“车道封闭”标志和施工车辆的传感器数据，确定该车道由于施工而被封闭。相应地，云服务中心520发送用于车辆通过障碍的建议操作模式(例如：指示车辆变道另一条道路上)。云服务中心520观察其操作环境500内的视频流，并且已确认自动驾驶车辆能安全并成功地穿过障碍时，对该自动驾驶车辆所使用的操作步骤可以被添加到驾驶信息地图中。相应地，这一信息可以发送到该区域内可能遇到相同障碍的其它车辆，以便辅助其它车辆识别出封闭的车道并顺利通过。
在一些实施例中，所公开的方法可以实施为以机器可读格式，被编码在计算机可读存储介质上的或者被编码在其它非瞬时性介质或者制品上的计算机程序指令。图6示意性地示出根据这里展示的至少一些实施例而布置的示例计算机程序产品的概念性局部视图，示例计算机程序产品包括用于在计算设备上执行计算机进程的计算机程序。在一个实施例中，示例计算机程序产品600是使用信号承载介质601来提供的。信号承载介质601可以包括一个或多个程序指令602，其当被一个或多个处理器运行时可以提供以上针对图2至图5描述的全部功能或者部分功能，或者可以提供后续实施例中描述的全部或部分功能。例如，参考图7中所示的实施例，S701至S704中的一个或多个特征可以由与信号承载介质601相关联的一个或多个指令来承担。此外，图6中的程序指令602也描述示例指令。
在一些示例中，信号承载介质601可以包含计算机可读介质603，诸如但不限于，硬盘驱动器、紧密盘(CD)、数字视频光盘(DVD)、数字磁带、存储器、只读存储记忆体(read-only memory，ROM)或随机存储记忆体(random access memory，RAM)等等。在一些实施方式中，信号承载介质601可以包含计算机可记录介质604，诸如但不限于，存储器、读/写(R/W)CD、R/W DVD、等等。在一些实施方式中，信号承载介质601可以包含通信介质605，诸如但不限于，数字和/或模拟通信介质(例如，光纤 电缆、波导、有线通信链路、无线通信链路、等等)。因此，例如，信号承载介质601可以由无线形式的通信介质605(例如，遵守IEEE 802.11标准或者其它传输协议的无线通信介质)来传达。一个或多个程序指令602可以是，例如，计算机可执行指令或者逻辑实施指令。在一些示例中，诸如针对图2至图4描述的计算设备可以被配置为，响应于通过计算机可读介质603、和/或计算机可记录介质604和/或通信介质605中的一个或多个传达到计算设备的程序指令602，提供各种操作、功能、或者动作。应该理解，这里描述的布置仅仅是用于示例的目的。因而，本领域技术人员将理解，其它布置和其它元素(例如，机器、接口、功能、顺序、和功能组等等)能够被取而代之地使用，并且一些元素可以根据所期望的结果而一并省略。另外，所描述的元素中的许多是可以被实现为离散的或者分布式的组件的、或者以任何适当的组合和位置来结合其它组件实施的功能实体。
为了提高车辆滚动阻力系数的辨识精度，即为了提高本申请实施例中的目标车辆滚动阻力系数的辨识精度，保证车辆行驶过程中的经济性、动力性和安全性，本申请提出了一种获取车辆滚动阻力系数的方法，该方法的执行主体可以为车辆(例如自动驾驶车辆)或者车辆之外的其他设备，也可以是车辆或者车辆之外的其他设备上的处理器，例如上述内容中提到的处理器301以及处理器430等。如图7所示，以目标车辆为例，该方法包括步骤S701～S704：
S701、获取目标车辆的理论驱动力以及目标车辆的实际驱动力。
其中，目标车辆的实际驱动力是根据目标车辆的电机扭矩、目标车辆的油门踏板开度以及目标车辆的制动踏板开度得到的。若目标车辆的刹车踏板开度为0，则该目标车辆在当前时刻未刹车，若目标车辆的刹车踏板开度大于0，则该目标车辆在当前时刻处于刹车状态。同理，若目标车辆的油门踏板开度为0，则该目标车辆在当前时刻未加速，若目标车辆的油门踏板开度大于0，则该目标车辆在当前时刻处于加速状态。
可选的，获取DR传感器监测到的数据，并利用DR技术对监测到的数据进行计算，以得到目标车辆的状态信息，该目标车辆的状态信息包括目标车辆所处道路的道路坡度、目标车辆的速度、目标车辆的加速度、目标车辆的电机扭矩、目标车辆的油门踏板开度、目标车辆的刹车踏板开度等信息。然后，根据目标车辆的电机扭矩、目标车辆的油门踏板开度和目标车辆的制动踏板开度，来获取目标车辆的实际驱动力。
在一种可能的实现方式中，目标车辆的电机扭矩、目标车辆的油门踏板开度以及目标车辆的刹车踏板开度，可以是通过对角度传感器等传感器监测到的数据进行计算得到的，也可以是从目标车辆的行进***或控制***直接获取到的。
在一种可能的实现方式中，上述DR传感器包括陀螺仪、加速度计、速度传感器、角度传感器、视觉传感器等。
在一种可能的实现方式中，利用传感器在同一时刻监测到的数据进行计算，得到目标车辆所处道路的道路坡度、目标车辆的速度、目标车辆的加速度，还得到目标车辆的电机扭矩、目标车辆的油门踏板开度以及目标车辆的刹车踏板开度等信息，这些信息之间存在关联关系，例如时间上的关联关系。因此，可以根据目标车辆的刹车踏板开度，对传感器获取到的数据以及根据该数据计算得到的上述信息进行筛选，然后 利用通过筛选的数据和信息，经过本步骤S701来获取目标车辆的理论驱动力，并利用目标车辆的电机扭矩、目标车辆的油门踏板开度以及目标车辆的刹车踏板开度等信息，获取目标车辆的实际驱动力。
