Vehicle control method and device, control equipment and storage medium

Abstract

The invention relates to a vehicle control method and device, control equipment and a storage medium, and the method comprises the steps: determining a slope value according to multiple pieces of sensor data sensed by a vehicle; according to the slope value, the measurement state information corresponding to the vehicle is compensated, and the compensated state information is obtained; and controlling the vehicle based on the compensated state information. The gradient value is determined through the multi-dimensional sensor data, so that the precision of the gradient value is improved, the measurement state information corresponding to the vehicle is compensated through the gradient value with higher precision, the influence of the gradient on the measurement state information corresponding to the vehicle is reduced, and the vehicle control accuracy and reliability are improved.

Description

Technical Field

[0001] The present application relates to the field of intelligent driving technology, and in particular to a vehicle control method, device, control equipment and storage medium. Background Art

[0002] At present, the vehicle's automatic emergency braking system (AEB) mainly relies on sensors to detect the distance and speed of obstacles ahead. It is an active safety technology that performs automatic emergency braking when the vehicle perceives and analyzes the risk of collision during driving. On flat roads, the measurement results of these sensors are relatively accurate, but on sloping roads, due to the influence of the terrain inclination, the measurement results of the sensors will deviate, resulting in misjudgment of the AEB system.

[0003] The related technology usually relies on the measurement data of sensors, which cannot effectively solve the error problem caused by the slope. Summary of the invention

[0004] The embodiments of the present application provide a vehicle control method, apparatus, control device and storage medium, which effectively solve the error problem caused by slope, improve the accuracy and reliability of vehicle control on a sloping road surface, and at least partially solve the above-mentioned technical problems.

[0005] In order to achieve the above object, according to a first aspect of the present application, a vehicle control method is provided, the method comprising:

[0006] Determine the slope value based on multiple sensor data sensed by the vehicle;

[0007] According to the slope value, the measured state information corresponding to the vehicle is compensated to obtain compensated state information;

[0008] The vehicle is controlled based on the compensated state information.

[0009] According to a second aspect of the present application, a vehicle control device is provided, the device comprising:

[0010] A determination module, for determining a slope value according to a plurality of sensor data sensed by the vehicle;

[0011] A compensation module, used for compensating the measured state information corresponding to the vehicle according to the slope value to obtain compensated state information;

[0012] A control module is used to control the vehicle based on the compensated state information.

[0013] According to a third aspect of the present application, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the above-mentioned vehicle control method is implemented.

[0014] According to a fourth aspect of the present application, a computer program product is provided, comprising a computer program, wherein the computer program implements the above-mentioned vehicle control method when executed by a processor.

[0015] According to a fifth aspect of the present application, a control device is provided, comprising: a memory on which a computer program is stored; and a processor for executing the computer program in the memory to implement the above-mentioned vehicle control method.

[0016] According to a sixth aspect of the present application, a vehicle is provided, comprising the above-mentioned control device.

[0017] The vehicle control method, device, storage medium, program product and vehicle of the embodiment of the present application determine the slope value according to multiple sensor data sensed by the vehicle; compensate the measured state information corresponding to the vehicle according to the slope value to obtain compensated state information; and control the vehicle based on the compensated state information. Since the slope value is determined by multi-dimensional sensor data, the accuracy of the slope value is improved, and the measured state information corresponding to the vehicle is compensated by the high-precision slope value, which reduces the influence of the slope on the measured state information corresponding to the vehicle, and improves the accuracy and reliability of vehicle control.

[0018] Other features and advantages of the present application will be described in detail in the subsequent detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present application, and those skilled in the art can obtain other drawings based on these drawings without creative work.

[0020] In order to more completely understand the present application and its beneficial effects, the following description will be given in conjunction with the accompanying drawings, wherein the same figure numbers represent the same parts in the following description.

[0021] Figure 1 is a flow chart of a vehicle control method provided in some embodiments of the present application;

[0022] Figure 2 is a schematic diagram of coordinate system conversion provided in some embodiments of the present application;

[0023] Figure 3is a schematic diagram of a process for determining a slope value provided in some embodiments of the present application;

[0024] Figure 4 is a flowchart of controlling an AEB system provided in some embodiments of the present application;

[0025] Figure 5 is a comparative schematic diagram of changes in distance and speed during AEB system control according to measurement state information and compensated state information provided in some embodiments of the present application;

[0026] Figure 6 is a schematic diagram of the structure of a vehicle control device provided in some embodiments of the present application;

[0027] Figure 7 is a schematic diagram of the structure of a control device provided in some embodiments of the present application;

[0028] Figure 8 is a schematic diagram of a vehicle provided in some embodiments of the present application. DETAILED DESCRIPTION

[0029] The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present application.

[0030] In the description of this application, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more features. In the description of this application, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0031] In the description of the present application, the word "for example" is used to mean "used as an example, illustration or explanation". Any embodiment described as "for example" in the present application is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is given to enable any technician in the field to implement and use the present invention. In the following description, details are listed for the purpose of explanation. It should be understood that a person of ordinary skill in the art can recognize that the present invention can be implemented without using these specific details. In other examples, well-known structures and processes will not be elaborated in detail to avoid obscuring the description of the present invention with unnecessary details. Therefore, the present invention is not intended to be limited to the embodiments shown, but is consistent with the widest scope consistent with the principles and features disclosed in the present application.

[0032] In the related art, the vehicle's AEB system is usually controlled by relying on the measurement results of a single sensor, which cannot effectively solve the error problem caused by the slope. Especially in the case of a large slope, the measurement results of a single sensor are easily interfered and cannot reflect the vehicle's driving environment, reducing the performance and reliability of the vehicle's AEB system.

[0033] In order to solve the above problems, the embodiment of the present application provides a vehicle control method, which determines the slope value according to multiple sensor data sensed by the vehicle; compensates the measured state information corresponding to the vehicle according to the slope value to obtain compensated state information; and controls the vehicle based on the compensated state information. Since the slope value is determined by multi-dimensional sensor data, the accuracy of the slope value is improved, and the measured state information corresponding to the vehicle is compensated by the high-precision slope value, which reduces the influence of the slope on the measured state information corresponding to the vehicle, and improves the accuracy and reliability of vehicle control.

[0034] See also Figure 1 , provides a vehicle control method, which is applied to a control device. The control device may be a terminal device or a server. The method includes:

[0035] Step S101, determining a slope value based on multiple sensor data sensed by the vehicle.

[0036] The slope value is the slope value of the vehicle when it is traveling on a sloped road.

[0037] Specifically, the control device can estimate the slope value based on multiple sensor data sensed by sensors on the vehicle. It can be understood that in this embodiment, the slope value is determined by combining multi-dimensional sensor data, avoiding the limitations of single sensor data, and can fully reflect the vehicle driving environment. It also takes into account the impact of multi-dimensional sensor data on the slope value calculation, which can improve the accuracy of the slope value.

