Vehicle speed estimation method and device

Abstract

The invention discloses a vehicle speed estimation method and device, relates to the technical field of automobiles, and can obtain a high-precision vehicle speed estimation value when a vehicle is automatically parked, so that the vehicle can be automatically parked more accurately. The method comprises the steps that first information of a vehicle is obtained, the wheel speed of wheels is corrected according to steering angles of a left wheel and a right wheel of a front axle of the vehicle, and an observed quantity matrix and an observed noise covariance matrix are determined according to the first information and the corrected wheel speed of the wheels, and performing Kalman filtering on the observed quantity matrix and the observation noise covariance matrix to obtain an estimated value of the vehicle speed. Wherein the first information comprises at least one of the longitudinal acceleration of the vehicle, the wheel speed of each wheel of the vehicle, the rotating speed of driving equipment of the vehicle, the torque of the driving equipment of the vehicle and the steering angle of each wheel of the vehicle.

Description

Technical Field

[0001] The present application relates to the field of automobile technology, and in particular to a vehicle speed estimation method and device. Background Art

[0002] Autonomous vehicle technology is rapidly developing, and the application of automatic parking technology is becoming increasingly common. However, current automatic parking technology still has some problems. For example, vehicles can only perform automatic parking operations in relatively spacious parking spaces. In narrow parking spaces, automatic parking is less effective. When the vehicle automatically parks on medium- and low-adhesion roads, the tires may slip, resulting in poor automatic parking results. Precise automatic parking requires high-precision vehicle speed estimation. However, related technologies have low accuracy in vehicle speed estimation during automatic parking. Summary of the Invention

[0003] The present application provides a vehicle speed estimation method and device, which can obtain a high-precision vehicle speed estimation value when the vehicle is automatically parked, so that the vehicle can park automatically more accurately.

[0004] To achieve the above objectives, this application provides the following technical solutions:

[0005] In a first aspect, the present application provides a vehicle speed estimation method, comprising: obtaining first information about the vehicle; the first information including at least one of the vehicle's longitudinal acceleration, the wheel speed of each wheel of the vehicle, the vehicle's drive device speed, the vehicle's drive device torque, and the steering angle of each wheel of the vehicle; correcting the wheel speeds according to the steering angles of the vehicle's left and right front axle wheels; and determining the vehicle's speed based on the first information and the corrected wheel speeds. The vehicle controller corrects the wheel speeds according to the steering angles of the vehicle's left and right front axle wheels to obtain a more accurate wheel speed, thereby improving the accuracy of vehicle speed estimation during automatic parking, thereby enabling the vehicle to perform automatic parking more accurately.

[0006] In one possible implementation, the wheel speed is corrected according to the steering angles of the left and right wheels of the vehicle's front axle, including: the magnitude of the steering angle of the left wheel of the vehicle's front axle is positively correlated with the degree of correction of the left wheel of the vehicle's front axle; the magnitude of the steering angle of the right wheel of the vehicle's front axle is positively correlated with the degree of correction of the right wheel of the vehicle's front axle.

[0007] In this way, when the vehicle's front axle left wheel and the steering angle are large, the vehicle controller can increase the correction degree (correction amount) of the front axle left wheel. When the vehicle's front axle right wheel and the steering angle are large, the vehicle controller can increase the correction degree of the front axle right wheel, thereby improving the accuracy of the front and rear left wheel speeds and right wheel speeds.

[0008] In one possible implementation, the vehicle speed is determined based on the first information and the corrected wheel speed, including: determining an observation matrix and an observation noise covariance matrix based on the first information and the corrected wheel speed; the observation matrix includes acceleration observation values and vehicle speed observation values; the observation noise covariance matrix includes acceleration observation noise and vehicle speed observation noise; and the vehicle speed is calculated based on the observation matrix and the observation noise covariance matrix.

[0009] In one possible implementation, the method further includes: determining a wheel speed confidence based on the corrected wheel speed, the longitudinal acceleration of the vehicle, and the torque of the vehicle's drive device; the wheel speed confidence represents the error relationship between the wheel speed and the vehicle speed; determining the vehicle speed observation noise based on the wheel speed confidence; and a mapping relationship exists between the wheel speed confidence and the vehicle speed observation noise.

[0010] The vehicle controller can determine the wheel speed confidence according to the slip or rotation state of the wheel, and then obtain the vehicle speed observation noise in the observation noise covariance matrix, thereby improving the estimation accuracy of the vehicle speed when the vehicle wheel slips or rotates.

[0011] In one possible implementation, an observation matrix is determined based on the first information and the corrected wheel speed, including: determining an acceleration observation value based on the longitudinal acceleration of the vehicle and the weighted model acceleration of the vehicle; the model acceleration of the vehicle is obtained based on the torque of the vehicle's driving device.

[0012] In this way, the vehicle controller determines the acceleration observation value based on the vehicle's longitudinal acceleration and the vehicle's model acceleration weights, thereby improving the accuracy of the acceleration observation value and further improving the estimation accuracy of the vehicle speed.

[0013] In one possible implementation, an observation matrix is determined based on the first information and the corrected wheel speed, including: determining a vehicle speed observation value based on the weighted wheel speed of the vehicle's wheels and the weighted speed of the vehicle's drive equipment; the weighted wheel speed of the wheel is obtained based on the wheel speed of each wheel.

[0014] In this way, the vehicle controller determines the vehicle speed observation value by weighting the weighted wheel speed of the vehicle's wheels and the vehicle's driving device speed, thereby improving the accuracy of the speed observation value and further improving the estimation accuracy of the vehicle speed.

[0015] In one possible implementation, the observation matrix and the observation noise covariance matrix are determined based on the first information and the corrected wheel speed, including: determining the observation matrix and the observation noise covariance matrix based on the first information and the corrected wheel speed and driving conditions; the driving conditions include starting conditions and parking conditions.

[0016] In this way, the vehicle controller determines the corresponding observation matrix and observation noise covariance matrix for different driving conditions, thereby improving the vehicle speed estimation accuracy under the corresponding driving conditions.

[0017] In one possible implementation, when the driving condition is a starting condition: the weight of the vehicle's longitudinal acceleration is greater than the weight of the vehicle's model acceleration; the weight of the vehicle's wheel weighted speed is less than the weight of the vehicle's drive device speed; and the acceleration observation noise is less than the vehicle speed observation noise.

[0018] In this way, the vehicle controller can improve the vehicle speed estimation accuracy during starting conditions.

[0019] In a possible implementation, when the driving condition is a parking condition: the weight of the longitudinal acceleration of the vehicle is smaller than the weight of the model acceleration of the vehicle; and the acceleration observation noise is smaller than the vehicle speed observation noise.

[0020] In this way, the vehicle controller can improve the vehicle speed estimation accuracy during parking conditions.

[0021] In one possible implementation, the first information also includes: at least one of the vehicle's steering wheel angle, the vehicle's yaw angular velocity, the vehicle's master cylinder pressure, the vehicle's drive device torque, the vehicle's traction control system TCS flag, the anti-lock braking system ABS flag, the electronic stability control system ESC flag, the accelerator pedal opening, the brake pedal opening, and the vehicle's gear information.

