WO2021227086A1

WO2021227086A1 - Method and apparatus for acquiring vehicle rolling resistance coefficient

Info

Publication number: WO2021227086A1
Application number: PCT/CN2020/090703
Authority: WO
Inventors: 周维; 陈效华; 刘亚林; 余瑶
Original assignee: 华为技术有限公司
Priority date: 2020-05-15
Filing date: 2020-05-15
Publication date: 2021-11-18
Also published as: CN112689585A; CN112689585B

Abstract

A method and apparatus for acquiring a vehicle rolling resistance coefficient, which method and apparatus relate to the technical field of automobiles; are used for determining a rolling resistance coefficient of a target vehicle (512, 513) according to the actual driving force of the target vehicle (512, 513), a theoretical driving force of the target vehicle (512, 513) and road surface image information, so as to improve the identification precision for a rolling resistance coefficient of a vehicle; and can be applied to new energy automobiles, intelligent automobiles and networked automobiles. The method comprises: acquiring a theoretical driving force of a target vehicle (512, 513) and the actual driving force of the target vehicle (512, 513) (S701); acquiring a first vehicle rolling resistance coefficient on the basis of the theoretical driving force and the actual driving force (S702); inputting, into a vehicle rolling resistance coefficient identification model, road surface image information of a road where the target vehicle (512, 513) is located, so as to obtain a value range for a vehicle rolling resistance coefficient corresponding to the road surface type of the road where the target vehicle (512, 513) is located (S703); and acquiring a rolling resistance coefficient of the target vehicle (512, 513) according to the value range of the vehicle rolling resistance coefficient corresponding to the road surface type of the road where the target vehicle (512, 513) is located and according to the first vehicle rolling resistance coefficient (S704).

Description

Method and device for obtaining vehicle rolling resistance coefficient

Technical field

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.

Background technique

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. Taking the low-speed driving state of the vehicle as an example, the value of the rolling resistance coefficient of the vehicle on different road types can be as shown in FIG. 1. 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.

In the prior art, methods for identifying the coefficient of rolling resistance of a vehicle can be roughly divided into two categories, namely online identification and offline identification. Among them, offline identification methods mainly include bench test methods and road taxi test methods. Generally, 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. However, 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. However, 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. Economy, power and safety.

Summary of the invention

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.

In order to achieve the above objectives, this application adopts the following technical solutions:

In the first aspect, 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. Among them, 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.

Through the above process, according to 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 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. Secondly, according to the road surface image information of the road on which the target vehicle is located and the vehicle rolling resistance coefficient identification model, 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. Type identification accuracy. Finally, 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 target vehicle rolling resistance coefficient can be obtained. 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.

In a possible implementation manner, 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. At this time, 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. 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, obtaining the target vehicle rolling resistance coefficient includes: using the second vehicle rolling resistance coefficient as the target vehicle rolling resistance coefficient.

Through the above process, 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.

In a possible implementation manner, 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. At this time, 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. 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, obtaining the vehicle rolling resistance coefficient of the target vehicle includes: using the target fusion result as the target vehicle rolling resistance coefficient.

Through the above process, 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.

In a possible implementation manner, 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. Among them, the Kalman filter is a centralized Kalman filter or a joint Kalman filter.

Through the above process, 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.

In a possible implementation manner, 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.

Through the above process, 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 effect of accuracy on determining the rolling resistance coefficient of the first vehicle, and improving the identification accuracy of the rolling resistance coefficient of the first vehicle, so as to ensure the power, economy and safety of the target vehicle during its driving.

In a possible implementation, 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 When calculating the acceleration of the target vehicle and the speed of the target vehicle to obtain the actual driving force of 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.

In a possible implementation manner, 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.

In a possible implementation manner, 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.

Through the above process, 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.

In a possible implementation manner, 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.

Through the above process, 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.

In the second aspect, 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. Then, 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. Among them, 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. Finally, 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.