[0038] In some embodiments, the sensor data includes at least one of inertial detection data collected by an inertial measurement sensor, radar detection data collected by a radar sensor, and visual detection data collected by a visual sensor.

[0039] The inertial measurement unit (IMU) includes an accelerometer, a gyroscope and a magnetometer, which are used to collect the acceleration, angular velocity and magnetic field strength of the vehicle respectively. The pitch angle and roll angle can be calculated using the gravity acceleration component measured by the accelerometer, and the first estimated slope value θ corresponding to the inertial measurement sensor can be calculated based on the pitch angle and roll angle. IMU .

[0040] The radar detection data collected by the radar sensor includes Doppler frequency shift and echo intensity. The Doppler frequency shift can reflect the relative speed of the target object, and the echo intensity is related to the distance and reflection characteristics of the target object. The radar sensor can detect the reflected signal of the road in front of the vehicle, and calculate the second slope value θ corresponding to the radar sensor by analyzing the Doppler frequency shift and echo intensity changes of these signals, combined with the vehicle's driving speed and the installation location of the radar sensor. radar .

[0041] The visual sensor may be a camera. The visual detection data collected by the visual sensor includes image data corresponding to the vehicle. The third estimated slope value θ corresponding to the visual sensor may be determined from the collected image data using image processing or deep learning methods. vision . Image processing methods include steps such as edge detection, feature extraction, and geometric analysis. For example, the edges and lane lines of the road are detected through image data, and the position and angle changes of the edges and lane lines of the road in the image data are analyzed to infer the slope of the road. Deep learning methods learn the feature representation of the slope directly from the image data by training a neural network model. For example, a convolutional neural network (CNN) can be used to analyze road images and output the slope estimation result.

[0042] Specifically, the slope value can be determined based on at least one of the inertial detection data, radar detection data, and visual detection data. As a preferred embodiment of this embodiment, inertial detection data, radar detection data, and visual detection data are selected at the same time, and corresponding data processing is performed respectively to calculate the corresponding first estimated slope value, second estimated slope value, and third slope value, and then the slope value is determined based on the first estimated slope value, the second estimated slope value, and the third slope value. Through multi-sensor fusion, that is, combining the data collected by the IMU, radar sensor, and visual sensor, it is possible to estimate the slope value from multiple angles, improve the accuracy of the slope value, and thus more comprehensively reflect the driving environment of the vehicle.

[0043] In some embodiments, determining the slope value based on multiple sensor data sensed by the vehicle includes: performing corresponding data processing on each sensor data to obtain processed sensor data; and determining the slope value based on the processed sensor data.

[0044] Specifically, for the sensor data collected by different sensors, corresponding data processing can be performed to obtain processed sensor data, and the first estimated slope value θ in the above embodiment can be used as the first estimated slope value θ. IMU , the second estimated slope value θ radar , the third estimated slope value θ vision For example, the first estimated slope value, the second estimated slope value, and the third estimated slope value are all processed sensor data, and the slope value is determined according to the processed sensor data corresponding to each sensor.

[0045] In some embodiments, determining the slope value based on the processed sensor data includes: performing data fusion on the processed sensor data corresponding to each sensor data to obtain the slope value.

[0046] Specifically, data fusion may be performed on the processed sensor data corresponding to each sensor, wherein the data fusion method includes but is not limited to a weighted average method, a Bayesian estimation method, a Kalman filtering method, a fuzzy logic method, an artificial intelligence method, and the like.

[0047] In a specific embodiment, the first estimated slope value θ is continued IMU , the second estimated slope value θ radar , the third estimated slope value θ vision Take the example to illustrate that the Kalman filter method is used for data fusion to obtain the slope value θ:

[0048] θ=kalmanFilter(θ IMU ,θ radar ,θ vision );

[0049] KalmanFilter() in the formula is a Kalman filter, which is a recursive algorithm used to estimate the state of a dynamic system.

[0050] It can be understood that in this embodiment, by combining the processed sensor data corresponding to the IMU, radar sensor and visual sensor, data fusion is used to reduce the error of each sensor when measuring alone, and the slope value is estimated from multiple angles, thereby improving the accuracy of slope value estimation, and being able to more comprehensively reflect the vehicle's driving environment. It can also significantly reduce the impact of the slope on the vehicle's corresponding measurement status information, thereby improving the accuracy and reliability of vehicle control.

[0051] In some embodiments, for the inertial detection data and the radar detection data, the data processing includes filtering processing, and for the visual detection data, the data processing includes image preprocessing.

[0052] Specifically, the inertial detection data and the radar detection data can be filtered to remove noise and distorted signals to improve the quality of the inertial detection data and the radar detection data, reduce the impact of noise and interference, and help improve the efficiency of determining the corresponding first estimated slope value and the second estimated slope value.

[0053] For visual inspection data, image preprocessing can be used, such as grayscale, denoising, and edge detection of visual inspection data, so as to facilitate subsequent feature extraction. Data processing corresponding to visual inspection data can also include image processing methods based on deep algorithms.

[0054] In a specific implementation, the visual detection data can be grayscaled, denoised (such as neighborhood-based spatial filtering, image compression denoising, etc.), and then edge detected (such as gradient-based edge detection, Canny-based edge detection, etc.) to obtain a preprocessed image, and then the Lanenet lane line detection algorithm is used to extract the road lane line fitting function, and the Hnet neural network structure based on deep learning is used to generate the perspective transformation matrix coefficients, and the preview point coordinates are obtained to determine the third estimated slope value corresponding to the visual sensor.

[0055] In some embodiments, for the inertial detection data, the data processing further includes compensating the inertial detection data based on real-time dynamic data.

[0056] Among them, real-time kinematic data (RTK data) is used to provide high-precision location information.

[0057] The inventors have found that the perception data collected by the IMU, i.e., the inertial detection data, will have large errors due to the accumulated errors and external interference caused by the long-term use of the IMU. Therefore, in this embodiment, the inertial detection data is compensated using RTK data to eliminate the accumulated errors of the IMU, ensuring the accuracy and real-time performance of the inertial detection data after the compensation process, which is conducive to improving the accuracy and real-time performance of determining the first estimated slope value, and solving the problem of accumulated errors existing in a single IMU.

[0058] In some embodiments, the step of determining the real-time dynamic data includes: using the coordinates and observation data of the base station to perform differential processing on the observation data of the mobile station to obtain the real-time dynamic data.

[0059] Specifically, the real-time dynamic data in this embodiment is data that has been subjected to differential processing. It can be understood that by performing differential processing on the original RTK data, systematic errors can be eliminated to ensure the high accuracy of the real-time dynamic data. The differential processing can improve the positioning accuracy by calculating the difference in the observation values ​​of the base station and the mobile station, that is, using the coordinates and observation data of the base station, the observation data of the mobile station is differentially processed to obtain the real-time dynamic data.