[0022] In one possible implementation, calculating the vehicle speed based on the observation matrix and the observation noise covariance matrix includes calculating the vehicle speed using a Kalman filter algorithm based on the observation matrix and the observation noise covariance matrix. The vehicle controller calculates the vehicle speed using a Kalman filter algorithm, ensuring real-time speed estimation.

[0023] In second aspect, the present application provides a vehicle speed estimation device, comprising: a transceiver module for obtaining first information of the vehicle; the first information includes at least one of the longitudinal acceleration of the vehicle, the wheel speed of each wheel of the vehicle, the speed of the driving device of the vehicle, the torque of the driving device of the vehicle, and the steering angle of each wheel of the vehicle; a processing module for correcting the wheel speed according to the steering angles of the left and right wheels of the front axle of the vehicle; and determining the vehicle speed based on the first information and the corrected wheel speed.

[0024] In a third aspect, the present application provides a vehicle comprising the device as described in the second aspect.

[0025] In a fourth aspect, the present application provides a computer-readable storage medium, which includes a computer program or instructions. When the computer program or instructions are run on the vehicle speed estimation device, the vehicle speed estimation device described in the second aspect executes the method described in the first aspect.

[0026] In a fifth aspect, the present application provides a computer program product, which includes: a computer program or instructions, which, when the computer program or instructions are run on a computer, enables the computer to execute the method described in the first aspect.

[0027] In a sixth aspect, the present application provides a chip system, comprising: a processor, the processor being used to call and run a computer program stored in a memory from the memory to execute any one of the methods provided in the implementation manner in the first aspect.

[0028] The technical effects corresponding to the second to sixth aspects and any implementation method of the second to sixth aspects can be referred to the technical effects corresponding to the above-mentioned first aspect and any implementation method of the first aspect, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 is a system schematic diagram of a vehicle provided in an embodiment of the present application;

[0030] Figure 2 is a schematic diagram of a vehicle estimation device provided in an embodiment of the present application;

[0031] Figure 3 This is a flow chart of a vehicle estimation method provided by an embodiment of the present application;

[0032] Figure 4 This is a schematic diagram of a vehicle provided in an embodiment of the present application;

[0033] Figure 5 is a schematic diagram of another vehicle estimation device provided in an embodiment of the present application;

[0034] Figure 6 Schematic diagram of the structure of a vehicle speed estimation device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0035] In the embodiments of this application, words such as "exemplary" or "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of this application should not be interpreted as being preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner.

[0036] In the embodiments of this application, the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of the technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of this application, unless otherwise specified, "plurality" means two or more.

[0037] In this application, the term "at least one" means one or more, and the term "plurality" means two or more. For example, "plurality of second messages" means two or more second messages. The terms "system" and "network" are often used interchangeably herein.

[0038] It is to be understood that the terminology used in the description of the various described examples herein is for the purpose of describing particular examples only and is not intended to be limiting.

[0039] It should also be understood that the term "and / or" as used herein refers to and encompasses any and all possible combinations of one or more of the listed items. The term "and / or" describes an association between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A exists alone, A and B exist simultaneously, or B exists alone. Furthermore, the character " / " in this application generally indicates that the associated objects are in an "or" relationship.

[0040] It should also be understood that in the various embodiments of the present application, the size of the serial number of each process does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0041] It should be understood that references throughout this specification to "one embodiment," "an embodiment," or "one possible implementation" mean that specific features, structures, or characteristics associated with that embodiment or implementation are included in at least one embodiment of this application. Therefore, the appearance of "in one embodiment," "in an embodiment," or "one possible implementation" throughout this specification does not necessarily refer to the same embodiment. Furthermore, these specific features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0042] Automatic parking technology is an assisted driving technology based on vehicle-mounted sensors, computers and control systems. It helps the driver complete the parking process by automatically controlling the vehicle's steering wheel, throttle and brake systems.

[0043] Alternatively, the vehicle can be controlled according to the vehicle speed to complete automatic parking. However, in the related art, the measurement accuracy of the vehicle speed in the automatic parking scenario is not high.

[0044] For example, to measure a vehicle's speed in an automated parking lot, traditional measurement methods typically use estimation algorithms such as radar and cameras to directly measure the vehicle's speed. However, this method's radar and camera information is easily affected by blind spots, making measurement difficult and costly. Alternatively, the vehicle's speed is estimated by wheel speed. However, automated parking involves multiple starting and stopping conditions. When the vehicle is starting, the wheel speed may be delayed; when the vehicle is parked, the wheel speed may experience large zero jumps. This results in low speed estimation accuracy.

[0045] To address the above issues, embodiments of the present application provide a vehicle speed estimation method that can determine the vehicle speed using first information and corrected wheel speeds. This method can improve the accuracy of vehicle speed estimation during automatic parking, thereby enabling more accurate automatic parking.

[0046] The vehicle controller can correct the wheel speeds based on the steering angles of the left and right front axle wheels, improving the accuracy of converting wheel speeds to vehicle speed at the center of mass, thereby improving the accuracy of vehicle speed estimation during automatic parking.

[0047] Optionally, the method can determine the observation matrix and the observation noise covariance matrix (also referred to as the observation noise matrix) through the first information and the corrected wheel speed; then, the vehicle speed is calculated based on the observation matrix and the observation noise covariance matrix.

[0048] Optionally, the observation matrix of the vehicle speed estimation algorithm includes acceleration observations and vehicle speed observations, and the observation noise covariance matrix includes acceleration observation noise and vehicle speed observation noise. The vehicle controller can also determine wheel speed confidence levels based on wheel slip or spinning, thereby deriving the vehicle speed observation noise from the observation noise covariance matrix. This improves the accuracy of vehicle speed estimation when the vehicle's wheels are slipping or spinning.

[0049] The acceleration observation value represents the acceleration of the vehicle after correction.

[0050] Acceleration measurement noise refers to the uncertainty or random variation in acceleration caused by various interference factors, including but not limited to mechanical vibration, temperature fluctuations, electromagnetic interference, and sensor nonlinearity and drift. The greater the acceleration measurement noise, the greater the degree of acceleration interference and the lower the reliability of the acceleration.

[0051] The observed vehicle speed value indicates the vehicle speed corrected for the wheel speed.

[0052] Vehicle speed observation noise refers to the uncertainty or random changes in vehicle speed caused by various interference factors, including but not limited to slip or rotation. The greater the vehicle speed observation noise, the higher the degree of vehicle speed interference and the lower the credibility of the vehicle speed.

[0053] Optionally, the vehicle controller can also calibrate the observation matrix and the observation noise covariance matrix based on the wheel driving conditions, where the driving conditions may include starting conditions and stopping conditions, to improve the accuracy of vehicle speed estimation in starting conditions and stopping conditions.

[0054] Optionally, the embodiments of the present application may be applied to electric vehicles, fuel vehicles, or hybrid electric vehicles, and the embodiments of the present application do not impose specific limitations on this.

