A method, system, medium and device for accurately detecting right turn of vehicle

Abstract

The present invention relates to the field of vehicle control technology, and more specifically, to a method, system, medium and device for accurately detecting a vehicle turning right. The scheme includes using an inertial measurement unit to collect information and obtain online collected information; transmitting the online collected information to a single-chip microcomputer through an SPI interface; preprocessing the data in the single-chip microcomputer to form a multi-dimensional data fusion data group; performing posture analysis online and combining GIS and bump height to make a comprehensive data validity judgment; performing average value analysis after obtaining six-axis data; and performing state judgment online based on the average value analysis result. The scheme performs accurate average value and change value judgment through fast data preprocessing combined with high-precision and data-fused three-axis Euler calculation to complete intelligent perception and control of the vehicle.

Description

Technical Field

[0001] The present invention relates to the field of vehicle control technology, and more specifically, to a method, system, medium and device for accurately detecting a vehicle turning right. Background Art

[0002] In the field of vehicle control, the implementation of accurate detection of vehicle right turns usually involves the use of advanced sensor technologies (such as IMU), complex data processing algorithms (such as Kalman filtering and machine learning), and multi-sensor fusion technology. These technologies can monitor the vehicle's motion state in real time, including acceleration, angular velocity, and posture changes, so as to accurately determine whether the vehicle is performing a right turn. The significance of this precise detection is to improve vehicle safety and driving experience. By responding to the vehicle's steering intention in a timely manner, the assisted driving system can intervene more intelligently, provide necessary support or warnings, and reduce the occurrence of traffic accidents. In addition, accurate detection of vehicle right turns is crucial to the development of autonomous driving technology. It provides reliable data support for advanced driver assistance systems and autonomous vehicles, and helps to achieve a smoother and safer driving transition.

[0003] Prior to the present invention, existing methods for accurately detecting right turns of vehicles mainly relied on single sensor data, such as gyroscopes or accelerometers, and inferred the vehicle's motion state by analyzing the changes in the output of these sensors. However, this method has obvious limitations: first, a single sensor is easily affected by external interference and noise, resulting in reduced data accuracy; second, the performance of sensors of different brands and models varies greatly, requiring frequent adjustments to the algorithm to adapt to different hardware conditions; finally, complex road environments and changeable driving behaviors increase the difficulty of accurate judgment. Therefore, improving data fusion capabilities and enhancing the robustness and adaptability of the system have become key technical difficulties and research focuses in this field. Summary of the invention

[0004] In view of the above problems, the present invention proposes a method, system, medium and equipment for accurately detecting right turns of vehicles. Through fast data preprocessing combined with high-precision and data fusion three-axis Euler calculation, accurate average and change value judgments are made to complete intelligent perception and control of the vehicle.

[0005] According to a first aspect of an embodiment of the present invention, a method for accurately detecting a right turn of a vehicle is provided.

[0006] In one or more embodiments, preferably, the method for accurately detecting a vehicle turning right includes:

[0007] Use inertial measurement unit to collect information and obtain online collection information;

[0008] The online collected information is transmitted to the single chip microcomputer via the SPI interface;

[0009] Preprocess the data in the single chip microcomputer to form a multi-dimensional data fusion data group;

[0010] Perform attitude analysis online and make comprehensive data validity judgments by combining GIS and bump height;

[0011] After obtaining the six-axis data, average value analysis was performed;

[0012] Based on the average value analysis results, the status is determined online.

[0013] In one or more embodiments, preferably, the collecting information using an inertial measurement unit to obtain online collected information specifically includes:

[0014] Online inertial measurement unit information collection, acceleration, angular velocity;

[0015] Obtain GPS information through the vehicle-mounted GPS;

[0016] Obtain bump height information through on-board sensors;

[0017] The acceleration, angular velocity and six-axis sensor acquisition data, GPS information and bump height information are combined as online acquisition information.