[0060] In a specific implementation, in order to collect inertial detection data and real-time dynamic data, the following steps can be performed:

[0061] R1: RTK equipment includes base station and rover. IMU equipment includes accelerometer, gyroscope and magnetometer. Data recording equipment is used to record inertial detection data and RTK data. Control equipment is used for data processing and analysis.

[0062] RTK and IMU device installation location: Choose a suitable location to install the IMU and RTK devices, ensuring that they are not affected by vehicle vibration and electromagnetic interference. Ensure that the devices have adequate power supply.

[0063] Base station: Select a fixed location with a known precise position and install a base station.

[0064] Rover: Mount the rover on a vehicle, making sure the antenna is on top of the vehicle, away from metal objects and electronic equipment.

[0065] IMU device installation, its location selection: install the IMU device in the center of the vehicle, as close to the center of mass of the vehicle as possible to reduce the error caused by vehicle movement. Fixing method: Use a fixing bracket to firmly mount the IMU device on the vehicle, and ensure that the mounting plane is parallel to the horizontal plane of the vehicle. Time synchronization: Ensure the time synchronization of RTK and IMU devices, which can be achieved through the GPS synchronization module. Configure the data recording device to ensure that the data of RTK and IMU can be recorded at the same time.

[0066] R2: Start the RTK base station and rover to ensure that the rover can receive the differential signal from the base station.

[0067] Calibrate the IMU device: Calibrate the IMU device under static conditions to ensure that the bias and scale factor errors of the accelerometer and gyroscope are minimal.

[0068] The test begins by placing the vehicle on a flat surface and recording static data. Dynamic testing then proceeds, including the vehicle's rollover and rolling motion.

[0069] After completing the above steps R1 and R2, the collection of inertial detection data and real-time dynamic data can be achieved.

[0070] In some embodiments, the step of compensating the inertial detection data based on the real-time dynamic data is performed after the step of filtering.

[0071] Specifically, the inertial detection data is first filtered to obtain filtered inertial detection data (referred to as filtered inertial data in this embodiment) to remove noise and ensure the stability and quality of the filtered inertial data. Then, the filtered inertial data is compensated based on real-time dynamic data to eliminate systematic errors and ensure the high accuracy of the processed filtered inertial data.

[0072] In some embodiments, the step of compensating the filtered inertial detection data based on the real-time dynamic data includes: performing coordinate system conversion based on the real-time dynamic data to obtain position data corresponding to the vehicle in the vehicle coordinate system; performing posture calculation on the filtered inertial detection data based on the position data to obtain processed perception data corresponding to the inertial detection data.

[0073] Among them, the ego-vehicle coordinate system is the coordinate system established by the autonomous driving vehicle itself, which usually follows the right-hand coordinate system. The coordinate origin is located on the ground plane at the center of the rear axle of the vehicle. The vehicle's forward direction is defined as the positive direction of the X-axis, the left side of the vehicle points to the positive direction of the Y-axis, and the direction from the ground plane to the sky is the positive direction of the Z-axis.

[0074] Specifically, the real-time dynamic data can be converted into the self-vehicle coordinate system to obtain the planar position data of the target vehicle relative to the vehicle (self-vehicle), thereby providing the precise position of the vehicle in its own coordinate system and simplifying the subsequent processing and fusion of sensor data.

[0075] Then, the attitude of the filtered inertial detection data is calculated based on the position data to obtain the processed perception data corresponding to the inertial detection data. In the attitude calculation, the Kalman filter algorithm plays a vital role. Kalman filtering is an efficient recursive filter that can estimate the state of a dynamic system from a series of noisy measurements. The attitude calculation steps are as follows:

[0076] M1: Initialize the state vector. The state vector contains information such as attitude and angular velocity represented by quaternion. The state vector can be initialized based on the inertial detection data after filtering.

[0077] M2: Predicting the current state can be done by integrating the angular velocity of the gyroscope. For example, predicting the current state based on the state of the previous moment and the control input (such as acceleration and angular velocity) can be done by integrating the angular velocity of the gyroscope.

[0078] M3: Update prediction, which can correct the quaternion based on the output of the accelerometer. The predicted current state can be compared with the position data, and the predicted value can be adjusted through the Kalman gain to obtain a more accurate state estimate;

[0079] M4: Repeat steps M2 and M3 to continuously improve the accuracy of the attitude solution in an iterative manner.

[0080] It is worth noting that in order to reduce the amount of calculation and improve real-time performance, the Kalman filter is simplified, such as using the direction cosine matrix to approximate the quaternion. The Kalman filter estimates the state by establishing a state model and an observation model, combining prior information and observation information. The key lies in the two steps M2 and M3. M2 estimates the current state based on the state of the previous moment, and M3 adjusts the predicted value based on the new observation data to obtain a more accurate state estimate, thereby improving the accuracy of the processed perception data corresponding to the inertial detection data.

[0081] In some embodiments, the coordinate system conversion is performed based on the real-time dynamic data to obtain the position data of the vehicle in the self-vehicle coordinate system corresponding to the vehicle, including: converting the real-time dynamic data from the earth coordinate system where it is located to the self-vehicle coordinate system corresponding to the vehicle to obtain the position data in the self-vehicle coordinate system.

[0082] Among them, the geodetic coordinate system is the coordinate system corresponding to the real-time dynamic data. The location data provided by the real-time dynamic data is usually the longitude, latitude and altitude in the WGS-84 coordinate system.

[0083] Specifically, the real-time dynamic data is converted from the earth coordinate system to the vehicle coordinate system corresponding to the vehicle to obtain the position data in the vehicle coordinate system. It can be understood that by converting the real-time dynamic data to the vehicle coordinate system corresponding to the vehicle to obtain the position data in the vehicle coordinate system, it is convenient to obtain the planar position data of the target vehicle relative to the vehicle, and then realize data analysis and processing in the vehicle coordinate system, and improve the position accuracy of the inertial detection data after filtering.

[0084] In some embodiments, the real-time dynamic data is converted from the earth coordinate system where it is located to the self-vehicle coordinate system corresponding to the vehicle to obtain the position data in the self-vehicle coordinate system, including: converting the real-time dynamic data from the earth coordinate system where it is located to the northeast celestial coordinate system to obtain the basic position data in the northeast celestial coordinate system; converting the basic position data in the northeast celestial coordinate system to the self-vehicle coordinate system to obtain the position data.

[0085] Among them, the East-North-Up (ENU) coordinate system is a local coordinate system, with the X-axis pointing to the east, the Y-axis pointing to the north, and the Z-axis pointing vertically upward to the sky.