[0055] Optionally, the embodiment of the present application is introduced by taking an electric vehicle as an example.

[0056] Figure 1 This is a schematic diagram of the structure of a vehicle provided in an embodiment of the present application. Vehicle 100 may include various subsystems, such as a travel system 110, a sensor system 120, a control system 130, one or more peripheral devices 140, a power supply 150, a computer system 160, and a user interface 170. Optionally, vehicle 100 may include more or fewer subsystems, and each subsystem may include multiple components. In addition, each subsystem and component of vehicle 100 may be interconnected via wired or wireless connections.

[0057] The propulsion system 110 may include components that provide powered motion for the vehicle 100. In one embodiment, the propulsion system 110 may include an engine 111, a transmission 112, an energy source 113, and wheels 114. The engine 111 may be an internal combustion engine, an electric motor, an air compression engine, or another combination of engines, such as a hybrid engine consisting of a gasoline engine and an electric motor, or a hybrid engine consisting of an internal combustion engine and an air compression engine. The engine 111 converts the energy source 113 into mechanical energy.

[0058] Examples of energy source 113 include gasoline, diesel, other petroleum-based fuels, propane, other compressed gas-based fuels, ethanol, solar panels, batteries, and other sources of electricity. Energy source 113 may also provide energy to other systems of vehicle 100.

[0059] The transmission 112 can transmit mechanical power from the engine 111 to the wheels 114. The transmission 112 may include a gearbox, a differential, and a drive shaft. In one embodiment, the transmission 112 may also include other components, such as a clutch. The drive shaft may include one or more shafts that can be coupled to one or more wheels 114.

[0060] The sensor system 120 may include several sensors that sense information about the environment around the vehicle 100. For example, the sensor system 120 may include a positioning system 121 (the positioning system may be a global positioning system (GPS), a BeiDou system, or other positioning systems), an inertial measurement unit (IMU) 122, a radar 123, a lidar 124, and a camera 125. The sensor system 120 may also include a wheel speed sensor, a steering wheel angle sensor, an electronic stability program (ESP), a motor control unit (MCU), a vehicle control unit (VCU) ( Figure 1 Not shown in ) etc.

[0061] Positioning system 121 may be used to estimate the geographic location of vehicle 100. IMU 122 is used to sense changes in position and orientation of vehicle 100 based on inertial acceleration. In one embodiment, IMU 122 may be a combination of an accelerometer and a gyroscope.

[0062] Radar 123 may utilize radio signals to sense objects within the surrounding environment of vehicle 100. In some embodiments, in addition to sensing objects, radar 123 may also be used to sense the speed and / or heading of the objects.

[0063] The lidar 124 may utilize laser light to sense objects in the environment of the vehicle 100. In some embodiments, the lidar 124 may include one or more laser sources, a laser scanner, and one or more detectors, among other system components.

[0064] The camera 125 may be used to capture multiple images of the surrounding environment of the vehicle 100, as well as multiple images of the interior of the vehicle cabin. The camera 125 may be a still camera or a video camera.

[0065] The control system 130 may control the operation of the vehicle 100 and its components. The control system 130 may include various elements, including a steering system 131 , a throttle 132 , a brake unit 133 , a computer vision system 134 , a path control system 135 , and an obstacle avoidance system 136 .

[0066] The steering system 131 is operable to adjust the forward direction of the vehicle 100. For example, in one embodiment, it may be a steering wheel system.

[0067] The throttle 132 is used to control the operating speed of the engine 111 , thereby controlling the speed of the vehicle 100 .

[0068] Braking unit 133 is used to control the deceleration of vehicle 100. Braking unit 133 can use friction to slow down wheels 114. In other embodiments, braking unit 133 can also convert the kinetic energy of wheels 114 into electric current. Braking unit 133 can also take other forms to slow the rotation speed of wheels 114 to control the speed of vehicle 100.

[0069] The computer vision system 134 can process and analyze images captured by the camera 125 to identify objects and / or features in the environment surrounding the vehicle 100 and the physical and facial features of the driver in the vehicle cockpit. The objects and / or features may include traffic signs, road conditions, and obstacles, and the physical and facial features of the driver may include the driver's behavior, line of sight, expression, etc.

[0070] The route control system 135 is used to determine the driving route of the vehicle 100. In some embodiments, the route control system 135 can combine data from sensors, the positioning system 121, and one or more predetermined maps to determine the driving route for the vehicle 100.

[0071] The obstacle avoidance system 136 is used to identify, assess, and avoid or otherwise negotiate potential obstacles in the environment of the vehicle 100 .

[0072] Of course, in one example, the control system 130 may include additional or alternative components other than those shown and described, or may reduce some of the components shown above.

[0073] The vehicle 100 interacts with external sensors, other vehicles, other computer systems, or users via peripheral devices 140. The peripheral devices 140 may include a wireless communication system 141, an onboard computer 142, a microphone 143, and / or a speaker 144.

[0074] In some embodiments, the peripheral device 140 provides a means for the user of the vehicle 100 to interact with the user interface 170. For example, the onboard computer 142 can provide information to the user of the vehicle 100. The energy flow can be displayed in the form of energy flow on the onboard computer 142, or energy saving effect information can be pushed. The user interface 170 can also operate the onboard computer 142 to receive user input. The onboard computer 142 can be operated through a touch screen. In other cases, the peripheral device 140 can provide a means for the vehicle 100 to communicate with other devices located in the vehicle. For example, the microphone 143 can receive audio (e.g., voice commands or other audio input) from the user of the vehicle 100. Similarly, the speaker 144 can output audio to the user of the vehicle 100.

[0075] The wireless communication system 141 may communicate wirelessly with one or more devices directly or via a communication network.

[0076] Power source 150 can provide power to various components of vehicle 100. In one embodiment, power source 150 can be a rechargeable lithium-ion or lead-acid battery. One or more battery packs of such batteries can be configured as a power source to provide power to various components of vehicle 100. In some embodiments, power source 150 and energy source 113 can be implemented together, such as in some all-electric vehicles.

[0077] Some or all functions of the vehicle 100 are controlled by a computer system 160. The computer system 160 may include at least one processor 161 that executes instructions 1621 stored in a non-transitory computer-readable medium, such as a data storage device 162. The computer system 160 may also be a plurality of computing devices that control individual components or subsystems of the vehicle 100 in a distributed manner.

[0078] The processor 161 may be any conventional processor, such as a commercially available central processing unit (CPU). Alternatively, the processor may be a dedicated device such as an application specific integrated circuit (ASIC) or other hardware based processor. Figure 1 The processor, memory, and other elements are functionally illustrated as being in the same physical housing, but one of ordinary skill in the art will understand that the processor, computer system, or memory may actually include multiple processors, computer systems, or memories that may be stored within the same physical housing, or may not be stored within the same physical housing.

[0079] In various aspects described herein, the processor can be located remotely from the vehicle and in wireless communication with the vehicle. In other aspects, some of the processes described herein are performed on a processor disposed within the vehicle while others are performed by a remote processor, including taking the necessary steps to perform a single maneuver.