[0018] In one or more embodiments, preferably, the online acquisition information is transmitted to the single-chip microcomputer via an SPI interface, specifically including:

[0019] Set the SPI interface channel;

[0020] All online collected information is transmitted to the single chip microcomputer at preset intervals through serial communication.

[0021] In one or more embodiments, preferably, the preprocessing of data in the single chip microcomputer to form a multi-dimensional data fusion data group specifically includes:

[0022] Obtaining real-time online acquisition information in the microcontroller storage area;

[0023] Performing online analysis on the online collected information, and calculating the integral deviation using a first calculation formula;

[0024] Determine whether the integral deviation satisfies the second calculation formula. If so, the data is considered to meet the requirements, and further filter the multi-dimensional data in the same time period and add a timestamp.

[0025] If the integral deviation does not satisfy the second calculation formula, the data is considered to not meet the requirements and is directly deleted;

[0026] The time-stamped data is used as raw data, and low-pass filtering and Kalman filtering are performed on the raw data to remove noise;

[0027] The first calculation formula is:

[0028] PC=

[0029] Among them, PC is the integral deviation, P(t) is the absolute value of the deviation of each data corresponding to the historical average value at time t, and n is the total time;

[0030] The second calculation formula is:

[0031] {Max(PC)-Min(PC)}÷Max(PC) <Y

[0032] Among them, Y is the preset contrast margin, Max() and Min() are the maximum and minimum value extraction functions in the data within 10 minutes.

[0033] In one or more embodiments, preferably, the online posture analysis and the comprehensive data validity judgment in combination with GIS and turbulence height specifically include:

[0034] Using a six-axis fusion algorithm to calculate the three-axis Euler angle, the three-axis Euler angle includes a pitch angle, a roll angle, and a yaw angle;

[0035] Determine whether the pitch angle, roll angle, and yaw angle have a jump that satisfies a third calculation formula;

[0036] If it exists, the historical map analysis module is started, and the historical map analysis module analyzes online whether the bump height of the road section passed in the last minute satisfies the fourth calculation formula. If not, it is considered that the current rotation is abnormal, and if it is satisfied, the current data is considered normal;

[0037] Further judging whether there is GIS auxiliary judgment data for the abnormal rotation, and if not, discarding the current data;

[0038] If it exists, the GIS data is used to determine whether the three-axis Euler angles satisfy the fifth calculation formula. If so, the data is considered valid, otherwise, the data is deleted;

[0039] The third calculation formula is:

[0040] YD>Y2

[0041] HD>Y3

[0042] PD>Y4

[0043] Wherein, YD is the average change rate of the pitch angle in the last minute, HD is the average change rate of the roll angle in the last minute, PD is the average change rate of the yaw angle in the last minute, Y2 is the second contrast margin, Y3 is the third contrast margin, and Y4 is the fourth contrast margin;

[0044] The fourth calculation formula is:

[0045] YF>ST

[0046] Among them, YF is the maximum bump height of the road section passed in 1 minute, and ST is the preset maximum bump;

[0047] The fifth calculation formula is:

[0048] YD-GP>Y5

[0049] HD-GP>Y6

[0050] PD-GP>Y7

[0051] Among them, GP is the GIS judgment value, Y5 is the fifth contrast margin, Y6 is the sixth contrast margin, and Y7 is the seventh contrast margin.

[0052] In one or more embodiments, preferably, the step of performing average value analysis after obtaining the six-axis data specifically includes:

[0053] After processing the six-axis data, the data is stored after a delay of 20ms and stored in the preset data link table;

[0054] The average change value is calculated for the first N data in the data linked list.

[0055] In one or more embodiments, preferably, the online status determination based on the average value analysis result specifically includes:

[0056] According to the comparison between the calculated average change value and the preset turning threshold, when it is determined that the sixth calculation formula is satisfied, the right turning state or right turning;

[0057] If it is not satisfied, the turning state is considered to be ended;

[0058] The sixth calculation formula is:

[0059] PB>Y8

[0060] Among them, PB is the average change value, and Y8 is the preset turning threshold.