[0086] Specifically, the real-time dynamic data is converted from the geodetic coordinate system to the ENU coordinate system, and the conversion formula of the seven gases is as follows:

[0087] E=[-sin(Latitude),cos(Latitude),0][ΔX]

[0088] N=[-sin(Longitude)*cos(Longitude),-sin(Longitude)*sin(Latitude),cos(Longitude)][ΔY]

[0089] U=[cos(Longitude)*cos(Longitude),cos(Longitude)*sin(Latitude),sin(Longitude)][ΔZ]

[0090] Among them, ΔX, ΔY, ΔZ, Latitude, and Longitude are real-time dynamic data.

[0091] Vector E represents the change in position in the east-west direction (longitude direction), ΔX is the displacement in the longitude direction, and latitude is the angle between a point on the earth's surface and the equator; vector N represents the change in position in the north-south direction (latitude direction), ΔY is the displacement in the latitude direction, and longitude is the angle between a point on the earth's surface and the prime meridian; vector U represents the change in position in the vertical direction (altitude direction), and ΔZ is the displacement in the vertical direction.

[0092] The basic position data in the ENU coordinate system is converted to the vehicle coordinate system. The conversion formula is as follows:

[0093] X=[cos(ψ),-sin(ψ),0][E]

[0094] Y=[sin(ψ),cos(ψ),0][N]

[0095] Z=[0,0,1][U]

[0096] Among them, the X-axis in the vehicle coordinate system is the vehicle's forward direction, the Y-axis is the vehicle's lateral direction, pointing to the left side of the vehicle, and the Z-axis is vertically upward and perpendicular to the ground.

[0097] ψ is the heading angle of the vehicle

[0098] X: represents the unit vector along the vehicle's forward direction after conversion to the vehicle coordinate system.

[0099] Y: represents the unit vector along the lateral direction of the vehicle (pointing to the left side of the vehicle) after conversion to the vehicle coordinate system.

[0100] Z: represents the unit vector pointing vertically upward (perpendicular to the ground) after conversion to the vehicle coordinate system.

[0101] X=[cos(ψ),-sin(ψ),0][E]:Indicates that the X-axis in the ego-vehicle coordinate system (the vehicle's forward direction) is obtained by rotating the east-axis unit vector [E] in the ENU coordinate system around the Z-axis by the heading angle ψ.

[0102] Y = [sin(ψ), cos(ψ), 0][N]: indicates that the Y axis in the ego-vehicle coordinate system (lateral to the vehicle, pointing to the left side of the vehicle) is obtained by rotating the north axis unit vector [N] in the ENU coordinate system around the Z axis by the heading angle ψ.

[0103] Z=[0,0,1][U]:Indicates that the Z axis in the ego-vehicle coordinate system (vertically upward, perpendicular to the ground) is the same as the celestial axis unit vector [U] in the ENU coordinate system and does not need to be rotated.

[0104] In this embodiment, the position and speed information in the ENU coordinate system can be converted into the vehicle coordinate system through the above conversion formula, thereby ensuring the accuracy of the position data.

[0105] In one specific embodiment, Figure 2 As shown, it is a schematic diagram of coordinate system transformation, where, in the radar coordinate system (vehicle coordinate system), the vector can be more easily combined with the vehicle's sensor data (such as radar, camera, etc.) for vehicle navigation and control.

[0106] Coordinate system transformation includes the following steps:

[0107] P1: Plane coordinates reported by the ego vehicle receiver (①): The ego vehicle (i.e., a vehicle equipped with an RTK receiver) receives signals from the satellite and calculates its position in the plane northeast coordinate system (ENU).

[0108] P2: Plane coordinates reported by the target vehicle receiver (②): The target vehicle also receives satellite signals and calculates its own position in the ENU coordinate system.

[0109] P3: Calculate the plane position vector of the target vehicle relative to the ego vehicle (②-①): By comparing the positions of the two vehicles in the ENU coordinate system, calculate the plane position vector of the target vehicle relative to the ego vehicle.

[0110] P4: Coordinate transformation: Transform the target vehicle's relative position vector from the ENU coordinate system to the radar coordinate system. This step involves the use of the heading angle to ensure that the vector is expressed in the correct direction.

[0111] Through the conversion formulas from the geodetic coordinate system (such as WGS-84) to the ENU coordinate system, and then to the vehicle coordinate system. These conversions are crucial for combining real-time dynamic data with the vehicle's sensor data to achieve effective data comparison and analysis in the same coordinate system.

[0112] like Figure 3 As shown in FIG. 1 , it is a schematic diagram of the process of determining the slope value, including:

[0113] Start the equipment: Start the RTK base station; Start the RTK rover; Ensure that the communication is normal;

[0114] Calibrate the IMU: calibrate under static conditions; ensure that the zero bias and scale factor errors are minimized;

[0115] Start testing: record static data; perform dynamic testing;

[0116] Data collection: collection of IMU data, RTK data, radar data and visual data;

[0117] Data preprocessing: IMU data filtering; RTK data differential processing; radar data filtering; visual data preprocessing;

[0118] Coordinate system conversion: geodetic coordinate system to ENU, ENU to vehicle coordinate system;

[0119] Attitude solution: Using Kalman filter; combining IMU and RTK data to estimate pitch and roll angles;

[0120] Combine IMU, radar and vision data; use Kalman filter to get more accurate slope value.

[0121] Step S102: Compensating the measured state information corresponding to the vehicle according to the slope value to obtain compensated state information.

[0122] The measured state information is the state information that characterizes the vehicle, such as the predicted position information of the vehicle, including the distance and speed of the vehicle after a certain period of time (such as the next moment), which is used to determine the possible position information of the vehicle after a certain period of time. The predicted position information may also include other parameters such as the driving direction, acceleration, and road curvature of the vehicle. The measured state information can be determined by the measurement results of the sensor. The compensated state information is the state information after the measured state information of the vehicle is compensated by the slope value, that is, the state information under the slope state corresponding to the slope value, that is, the state information that can more accurately characterize the vehicle state.

[0123] Specifically, the control device compensates for the measured status information corresponding to the vehicle according to the slope value, thereby reducing the impact of the slope on the measured status information corresponding to the vehicle, improving the accuracy of the compensated status information, and avoiding the misjudgment of vehicle control caused by the slope, so as to enable subsequent accurate and reliable control of the vehicle based on the compensated status information, thereby ensuring the safety and reliability of vehicle driving.

[0124] In some embodiments, in response to the vehicle entering a sloped road surface.

[0125] Specifically, the control device responds to the vehicle entering a sloping road surface, indicating that the vehicle is traveling on a sloping road surface. In order to avoid misjudgment of the influence of the slope on vehicle control, in this embodiment, the measured state information corresponding to the vehicle is compensated according to the slope value to obtain compensated state information; the vehicle is controlled based on the compensated state information to ensure the accuracy and reliability of vehicle control on the sloping road surface.