[0080] The user interface 170 is used to provide information to or receive information from a user of the vehicle 100. Optionally, the user interface 170 may include an interface for interacting with and exchanging information with the user through one or more input / output devices within the set of peripheral devices 140, wherein the one or more input / output devices within the set of peripheral devices 140 may be, for example, one or more of the wireless communication system 141, the onboard computer 142, the microphone 143, and the speaker 144.

[0081] Computer system 160 may control functions of vehicle 100 based on input received from various subsystems (eg, travel system 110 , sensor system 120 , and control system 130 ) and from user interface 170 .

[0082] Alternatively, one or more of the above components may be installed or associated separately from the vehicle 100. For example, the data storage device 162 may be partially or completely separate from the vehicle 100. The above components may be communicatively coupled together in a wired and / or wireless manner.

[0083] Optionally, the above components are just an example. In actual applications, the components in the above modules may be added or deleted according to actual needs. Figure 1 It should not be understood as limiting the embodiments of the present application.

[0084] The vehicle 100 may be a car, truck, motorcycle, bus, ship, airplane, helicopter, lawn mower, recreational vehicle, amusement park vehicle, construction equipment, tram, golf cart, train, etc., and the embodiments of the present application do not impose any particular limitation.

[0085] Figure 2 The system architecture diagram of a vehicle speed estimation device is shown, which includes an inertial measurement unit (IMU) 201, an electronic stability program (ESP) 202, a motor control unit (MCU) 203, a vehicle control unit (VCU) 204, and an electric power steering system (EPS) 205.

[0086] The inertial measurement unit 201 is used to obtain the longitudinal acceleration and yaw angular velocity of the vehicle, and send the obtained longitudinal acceleration and yaw angular velocity to the vehicle controller 204.

[0087] The vehicle electronic stability system 202 is used to obtain the wheel speed of each wheel of the vehicle and send the wheel speed of each vehicle to the vehicle controller 204. The vehicle electronic stability system 202 includes multiple sensors, wherein the wheel speed of each wheel is obtained by a wheel speed sensor.

[0088] In some embodiments, the vehicle electronic stability system 202 also includes a master cylinder pressure sensor for obtaining at least one of the vehicle's master cylinder pressure, a traction control system (TCS) flag, an anti-lock braking system (ABS) flag, and an electronic stability control system (ESC) flag, and sending the obtained information to the vehicle controller 204.

[0089] The motor controller 203 is used to obtain the torque and speed of the motor, and send the torque and speed of the motor to the vehicle controller 204.

[0090] In one embodiment, if the vehicle is a fuel vehicle or a hybrid vehicle, the system 200 may further include an engine controller 203' (not shown in the figure), which is used to obtain the engine torque and engine speed, and send the engine torque and engine speed to the vehicle controller 204.

[0091] The electric power steering system 205 is used for the vehicle's steering wheel angle and sends the vehicle's steering wheel angle to the vehicle controller 204 .

[0092] The vehicle controller 204 is configured to obtain at least one of the following: the accelerator pedal position, the brake pedal position, and the gear position of the vehicle. The vehicle controller 204 is further configured to receive the longitudinal acceleration and yaw rate of the vehicle from the inertial measurement unit 201. The vehicle controller 204 is further configured to receive the wheel speed of each wheel of the vehicle from the electronic stability control system 202. The vehicle controller 204 is further configured to receive other first information from the electronic stability control system 202, such as the vehicle's master cylinder pressure, the traction control system (TCS) flag, the anti-lock braking system (ABS) flag, and the electronic stability control system (ESC) flag. The vehicle controller 204 is further configured to receive the motor torque and motor speed from the motor controller 203. The vehicle controller 204 is further configured to receive the vehicle's steering wheel angle from the electric power steering system 205. The vehicle controller 204 is further configured to determine the vehicle speed based on the at least one of the above information.

[0093] The method provided in the embodiments of the present application is described below with reference to the accompanying drawings.

[0094] refer to Figure 3 , Figure 3 The following is a flow chart of a vehicle speed estimation method provided in an embodiment of the present application. The vehicle speed estimation method is applied to a vehicle controller (or a vehicle speed estimation device). The method may include the following steps:

[0095] S101: A vehicle controller obtains first vehicle information.

[0096] Optionally, the first information includes at least one of the longitudinal acceleration of the vehicle, the wheel speed of each wheel of the vehicle, the rotational speed of the driving device of the vehicle, and the torque of the driving device of the vehicle.

[0097] Optionally, the vehicle controller may receive first information obtained from a sensor in the vehicle.

[0098] Among them, the sensors in the vehicle may include an inertial measurement unit, an electronic stability system, a drive device controller, etc.

[0099] Optionally, an inertial measurement unit in the vehicle can obtain the longitudinal acceleration of the vehicle, where the longitudinal acceleration refers to the acceleration of the vehicle in the front-to-back direction.

[0100] In some embodiments, the inertial measurement unit may acquire acceleration observation noise of the vehicle.

[0101] In some embodiments, the inertial measurement unit can also obtain the vehicle's yaw rate.

[0102] The electronic stability system in a vehicle can obtain the wheel speed of each wheel of the vehicle.

[0103] In some embodiments, the vehicle electronic stability system may further obtain at least one of the vehicle's master cylinder pressure, a traction control system TCS flag, an anti-lock braking system ABS flag, and an electronic stability control system ESC flag.

[0104] Optionally, when the vehicle is an electric vehicle, the vehicle's driving device is a motor; when the vehicle is a fuel vehicle, the vehicle's driving device is an engine; when the vehicle is a hybrid vehicle, the vehicle's driving device is an engine and a motor.

[0105] For hybrid electric vehicles, when the vehicle requires more power, the engine starts and provides additional power. At this time, the speed of the vehicle's drive device generally refers to the engine's speed. When the vehicle requires less power or is traveling at low speeds, the motor may operate independently or in conjunction with the engine. In this case, the speed of the vehicle's drive device generally refers to the speed of the motor. It should be understood that different hybrid electric vehicle systems may adopt different control strategies and operating modes. The speed of the vehicle's drive device described above is merely an example and is not specifically limited in this embodiment of the present application.

[0106] Optionally, the embodiment of the present application assumes that the vehicle is an electric vehicle, and the driving device of the vehicle is introduced by taking a motor as an example.

[0107] Optionally, a motor controller in the vehicle can obtain the speed of the vehicle's motor and the speed of the motor.

[0108] Sensors in the vehicle may also include an electric power steering system that can acquire the vehicle's steering wheel angle.

[0109] In some embodiments, the sensor in the vehicle may also be a vehicle controller, which may obtain at least one of the following information: the accelerator pedal opening, the brake pedal opening, and the vehicle gear position information.

[0110] S102: The vehicle controller corrects the wheel speeds according to the steering angles of the left and right wheels on the front axle of the vehicle.

[0111] Optionally, after the vehicle controller receives the first information from the vehicle sensor, the vehicle state estimation (VSE) module in the vehicle controller can correct the wheel speed according to the steering angles of the left and right wheels of the vehicle's front axle.