[0061] According to a second aspect of an embodiment of the present invention, a system for accurately detecting a vehicle turning right is provided.

[0062] In one or more embodiments, preferably, the system for accurately detecting a vehicle turning right includes:

[0063] A data acquisition module is used to collect information using an inertial measurement unit to obtain online collected information;

[0064] A data transmission module, used for transmitting the online collected information to the single chip microcomputer through the SPI interface;

[0065] The data preprocessing module is used to preprocess the data in the single chip microcomputer to form a multi-dimensional data fusion data group;

[0066] Attitude calculation module, used for online attitude analysis and comprehensive data validity judgment in combination with GIS and bump height;

[0067] Data processing module, used to obtain six-axis data and then perform average value analysis;

[0068] The status judgment module is used to perform status judgment online based on the average value analysis results.

[0069] According to a third aspect of an embodiment of the present invention, there is provided a computer-readable storage medium on which computer program instructions are stored. When the computer program instructions are executed by a processor, the method as described in any one of the first aspect of the embodiment of the present invention is implemented.

[0070] According to a fourth aspect of an embodiment of the present invention, there is provided an electronic device, comprising a memory and a processor, wherein the memory is used to store one or more computer program instructions, wherein the one or more computer program instructions are executed by the processor to implement any one of the methods described in the first aspect of the embodiment of the present invention.

[0071] The technical solution provided by the embodiments of the present invention may have the following beneficial effects:

[0072] In the solution of the present invention, a comprehensive determination method for multi-dimensional data fusion is proposed. After acquiring the data, multi-dimensional verification and analysis of the data are performed before filtering.

[0073] In the solution of the present invention, the three-axis Euler angle in the six-axis fusion algorithm is judged, and on this basis, the right turn judgment of multi-dimensional data fusion is realized by combining the road bumps and road turning position analysis.

[0074] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description, claims, and drawings.

[0075] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0077] Figure 1 The present invention is a flowchart of a method for accurately detecting a vehicle turning right according to an embodiment of the present invention.

[0078] Figure 2 The present invention is a flowchart of using an inertial measurement unit to collect information and obtain online collected information in a method for accurately detecting a right turn of a vehicle according to an embodiment of the present invention.

[0079] Figure 3 The present invention is a flowchart of a method for accurately detecting a vehicle turning right in one embodiment of the present invention, in which the online collected information is transmitted to a single-chip computer through an SPI interface.

[0080] Figure 4 It is a flow chart of performing data preprocessing on a single chip microcomputer to form a multi-dimensional data fusion data group in a method for accurately detecting a right turn of a vehicle according to an embodiment of the present invention.

[0081] Figure 5 The present invention is a flowchart of an implementation method for accurately detecting a vehicle right turn in an embodiment of the present invention, which performs posture analysis online and performs comprehensive data validity judgment in combination with GIS and bump height.

[0082] Figure 6 The present invention is a flowchart of performing average value analysis after obtaining six-axis data in a method for accurately detecting a right turn of a vehicle according to an embodiment of the present invention.

[0083] Figure 7 The present invention is a flowchart of an implementation method for accurately detecting a vehicle turning right, for performing state determination online based on average value analysis results, in accordance with an embodiment of the present invention.

[0084] Figure 8 The present invention is a structural diagram of a system for accurately detecting a vehicle turning right according to an embodiment of the present invention.

[0085] Fig. 9 It is a structural diagram of an electronic device in one embodiment of the present invention. DETAILED DESCRIPTION

[0086] In some of the processes described in the specification and claims of the present invention and the above-mentioned figures, multiple operations that appear in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this article or executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish different operations, and the serial numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions of "first", "second", etc. in this article are used to distinguish different messages, devices, modules, etc., do not represent the order of precedence, and do not limit the "first" and "second" to be different types.