[0126] In some embodiments, compensating the measured state information corresponding to the vehicle according to the slope value to obtain the compensated state information includes: determining the compensated roll angle and the compensated pitch angle corresponding to the slope value; determining the compensation coefficient of the measured state information according to the compensated roll angle and the compensated pitch angle; compensating the measured state information according to the compensation coefficient to obtain the compensated state information.

[0127] Specifically, the slope value can be first converted into the corresponding compensation roll angle and compensation pitch angle. For example, the slope value θ and the compensation roll angle θ are p and compensated pitch angle θ r The mathematical relationship between them is as follows:

[0128]

[0129] Then, according to the compensated roll angle and the compensated pitch angle, for example, the cosine value and sine value of the compensated roll angle and the compensated pitch angle are used as compensation coefficients of the measured state information. Finally, the measured state information is compensated according to the compensation coefficient to obtain compensated state information. It can be understood that in this embodiment, since the slope value has a high accuracy, the compensation coefficient has a high accuracy, thereby improving the accuracy of the compensated state information, so as to subsequently improve the accuracy and reliability of vehicle control based on the compensated state information.

[0130] In some embodiments, the measurement status information includes measurement status information obtained through perception data of the radar sensor and / or the visual sensor.

[0131] Specifically, the measurement state information can be information that can characterize the state of the vehicle obtained by analyzing the perception data of the radar sensor and / or the visual sensor, and reflects the relative state of the vehicle and the surrounding environment (especially the obstacle in front). Among them, the perception data of the radar sensor and / or the visual sensor is the measurement data of the corresponding sensor, and the measurement result obtained by performing measurement state analysis on the result of state analysis of the measurement data is the measurement state information. For example, for a visual sensor, its perception data is an image, and a series of processing is performed on the image to determine the corresponding measurement state information, such as the distance and speed of the vehicle, which are the distance and speed between the vehicle and the obstacle in front, respectively. As a preference of this embodiment, the measurement state information of the radar sensor and the visual sensor can be selected to utilize the comprehensive accuracy advantage of the multi-dimensional measurement state information to improve the accuracy of the measurement state information.

[0132] In some embodiments, the measurement status information includes measurement speed and / or measurement distance.

[0133] The measurement status information may be the measurement speed in the measurement result corresponding to the vehicle and / or the measurement distance may be the relative distance between the front obstacle and the vehicle, and the measurement speed may refer to the relative speed between the front obstacle and the vehicle.

[0134] Specifically, the measured speed and / or measured distance may be compensated to obtain compensated speed and distance, and then based on the compensated speed and distance, precise control of the vehicle's AEB system may be achieved, thereby improving the performance and overall stability of the AEB system.

[0135] In some embodiments, compensating the measurement state information according to the compensation coefficient to obtain the compensated state information includes: compensating each measurement state information according to the compensation coefficient to obtain compensated state data; and determining the compensated state information according to each compensated state data.

[0136] Specifically, each measurement state information is compensated according to the compensation coefficient to obtain the corresponding compensated state data, and then each compensated state data is processed. In some embodiments, each compensated state data may be fused, wherein the data fusion includes weighted average method, Bayesian estimation method, Kalman filtering method, fuzzy logic method, artificial intelligence method, etc. As the preferred embodiment of this embodiment, the Kalman filtering method is selected to optimally estimate the state of the moving vehicle through a recursive algorithm, which is used for real-time data processing and prediction, thereby improving the real-time and accuracy of the prediction of the compensated state information.

[0137] It can be understood that in this embodiment, by fusing the compensated state data, the driving environment of the vehicle can be more comprehensively reflected, and the influence of the slope on the measured state information corresponding to the vehicle can be significantly reduced, thereby improving the accuracy and reliability of vehicle control.

[0138] In some embodiments, the compensated state information corresponding to the measured distance includes a first direction distance, a second direction distance and a third direction distance, and the compensation coefficient includes a first compensation coefficient corresponding to the first direction distance, a second compensation coefficient corresponding to the second direction distance and a third compensation coefficient corresponding to the third direction distance; the first compensation coefficient is determined based on the cosine value of the compensated roll angle and the cosine value of the compensated pitch angle; the second compensation coefficient is determined based on the cosine value of the compensated roll angle and the sine value of the compensated pitch angle; the third compensation coefficient is determined based on the sine value of the compensated roll angle.

[0139] The first direction distance, the second direction distance and the third direction distance may be an X direction distance, a Y direction distance and a Z vector distance respectively.

[0140] Specifically, the compensated state information corresponding to the measured distance includes the first direction distance d x , the second direction distance d y The distance d in the third direction z , and its corresponding calculation formula is as follows:

[0141] d x＝ d·cos(θ p )·cos(θ r )

[0142] d x =d·cos(θp)·sin(θr)

[0143] d z＝ d·sin(θ p )

[0144] Where d is the measured distance, cos(θ p )·cos(θ r ) is the first compensation coefficient corresponding to the first direction distance, cos(θp)·sin(θr) is the second compensation coefficient corresponding to the second direction distance, sin(θ p ) is the third compensation coefficient corresponding to the distance in the third direction.

[0145] Similarly, the compensated state information corresponding to the measured speed includes the first direction speed v x , speed in the second direction v y and the third direction velocity v z , and its corresponding calculation formula is as follows:

[0146] v x =v r ·cos(θp)·cos(θr)

[0147] v y =v r ·cos(θp)·sin(θr)

[0148] v z＝ v r ·sin(θ p )

[0149] Where v is the measured speed, cos(θ p )·cos(θ r ) is the first compensation coefficient corresponding to the first direction speed, cos(θp)·sin(θr) is the second compensation coefficient corresponding to the second direction speed, sin(θ p ) is the third compensation coefficient corresponding to the speed in the third direction.

[0150] Step S103: controlling the vehicle based on the compensated state information.

[0151] Among them, the control of the vehicle may be the control of the AEB system.

[0152] Specifically, the vehicle is controlled according to the compensated state information. Since the measured state information corresponding to the vehicle is compensated by a higher-precision slope value, the influence of the slope on the measured state information corresponding to the vehicle is reduced, thereby improving the accuracy and reliability of vehicle control.

[0153] In some embodiments, an automatic emergency braking system of the vehicle is controlled based on the compensated state information.

[0154] Specifically, the compensated state information can be used as the perception data of the AEB system to control the AEB system. It can be understood that since the compensated state information is obtained after compensation and correction based on the slope value, the compensated state information can fully reflect the vehicle's surrounding environment information, thereby ensuring the accuracy and reliability of the AEB system control on a sloped road surface, and improving the overall stability and performance of the AEB system.