[0112] Alternatively, the vehicle controller may determine the steering angle of each wheel of the vehicle based on the steering wheel angle and the ratio of the steering wheel angle to the wheel angle. The ratio of the steering wheel angle to the wheel angle is a preset value that can be obtained through experiments before the vehicle leaves the factory.

[0113] For example, the vehicle controller can determine the steering angle of the left front wheel of the vehicle based on the steering wheel angle and the ratio of the steering wheel angle to the steering angle of the left front wheel of the vehicle; the vehicle controller can also determine the steering angle of the right front wheel of the vehicle based on the steering wheel angle and the ratio of the steering wheel angle to the steering angle of the right front wheel of the vehicle.

[0114] Optionally, since the sensor is susceptible to strong electromagnetic interference and the influence of external environmental temperature and humidity, and the first information obtained by the sensor will inevitably have transmission errors and signal noise during the transmission process, the first information obtained by the vehicle controller will have signal noise and invalid values, which causes the first information to be distorted.

[0115] In this regard, the vehicle controller may preprocess the first information to remove signal noise and invalid values from the first information.

[0116] Optionally, the preprocessing method may be filtering processing, smoothing processing, etc., so as to improve the reliability of the first information and reduce errors.

[0117] It should be understood that the above-mentioned preprocessing method of the first information is only an example, and the first information can also be preprocessed in other ways. The embodiment of the present application does not specifically limit the preprocessing method.

[0118] In some embodiments, when the steering wheel angle of the vehicle is large, the wheel speed may have a large error. The vehicle controller can perform steering correction on the wheel speed and convert the wheel speed of each wheel of the vehicle to the center of mass of the vehicle to correct the wheel speed.

[0119] Optionally, the wheel speed may be corrected for steering, as shown in the following formulas (1)-(4):

[0120] Vx FL =V FL +ΔV FLLon k δfl -ΔV FLLat (1)

[0121]

[0122]

[0123]

[0124] Among them, reference Figure 4 , Respectively represent the wheel speed of the front left wheel, the wheel speed of the front right wheel, the wheel speed of the rear left wheel, and the wheel speed of the rear right wheel after correction, ΔV FLLon Indicates the longitudinal correction of the left wheel on the front axle, ΔV FRLon Indicates the longitudinal correction amount of the right wheel on the front axle, k δfl Indicates the tire steering angle correction coefficient of the left wheel on the front axle, k δfr Indicates the tire steering angle correction coefficient of the right wheel on the front axle, ΔV FLLat Indicates the lateral correction of the left wheel on the front axle, ΔV FRLat Indicates the lateral correction amount of the right wheel on the front axle, ΔV RLLat Indicates the lateral correction of the left wheel on the rear axle, ΔV RRLat Indicates the lateral correction amount of the right wheel on the rear axle.

[0125] Optionally, the greater the steering angle of the tire on the left front axle, the greater the steering angle coefficient of the tire on the left front axle; and the greater the steering angle of the tire on the right front axle, the greater the steering angle coefficient of the tire on the right front axle. There is a positive correlation between the steering angle and the steering angle coefficient.

[0126] In one embodiment, the wheel speed is subjected to steering correction, which may be specifically expressed as follows:

[0127]

[0128]

[0129]

[0130]

[0131] Among them, reference Figure 4 , Respectively represent the wheel speed of the front left wheel, the wheel speed of the front right wheel, the wheel speed of the rear left wheel, and the wheel speed of the rear right wheel after correction, V FL 、V FR 、V RL 、V RR Respectively represent the original (before correction) front left wheel speed, the original front right wheel speed, the original rear left wheel speed, and the original rear right wheel speed, ω z represents the vehicle's yaw rate, L f Indicates the distance between the center of mass of the vehicle and the front axle, L r Indicates the distance from the center of mass to the rear axle, δ fl Indicates the left front wheel tire steering angle, T f represents the front axle track of the vehicle, δ fr Indicates the steering angle of the right front wheel tire, T r Indicates the rear axle track.

[0132] Optionally, the steering angle and the steering angle coefficient may be nonlinearly positively correlated or linearly positively correlated, and this embodiment of the present application does not impose any specific limitation on this.

[0133] Optionally, considering the Ackerman steering characteristics, the steering angles of the two front wheels of the vehicle are inconsistent when the vehicle is automatically parked (at low speed). Therefore, the embodiment of the present application uses the steering angles of the left and right wheels of the vehicle's front axle to correct the wheel speeds on the left and right sides of the front axle using their respective tire steering angles to obtain the corrected wheel speed of each wheel, so as to improve the wheel speed accuracy of the wheel, and subsequently improve the estimation accuracy of the vehicle speed.

[0134] S103: The vehicle controller determines the vehicle speed according to the first information and the corrected wheel speeds.

[0135] Optionally, the VSE module in the vehicle controller can determine an observation covariance matrix and an observation noise covariance matrix based on the first information and the corrected wheel speeds. The observation matrix of the vehicle speed estimation algorithm includes acceleration observations and vehicle speed observations. The observation noise covariance matrix includes acceleration observation noise and vehicle speed observation noise. The vehicle controller can then calculate the vehicle speed using a Kalman filter based on the observation covariance matrix and the observation noise covariance matrix.

[0136] Optionally, the vehicle controller may use the corrected wheel speed of each wheel to calculate the wheel acceleration of each wheel through differential filtering, and perform filtering on the wheel acceleration of each wheel.

[0137] Optionally, under certain motor torque and / or master cylinder pressure, if the vehicle is driving normally, the difference between the wheel acceleration and the vehicle's longitudinal acceleration should be less than a preset threshold. If the difference between the wheel acceleration and the vehicle's longitudinal acceleration is greater than the preset threshold, it indicates that the vehicle is slipping or spinning, and the wheel speed confidence level will be reduced accordingly.

[0138] In one embodiment, the vehicle controller can also assist in determining whether a wheel of the vehicle is slipping or spinning based on the TCS flag, ABS flag, and ECS flag.

[0139] The wheel speed confidence level indicates the error between the wheel speed and the vehicle speed, and the wheel speed confidence level ranges from 0 to 1. A lower wheel speed confidence level indicates a smaller error between the wheel speed and the vehicle speed; a higher wheel speed confidence level indicates a larger error between the wheel speed and the vehicle speed.

[0140] Alternatively, the vehicle controller can calculate the vehicle's longitudinal force based on the motor torque. The vehicle controller can then determine the wheel speed confidence level for the wheel speed based on the difference between the wheel acceleration and the wheel's longitudinal acceleration, the vehicle's longitudinal force, and a difference-wheel speed confidence table.

[0141] When the difference between the wheel acceleration and the longitudinal acceleration of the vehicle gradually increases, the wheel speed confidence gradually decreases; when the longitudinal force of the vehicle is large, the vehicle slip or sliding degree is generally small, the preset threshold is large, and the wheel speed confidence is small.