In a possible implementation manner, 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. Then, the processing unit is specifically further configured to use the second vehicle rolling resistance coefficient as the target vehicle rolling resistance coefficient.

In a possible implementation manner, 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.

In a possible implementation manner, 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. Among them, the Kalman filter is a centralized Kalman filter or a joint Kalman filter.

In a possible implementation manner, 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. Among them, the longitudinal dynamics model of the target vehicle is used to represent the motion law of the longitudinal motion of the target vehicle.

In a possible implementation manner, 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.

In a possible implementation manner, 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. Wherein, 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.

In a possible implementation, 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. Wherein, 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.

In a third aspect, 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.

In a fourth 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.

In a fifth aspect, 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. Methods.

Description of the drawings

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;

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;

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.

Detailed ways

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. Or, implement 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.

Referring to Fig. 2, for example, 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. Among them, 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. Its basic principle is to use data collected by sensors such as direction sensors and speed sensors to calculate the instantaneous position of the vehicle to achieve continuous autonomous positioning of the vehicle. 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. Among them, 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 value range of the rolling resistance coefficient of the vehicle, and the rolling resistance coefficient of the vehicle, that is, the second rolling resistance coefficient of the vehicle. Optionally, 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.

In a possible implementation of the embodiments of the present application, as shown in FIG. 3, 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), and an external universal serial bus (USB) port 311. Wherein, optionally, 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. Optionally, the processor 301 may also be a dedicated device such as an application specific integrated circuit (ASIC). Optionally, the processor 301 may also be a neural network processor or a combination of a neural network processor and the foregoing traditional processors.

Optionally, in the various embodiments described in the present application, as above, 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. In other respects, 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. Optionally, 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). Optionally, 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.

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.

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

deploying server 313 download application 319.

For another example, 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. For example, the sensor 322 can detect animals, cars, obstacles, and/or pedestrian crossings. Furthermore, the sensor 322 can also detect the environment around the aforementioned objects such as animals, cars, obstacles and/or pedestrian crossings. For example: 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. Optionally, the computer system can also be located on a self-driving car. Optionally, the sensor 322 may be at least one of a camera, an infrared sensor, a chemical detector, and a microphone.

In other embodiments of the present application, the computer system may also receive information from other computer systems or transfer information to other computer systems. Or, 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. As shown in FIG. 4, 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.

In one example, the computer system 410 may include a server with multiple computers, such as a load balancing server group. In order to receive, process, and transmit data from the computer system, 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.

In one example, the data 460 of the server 420 may include providing road-related information. For example, 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.

Refer to Figure 5 for an example of the interaction between the autonomous driving vehicle and the cloud service center (cloud server). 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.

Exemplarily, the cloud service center 520 may provide a part of the map to the vehicle 513 and the vehicle 512 through the network 511. In other examples, operations can be divided between different locations. For example, multiple cloud service centers can receive, confirm, combine, and/or send information reports. In some examples, information reports and/or sensor data can also be sent between vehicles. Other configurations are also possible.

In some examples, 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. Correspondingly, 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). When the cloud service center 520 observes the video stream in its operating environment 500 and has confirmed that the autonomous driving vehicle can safely and successfully pass the obstacle, the operation steps used for the autonomous driving vehicle can be added to the driving information map. Correspondingly, 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.

In some embodiments, 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. In one embodiment, 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. For example, referring to the embodiment shown in FIG. 7, one or more features in S701 to S704 may be undertaken by one or more instructions associated with the signal bearing medium 601. In addition, the program instructions 602 in FIG. 6 also describe example instructions.

In some examples, 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. In some embodiments, 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. In some embodiments, 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.). Therefore, for example, 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. In some examples, 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. Thus, those skilled in the art will understand that other arrangements and other elements (for example, machines, interfaces, functions, sequences, and functional groups, etc.) can be used instead, and some elements can be omitted altogether depending on the desired result . In addition, many of the described elements are functional entities that can be implemented as discrete or distributed components, or combined with other components in any appropriate combination and position.