示例性的，为了使得用于获取目标车辆的实际驱动力和目标车辆的理论驱动力的信息，符合下述示例中给出的目标车辆的纵向动力学模型，可将目标车辆的刹车踏板开度为0，即目标车辆处于不刹车的状态作为筛选条件。若目标车辆的刹车踏板开度为0时，则可获取与该目标车辆的刹车踏板开度在同一时刻确定的目标车辆所处道路的道路坡度、目标车辆速度、目标车辆加速度、目标车辆的电机扭矩、目标车辆的油门踏板开度等信息，以及用于确定该信息的传感器所获取到的数据通过筛选。
可选的，将目标车辆所处道路的道路坡度、目标车辆的加速度以及目标车辆的速度输入目标车辆的纵向动力学模型，可得到目标车辆的理论驱动力。其中，目标车辆的纵向动力学模型用于表示目标车辆的纵向运动的运动规律。目标车辆的理论驱动力与目标车辆的滚动阻力系数和车辆载荷相关联，也就是说，本步骤S701中所求得的目标车辆的理论驱动力为关于车辆滚动阻力系数与车辆载荷的表达式。另外，车辆滚动阻力系数为车辆的车轮在一定条件下滚动时所需的车辆驱动力与车辆载荷的比值，该车辆滚动阻力系数与其车轮轮胎的结构材料、轮胎充气气压、路面类型、道路状况、车辆的行驶速度、车辆的受力情况等相关。
示例性的，上述提到的一定条件可以是空气阻力为0，目标车辆当前所在的道路为平坦道路，且目标车辆以匀速行驶等，也就是说，目标车辆当前所在的道路是没有坡度的，且不考虑目标车辆的加速度以及空气阻力等。
示例性的，目标车辆的纵向动力学模型为F t＝μmg cosθ+mg sinθ+0.5ρC dAv 2+δma，其中，F t表示目标车辆的驱动力，即目标车辆的理论驱动力，μ表示车辆滚动阻力系数，m表示车辆载荷，g表示重力系数，θ表示目标车辆所处道路的道路坡度，μmg cosθ表示滚动阻力，mg sinθ表示坡度阻力，ρ表示空气密度，C d表示空气阻力系数，A表示目标车辆的迎风面积，v表示目标车辆的速度，0.5ρC dAv 2表示空气阻力，δ表示转动惯量系数，a表示目标车辆的加速度，δma表示加速阻力。已知重力系数、空气密度、空气阻力系数、目标车辆的迎风面积以及转动惯量系数，将目标车辆的速度、目标车辆的加速度、目标车辆所处道路的道路坡度输入该目标车辆的纵向动力学模型中，即可得到目标车辆的理论驱动力。
S702、基于目标车辆的理论驱动力和目标车辆的实际驱动力，获取第一车辆滚动阻力系数。
其中，第一车辆滚动阻力系数为使得目标车辆的实际驱动力与目标车辆的理论驱动力的差值最小的车辆滚动阻力系数。
可选的，基于最小二乘法，根据上述步骤S701中得到的目标车辆的实际驱动力和目标车辆的理论驱动力，来获取使得目标车辆的实际驱动力与目标车辆的理论驱动力的差值最小的车辆滚动阻力系数为第一车辆滚动阻力系数。
在一种可能的实现方式中，基于最小二乘法，还可获取到使得目标车辆的实际驱动力和目标车辆的理论驱动力的差值最小时的车辆载荷为目标车辆载荷。
示例性的，目标车辆的实际驱动力为F′ t(k)，目标车辆的理论驱动力为 F t(k)＝μmgcosθ(k)+mgsinθ(k)+δm a(k)+0.5ρCdAv 2(k)，则目标车辆的实际驱动力与目标车辆的理论驱动力的差值为|F′ t(k)-F t(k)|。从k＝1时刻到k＝N(N>＝1)时刻的目标车辆的实际驱动力与目标车辆的理论驱动力的差值的平方和为L，
Figure PCTCN2020090703-appb-000001
目标车辆的实际驱动力与目标车辆的理论驱动力的差值最小，也就是说，L的取值最小。为了确定第一车辆滚动阻力系数μ 1，也就是使得目标车辆的实际驱动力F′ t(k)与目标车辆的理论驱动力F t(k)的差值最小的车辆滚动阻力系数，令以μ为未知数的L的导数为0，且以m为未知数的L的导数为0，则此时的车辆滚动阻力系数即为第一车辆滚动阻力系数μ 1。可选的，此时的车辆载荷即为目标车辆载荷m。
示例性的，在上述示例中，μ与m之间存在非线性的关联关系，可能会使得数据处理过程较为复杂，在本示例中令μm＝p，则可以将μ与m之间的非线性关系转化为p与m之间的线性关系，使得数据处理过程易于实施。目标车辆的实际驱动力为F′ t(k)，目标车辆的理论驱动力为F t(k)＝pg cosθ(k)+mgsinθ(k)+δm a(k)+0.5ρCdAv 2(k)，则目标车辆的实际驱动力与目标车辆的理论驱动力的差值为|F′ t(k)-F t(k)|。从k＝1时刻到k＝N(N>＝1)时刻的目标车辆的实际驱动力与目标车辆的理论驱动力的差值的平方和为L，
Figure PCTCN2020090703-appb-000002
目标车辆的实际驱动力与目标车辆的理论驱动力的差值最小，也就是说，L的取值最小。为了确定第一车辆滚动阻力系数μ 1，也就是使得目标车辆的实际驱动力F′ t(k)与目标车辆的理论驱动力F t(k)的差值最小的车辆滚动阻力系数，令以p为未知数的L的导数为0，且以m为未知数的L的导数为0，则此时的p＝μ 1m 1，μ 1即为第一车辆滚动阻力系数。可选的，m 1即为目标车辆载荷m。