[0142] Optionally, the difference-wheel speed confidence table may be specifically as shown in Table 1 below:

[0143] Table 1

[0144]

[0145] Among them, S represents a small value, M represents a medium value, and L represents a large value.

[0146] Optionally, the difference S between the wheel acceleration and the longitudinal acceleration of the vehicle can be 5 m / s 2 , the range of M value can be 5m / s 2 -10m / s 2 , L value can be 10m / s 2 ; The S value of the vehicle's longitudinal force can be 5m / s 2 , the M value can range from 6000N to 12000N, and the L value can be 12000N; the S value of the wheel speed confidence can be 0.2, the M value can range from 0.2 to 0.8, and the L value can be 0.8.

[0147] For example, when the difference between the wheel acceleration and the longitudinal acceleration of the vehicle is less than or equal to 5 m / s 2 When the longitudinal force of the vehicle is less than or equal to 6000N, the wheel speed confidence level is greater than 0.8; when the difference between the wheel acceleration and the longitudinal acceleration of the vehicle is greater than 5m / s 2 And less than 10m / s 2 When the longitudinal force of the vehicle is greater than 6000N and less than 12000N, the wheel speed confidence level is between 0.8 and 0.2; when the difference between the wheel acceleration and the longitudinal acceleration of the vehicle is greater than or equal to 10m / s 2 , when the longitudinal force of the vehicle is greater than or equal to 12000N, the wheel speed confidence is less than 0.2.

[0148] In one embodiment, the vehicle controller may also assist in determining the wheel speed confidence according to the accelerator pedal opening.

[0149] For example, when the accelerator pedal opening is small, the wheel speed confidence is high; when the accelerator pedal opening is large, the wheel may slip or spin, and the wheel speed confidence is low.

[0150] In another embodiment, the vehicle controller may also assist in determining the wheel speed confidence according to the brake pedal opening.

[0151] For example, when the brake pedal opening is small, the wheel speed confidence is high; when the brake pedal opening is large, the wheel may slip or spin, and the wheel speed confidence is low.

[0152] Optionally, the vehicle controller may calculate the vehicle speed observation noise based on the wheel acceleration of each vehicle and the corresponding wheel speed confidence.

[0153] For example, if the vehicle has four wheels and the vehicle acceleration is 3 m / s 2 , the wheel acceleration of the left wheel on the front axle is 10m / s 2 , the wheel speed confidence level is 0.2; the wheel acceleration of the right wheel on the front axle is 5m / s 2 , the wheel speed confidence level is 0.8; the wheel acceleration of the left wheel on the rear axle is 12m / s 2 , the wheel speed confidence level is 0.1; the wheel acceleration of the right wheel on the rear axle is 8m / s 2 , the wheel speed confidence is 0.6. Then the weighted wheel speed confidence can be (0.2+0.8+0.1+0.6) / 4=0.425.

[0154] Optionally, there is a mapping relationship between the wheel speed confidence and the vehicle speed observation noise. The mapping relationship can be measured by a staff member and the mapping relationship can be nonlinear.

[0155] Optionally, the wheel speed confidence 0-1 can be mapped to the vehicle speed observation noise 15-1. The smaller the wheel speed confidence, the greater the vehicle speed observation noise; and the greater the wheel speed confidence, the smaller the vehicle speed observation noise.

[0156] For example, the weighted wheel speed confidence of 0.425 may be mapped to the vehicle speed observation noise of 10.

[0157] In this way, the wheel speed confidence is determined by the corrected wheel speed of each wheel, the longitudinal acceleration of the vehicle, and the longitudinal force of the vehicle, and the vehicle speed observation noise value is determined based on this, thereby improving the vehicle speed estimation accuracy when the wheel slips or spins.

[0158] Optionally, the vehicle controller may determine the vehicle speed observation value based on the weighted wheel speed of the vehicle's wheels and the vehicle's motor speed, and correct the vehicle's weighted wheel speed using the vehicle's motor speed.

[0159] Optionally, the vehicle controller may calculate the weighted wheel speed of each wheel according to the wheel speed of the vehicle, and determine the vehicle speed observation value according to the weighted wheel speed and the motor speed weighting.

[0160] For example, if the wheel speed of the left front wheel is 5 m / s, the wheel speed confidence level is 0.2; the wheel speed of the right front wheel is 14 m / s, the wheel speed confidence level is 0.8; the wheel speed of the left rear wheel is 3 m / s, the wheel speed confidence level is 0.1; and the wheel speed of the right rear wheel is 8 m / s, the wheel speed confidence level is 0.6. The weighted wheel speed of each wheel can be (5*0.2+14*0.8+3*0.1+8*0.6) / 4=4 m / s. For example, the equivalent vehicle speed obtained from the motor speed measured by the motor controller can be 5 m / s.

[0161] For example, if the weighted wheel speed and the motor speed have the same weight, the observed vehicle speed may be (4*0.5+5*0.5)=4.5 m / s. It should be noted that, generally, the weight of the motor speed is not high.

[0162] Optionally, the vehicle controller may obtain an acceleration observation value based on the vehicle's longitudinal acceleration and the vehicle's model acceleration, and correct the vehicle's longitudinal acceleration using the vehicle's model acceleration.

[0163] The vehicle's model acceleration can be calculated based on the vehicle's longitudinal force and mass. Specifically, the following formula (9) can be used:

[0164] a=F / m (9)

[0165] Where a represents the model acceleration of the vehicle, F represents the longitudinal force of the vehicle, and m represents the mass of the vehicle.

[0166] For example, the longitudinal force F of the vehicle may be 3600N, the mass m of the vehicle may be 900kg, and the model acceleration of the vehicle may be 4m / s 2 , the longitudinal acceleration of the vehicle is 6m / s 2 .

[0167] For example, if the vehicle's longitudinal acceleration and the vehicle's model acceleration weights are the same, the acceleration observation value can be (4*0.5+6*0.5)=5m / s 2 .

[0168] Optionally, the vehicle controller can obtain acceleration observation noise through an inertial measurement unit. For example, the acceleration observation noise can be 8.

[0169] In this way, the vehicle controller can obtain the acceleration observation value, acceleration observation noise, vehicle speed observation value, and vehicle speed observation noise, thereby obtaining the observation covariance matrix and the observation noise covariance matrix. The Kalman filter algorithm can then be used to calculate the vehicle speed, ensuring real-time speed estimation.

[0170] Optionally, the state space equations of the acceleration observation value and the vehicle speed observation value can be shown as follows (10) and (11):

[0171] ax k =1*ax k-1 +0*vx k-1 (10)

[0172] vx k =T*ax k-1 +1*vx k-1 (11)

[0173] Alternatively, the system observation equations for the acceleration observation value and the vehicle speed observation value can be expressed as follows:

[0174] ax zk =1*ax k +0*vx k (12)

[0175] vx zk =0*ax k +1*vx k (13)

[0176] Among them, ax k Indicates the predicted acceleration value at the current moment, ax k-1 Indicates the estimated value of acceleration at the previous moment, ax zk Indicates the acceleration observation value at the current moment, vx k Indicates the predicted speed value at the current moment, vxk-1 Indicates the estimated value of the speed at the previous moment, vx zk Indicates the velocity observation value at the current moment.