[0087] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0088] In the field of vehicle control, the implementation of accurate detection of vehicle right turns usually involves the use of advanced sensor technologies (such as IMU), complex data processing algorithms (such as Kalman filtering and machine learning), and multi-sensor fusion technology. These technologies can monitor the vehicle's motion state in real time, including acceleration, angular velocity, and posture changes, so as to accurately determine whether the vehicle is performing a right turn. The significance of this precise detection is to improve vehicle safety and driving experience. By responding to the vehicle's steering intention in a timely manner, the assisted driving system can intervene more intelligently, provide necessary support or warnings, and reduce the occurrence of traffic accidents. In addition, accurate detection of vehicle right turns is crucial to the development of autonomous driving technology. It provides reliable data support for advanced driver assistance systems and autonomous vehicles, and helps to achieve a smoother and safer driving transition.

[0089] Prior to the present invention, existing methods for accurately detecting right turns of vehicles mainly relied on single sensor data, such as gyroscopes or accelerometers, and inferred the vehicle's motion state by analyzing the changes in the output of these sensors. However, this method has obvious limitations: first, a single sensor is easily affected by external interference and noise, resulting in reduced data accuracy; second, the performance of sensors of different brands and models varies greatly, requiring frequent adjustments to the algorithm to adapt to different hardware conditions; finally, complex road environments and changeable driving behaviors increase the difficulty of accurate judgment. Therefore, improving data fusion capabilities and enhancing the robustness and adaptability of the system have become key technical difficulties and research focuses in this field.

[0090] In an embodiment of the present invention, a method for accurately detecting a vehicle turning right is provided. The solution uses fast data preprocessing combined with high-precision and data-fused three-axis Euler calculation to accurately judge the average value and change value, thereby completing intelligent perception and control of the vehicle.

[0091] According to a first aspect of an embodiment of the present invention, a method for accurately detecting a right turn of a vehicle is provided.

[0092] Figure 1 The present invention is a flowchart of a method for accurately detecting a vehicle turning right according to an embodiment of the present invention.

[0093] In one or more embodiments, preferably, the method for accurately detecting a vehicle turning right includes:

[0094] Use the inertial measurement unit to collect information and obtain online collected information; transmit the online collected information to the single-chip microcomputer through the SPI interface; pre-process the data in the single-chip microcomputer to form a multi-dimensional data fusion data group; perform posture analysis online and combine GIS and bump height to make a comprehensive data validity judgment; perform average value analysis after obtaining six-axis data; and perform state judgment online based on the average value analysis result. In an embodiment of the present invention, first, the vehicle motion data is collected using an IMU sensor and transmitted to the single-chip microcomputer in real time through the SPI interface. Subsequently, the raw data is low-pass filtered and Kalman filtered to remove noise and improve data quality. Then, the six-axis fusion algorithm is applied to calculate the three-axis Euler angle to obtain the precise posture information of the vehicle. After processing the six-axis data, the system will delay for 20ms, then perform a two-way linked list calculation, and calculate the average change value of the first N data. Finally, based on the comparison of the average change value with the preset threshold, it is determined whether the vehicle is in a right turn state or whether the right turn has ended. This system not only improves vehicle safety and driving experience, but also provides reliable data support for the development of autonomous driving technology.

[0095] Figure 2 The present invention is a flowchart of using an inertial measurement unit to collect information and obtain online collected information in a method for accurately detecting a right turn of a vehicle according to an embodiment of the present invention.

[0096] like Figure 2 As shown, in one or more embodiments, preferably, the collecting of information using an inertial measurement unit to obtain online collected information specifically includes:

[0097] Inertial measurement unit information is collected online to collect acceleration and angular velocity; GPS information is obtained through the vehicle-mounted GPS; bump height information is obtained through the vehicle-mounted sensor; the acceleration, angular velocity and six-axis sensor collection data, GPS information and bump height information are combined as online collection information. In an embodiment of the present invention, first, through an inertial measurement unit (IMU) installed on the vehicle, we continuously monitor and record the acceleration and angular velocity information of the vehicle. IMU is a self-contained measurement device that can detect and report the motion state of a system relative to an inertial reference system, including acceleration and rotation rate. In this embodiment, the IMU is fixed inside the vehicle, and its three-axis accelerometer is used to measure the linear acceleration of the vehicle in three-dimensional space, while the three-axis gyroscope is used to measure the rotational angular velocity of the vehicle around three mutually perpendicular axes. Secondly, the vehicle-mounted GPS module is used to obtain accurate location information. GPS is a satellite-based navigation system that can provide real-time geographic location, speed and time information of the vehicle worldwide. In this system, the GPS receiver continuously tracks multiple satellite signals to determine the latitude and longitude coordinates and movement speed of the vehicle. Next, the bump height sensor installed on the vehicle chassis or other key positions is used to detect the vertical displacement of the vehicle caused by the uneven road surface. This sensor usually works based on the piezoelectric effect or the principle of capacitance change, and can convert mechanical vibration into electrical signal output, thereby reflecting the degree of bump experienced by the vehicle. Finally, the acceleration and angular velocity data obtained from the IMU, the geographic location and speed information provided by the GPS, and the bump intensity data recorded by the bump height sensor are integrated to form a comprehensive online acquisition information package. This data package not only contains the basic kinematic parameters of the vehicle, but also covers the impact of environmental factors on the vehicle's driving state, providing rich original data for subsequent posture analysis, data processing and state judgment. For example, when a vehicle is driving on a rough road, the IMU will detect frequently changing acceleration and angular velocity, the GPS will show that the vehicle's position changes slowly but the speed fluctuates greatly, and the bump height sensor will also report a high bump value. This information is recorded in real time and transmitted to the central processing unit for analysis and processing by wireless or wired means.

[0098] Figure 3 The present invention is a flowchart of a method for accurately detecting a vehicle turning right in one embodiment of the present invention, in which the online collected information is transmitted to a single-chip computer through an SPI interface.

[0099] like Figure 3 As shown, in one or more embodiments, preferably, the online acquisition information is transmitted to the single-chip microcomputer via the SPI interface, specifically including:

[0100] Set up the SPI interface channel; transmit all the online acquisition information to the single-chip microcomputer at preset intervals through serial communication. In an embodiment of the present invention, first, it is necessary to set up the SPI interface channel. SPI is a synchronous serial communication protocol, which is usually used for short-distance high-speed data transmission. In this embodiment, the SPI interface is configured as a four-wire mode, including a clock line (SCLK), a master device output / slave device input line (MOSI), a master device input / slave device output line (MISO) and a slave device select line (SS). These lines establish a connection between the microcontroller and the sensor module to ensure that the data can be transmitted correctly. Next, the SPI interface is initialized. This includes configuring the SPI control register to set the appropriate communication rate, data bit order (such as MSB first or LSB first), and whether to enable certain functions (for example, whether the CS chip select signal is required). In addition, it is necessary to adjust the relevant parameters according to the specific hardware used, such as selecting a suitable clock source and frequency division factor to meet the timing requirements of the system. Then, all the online acquisition information is sent to the single-chip microcomputer at preset time intervals through serial communication. In this step, each sensor node packages the data it collects into a data frame and sends it out through the SPI bus. The microcontroller, as the host, periodically initiates SPI transaction requests to read data from each sensor. Each transaction may involve the transmission of a single byte or multiple bytes, depending on the data format of the sensor and the required accuracy. For example, assuming that an accelerometer generates a new set of three-dimensional acceleration data every 10 milliseconds, the microcontroller can trigger an SPI read operation every 10 milliseconds to obtain the latest data from the accelerometer. Similarly, for angular velocity meters and other types of sensors, similar methods can be used to collect data periodically. Finally, all data received by the microcontroller will be stored in its internal memory for subsequent processing. This data may include information such as the vehicle's real-time position, speed, acceleration, angular velocity, and bump height, which are essential for realizing functions such as advanced driver assistance systems (ADAS). In summary, through the above steps, we can effectively use the SPI interface to transmit data collected by various sensors to the microcontroller in real time, providing support for further data analysis and decision-making.