需要说明的是，通过上述过程，本申请可以按照最小二乘法，根据目标车辆的实际驱动力和目标车辆的理论驱动力，来获取第一车辆滚动阻力系数以及目标车辆载荷，以在提高车辆滚动阻力系数的辨识精度的基础上，提高车辆载荷的辨识精度，从而进一步保证车辆在行驶过程中的动力性、经济性和安全性。
S703、将目标车辆所处道路的路面图像信息输入车辆滚动阻力系数辨识模型，得到车辆所处道路的路面类型对应的车辆滚动阻力系数的取值范围。
其中，车辆滚动阻力系数辨识模型根据车辆滚动阻力系数数据库训练得到，更具体地，车辆滚动阻力系数辨识模型根据车辆滚动阻力系数数据库以及深度学习算法进行模型训练得到。该车辆滚动阻力系数辨识模型用于根据车辆所处道路的路面图像信息进行识别，确定车辆所处道路的路面类型对应的车辆滚动阻力系数的取值范围。车辆滚动阻力系数数据库用于存储不同车辆在不同类型路面上的车辆滚动阻力系数，以及不同类型路面对应的车辆滚动阻力系数的取值范围。
在一种可能的实现方式中，车辆滚动阻力系数数据库可以根据路面类型对其存储的路面图像信息进行编号，针对每一编号分别存储该编号对应的路面类型的图像信息、该路面类型对应的车辆滚动阻力系数的取值范围，以及不同车辆在该路面类型的道路上通过时的车辆滚动阻力系数。可选的，车辆滚动阻力系数数据库中还可以存储不同轮胎类型的车辆在不同类型路面上行驶时的车辆滚动阻力系数等。
示例性的，车辆滚动阻力系数数据库可以存储有图8所示数据。其中，编号1-10对应的路面类型分别为干燥的土路、结冰的沥青路、干燥的砂石路、泥土路、雨天水泥路、干燥的水泥路、干燥的石块路、雨天沥青路以及晴天沥青路，这10个路面类型对应的车辆滚动阻力系数分别为[a1，a2]、[a3，a4]、[a5，a6]、[a7，a8]、[a9，a10]、[a11，a12]、[a13，a14]、[a15，a16]以及[a17，a18]，且该车辆滚动阻力系数数据库中还存储有每一路面类型的图像信息，每一路面类型对应的路面图像信息中至少包含一张图像，例如图8中所示b1-b18等。
示例性的，以深度学习算法中的卷积神经网络为例，根据车辆滚动阻力系数数据库以及深度学习算法进行模型训练的过程可如图9所示，将车辆滚动阻力系数数据库中的路面图像信息输入深度学习模型中，得到路面类型以及车辆在当前状况下的车辆滚动阻力系数μ 2。也就是说，可以得到输入该深度模型中的路面图像信息的路面类型所对应的车辆滚动阻力系数的取值范围[μ min，μ max]以及车辆在当前状况下的车辆滚动阻力系数μ 2。其中，该深度学习模型中包括卷积层(convolutional layer)1、池化层(pooling layer)1、卷积层2、池化层2以及全连接层(fully-connected layer)。
可选的，将目标车辆所处道路的路面图像信息输入到车辆滚动阻力系数辨识模型，由该车辆滚动阻力系数辨识模型对输入的路面图像信息进行识别，输出该路面图像信息所对应的路面类型，也就是目标车辆所处道路的路面类型。然后，根据该路面类型在车辆滚动阻力系数数据库中进行查询，确定该路面类型所对应的车辆滚动阻力系数的取值范围。其中，该路面图像信息可以是通过视觉传感器等获取到的目标车辆所处道路的路面的图像信息。通过该过程，本申请可以减少单一环境的限制，获取目标车辆在各种环境下的车辆滚动阻力系数，以提高车辆滚动阻力系数的辨识精度，保证目标车辆在其行驶过程中的经济性、安全性和动力性。
S704、根据目标车辆所处道路的路面类型对应的车辆滚动阻力系数的取值范围和第一车辆滚动阻力系数，获取目标车辆滚动阻力系数。
可选的，在上述步骤S703中，将目标车辆所处道路的路面图像信息输入车辆滚动阻力系数辨识模型后，可得到目标车辆所处道路的路面类型对应的车辆滚动阻力系数，还可得到第二车辆滚动阻力系数。其中，该第二车辆滚动阻力系数为将目标车辆所处道路的路面图像信息输入到车辆滚动阻力系数辨识模型中得到的，是根据该车辆滚动阻力系数辨识模型估算出来的目标车辆在其当前所处道路上时的车辆滚动阻力系数。另，由于第二车辆滚动阻力系数与目标车辆所处道路的路面类型对应的车辆滚动阻力系数的取值范围，均是基于车辆滚动阻力系数辨识模型对目标车辆所处道路的路面图像信息进行识别得到的，第二车辆滚动阻力系数大于车辆所处道路的路面类型对应的取值范围的最小取值，且第二车辆滚动阻力系数小于该取值范围的最大取值。也就是说，第二车辆滚动阻力系数的取值在目标车辆所处道路的道路类型对应的车辆滚动阻力系数的取值范围内。