In order to improve the identification accuracy of the rolling resistance coefficient of the vehicle, that is, in order to improve the identification accuracy of the rolling resistance coefficient of the target vehicle in the embodiments of the present application, and to ensure the economy, power, and safety of the vehicle during driving, this application proposes an acquisition method. The method of vehicle rolling resistance coefficient. 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 processor 301 and the processor 430 etc. As shown in Fig. 7, taking the target vehicle as an example, the method includes steps S701 to S704:

S701. Acquire the theoretical driving force of the target vehicle and the actual driving force of the target vehicle.

Among them, 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.

Optionally, obtain the data monitored by the DR sensor, and use the DR technology to calculate the monitored data to obtain the status information of the target vehicle. 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.

In a possible implementation, 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.

In a possible implementation manner, the aforementioned DR sensor includes a gyroscope, an accelerometer, a speed sensor, an angle sensor, a vision sensor, and the like.

In a possible implementation manner, 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.

Exemplarily, in order to obtain information about the actual driving force of the target vehicle and the theoretical driving force of the target vehicle, conform to the longitudinal dynamics model of the target vehicle given in the following example, 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.

Optionally, 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. Among them, 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. In addition, 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.

Exemplarily, 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.

Exemplarily, the longitudinal dynamics model of the target vehicle is F _t =μmg cosθ+mg sinθ+0.5ρC _d Av ² +δma, where F _t represents the driving force of the target vehicle, that is, the theoretical driving force of the target vehicle, and μ represents Vehicle rolling resistance coefficient, m represents vehicle load, g represents gravity coefficient, θ represents the road gradient of the road where the target vehicle is located, μmg cosθ represents rolling resistance, mg sinθ represents gradient resistance, ρ represents air density, and C _d represents air resistance coefficient. A represents the windward area of the target vehicle, v represents the speed of the target vehicle, 0.5ρC _d Av ² represents the air resistance, δ represents the moment of inertia coefficient, a represents the acceleration of the target vehicle, and δma represents the acceleration resistance. Know the gravity coefficient, air density, air resistance coefficient, the windward area of the target vehicle and the moment of inertia coefficient, and input the speed of the target vehicle, the acceleration of the target vehicle, and the road gradient of the road where the target vehicle is located into the longitudinal dynamics model of the target vehicle In, the theoretical driving force of the target vehicle can be obtained.

S702. Obtain a 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 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.

Optionally, based on the least squares method, 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.

In a possible implementation manner, based on the least square method, 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 theoretical target vehicle drive force is _{F t (k) = μmgcosθ (} k) + mgsinθ (k) + δm a (k) + 0.5ρCdAv 2 (k), the difference between the actual driving force of the target vehicle and the theoretical driving force of the target vehicle is |F' _t (k)-F _t (k)|. The sum of squares of the difference between the actual driving force of the target vehicle and the theoretical driving force of the target vehicle from k=1 to k=N (N>=1) is L,

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. In order to determine the first vehicle rolling resistance coefficient μ ₁ , that 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, let The derivative of L where μ is an unknown number is 0, and the derivative of L where m is an unknown number is 0, then the vehicle rolling resistance coefficient at this time is the first vehicle rolling resistance coefficient μ ₁ . Optionally, the vehicle load at this time is the target vehicle load m.

Exemplarily, in the above example, there is a nonlinear relationship between μ and m, which may make the data processing process more complicated. In this example, if μm=p, the nonlinear relationship between μ and m can be changed. The relationship is transformed into a linear relationship between p and m, making the data processing process easy to implement. The actual target vehicle drive force is F _'t (k), the theoretical target vehicle drive force is _{F t (k) = pg cosθ} (k) + mgsinθ (k) + δm a (k) + 0.5ρCdAv 2 (k) , Then the difference between the actual driving force of the target vehicle and the theoretical driving force of the target vehicle is |F' _t (k)-F _t (k)|. The sum of squares of the difference between the actual driving force of the target vehicle and the theoretical driving force of the target vehicle from k=1 to k=N (N>=1) is L,

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. In order to determine the first vehicle rolling resistance coefficient μ ₁ , that 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, let The derivative of L where p is an unknown number is 0, and the derivative of L where m is an unknown number is 0, then p=μ ₁ m ₁ at this time, and μ _{1 is} the first vehicle rolling resistance coefficient. Optionally, m _{1 is} the target vehicle load m.