[0101] Figure 4 It is a flow chart of performing data preprocessing on a single chip microcomputer to form a multi-dimensional data fusion data group in a method for accurately detecting a right turn of a vehicle according to an embodiment of the present invention.

[0102] like Figure 4 As shown, in one or more embodiments, preferably, the preprocessing of data in the single chip microcomputer to form a multi-dimensional data fusion data group specifically includes:

[0103] Obtaining real-time online acquisition information in the storage area of ​​the single-chip computer; performing online analysis on the online acquisition information, and calculating the integral deviation using the first calculation formula; judging whether the integral deviation satisfies the second calculation formula, if so, it is considered that the data meets the requirements, and further filtering the multi-dimensional data in the same time period is performed, and timestamping is performed; if the integral deviation does not satisfy the second calculation formula, it is considered that the data does not meet the requirements and is directly deleted; the timestamped data is used as the original data, and the original data is low-pass filtered and Kalman filtered to remove noise; the first calculation formula is:

[0104] PC=

[0105] Among them, PC is the integral deviation, P(t) is the absolute value of the deviation of each data corresponding to the historical average value at time t, and n is the total time;

[0106] The second calculation formula is:

[0107] {Max(PC)-Min(PC)}÷Max(PC) <Y

[0108] Among them, Y is the preset contrast margin, Max() and Min() are the maximum and minimum value extraction functions in the data within 10 minutes.

[0109] First, the acceleration and angular velocity information of the vehicle is collected in real time through the inertial measurement unit (IMU). At the same time, the vehicle's geographic location information, including latitude, longitude and altitude, is obtained using the on-board GPS module. In addition, the bump height sensor installed on the vehicle chassis or other key positions is used to detect the vertical displacement caused by uneven road surface during vehicle driving. These data together form the basis for online information collection. The above collected data is transmitted to the microcontroller through the SPI interface. In this process, it is necessary to set the SPI interface channel and configure relevant communication parameters, such as baud rate, data bit order, etc., to ensure accurate data transmission. After the microcontroller receives the data, it first obtains the real-time collected online information in the storage area. Then, the online collected information is analyzed online, and the integral deviation (PC) is calculated using the first calculation formula. Specifically, PC is the integral deviation, P(t) is the absolute value of the deviation of each data corresponding to the historical average value at time t, and n is the total duration. Next, it is determined whether the integral deviation satisfies the second calculation formula. If it is satisfied, the data is considered to meet the requirements, and further multi-dimensional data filtering is performed in the same time period and timestamped; if it is not satisfied, the data is considered to not meet the requirements and is directly deleted. For the filtered data, multi-dimensional data fusion is performed. This includes combining acceleration, angular velocity, GPS information, and bump height information to form a comprehensive data set. At the same time, in order to remove noise and improve data quality, the timestamped data is low-pass filtered and Kalman filtered as raw data. After the above preprocessing steps, a clean and accurate multi-dimensional data fusion data set is finally obtained. This data set can be used for subsequent advanced applications such as posture analysis, data processing, and state judgment.

[0110] Figure 5 The present invention is a flowchart of an implementation method for accurately detecting a vehicle right turn in an embodiment of the present invention, which performs posture analysis online and performs comprehensive data validity judgment in combination with GIS and bump height.