[0155] In a specific embodiment, Figure 4As shown in the figure, it is a flowchart for controlling the AEB system: the position of the vehicle can be predicted based on the compensated state information to obtain the predicted position, and then based on the predicted position and the monitoring and analysis of the vehicle's surrounding environment, it is evaluated whether the vehicle has the risk of collision with other vehicles, pedestrians or other obstacles. If the risk of collision is assessed, a forward collision warning (FCW) will be issued. The driver is notified that a collision may be imminent so that the driver can take measures to avoid the collision. If the driver does not respond to the warning in time or the risk of collision still exists, the system will automatically start the AEB system, and the AEB system will automatically apply the vehicle's brakes to reduce the speed of the collision or avoid the collision completely.

[0156] In a specific embodiment, Figure 5 As shown, it is a comparative schematic diagram of the changes in distance and speed during the AEB system control process based on the measured state information and the compensated state information. The AEB system control process is as follows:

[0157] T1: Distance judgment: judge the actual distance between the vehicle and the obstacle ahead based on the compensated state information corresponding to the measured distance;

[0158] T2: Speed ​​judgment: judge the relative speed between the vehicle and the obstacle ahead based on the compensated state information corresponding to the measured speed.

[0159] T3: Decision-making: Based on the distance and speed information, decide whether to initiate emergency braking.

[0160] like Figure 5 As shown in the figure, one group represents the unprocessed raw radar and visual sensor data, that is, the measurement state information (L1, L3), and the other group represents the compensated state information (L2, L4). These data are used to show how the measured values ​​of distance and speed change over time.

[0161] Distance measurement comparison: The upper sub-graph shows the change of distance measurement values. L1 represents the original distance measurement data, that is, the measured distance in the measurement state information, which has certain fluctuations and noise; L2 is the compensated state information corresponding to the measured distance, which can be seen to have smaller fluctuations and is smoother, indicating that data fusion technology effectively reduces the impact of noise and improves the accuracy of distance measurement.

[0162] Speed ​​measurement comparison: The lower sub-figure shows the speed measurement values. Similarly, L3 represents the original speed measurement data, that is, the measured speed in the measurement state information, which has large fluctuations; while L4 is the compensated state information corresponding to the measured speed, and the fluctuations are significantly reduced, which shows that the accuracy of speed measurement has been improved through data fusion technology.

[0163] Therefore, the compensated state information is used as the perception data of the AEB system as a whole, which improves the overall stability of the AEB system.

[0164] The above vehicle control method determines the slope value according to multiple sensor data sensed by the vehicle; compensates the measured state information corresponding to the vehicle according to the slope value to obtain compensated state information; and controls the vehicle based on the compensated state information. Since the slope value is determined by multi-dimensional sensor data, the accuracy of the slope value is improved, and the measured state information corresponding to the vehicle is compensated by the high-precision slope value, which reduces the influence of the slope on the measured state information corresponding to the vehicle, and improves the accuracy and reliability of vehicle control.

[0165] It should be understood that, although the steps in the flowcharts involved in the above embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above embodiments may include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0166] Based on the same inventive concept, the present application also provides a vehicle control device for implementing the vehicle control method involved in the above-mentioned embodiment in which the control device is the execution subject. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above-mentioned method, so the specific limitations in one or more vehicle control device embodiments provided below can refer to the limitations of the vehicle control method involved in the embodiment in which the control device is the execution subject, and will not be repeated here.

[0167] In some embodiments, Figure 6 As shown, a vehicle control device is provided, which can be integrated in a control device, including:

[0168] The determination module 601 is used to determine the slope value according to the multiple sensor data perceived by the vehicle; the determination module 601, the compensation module 602 and the control module 603, wherein:

[0169] A compensation module 602 is used to compensate the measured state information corresponding to the vehicle according to the slope value to obtain compensated state information;

[0170] The control module 603 is used to control the vehicle based on the compensated state information.

[0171] In some embodiments, the sensor data includes at least one of inertial detection data collected by an inertial measurement sensor, radar detection data collected by a radar sensor, and visual detection data collected by a visual sensor.

[0172] In some embodiments, the determination module is specifically used to perform corresponding data processing on each sensor data to obtain processed sensor data; and determine the slope value based on the processed sensor data.

[0173] In some embodiments, the determination module is specifically used to use the coordinates and observation data of the reference station to perform differential processing on the observation data of the mobile station to obtain the real-time dynamic data.

[0174] In some embodiments, the determination module is specifically used to perform coordinate system conversion based on the real-time dynamic data to obtain the position data of the vehicle in the self-vehicle coordinate system; perform posture calculation on the filtered inertial detection data based on the position data to obtain processed perception data corresponding to the inertial detection data.

[0175] In some embodiments, the determination module is specifically used to convert the real-time dynamic data from the earth coordinate system where the data is located to the vehicle coordinate system corresponding to the vehicle, so as to obtain the position data in the vehicle coordinate system.

[0176] In some embodiments, the determination module is further used to convert the real-time dynamic data from the geodetic coordinate system to the northeast celestial coordinate system to obtain the basic position data in the northeast celestial coordinate system;

[0177] The basic position data in the northeast celestial coordinate system is converted to the vehicle coordinate system to obtain the position data.

[0178] In some embodiments, the determination module is further configured to perform data fusion on the processed sensor data corresponding to each sensor data to obtain the slope value.

[0179] In some embodiments, the compensation module is specifically used to determine the compensated roll angle and the compensated pitch angle corresponding to the slope value; determine the compensation coefficient of the measurement state information according to the compensated roll angle and the compensated pitch angle; compensate the measurement state information according to the compensation coefficient to obtain the compensated state information.

[0180] In some embodiments, the compensation module is further used to compensate each measurement state information according to the compensation coefficient to obtain compensated state data; and determine the compensated state information according to each compensated state data.

[0181] In some embodiments, the compensation module is further configured to perform fusion processing on each of the compensated state data to obtain the compensated state information.

[0182] In some embodiments, the control module is specifically configured to control the automatic emergency braking system of the vehicle according to the compensated state information.

[0183] Each module in the above-mentioned devices can be implemented in whole or in part by software, hardware and a combination thereof. Each module can be embedded in or independent of the processor in the control device in the form of hardware, or can be stored in the memory in the control device in the form of software, so that the processor can call and execute the operations corresponding to each module above.

[0184] In some embodiments, a control device is provided, whose internal structure diagram can be as follows: Figure 7 As shown. The control device includes a processor, a memory, an input / output interface, a communication interface, a display unit and an input device. The processor, the memory and the input / output interface are connected via a system bus, and the communication interface, the display unit and the input device are connected to the system bus via the input / output interface. The processor of the control device is used to provide computing and control capabilities. The memory of the control device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the control device is used to exchange information between the processor and an external device. The communication interface of the control device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be implemented through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a vehicle control method is implemented.