在一种可能的实现方式中，若第一车辆滚动阻力系数小于上述目标车辆所处道路的路面类型对应的车辆滚动阻力系数的取值范围中的最小取值，或者第一车辆滚动阻力系数大于该取值范围中的最大取值，也就是说，该第一车辆滚动阻力系数不在目标车辆所处道路的路面类型对应的车辆滚动阻力系数的取值范围中，则可视为该第一车 辆滚动阻力系数与实际车辆滚动阻力系数的差值较大，因此在获取目标车辆滚动阻力系数的过程中，不考虑该第一车辆滚动阻力系数，而是直接将第二车辆滚动阻力系数为目标车辆滚动阻力系数，以保证所获取到的目标车辆滚动阻力系数的准确度。若第一车辆滚动阻力系数大于等于该取值范围中的最小取值，且小于等于该取值范围中的最大取值，也就是说，该第一车辆滚动阻力系数在目标车辆所处道路的路面类型对应的车辆滚动阻力系数的取值范围中，则可视为第一车辆滚动阻力系数与实际车辆滚动阻力系数的差值较小。那么，对第一车辆滚动阻力系数与第二车辆滚动阻力系数进行融合，得到的目标融合结果，并将该目标融合结果作为目标车辆滚动阻力系数，以达到综合考虑第一车辆滚动阻力系数与第二车辆滚动阻力系数的效果，提高确定目标车辆滚动阻力系数的辨识精度，保证目标车辆在其行驶过程中的动力性、安全性、经济性等。
示例性的，如图10所示，以目标车辆所处道路的路面类型对应的车辆滚动阻力系数的取值范围为[μ min，μ max]为例，且μ 1为第一车辆滚动阻力系数，μ 2为第二车辆滚动阻力系数。若μ 1<＝μ min，则μ＝μ 2，其中，μ为目标车辆滚动阻力系数。若μ 1>μ min且μ 1>＝μ max，则将第一车辆滚动阻力系数与第二车辆滚动阻力系数进行融合所得到的目标融合结果，作为目标车辆滚动阻力系数μ。若μ 1<μ max，则确定μ＝μ 2。
通过上述过程，本申请可以根据目标车辆在其当前所处道路上的车辆滚动阻力系数的取值范围、第一车辆滚动阻力系数，以及利用车辆滚动阻力系数辨识模型得到的第二车辆滚动阻力系数，将第二车辆滚动阻力系数或者第一车辆滚动阻力系数和第二车辆滚动阻力系数的目标融合结果，作为目标车辆滚动阻力系数，以提高车辆滚动阻力系数的辨识精度，保证车辆在行驶过程中的动力性、经济性和安全性。
需要说明的是，通过上述过程，本申请可以根据目标车辆的电机扭矩、目标车辆的油门踏板开度以及目标车辆的制动踏板开度来确定目标车辆的实际驱动力，以减少利用目标车辆所处道路的道路坡度、目标车辆的加速度以及目标车辆的速度进行计算来确定目标车辆的实际驱动力时，目标车辆所处道路的道路坡度、目标车辆的加速度以及目标车辆的速度的测量精度对车辆行驶过程中的车辆驱动力计算的影响，从而提高目标车辆的实际驱动力的辨识精度。其次，将车辆坡度、车辆加速度以及车辆速度输入目标车辆的纵向动力学模型，得到目标车辆的理论驱动力，然后根据目标车辆的实际驱动力和与车辆滚动阻力系数、车辆载荷相关联的目标车辆的理论驱动力，确定第一车辆滚动阻力系数，以减少目标车辆所处道路的道路坡度、目标车辆的加速度以及目标车辆的速度对第一车辆滚动阻力系数的辨识精度的影响，提高第一车辆滚动阻力系数的辨识精度。再次，根据路面图像信息和车辆滚动阻力系数辨识模型来确定第二车辆滚动阻力系数和车辆所处道路的路面类型，可以减少单一车辆行驶环境的限制，确定车辆在复杂的行驶环境下的车辆滚动阻力系数，提高车辆滚动阻力系数的辨识精度。最后，根据路面类型、第一车辆滚动阻力系数、第二车辆滚动阻力系数，以及第一车辆滚动阻力系数和第二车辆滚动阻力系数的融合结果，来确定目标滚动阻力系数，进一步提高车辆滚动阻力系数的辨识精度，保证目标车辆在行驶过程中的动力性、经济性和安全性。
在一种可能的实现方式中，将第一车辆滚动阻力系数与第二车辆滚动阻力系数， 输入卡尔曼滤波器进行滤波融合，得到目标融合结果，并确定该目标融合结果作为目标车辆滚动阻力系数。其中，该卡尔曼滤波器为集中式的卡尔曼滤波器或者联合式的卡尔曼滤波器。
首先对卡尔曼滤波进行简要介绍：卡尔曼滤波就是以k-1时刻的状态量x k-1为准，对k时刻的状态量进行预测，得到x k/k-1，同时对k时刻的值进行观测，得到z k，再在预测得到的x k/k-1与观测得到的z k之间进行分析，或者说利用观测得到的z k对预测得到的x k/k-1进行修正，从而得到对k时刻的状态量的估计x k的过程。
然后结合卡尔曼滤波的公式对卡尔曼滤波过程进行简要介绍：先引入一个离散控制过程的***，该***可以用x(k)＝Ax(k-1)+Bu(k)+w(k)来描述，***的测量值为z(k)＝Hx(k)+v(k)。其中，x(k)是k时刻的***状态，x(k-1)是k-1时刻的***状态，u(k)是k时刻对该***的控制量。A和B是***参数，H是测量***的参数，对于多模型***，A、B、H可以是矩阵。w(k)、v(k)分别表示过程和测量的噪声，可以被假设为高斯白噪声，w(k)与v(k)的协方差为分别为Q和R。在此处，设置P和Q不随***状态变化而变化。此时，该卡尔曼滤波过程为信息最优估计的过程。以控制量为0为例，根据k-1时刻的***状态对k时刻的***状态进行预测，得到x(k/k-1)＝Ax(k-1/k-1)+Bu(k)。x(k/k-1)为根据k-1时刻的***状态对k时刻的***状态进行预测得到的预测值，x(k-1/k-1)为k-1时刻的***状态的估计值。对应于x(k/k-1)的协方差为P(k/k-1)＝AP(k-1/k-1)A’+Q，其中，P(k/k-1)为x(k/k-1)对应的协方差，P(k-1/k-1)为x(k-1/k-1)对应的协方差，A’为A的转置矩阵，Q是***过程的协方差。然后，获取k时刻的***状态的测量值z(k)，并结合预测值x(k/k-1)和测量值z(k)，确定k时刻的***状态的估计值x(k/k)＝x(k/k-1)+Kg(k)(z(k)-Hx(k/k-1))，其中，Kg(k)＝P(k/k-1)H’/(HP(k/k-1)H’+R)为k时刻的卡尔曼增益，H’为H的转置矩阵，R为测量的协方差。最后，对k时刻的x(k/k)的协方差进行更新，即P(k/k)＝(I-Kg(k)H)P(k/k-1)。其中I为1的矩阵。