It should be noted that through the above process, 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. On the basis of 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.

Among them, 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.

In a possible implementation, 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 value range of the rolling resistance coefficient, and the rolling resistance coefficient of different vehicles when passing on the road of the road type. Optionally, 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.

Exemplarily, the vehicle rolling resistance coefficient database may store the data shown in FIG. 8. Among them, 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], and 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.

Exemplarily, taking the convolutional neural network in the deep learning algorithm as an example, 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 μ ₂ under the current conditions of the vehicle. _{That is, the value range [μ min} , μ _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 μ 2} of the vehicle under the current condition can be obtained. Among them, 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.

Optionally, 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. Wherein, 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. Through this process, 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.

Optionally, in the above step S703, after 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, 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. Rolling resistance coefficient of the vehicle. Wherein, 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. In addition, because 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.

In a possible implementation manner, if 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. If 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. In 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. Then, 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.

Exemplarily, as shown in FIG. 10, the value range of the vehicle rolling resistance coefficient corresponding to the road surface type of the target vehicle is [μ _min , μ _max ] as an example, and μ ₁ is the first vehicle rolling resistance coefficient , Μ ₂ is the second vehicle rolling resistance coefficient. If μ ₁ <= μ _min , then μ= μ ₂ , where μ is the target vehicle rolling resistance coefficient. If μ ₁ > μ _min and μ ₁ >= μ _max , the target fusion result obtained by fusing the first vehicle rolling resistance coefficient and the second vehicle rolling resistance coefficient is used as the target vehicle rolling resistance coefficient μ. If μ ₁ <μ _max , then μ=μ ₂ is determined.

Through the above process, 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.

It should be noted that through the above process, 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. When calculating the road gradient of the road, 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. Secondly, input the vehicle gradient, vehicle acceleration and vehicle speed into the longitudinal dynamics model of the target vehicle to obtain the theoretical driving force of the target vehicle, and then according to the actual driving force of the target vehicle and the target vehicle associated with the vehicle rolling resistance coefficient and the vehicle load 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. Thirdly, according to 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. Finally, according to the road type, the first vehicle rolling resistance coefficient, the second vehicle rolling resistance coefficient, and the fusion result of the first vehicle rolling resistance coefficient and the second vehicle rolling resistance coefficient, 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.

In a possible implementation manner, 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 . Wherein, the Kalman filter is a centralized Kalman filter or a joint Kalman filter.

First, a brief introduction to 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.}

Then combined with the Kalman filter formula to briefly introduce the Kalman filter process: first introduce a discrete control process system, the system can be x(k)=Ax(k-1)+Bu(k)+w(k) To describe, the measured value of the system is z(k)=Hx(k)+v(k). Among them, x(k) is the system state at time k, x(k-1) is the system state at time k-1, and u(k) is the control amount of the system at time k. A and B are system parameters, and H is a parameter of the measurement system. For a multi-model 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. Here, the settings P and Q do not change with the change of the system state. At this time, the Kalman filtering process is the process of information optimal estimation. Taking the control variable as 0 as an example, predict the system state at time k according to the system state at time k-1, and obtain x(k/k-1)=Ax(k-1/k-1)+Bu(k) . 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, and x(k-1/k-1) is the estimation of the system state at time k-1 value. The covariance corresponding to x(k/k-1) is P(k/k-1)=AP(k-1/k-1)A'+Q, where P(k/k-1) is x (k/k-1) corresponds to the covariance, P(k-1/k-1) is the covariance corresponding to x(k-1/k-1), A'is the transpose matrix of A, and Q is the system The covariance of the process. Then, obtain the measured value z(k) of the system state at time k, and combine the predicted value x(k/k-1) and the measured value z(k) to determine the estimated value of the system state at time k x(k/k )=x(k/k-1)+Kg(k)(z(k)-Hx(k/k-1)), where Kg(k)=P(k/k-1)H'/( HP(k/k-1)H'+R) is the Kalman gain at time k, H'is the transpose matrix of H, and R is the measured covariance. Finally, update the covariance of x(k/k) at time k, that is, P(k/k)=(I-Kg(k)H)P(k/k-1). Where I is a matrix of 1.