[0185] Optionally, the control device further includes a display unit. The display unit of the control device is used to form a visually visible picture, and may be a display screen, a projection device, or a virtual reality imaging device. The display screen may be a liquid crystal display screen or an electronic ink display screen, and the input device of the control device may be a touch layer covered on the display screen, or a key, trackball, or touchpad provided on the housing of the control device, or an external keyboard, touchpad, or mouse, etc.

[0186] Those skilled in the art will understand that Figure 7 The structure shown in the figure is merely a block diagram of a partial structure related to the scheme of the present application, and does not constitute a limitation on the control device to which the scheme of the present application is applied. The specific control device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0187] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The processor involved in each embodiment provided in this application can be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic unit, a data processing logic unit based on quantum computing, etc., but is not limited thereto.

[0188] Correspondingly, an embodiment of the present application also provides a control device, which may be a terminal device or a server.

[0189] like Figure 7 As shown, Figure 7 A schematic diagram of the structure of a control device provided in an embodiment of the present application. The control device 1000 includes a processor 1001 having one or more processing cores, a memory 1002 having one or more computer-readable storage media, and a computer program stored in the memory 1002 and executable on the processor. The processor 1001 is electrically connected to the memory 1002. It will be understood by those skilled in the art that the control device structure shown in the figure does not constitute a limitation on the control device, and may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0190] The processor 1001 is the control center of the control device 1000, and uses various interfaces and lines to connect various parts of the entire control device 1000. By running or loading software programs and / or units stored in the memory 1002, and calling data stored in the memory 1002, the processor 1001 executes various functions of the control device 1000 and processes data, thereby monitoring the control device 1000 as a whole. The processor 1001 can be a central processing unit CPU, a graphics processing unit GPU, a network processor (Network Processor, NP), etc., and can implement or execute the various methods, steps and logic block diagrams disclosed in the embodiments of the present application.

[0191] In the embodiment of the present application, the processor 1001 in the control device 1000 will load the instructions corresponding to the processes of one or more applications into the memory 1002 according to the following steps, and the processor 1001 will run the application stored in the memory 1002, so as to realize various functions, for example: determine the slope value according to the multiple sensor data perceived by the vehicle; compensate the measured state information corresponding to the vehicle according to the slope value to obtain the compensated state information; control the vehicle based on the compensated state information. The specific implementation of each of the above operations can be referred to the previous embodiment, which will not be repeated here.

[0192] Alternatively, if Figure 7 As shown, the control device 1000 further includes: a touch screen 1003, a radio frequency circuit 1004, an audio circuit 1005, an input unit 1006, and a power supply 1007. The processor 1001 is electrically connected to the touch screen 1003, the radio frequency circuit 1004, the audio circuit 1005, the input unit 1006, and the power supply 1007, respectively. Those skilled in the art can understand that Figure 7 The control device structure shown in the figure does not constitute a limitation on the control device, and may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0193] The touch display screen 1003 can be used to display a graphical user interface and receive operation instructions generated by the user acting on the graphical user interface. The touch display screen 1003 may include a display panel and a touch panel. Among them, the display panel can be used to display information input by the user or information provided to the user and various graphical user interfaces of the control device, and these graphical user interfaces can be composed of graphics, text, icons, videos and any combination thereof. Optionally, the display panel can be configured in the form of a liquid crystal display (LCD), an organic light-emitting diode (OLED), etc. The touch panel can be used to collect the user's touch operation on or near it (such as the user uses any suitable object or attachment such as a finger, a stylus, etc. on the touch panel or near the touch panel), and generate corresponding operation instructions, and the operation instructions execute corresponding programs. Optionally, the touch panel may include two parts: a touch detection device and a touch controller. Among them, the touch detection device detects the user's touch orientation, detects the signal brought by the touch operation, and transmits the signal to the touch controller; the touch controller receives the touch information from the touch detection device, converts it into the touch point coordinates, and then sends it to the processor 1001, and can receive the command sent by the processor 1001 and execute it. The touch panel can cover the display panel. When the touch panel detects a touch operation on or near it, it is transmitted to the processor 1001 to determine the type of touch event, and then the processor 1001 provides a corresponding visual output on the display panel according to the type of touch event. In an embodiment of the present application, the touch panel and the display panel can be integrated into the touch display screen 1003 to realize the input and output functions. However, in some embodiments, the touch panel and the touch panel can be used as two independent components to realize the input and output functions. That is, the touch display screen 1003 can also be used as a part of the input unit 1006 to realize the input function.

[0194] The RF circuit 1004 may be used to send and receive RF signals, so as to establish wireless communication with a network device or other control device through wireless communication, and to send and receive signals between the network device or other control device.

[0195] The audio circuit 1005 can be used to provide an audio interface between the user and the control device through a speaker and a microphone. The audio circuit 1005 can transmit the electrical signal converted from the received audio data to the speaker, which is converted into a sound signal for output; on the other hand, the microphone converts the collected sound signal into an electrical signal, which is received by the audio circuit 1005 and converted into audio data, and then the audio data is output to the processor 1001 for processing, and then sent to another control device through the radio frequency circuit 1004, or the audio data is output to the memory 1002 for further processing. The audio circuit 1005 may also include an earphone jack to provide communication between an external headset and the control device.

[0196] The input unit 1006 may be used to receive input numbers, character information or user feature information (such as fingerprint, iris, facial information, etc.), and to generate keyboard, mouse, joystick, optical or trackball signal inputs related to user settings and function control.

[0197] The power supply 1007 is used to supply power to various components of the control device 1000. Optionally, the power supply 1007 can be logically connected to the processor 1001 through a power management system, so that the power management system can manage charging, discharging, and power consumption. The power supply 1007 can also include one or more DC or AC power supplies, recharging systems, power failure detection circuits, power converters or inverters, power status indicators, and other arbitrary components.

[0198] although Figure 7 Not shown, the control device 1000 may also include a camera, a sensor, a wireless fidelity module, a Bluetooth module, etc., which will not be described in detail here.

[0199] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0200] A person of ordinary skill in the art will appreciate that all or part of the steps in the various methods of the above embodiments may be completed by instructions, or by controlling related hardware through instructions. The instructions may be stored in a computer-readable storage medium and loaded and executed by a processor.

[0201] To this end, an embodiment of the present application provides a computer-readable storage medium, in which a plurality of computer programs are stored, and the computer program can be loaded by a processor to execute any vehicle control method provided in the embodiment of the present application. The computer program can execute the following steps of the vehicle control method: determine the slope value according to a plurality of sensor data perceived by the vehicle; compensate the measured state information corresponding to the vehicle according to the slope value to obtain compensated state information; and control the vehicle based on the compensated state information. The specific implementation of each of the above operations can be found in the previous embodiments, which will not be repeated here.