最后，结合图11-图12中给出的示例，对本申请实施例中的第一车辆滚动阻力系数和第二车辆滚动阻力系数进行滤波融合，得到目标融合结果的过程进行介绍：
示例性的，如图11所示，以卡尔曼滤波器为集中式的卡尔曼滤波器为例，将第一车辆滚动阻力系数μ 1(k)与第二车辆滚动阻力系数μ 2(k)的差值(预测值)输入卡尔曼滤波器，即将图中所示的μ 1(k)-μ 2(k)输入卡尔曼滤波器，并得到该卡尔曼滤波器输出的μ 1(k)-μ 2(k)的估计值μ 3(k)。然后将μ 3(k)与μ 1(k)的差值μ(k)作为目标融合结果输出，或者将μ(k)也可以是μ 3(k)与μ 1(k)的和作为目标融合结果输出。也就是说，目标融合结果μ(k)可作为本申请实施例所求的目标车辆滚动阻力系数。
示例性的，如图12所示，以第一卡尔曼滤波器和第二卡尔曼滤波器构成联合式的卡尔曼滤波器为例，其中，第一卡尔曼滤波器和第二卡尔曼滤波器均为车辆滚动阻力系数卡尔曼滤波器，即用于对车辆滚动阻力系数进行滤波融合的滤波器。将第一车辆滚动阻力系数μ 1(k)与第二车辆滚动阻力系数μ 2(k)作为预测值分别输入第一卡尔曼滤波器和第二卡尔曼滤波器，并得到图中所示的μ 3(k)和μ 4(k)。然后对第一 卡尔曼滤波器和第二卡尔曼滤波器输出的估计值μ 3(k)和μ 4(k)进行状态融合，例如为μ 3(k)和μ 4(k)设置分配系数，该分配系数也可以理解为权重，对μ 3(k)和μ 4(k)进行加权求和，从而得到目标融合结果μ(k)，该目标融合结果μ(k)可作为目标车辆滚动阻力系数。
示例性的，在经过图12的示例，得到目标融合结果后，还可以将该目标融合结果以一定的分配系数反馈给第一卡尔曼滤波器以及第二卡尔曼滤波器，分配系数之和为1，以进一步提高滤波融合的过程中的分配系数设置的合理性，提高所得目标融合结果的精度。
需要说明的是，通过上述过程，本申请可以利用卡尔曼滤波器，对第一车辆滚动阻力系数和第二滚动阻力系数进行滤波融合，并将得到的滤波结果确定为目标车辆滚动阻力系数，以提高车辆滚动阻力系数的辨识精度，保证目标车辆在行驶过程中的动力性、经济性和安全性。
在一种可能的实现方式中，上述卡尔曼滤波器也可以替换为粒子滤波器等其他的滤波器，也可以将卡尔曼滤波器替换为深度学习等算法。例如，利用深度学习算法对第一车辆滚动阻力系数与第二车辆滚动阻力系数进行融合，得到目标融合结果，并确定该融合结果为目标车辆滚动阻力系数，从而提高车辆滚动阻力系数的辨识精度，保证车辆在行驶过程中的动力性、经济性和安全性。
在一种可能的实现方式中，将步骤S701中得到的目标车辆所处道路的道路坡度、目标车辆的加速度、目标车辆的速度以及步骤S704中得到的目标车辆滚动阻力系数，输入到目标车辆的纵向动力学模型中，输出使得第一车辆滚动阻力系数与第二车辆滚动阻力系数的差值最小的车辆载荷，并确定该车辆载荷为目标车辆载荷。
通过上述过程，本申请可以根据已获取到的目标车辆滚动阻力系数和车辆的纵向动力学模型，来确定目标车辆载荷，以进一步提高目标车辆荷载的辨识精度，从而保证车辆行驶过程中的动力性、经济性和安全性。
本申请实施例可以根据上述方法示例对车辆获取车辆滚动阻力系数的装置进行功能模块的划分，在采用对应各个功能划分各个功能模块的情况下，图13示出上述实施例中所涉及的获取车辆滚动阻力系数的装置的一种可能的结构示意图。如图13所示，获取车辆滚动阻力系数的装置包括获取单元1301以及处理单元1302。当然，获取车辆滚动阻力系数的装置还可以包括其他模块，或者获取车辆滚动阻力系数的装置可以包括更少的模块。
获取单元1301用于获取目标车辆的理论驱动力和该目标车辆的实际驱动力。
具体的，获取单元1301用于获取目标车辆所处道路的道路坡度、目标车辆的加速度、目标车辆的速度以及目标车辆的实际驱动力。然后，将目标车辆所处道路的道路坡度、目标车辆的加速度、以及目标车辆的速度输入目标车辆的纵向动力学模型，得到目标车辆的理论驱动力。其中，该目标车辆的纵向动力学模型用于表示目标车辆的纵向运动的运动规律。
处理单元1302用于基于目标车辆的理论驱动力和目标车辆的实际驱动力，获取第一车辆滚动阻力系数。其中，该第一车辆滚动阻力系数使得目标车辆的实际驱动力和理论驱动力的差值最小。
在一种可能的实现方式中，处理单元1302具体用于基于最小二乘法，根据目标车辆的实际驱动力与理论驱动力，获取第一车辆滚动阻力系数。
在一种可能的实现方式中，处理单元1302具体还用于基于最小二乘法，根据目标车辆的理论驱动力和实际驱动力，获取目标车辆载荷。其中，该目标车辆载荷为使得目标车辆的实际驱动力与其理论驱动力的差值最小的车辆载荷。