Finally, in conjunction with the examples given in Figures 11-12, the process of filtering and fusing the first vehicle rolling resistance coefficient and the second vehicle rolling resistance coefficient in the embodiment of the present application to obtain the target fusion result is introduced:

Exemplarily, as shown in FIG. 11, taking the Kalman filter as a centralized Kalman filter as an example, the first vehicle rolling resistance coefficient μ ₁ (k) and the second vehicle rolling resistance coefficient μ ₂ (k) The difference (predicted value) of is input to the Kalman filter, that is, μ ₁ (k)-μ ₂ (k) shown in the figure is input to the Kalman filter, and μ ₁ (k) output by the Kalman filter is obtained -μ ₂ (k) is the estimated value μ ₃ (k). Then the difference μ _{(k) between μ 3} (k) and μ ₁ (k) is output as the target fusion result, or μ(k) can also be the sum of μ ₃ (k) and μ ₁ (k) as the target Fusion result output. That is to say, the target fusion result μ(k) can be used as the target vehicle rolling resistance coefficient obtained in the embodiment of the present application.

Exemplarily, as shown in FIG. 12, 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 μ ₁ (k) and the second vehicle rolling resistance coefficient μ ₂ (k) are respectively input into the first Kalman filter and the second Kalman filter as predicted values, and obtain the μ ₃ (k) and μ ₄ (k). Then perform state fusion _{on the estimated values μ 3} (k) and μ ₄ (k) output by the first Kalman filter and the second Kalman filter, for example, set the distribution coefficients _{for μ 3} (k) and μ _{4 (k)} , The distribution coefficient can also be understood as a weight, _{and the weighted summation of μ 3} (k) and μ ₄ (k) is performed to obtain the target fusion result μ(k), which can be used as the target vehicle rolling OK.

Exemplarily, after the target fusion result is obtained through the example in FIG. 12, 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. To further improve the rationality of the allocation coefficient setting in the process of filtering and fusion, and to improve the accuracy of the obtained target fusion result.

It should be noted that, through the above process, 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.

In a possible implementation manner, 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. For example, 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.

In a possible implementation manner, 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. In 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.

Through the above process, 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. In the case of dividing each functional module corresponding to each function, 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. Of course, 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.

Specifically, 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. Among them, 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.

In a possible implementation manner, 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.

In a possible implementation manner, 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. Wherein, 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.

Then, 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.

Among them, 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. .

Finally, 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.

In a possible implementation manner, 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. Subsequently, the processing unit 1302 is further configured to use the second vehicle rolling resistance coefficient as the target vehicle rolling resistance coefficient.

In a possible implementation manner, 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.

In a possible implementation manner, 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. Among them, the Kalman filter is a centralized Kalman filter or a joint Kalman filter.

In a possible implementation, 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. Wherein, 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.

For the specific working process of the server and the device described above, reference may be made to the corresponding process in the following method embodiment, which will not be repeated here.

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 method of obtaining the rolling resistance coefficient of the vehicle described above.

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.

Those skilled in the art can clearly understand that, for the convenience and conciseness of description, the specific working process of the system, device and unit described above can refer to the corresponding process in the foregoing method embodiment, which will not be repeated here.

The above-mentioned embodiments may be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using a software program, 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. 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.

Wherein, 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. For example, 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)).

Through the description of the above embodiments, those skilled in the art can clearly understand that for the convenience and brevity of the description, only the division of the above-mentioned functional modules is used as an example for illustration. In practical applications, the above-mentioned functions can be allocated according to needs. It is completed by different functional modules, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above.