[0177] Optionally, the Kalman filter equations are as follows (14)-(18):

[0178]

[0179]

[0180]

[0181]

[0182]

[0183] in is the predicted value of the state, is the estimated value of the state, T represents the period, R k is the observation noise covariance matrix of the input. Fk is the state transfer matrix, B k is the control vector, is the predicted covariance matrix of the state at time k, Q k is the process noise covariance matrix, z is the observed value of the state, and H is the observation matrix. The matrix vectors are as follows: Among them, ax represents the acceleration at the current moment, vx represents the speed at the current moment, Q k represents the process noise covariance matrix, Q k The larger the value, the higher the credibility of the observation. 11 ,q 12 ,q 21 ,q 22 The four values represent the credibility of the state space equation, R k represents the observation noise covariance matrix, r 11 represents the acceleration observation noise, r 21 Indicates vehicle speed observation noise.

[0184] The vehicle controller can calculate the estimated value of the vehicle speed based on the above Kalman filter algorithm.

[0185] In one embodiment, the vehicle controller can also identify the vehicle's driving conditions and correct the observation covariance matrix and the observation noise covariance matrix based on the vehicle's driving conditions to obtain corrected observation covariance matrix and observation noise covariance matrix. The vehicle's driving conditions may include starting conditions and parking conditions.

[0186] Optionally, the vehicle controller may perform operating condition identification based on the first information to determine the current driving condition of the vehicle.

[0187] For example, when the vehicle's longitudinal acceleration is greater than zero, it indicates that the vehicle is in a starting state; when the vehicle's longitudinal acceleration is less than zero, it indicates that the vehicle is in a stopping state. Alternatively, when the vehicle's motor speed increases, it indicates that the vehicle is in a starting state; when the vehicle's motor speed decreases, it indicates that the vehicle is in a stopping state. Alternatively, when the vehicle's gear position information is forward, it indicates that the vehicle is in a starting state; when the vehicle's gear position information is park, it indicates that the vehicle is in a stopping state.

[0188] It should be understood that the above-mentioned method for identifying the vehicle driving condition is only an example. The vehicle controller can also determine the vehicle driving condition based on other first information. This application does not impose any specific restrictions on this.

[0189] In one embodiment, when the vehicle is in a starting condition, there is a delay in the wheel speed, and the motor speed and longitudinal acceleration have a smaller delay than the wheel speed (the delay is about 30ms less), and the motor speed and longitudinal acceleration measured by the inertial measurement unit are more reliable.

[0190] Optionally, when calculating the acceleration observation value, the vehicle controller may increase the weight of the longitudinal acceleration and reduce the weight of the vehicle's model acceleration, so that the weight of the vehicle's longitudinal acceleration is greater than the weight of the vehicle's model acceleration.

[0191] For example, the weight of the longitudinal acceleration of the vehicle may be 0.8, and the weight of the model acceleration of the vehicle may be 0.2.

[0192] Optionally, when calculating the vehicle speed observation value, the vehicle controller can increase the weight of the vehicle's motor speed and reduce the weight of the vehicle's wheel weighted speed, so that the weight of the vehicle's wheel weighted speed is less than the weight of the vehicle's drive device speed.

[0193] For example, the weight of the weighted wheel speed of the vehicle's wheels may be 0.9, and the weight of the rotation speed of the vehicle's driving device may be 0.1.

[0194] Optionally, the vehicle controller may directly assign values to the acceleration observation noise and the vehicle speed observation noise, wherein the acceleration observation noise is smaller than the vehicle speed observation noise.

[0195] For example, the acceleration observation noise may be 2, and the vehicle speed observation noise may be 13.

[0196] In this way, the vehicle controller can obtain the corrected observation covariance matrix and observation noise covariance matrix for the vehicle in the starting condition. The vehicle controller can then estimate the vehicle speed based on these corrected observation covariance matrix and observation noise covariance matrix. This can improve the accuracy of vehicle speed estimation during starting.

[0197] In another embodiment, when the vehicle is in a parking state, the wheel speed will jump to zero in advance. At this time, the longitudinal acceleration measured by the inertial measurement unit will fluctuate, and the reliability of the model acceleration is higher than the longitudinal acceleration.

[0198] Optionally, when calculating the acceleration observation value, the vehicle controller may reduce the weight of the longitudinal acceleration and increase the weight of the vehicle's model acceleration, so that the weight of the vehicle's longitudinal acceleration is less than the weight of the vehicle's model acceleration.

[0199] For example, the weight of the longitudinal acceleration of the vehicle may be 0.3, and the weight of the model acceleration of the vehicle may be 0.7.

[0200] In this way, the reliability of the fused acceleration observation value is higher, which can increase the confidence of the acceleration noise, that is, reduce the value of the acceleration noise. As a result, the acceleration observation noise is smaller than the vehicle speed observation noise.

[0201] For example, the acceleration observation noise may be 3, and the vehicle speed observation noise may be 12.

[0202] In this way, the vehicle controller can obtain the corrected observation covariance matrix and observation noise covariance matrix when the vehicle is parked. The vehicle controller can then estimate the vehicle speed based on these corrected observation covariance matrix and observation noise covariance matrix. This improves the accuracy of vehicle speed estimation when the vehicle is parked.

[0203] In another implementation, the method flow chart of the vehicle speed estimation device can be as follows: Figure 5 The inertial measurement unit 201, the vehicle electronic stability system 202, the motor controller 203, the vehicle controller 204, and the electric power steering system 205 send the acquired first information of the vehicle to the vehicle state estimation module in the vehicle controller (for details, please refer to Figure 2 The vehicle state estimation module calculates the observation matrix and the observation noise covariance matrix according to the vehicle state estimation algorithm (see steps S101-S103 for details). The vehicle controller then processes the observation matrix and the observation noise covariance matrix using a Kalman filter algorithm to obtain a high-precision vehicle speed estimate. The vehicle controller can apply the speed estimate to the vehicle control algorithm to achieve precise control of the vehicle based on the high-precision vehicle speed.

[0204] It should be understood that some operations in the processes of the above-mentioned method embodiments are optionally combined, and / or the order of some operations is optionally changed. In addition, the execution order between the steps of each process is only exemplary and does not constitute a limitation on the execution order between the steps. There may also be other execution orders between the steps. It is not intended to indicate that the execution order is the only order in which these operations can be performed. Those of ordinary skill in the art will think of many ways to reorder the operations described herein. In addition, it should be noted that the process details involved in a certain embodiment of this invention are also applicable to other embodiments in a similar manner, or different embodiments can be used in combination.

[0205] Furthermore, some steps in the method embodiments may be equivalently replaced with other possible steps. Alternatively, some steps in the method embodiments may be optional and may be deleted in certain usage scenarios. Alternatively, other possible steps may be added to the method embodiments.

[0206] Furthermore, the above method embodiments may be implemented separately or in combination.