然后，处理单元1302还用于将目标车辆所处道路的路面图像信息，输入到车辆滚动阻力系数辨识模型中，得到目标车辆所处道路的路面类型对应的车辆滚动阻力系数的取值范围。
其中，车辆滚动阻力系数辨识模型根据车辆滚动阻力系数数据库训练得到，车辆滚动阻力系数数据库包括不同车辆在不同类型路面上的车辆滚动阻力系数，以及不同类型路面对应的车辆滚动阻力系数的取值范围。
最后，处理单元1302，还用于根据目标车辆所处道路的路面类型对应的车辆滚动阻力系数的取值范围和第一车辆滚动阻力系数，获取目标车辆滚动阻力系数。
在一种可能的实现方式中，第一车辆滚动阻力系数不在目标车辆所处道路的路面类型对应的车辆滚动阻力系数的取值范围内，处理单元1302具体用于将目标车辆所处道路的路面图像信息输入车辆滚动阻力系数辨识模型，得到目标车辆所处道路的路面类型对应的车辆滚动阻力系数的取值范围，以及第二车辆滚动阻力系数。随后，处理单元1302还用于将第二车辆滚动阻力系数作为目标车辆滚动阻力系数。
在一种可能的实现方式中，第一车辆滚动阻力系数在目标车辆所处道路的路面类型对应的车辆滚动阻力系数的取值范围内，处理单元1302具体还用于将第一车辆滚动阻力系数和第二车辆滚动阻力系数进行融合，得到目标融合结果。然后，处理单元1302具体还将上述目标融合结果作为目标车辆滚动阻力系数。
在一种可能的实现方式中，处理单元1302还用于将第一车辆滚动阻力系数以及第二车辆滚动阻力系数，输入卡尔曼滤波器进行滤波融合，得到目标融合结果。其中，卡尔曼滤波器为集中式卡尔曼滤波器或者联合式卡尔曼滤波器。
在一种可能的实现方式中，处理单元1302具体还用于将目标车辆所处道路的道路坡度、目标车辆的加速度、目标车辆的速度和目标车辆滚动阻力系数输入车辆的纵向动力学模型，得到目标车辆载荷。其中，该目标车辆载荷为目标车辆的实际驱动力与其理论驱动力的差值最小时的车辆载荷。
上述描述的服务器和装置的具体工作过程，可以参考下述方法实施例中的对应过程，在此不再赘述。
本申请实施例提供一种存储一个或多个程序的计算机可读存储介质，所述一个或多个程序包括指令，所述指令当被计算机执行时使计算机执行上述实施例的步骤S701-S704所述的获取车辆滚动阻力系数的方法。
本申请实施例还提供一种包含指令的计算机程序产品，当指令在计算机上运行时，使得计算机执行上述实施例步骤S701-S704所述的获取车辆滚动阻力系数的方法。
所属领域的技术人员可以清楚地了解到，为描述的方便和简洁，上述描述的***、装置和单元的具体工作过程，可以参考前述方法实施例中的对应过程，在此不再赘述。
上述实施例可以全部或部分通过软件，硬件，固件或者其任意组合实现。当使用 软件程序实现时，上述实施例可以全部或部分地以计算机程序产品的形式出现，计算机程序产品包括一个或多个计算机指令。在计算机上加载和执行计算机程序指令时，全部或部分地产生按照本申请实施例的流程或功能。
其中，所述计算机可以是通用计算机、专用计算机、计算机网络、或者其他可编程装置。计算机指令可以存储在计算机可读存储介质中，或者从一个计算机可读存储介质向另一个计算机可读存储介质传输，例如，计算机指令可以从一个网站站点、计算机、服务器或数据中心通过有线(例如同轴电缆、光纤、数字用户线(digital subscriber line，DSL))或无线(例如红外、无线、微波等)方式向另一个网站站点、计算机、服务器或数据中心传输。计算机可读存储介质可以是计算机能够存取的任何可用介质或者是包含一个或多个可用介质集成的服务器、数据中心等数据存储设备。该可用介质可以是磁性介质，(例如，软盘，硬盘、磁带)、光介质(例如，DVD)或者半导体介质(例如固态硬盘(solid state disk，SSD))等。
通过以上的实施方式的描述，所属领域的技术人员可以清楚地了解到，为描述的方便和简洁，仅以上述各功能模块的划分进行举例说明，实际应用中，可以根据需要而将上述功能分配由不同的功能模块完成，即将装置的内部结构划分成不同的功能模块，以完成以上描述的全部或者部分功能。
在本申请所提供的几个实施例中，应该理解到，所揭露的装置和方法，可以通过其它的方式实现。例如，以上所描述的装置实施例仅仅是示意性的，例如，所述模块或单元的划分，仅仅为一种逻辑功能划分，实际实现时可以有另外的划分方式，例如多个单元或组件可以结合或者可以集成到另一个装置，或一些特征可以忽略，或不执行。另一点，所显示或讨论的相互之间的耦合或直接耦合或通信连接可以是通过一些接口，装置或单元的间接耦合或通信连接，可以是电性，机械或其它的形式。
所述作为分离部件说明的单元可以是物理上分开的，或者也可以不是物理上分开的，作为单元显示的部件可以是一个物理单元或多个物理单元，即可以位于一个地方，或者也可以分布到多个不同地方。在应用过程中，可以根据实际的需要选择其中的部分或者全部单元来实现本实施例方案的目的。
另外，在本申请各个实施例中的各功能单元可以集成在一个处理单元中，也可以是各个单元单独物理存在，也可以两个或两个以上单元集成在一个单元中。上述集成的单元既可以采用硬件的形式实现，也可以采用软件功能单元的形式实现。