In the several embodiments provided in this application, it should be understood that the disclosed device and method may be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, 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. In addition, 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.

In addition, 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.

If 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. Based on this understanding, 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. .

The above are only specific implementations of this application, but the protection scope of this application is not limited to this. Any change or replacement within the technical scope disclosed in this application shall be covered by the protection scope of this application. .

Claims

A method for obtaining a coefficient of rolling resistance of a vehicle, characterized in that the method comprises:

Acquiring the theoretical driving force of the target vehicle and the actual driving force of the target vehicle;

Obtaining a first vehicle rolling resistance coefficient based on the theoretical driving force and the actual driving force, where the first vehicle rolling resistance coefficient minimizes the difference between the actual driving force and the theoretical driving force;

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; wherein, the vehicle rolling resistance coefficient The identification model is obtained by training based on a vehicle rolling resistance coefficient database, the vehicle rolling resistance coefficient database including vehicle rolling resistance coefficients of different vehicles on different types of roads, and value ranges of vehicle rolling resistance coefficients corresponding to different types of roads;

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 method for obtaining a vehicle rolling resistance coefficient according to claim 1, wherein 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;

The inputting 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 includes:

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 and the second vehicle rolling resistance coefficient corresponding to the road surface type of the road on which the target vehicle is located ；

The obtaining 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 includes:

The second vehicle rolling resistance coefficient is used as the target vehicle rolling resistance coefficient.
The method for obtaining a vehicle rolling resistance coefficient according to claim 1, wherein 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 target vehicle;

The inputting 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 includes:

Inputting 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 and the second vehicle rolling resistance coefficient corresponding to the road surface type of the road on which the target vehicle is located;

The method also includes:

Fusing the first vehicle rolling resistance coefficient and the second vehicle rolling resistance coefficient to obtain a target fusion result;

Wherein, the obtaining 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 includes:

The target fusion result is used as the target vehicle rolling resistance coefficient.
The method for obtaining a rolling resistance coefficient of a vehicle according to claim 3, wherein said fusing the first rolling resistance coefficient of the vehicle with the second rolling resistance coefficient of the vehicle to obtain a target fusion result comprises:

The first vehicle rolling resistance coefficient and the second vehicle rolling resistance coefficient are input to a Kalman filter for filtering and fusion to obtain the target fusion result, wherein the Kalman filter is a centralized Kalman filter Or combined Kalman filter.
The method for obtaining a coefficient of rolling resistance of a vehicle according to any one of claims 1 to 4, wherein said obtaining the theoretical driving force of the target vehicle and the actual driving force of the target vehicle comprises:

Acquiring 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;

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 vehicle is used to represent the motion law of the longitudinal motion of the target vehicle.
The method for obtaining a rolling resistance coefficient of a vehicle according to any one of claims 1 to 5, wherein the obtaining a first rolling resistance coefficient of a vehicle based on the theoretical driving force and the actual driving force comprises:

Based on the least square method, the first vehicle rolling resistance coefficient is obtained according to the theoretical driving force and the actual driving force.
The method for obtaining a rolling resistance coefficient of a vehicle according to any one of claims 1-6, wherein the method further comprises:

Based on the least square method, a target vehicle load is obtained according to the theoretical driving force and the actual driving force, and the target vehicle load is the vehicle load that minimizes the difference between the actual driving force and the theoretical driving force.
The method for obtaining a rolling resistance coefficient of a vehicle according to any one of claims 1-6, wherein the method further comprises:

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 are input into the longitudinal dynamics model of the target vehicle to obtain the target vehicle load. The target vehicle load is the vehicle load when the difference between the actual driving force and the theoretical driving force is the smallest.
A device for obtaining a coefficient of rolling resistance of a vehicle, characterized in that the device includes an obtaining unit and a processing unit:

The obtaining unit is used to obtain the theoretical driving force and the actual driving force of the target vehicle;