[0207] In the embodiment of the present application, the vehicle speed estimation device can be divided into functional modules according to the above method example. When each functional module is divided according to each function, Figure 6 FIG. 1 is a schematic diagram showing a possible structure of the vehicle speed estimation device involved in the above embodiment. Figure 6 As shown, the vehicle speed estimation device includes a transceiver module 1101 and a processing module 1102. Of course, the vehicle speed estimation device may also include other modules, or the vehicle speed estimation device may include fewer modules. This embodiment of the present application is not limited to this.

[0208] The transceiver module 1101 is configured to obtain first vehicle information; the first information includes at least one of the vehicle's longitudinal acceleration, the speed of each wheel, the vehicle's drive device speed, the vehicle's drive device torque, and the vehicle's steering angle. The first information may also include the vehicle's steering wheel angle, the vehicle's yaw rate, the vehicle's master cylinder pressure, the vehicle's drive device torque, the traction control system (TCS) flag, the anti-lock braking system (ABS) flag, the electronic stability control system (ESC) flag, the accelerator pedal position, the brake pedal position, and vehicle gear information.

[0209] The processing module 1102 is configured to correct the wheel speeds according to the steering angles of the left and right wheels of the front axle of the vehicle; and determine the vehicle speed according to the first information and the corrected wheel speeds.

[0210] The specific working process of the system described above can refer to the corresponding process in the above method embodiment, which will not be repeated here.

[0211] An embodiment of the present application provides a computer-readable storage medium storing one or more programs, wherein the one or more programs include instructions, which, when executed by a computer, enable the computer to execute the vehicle speed estimation method described in steps S101-S103 above.

[0212] An embodiment of the present application further provides a computer program product comprising instructions, which, when executed on a computer, enables the computer to execute the vehicle speed estimation method described in steps S101 to S103 of the above embodiment.

[0213] The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. 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 one website, computer, server, or data center to another website, computer, server, or data center via a wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) method. A computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a magnetic tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid state drive (SSD)).

[0214] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the modules or units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0215] The units described as separate components may or may not be physically separate, and the components shown as units may be one physical unit or multiple physical units, that is, they may be located in one place or distributed in multiple places. In practice, some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0216] The above description is only a specific implementation method of the present application, but the protection scope of the present application is not limited thereto. Any changes or replacements within the technical scope disclosed in the present application should be included in the protection scope of the present application.

Claims

1. A vehicle speed estimation method, characterized in that: The method comprises: Acquiring first information of the vehicle; the first information includes at least one of the longitudinal acceleration of the vehicle, the wheel speed of each wheel of the vehicle, the speed of the driving device of the vehicle, the torque of the driving device of the vehicle, and the steering angle of each wheel of the vehicle; Correcting the wheel speeds according to the steering angles of the left and right wheels on the front axle of the vehicle; The vehicle speed is determined according to the first information and the corrected wheel speeds.

2. The method according to claim 1, characterized in that The correcting the wheel speed according to the steering angles of the left and right wheels of the front axle of the vehicle includes: The magnitude of the left front wheel of the vehicle and the steering angle is positively correlated with the degree of correction of the left front wheel of the vehicle; the magnitude of the right front wheel of the vehicle and the steering angle is positively correlated with the degree of correction of the right front wheel of the vehicle.

3. The method according to claim 1 or 2, characterized in that The determining the vehicle speed according to the first information and the corrected wheel speeds includes: Determining an observation matrix and an observation noise covariance matrix based on the first information and the corrected wheel speed; the observation matrix includes acceleration observation values and vehicle speed observation values; the observation noise covariance matrix includes acceleration observation noise and vehicle speed observation noise; The vehicle speed is calculated based on the observation matrix and the observation noise covariance matrix.

4. The method according to claim 3, characterized in that The method further comprises: determining a wheel speed confidence level based on the corrected wheel speed, the longitudinal acceleration of the vehicle, and the driving device torque of the vehicle; wherein the wheel speed confidence level represents an error relationship between the wheel speed and the vehicle speed; The vehicle speed observation noise is determined according to the wheel speed confidence; and a mapping relationship exists between the wheel speed confidence and the vehicle speed observation noise.

5. The method according to claim 3 or 4, characterized in that The step of determining the observation matrix according to the first information and the corrected wheel speed includes: The acceleration observation value is determined based on the longitudinal acceleration of the vehicle and a weighted model acceleration of the vehicle; the model acceleration of the vehicle is obtained based on the torque of the driving device of the vehicle.

6. The method according to claim 5, characterized in that The step of determining the observation matrix according to the first information and the corrected wheel speed includes: The vehicle speed observation value is determined by weighting the weighted wheel speed of the vehicle's wheels and the rotation speed of the vehicle's driving equipment; the weighted wheel speed of the wheel is obtained according to the wheel speed of each wheel.

7. The method according to claim 5, characterized in that The step of determining an observation matrix and an observation noise covariance matrix based on the first information and the corrected wheel speed includes: An observation matrix and an observation noise covariance matrix are determined according to the first information and the corrected wheel speed and driving conditions; the driving conditions include a starting condition and a parking condition.

8. The method according to claim 7, characterized in that When the driving condition is a starting condition: The weight of the longitudinal acceleration of the vehicle is greater than the weight of the model acceleration of the vehicle; The weight of the weighted wheel speed of the vehicle's wheels is less than the weight of the rotational speed of the vehicle's drive equipment; The acceleration observation noise is smaller than the vehicle speed observation noise.

9. The method according to claim 7, characterized in that When the driving condition is a parking condition: The weight of the longitudinal acceleration of the vehicle is less than the weight of the model acceleration of the vehicle; The acceleration observation noise is smaller than the vehicle speed observation noise.

10. The method according to any one of claims 1 to 9, characterized in that The first information also includes: at least one of the vehicle's steering wheel angle, the vehicle's yaw angular velocity, the vehicle's master cylinder pressure, the vehicle's drive device torque, the vehicle's traction control system TCS flag, the anti-lock braking system ABS flag, the electronic stability control system ESC flag, the accelerator pedal opening, the brake pedal opening, and the vehicle's gear information.

11. The method according to any one of claims 3 to 10, characterized in that: The calculating the vehicle speed according to the observation matrix and the observation noise covariance matrix includes: The vehicle speed is calculated using the Kalman algorithm based on the observation matrix and the observation noise covariance matrix.

12. A vehicle speed estimation device, characterized in that: include: a transceiver module configured to obtain first information of the vehicle; the first information comprising at least one of the longitudinal acceleration of the vehicle, the speed of each wheel of the vehicle, the rotational speed of a driving device of the vehicle, the torque of the driving device of the vehicle, and the steering angle of each wheel of the vehicle; A processing module is used to correct the wheel speeds of the wheels according to the steering angles of the left and right wheels of the front axle of the vehicle; and determine the vehicle speed according to the first information and the corrected wheel speeds.

13. A vehicle, characterized in that: The vehicle speed estimation device according to claim 12 is included.

14. A computer-readable storage medium storing instructions, characterized in that: When the instructions are executed on an electronic device, the electronic device is caused to execute the method according to any one of claims 1 to 11.

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