所述集成的单元如果以软件功能单元的形式实现并作为独立的产品销售或使用时，可以存储在一个计算机可读取存储介质中。基于这样的理解，本申请实施例的技术方案本质上或者说对现有技术做出贡献的部分或者该技术方案的部分可以以软件产品的形式体现出来，该计算机软件产品存储在一个存储介质中，包括若干指令用以使得一个设备(可以是个人计算机，服务器，网络设备，单片机或者芯片等)或处理器(processor)执行本申请各个实施例所述方法的全部或部分步骤。而前述的存储介质包括：U盘、移动硬盘、只读存储器(read-only memory，ROM)、随机存取存储器(random access memory，RAM)、磁碟或者光盘等各种可以存储程序代码的介质。
以上所述，仅为本申请的具体实施方式，但本申请的保护范围并不局限于此，任何在本申请揭露的技术范围内的变化或替换，都应涵盖在本申请的保护范围之内。

Claims (19)

  1. 一种获取车辆滚动阻力系数的方法，其特征在于，所述方法包括：
    获取目标车辆的理论驱动力和所述目标车辆的实际驱动力；
    基于所述理论驱动力和所述实际驱动力，获取第一车辆滚动阻力系数，所述第一车辆滚动阻力系数使得所述实际驱动力与所述理论驱动力的差值最小；
    将所述目标车辆所处道路的路面图像信息输入车辆滚动阻力系数辨识模型，得到所述目标车辆所处道路的路面类型对应的车辆滚动阻力系数的取值范围；其中，所述车辆滚动阻力系数辨识模型根据车辆滚动阻力系数数据库训练得到，所述车辆滚动阻力系数数据库包括不同车辆在不同类型路面上的车辆滚动阻力系数，以及不同类型路面对应的车辆滚动阻力系数的取值范围；
    根据所述目标车辆所处道路的路面类型对应的车辆滚动阻力系数的取值范围和所述第一车辆滚动阻力系数，获取目标车辆滚动阻力系数。
  2. 根据权利要求1所述的获取车辆滚动阻力系数的方法，其特征在于，所述第一车辆滚动阻力系数不在所述目标车辆所处道路的路面类型对应的车辆滚动阻力系数的取值范围内；
    所述将所述目标车辆所处道路的路面图像信息输入车辆滚动阻力系数辨识模型，得到所述目标车辆所处道路的路面类型对应的车辆滚动阻力系数的取值范围，包括：
    将所述目标车辆所处道路的路面图像信息输入所述车辆滚动阻力系数辨识模型，得到所述目标车辆所处道路的路面类型对应的车辆滚动阻力系数的取值范围和第二车辆滚动阻力系数；
    所述根据所述目标车辆所处道路的路面类型对应的车辆滚动阻力系数的取值范围和所述第一车辆滚动阻力系数，获取目标车辆滚动阻力系数，包括：
    将所述第二车辆滚动阻力系数作为所述目标车辆滚动阻力系数。
  3. 根据权利要求1所述的获取车辆滚动阻力系数的方法，其特征在于，所述第一车辆滚动阻力系数在所述目标车辆所处道路的路面类型对应的车辆滚动阻力系数的取值范围内；
    所述将所述目标车辆所处道路的路面图像信息输入车辆滚动阻力系数辨识模型，得到所述目标车辆所处道路的路面类型对应的车辆滚动阻力系数的取值范围，包括：
    将所述目标车辆所处道路的路面图像信息输入车辆滚动阻力系数辨识模型，得到所述目标车辆所处道路的路面类型对应的车辆滚动阻力系数的取值范围和第二车辆滚动阻力系数；
    所述方法还包括：
    将所述第一车辆滚动阻力系数与所述第二车辆滚动阻力系数进行融合，得到目标融合结果；
    其中，所述根据所述目标车辆所处道路的路面类型对应的车辆滚动阻力系数的取值范围和所述第一车辆滚动阻力系数，获取目标车辆滚动阻力系数，包括：
    将所述目标融合结果作为所述目标车辆滚动阻力系数。
  4. 根据权利要求3所述的获取车辆滚动阻力系数的方法，其特征在于，所述将所述第一车辆滚动阻力系数与所述第二车辆滚动阻力系数进行融合，得到目标融合结 果，包括：
    将所述第一车辆滚动阻力系数以及所述第二车辆滚动阻力系数，输入卡尔曼滤波器进行滤波融合，得到所述目标融合结果，其中，所述卡尔曼滤波器为集中式卡尔曼滤波器或者联合式卡尔曼滤波器。
  5. 根据权利要求1-4任一项所述的获取车辆滚动阻力系数的方法，其特征在于，所述获取目标车辆的理论驱动力和所述目标车辆的实际驱动力，包括：
    获取所述目标车辆所处道路的道路坡度、所述目标车辆的加速度、所述目标车辆的速度以及所述目标车辆的实际驱动力；
    将所述目标车辆所处道路的道路坡度、所述目标车辆的加速度、以及所述目标车辆的速度输入所述目标车辆的纵向动力学模型，得到所述目标车辆的理论驱动力，所述目标车辆的纵向动力学模型用于表示所述目标车辆的纵向运动的运动规律。
  6. 根据权利要求1-5任一项所述的获取车辆滚动阻力系数的方法，其特征在于，所述基于所述理论驱动力和所述实际驱动力，获取第一车辆滚动阻力系数，包括：
    基于最小二乘法，根据所述理论驱动力和所述实际驱动力，得到所述第一车辆滚动阻力系数。
  7. 根据权利要求1-6任一项所述的获取车辆滚动阻力系数的方法，其特征在于，所述方法还包括：
    基于最小二乘法，根据所述理论驱动力和所述实际驱动力，得到目标车辆载荷，所述目标车辆载荷为使得所述实际驱动力与所述理论驱动力的差值最小的车辆载荷。