The processing unit is used for:

Based on the theoretical driving force and the actual driving force, a first vehicle rolling resistance coefficient is obtained, and the first vehicle rolling resistance coefficient minimizes the difference between the actual driving force and the theoretical driving force; and setting the target The road surface image information of the road on which the 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 target vehicle is obtained; wherein, the vehicle rolling resistance coefficient identification model is based on the vehicle The rolling resistance coefficient database is obtained through training, and the vehicle rolling resistance coefficient database includes vehicle rolling resistance coefficients of different vehicles on different types of roads, and value ranges of vehicle rolling resistance coefficients corresponding to different types of roads;

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 device for obtaining a vehicle rolling resistance coefficient according to claim 9, wherein 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 processing unit is specifically used for:

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 and the second vehicle rolling resistance coefficient corresponding to the road surface type of the road on which the target vehicle is located ；

The second vehicle rolling resistance coefficient is used as the target vehicle rolling resistance coefficient.
The device for obtaining a coefficient of rolling resistance of a vehicle according to claim 9, wherein the first coefficient of rolling resistance of the vehicle is within the value range of the coefficient of rolling resistance of the vehicle corresponding to the type of road surface on which the target vehicle is located;

The processing unit is specifically used for:

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 and the second vehicle rolling resistance coefficient corresponding to the road surface type of the road on which the target vehicle is located ；

Fusing the first vehicle rolling resistance coefficient and the second vehicle rolling resistance coefficient to obtain a target fusion result;

The target fusion result is used as the target vehicle rolling resistance coefficient.
The device for obtaining a coefficient of rolling resistance of a vehicle according to claim 11, wherein the processing unit is specifically configured to:

The first vehicle rolling resistance coefficient and the second vehicle rolling resistance coefficient are input to a Kalman filter for filtering and fusion to obtain the target fusion result, wherein the Kalman filter is a centralized Kalman filter Or combined Kalman filter.
The device for obtaining a coefficient of rolling resistance of a vehicle according to any one of claims 9-12, wherein the obtaining unit is specifically configured to:

Acquiring 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;

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 vehicle is used to represent the motion law of the longitudinal motion of the target vehicle.
The device for obtaining a coefficient of rolling resistance of a vehicle according to any one of claims 9-13, wherein the processing unit is specifically configured to:

Based on the least square method, the first vehicle rolling resistance system is obtained according to the theoretical driving force and the actual driving force.
The device for obtaining a coefficient of rolling resistance of a vehicle according to any one of claims 9-14, wherein the processing unit is further configured to:

Based on the least square method, a target vehicle load is obtained according to the theoretical driving force and the actual driving force, and the target vehicle load is the vehicle load that minimizes the difference between the actual driving force and the theoretical driving force.
The device for obtaining a coefficient of rolling resistance of a vehicle according to any one of claims 9-14, wherein:

The processing unit is further configured to: input 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 into the longitudinal dynamics of the target vehicle Model to obtain a target vehicle load, where the target vehicle load is the vehicle load when the difference between the actual driving force and the theoretical driving force is the smallest.
A device for obtaining a coefficient of rolling resistance of a vehicle, characterized in that the device for obtaining a coefficient of rolling resistance of a vehicle comprises: a processor and a memory; wherein the memory is used to store computer program instructions, and the processor runs the computer program instructions to make the The device for obtaining a coefficient of rolling resistance of a vehicle implements the method for obtaining a coefficient of rolling resistance of a vehicle according to any one of claims 1-8.
A computer-readable storage medium comprising computer instructions, when the computer instructions are executed by a processor, cause the device for obtaining a rolling resistance coefficient of a vehicle to execute the method for obtaining a rolling resistance coefficient of a vehicle according to any one of claims 1-8.
A computer program product, which is characterized in that when the computer program product runs on a processor, the device for obtaining a rolling resistance coefficient of a vehicle executes the method for obtaining a rolling resistance coefficient of a vehicle according to any one of claims 1-8.