[0202] The computer-readable storage medium may include: a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0203] Since the computer program stored in the computer-readable storage medium can execute any one of the vehicle control methods provided in the embodiments of the present application, the beneficial effects that can be achieved by any one of the vehicle control methods provided in the embodiments of the present application can be achieved. Please refer to the previous embodiments for details and will not be repeated here.

[0204] According to one aspect of the present application, a computer program product or a computer program is also provided, the computer program product or the computer program including computer instructions, the computer instructions being stored in a computer-readable storage medium. A processor of a control device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the control device executes the methods provided in various optional implementations in the above-mentioned embodiments.

[0205] According to one aspect of the present application, Figure 8 As shown, a vehicle 10 is also provided, which includes the above control device. The vehicle has all the beneficial effects of the above control device, etc., which will not be described in detail in this application.

[0206] The vehicle may be a fuel vehicle, a plug-in hybrid vehicle or a new energy vehicle, etc., and this application does not make any specific limitation on this.

[0207] In the above-mentioned vehicle control device, computer-readable storage medium, control device, and computer program product embodiments, the description of each embodiment has its own emphasis. For parts not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working process and beneficial effects of the above-mentioned vehicle control device, computer-readable storage medium, computer program product, control device and its corresponding units can refer to the description of the vehicle control method in the above embodiment, and will not be repeated here.

[0208] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0209] The above are only preferred embodiments of the present application and do not constitute any form of limitation to the present application. Although the descriptions of various embodiments in the embodiments of the present application have different focuses, for parts not described in detail in a certain embodiment, reference can be made to the relevant embodiments of other embodiments. However, any simple modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present application without departing from the content of the technical solution of the present application are still within the scope of the technical solution of the present application.

Claims

1. A vehicle control method, characterized in that: The method comprises: Determine the slope value based on multiple sensor data sensed by the vehicle; According to the slope value, the measured state information corresponding to the vehicle is compensated to obtain compensated state information; The vehicle is controlled based on the compensated state information.

2. The method according to claim 1, characterized in that The sensor data includes at least one of inertial detection data collected by an inertial measurement sensor, radar detection data collected by a radar sensor, and visual detection data collected by a visual sensor.

3. The method according to claim 2, characterized in that The step of determining the slope value according to the plurality of sensor data sensed by the vehicle includes: For each sensor data, corresponding data processing is performed to obtain processed sensor data; Based on the processed sensor data, a slope value is determined.

4. The method according to claim 3, characterized in that For the inertial detection data and the radar detection data, the data processing includes filtering processing, and for the visual detection data, the data processing includes image preprocessing.

5. The method according to claim 4, characterized in that For the inertial detection data, the data processing further includes compensating the inertial detection data based on real-time dynamic data.

6. The method according to claim 5, characterized in that The step of determining the real-time dynamic data includes: The coordinates and observation data of the reference station are used to perform differential processing on the observation data of the mobile station to obtain the real-time dynamic data.

7. The method according to claim 5, characterized in that The step of compensating the inertial detection data based on the real-time dynamic data is performed after the step of filtering.

8. The method according to claim 7, characterized in that The step of compensating the filtered inertial detection data based on the real-time dynamic data comprises: Performing coordinate system conversion based on the real-time dynamic data to obtain position data of the vehicle in the vehicle coordinate system; Performing attitude calculation on the filtered inertial detection data based on the position data to obtain processed sensor data corresponding to the inertial detection data.

9. The method according to claim 8, characterized in that The coordinate system conversion is performed based on the real-time dynamic data to obtain the position data of the vehicle in the vehicle coordinate system, including: The real-time dynamic data is converted from the earth coordinate system to the vehicle coordinate system corresponding to the vehicle to obtain the position data in the vehicle coordinate system.

10. The method according to claim 9, characterized in that The step of converting the real-time dynamic data from the earth coordinate system to the vehicle coordinate system corresponding to the vehicle to obtain the position data in the vehicle coordinate system includes: Convert the real-time dynamic data from the geodetic coordinate system to the northeast celestial coordinate system to obtain basic position data in the northeast celestial coordinate system; The basic position data in the northeast celestial coordinate system is converted to the vehicle coordinate system to obtain the position data.

11. The method according to claim 3, characterized in that The step of determining a slope value based on the processed sensor data comprises: The processed sensor data corresponding to each sensor data is fused to obtain the slope value.

12. The method according to any one of claims 1 to 11, characterized in that: The step of compensating the measured state information corresponding to the vehicle according to the slope value to obtain compensated state information includes: Determine a compensation roll angle and a compensation pitch angle corresponding to the slope value; Determining a compensation coefficient of the measurement state information according to the compensated roll angle and the compensated pitch angle; The measurement state information is compensated according to the compensation coefficient to obtain the compensated state information.

13. The method according to claim 12, characterized in that The measurement status information includes measurement status information obtained through perception data of the radar sensor and / or the visual sensor.

14. The method according to claim 13, characterized in that The measurement status information includes measurement speed and / or measurement distance.

15. The method according to claim 14, characterized in that The compensating the measurement state information according to the compensation coefficient to obtain the compensated state information includes: According to the compensation coefficient, each of the measurement state information is compensated respectively to obtain compensated state data; The post-compensation state information is determined according to each of the post-compensation state data.

16. The method according to claim 15, characterized in that The step of determining the post-compensation state information according to each post-compensation state data comprises: The compensated state data are fused to obtain the compensated state information.

17. The method according to claim 14, characterized in that The compensated state information corresponding to the measured distance includes a first direction distance, a second direction distance, and a third direction distance, and the compensation coefficient includes a first compensation coefficient corresponding to the first direction distance, a second compensation coefficient corresponding to the second direction distance, and a third compensation coefficient corresponding to the third direction distance; The first compensation coefficient is determined according to the cosine value of the compensation roll angle and the cosine value of the compensation pitch angle; The second compensation coefficient is determined according to the cosine value of the compensation roll angle and the sine value of the compensation pitch angle; The third compensation coefficient is determined according to the sine value of the compensation roll angle.

18. The method according to any one of claims 1 to 11, characterized in that: The controlling the vehicle based on the compensated state information includes: The automatic emergency braking system of the vehicle is controlled according to the compensated state information.

19. The method according to any one of claims 1 to 11, characterized in that: Also includes: In response to the vehicle entering a sloped road surface.

20. A vehicle control device, characterized in that: The device comprises: A determination module, for determining a slope value according to a plurality of sensor data sensed by the vehicle; A compensation module, used for compensating the measured state information corresponding to the vehicle according to the slope value to obtain compensated state information; A control module is used to control the vehicle based on the compensated state information.

21. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the vehicle control method according to any one of claims 1 to 19 is implemented.

22. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the vehicle control method according to any one of claims 1 to 19 is implemented.

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

24. A vehicle, characterized in that: Comprising a control device as claimed in claim 23.

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