  8. 根据权利要求1-6任一项所述的获取车辆滚动阻力系数的方法，其特征在于，所述方法还包括：
    将所述目标车辆所处道路的道路坡度、所述目标车辆的加速度、所述目标车辆的速度以及所述目标车辆滚动阻力系数输入所述目标车辆的纵向动力学模型，得到目标车辆载荷，所述目标车辆载荷为所述实际驱动力与所述理论驱动力的差值最小时的车辆载荷。
  9. 一种获取车辆滚动阻力系数的装置，其特征在于，所述装置包括获取单元、处理单元：
    所述获取单元用于：获取目标车辆的理论驱动力和实际驱动力；
    所述处理单元用于：
    基于所述理论驱动力和所述实际驱动力，获取第一车辆滚动阻力系数，所述第一车辆滚动阻力系数使得所述实际驱动力与所述理论驱动力的差值最小；将所述目标车辆所处道路的路面图像信息输入车辆滚动阻力系数辨识模型，得到所述目标车辆所处道路的路面类型对应的车辆滚动阻力系数的取值范围；其中，所述车辆滚动阻力系数辨识模型根据车辆滚动阻力系数数据库训练得到，所述车辆滚动阻力系数数据库包括不同车辆在不同类型路面上的车辆滚动阻力系数，以及不同类型路面对应的车辆滚动阻力系数的取值范围；
    根据所述目标车辆所处道路的路面类型对应的车辆滚动阻力系数的取值范围和所述第一车辆滚动阻力系数，获取目标车辆滚动阻力系数。
  10. 根据权利要求9所述的获取车辆滚动阻力系数的装置，其特征在于，所述第 一车辆滚动阻力系数不在所述目标车辆所处道路的路面类型对应的车辆滚动阻力系数的取值范围内；
    所述处理单元具体用于：
    将所述目标车辆所处道路的路面图像信息输入所述车辆滚动阻力系数辨识模型，得到所述目标车辆所处道路的路面类型对应的车辆滚动阻力系数的取值范围和第二车辆滚动阻力系数；
    将所述第二车辆滚动阻力系数作为所述目标车辆滚动阻力系数。
  11. 根据权利要求9所述的获取车辆滚动阻力系数的装置，其特征在于，所述第一车辆滚动阻力系数在所述目标车辆所处道路的路面类型对应的车辆滚动阻力系数的取值范围内；
    所述处理单元具体用于：
    将所述目标车辆所处道路的路面图像信息输入所述车辆滚动阻力系数辨识模型，得到所述目标车辆所处道路的路面类型对应的车辆滚动阻力系数的取值范围和第二车辆滚动阻力系数；
    将所述第一车辆滚动阻力系数与所述第二车辆滚动阻力系数进行融合，得到目标融合结果；
    将所述目标融合结果作为所述目标车辆滚动阻力系数。
  12. 根据权利要求11所述的获取车辆滚动阻力系数的装置，其特征在于，所述处理单元具体用于：
    将所述第一车辆滚动阻力系数以及所述第二车辆滚动阻力系数，输入卡尔曼滤波器进行滤波融合，得到所述目标融合结果，其中，所述卡尔曼滤波器为集中式卡尔曼滤波器或者联合式卡尔曼滤波器。
  13. 根据权利要求9-12任一项所述的获取车辆滚动阻力系数的装置，其特征在于，所述获取单元具体用于：
    获取所述目标车辆所处道路的道路坡度、所述目标车辆的加速度、所述目标车辆的速度以及所述目标车辆的实际驱动力；
    将所述目标车辆所处道路的道路坡度、所述目标车辆的加速度、以及所述目标车辆的速度输入所述目标车辆的纵向动力学模型，得到所述目标车辆的理论驱动力，所述目标车辆的纵向动力学模型用于表示所述目标车辆的纵向运动的运动规律。
  14. 根据权利要求9-13任一项所述的获取车辆滚动阻力系数的装置，其特征在于，所述处理单元具体用于：
    基于最小二乘法，根据所述理论驱动力和所述实际驱动力，得到所述第一车辆滚动阻力系。
  15. 根据权利要求9-14任一项所述的获取车辆滚动阻力系数的装置，其特征在于，所述处理单元还用于：
    基于最小二乘法，根据所述理论驱动力和所述实际驱动力，得到目标车辆载荷，所述目标车辆载荷为使得所述实际驱动力与所述理论驱动力的差值最小的车辆载荷。
  16. 根据权利要求9-14任一项所述的获取车辆滚动阻力系数的装置，其特征在于，
    所述处理单元还用于：将所述目标车辆所处道路的道路坡度、所述目标车辆的加 速度、所述目标车辆的速度以及所述目标车辆滚动阻力系数输入所述目标车辆的纵向动力学模型，得到目标车辆载荷，所述目标车辆载荷为所述实际驱动力与所述理论驱动力的差值最小时的车辆载荷。
  17. 一种获取车辆滚动阻力系数的装置，其特征在于，所述获取车辆滚动阻力系数的装置包括：处理器和存储器；其中，存储器用于存储计算机程序指令，处理器运行计算机程序指令以使所述获取车辆滚动阻力系数的装置执行权利要求1-8任一项所述的获取车辆滚动阻力系数的方法。
  18. 一种计算机可读存储介质，包括计算机指令，当该计算机指令被处理器运行时，使得获取车辆滚动阻力系数的装置执行权利要求1-8任一项所述的获取车辆滚动阻力系数的方法。
  19. 一种计算机程序产品，其特征在于，当该计算机程序产品在处理器上运行时，使得获取车辆滚动阻力系数的装置执行权利要求1-8任一项所述的获取车辆滚动阻力系数的方法。
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