[0111] like Figure 5 As shown, in one or more embodiments, preferably, the online posture analysis and the comprehensive data validity judgment in combination with GIS and bump height specifically include:

[0112] Use the six-axis fusion algorithm to calculate the three-axis Euler angle, which includes the pitch angle, the roll angle and the yaw angle; determine whether the pitch angle, the roll angle and the yaw angle have a jump that satisfies the third calculation formula; if so, start the historical map analysis module, and the historical map analysis module online analyzes whether the bump height of the road section passed in the last minute satisfies the fourth calculation formula. If not, it is considered that the current rotation is abnormal, and if it is satisfied, it is considered that the current data is normal; further determine whether there is GIS auxiliary judgment data for the abnormal rotation, if not, the current data is discarded; if so, determine whether the three-axis Euler angle satisfies the fifth calculation formula through GIS data, if it is satisfied, the data is considered valid, otherwise the data is deleted; the third calculation formula is:

[0113] YD>Y2

[0114] HD>Y3

[0115] PD>Y4

[0116] Wherein, YD is the average change rate of the pitch angle in the last minute, HD is the average change rate of the roll angle in the last minute, PD is the average change rate of the yaw angle in the last minute, Y2 is the second contrast margin, Y3 is the third contrast margin, and Y4 is the fourth contrast margin;

[0117] The fourth calculation formula is:

[0118] YF>ST

[0119] Among them, YF is the maximum bump height of the road section passed in 1 minute, and ST is the preset maximum bump;

[0120] The fifth calculation formula is:

[0121] YD-GP>Y5

[0122] HD-GP>Y6

[0123] PD-GP>Y7

[0124] Among them, GP is the GIS judgment value, Y5 is the fifth contrast margin, Y6 is the sixth contrast margin, and Y7 is the seventh contrast margin.

[0125] Using the six-axis fusion algorithm, the three-axis Euler angles of the vehicle, including pitch, roll, and yaw, are calculated from the data of the accelerometer and gyroscope. These angles describe the rotational posture of the vehicle in three-dimensional space. The third calculation formula is used to determine whether there is an abnormal jump in the pitch, roll, and yaw angles. This formula determines whether there is a jump by comparing the average change rate of each angle in the last minute with the preset comparison margin. If the change rate of any angle exceeds the corresponding comparison margin (Y2, Y3, Y4), it is considered that there is a jump. If an angle jump is detected, the historical map analysis module is started. This module analyzes the bump height data of the road section passed in the last minute to determine whether it meets the fourth calculation formula. If not, it is considered that the current rotation is abnormal; if it is, it is considered that the data is normal. For abnormal rotation, it is further determined whether there is GIS auxiliary judgment data. If there is no GIS data support, the current data is discarded; if there is GIS data, the fifth calculation formula is used for further verification. This formula determines the validity of the data by comparing the difference between the change rate of each angle and the GIS judgment value. If the difference is within an acceptable range (defined by Y5, Y6, and Y7), the data is considered valid. The third calculation formula: used to judge angle jumps, where YD, HD, and PD are the average change rates of the pitch angle, roll angle, and yaw angle in the last minute, respectively, and Y2, Y3, and Y4 are preset comparison margins. The fourth calculation formula: used to judge whether the bump height exceeds the preset maximum bump value ST, where YF is the maximum bump height within 1 minute. The fifth calculation formula: used to make the final data validity judgment in combination with GIS data, where GP is the GIS judgment value, and Y5, Y6, and Y7 are preset comparison margins.

[0126] Figure 6 The present invention is a flowchart of performing average value analysis after obtaining six-axis data in a method for accurately detecting a right turn of a vehicle according to an embodiment of the present invention.

[0127] like Figure 6 As shown, in one or more embodiments, preferably, the obtaining of the six-axis data and performing average value analysis specifically includes:

[0128] After processing the six-axis data, the data is stored after a delay of 20ms and stored in a preset data linked list; the average change value is calculated for the first N data in the data linked list. In an embodiment of the present invention, the six-axis data of the vehicle is collected by an inertial measurement unit (IMU), including three-axis acceleration and three-axis angular velocity. These data reflect the motion state of the vehicle in three-dimensional space in real time. The collected six-axis data is processed, which may include filtering, denoising and other operations to ensure the accuracy and reliability of the data. After processing the six-axis data, a 20 millisecond delay is set. This delay may be to wait for the synchronization of other related data, or to ensure the integrity and consistency of the data. After the delay ends, the processed data is stored in a preset data linked list. A data linked list is a data structure that allows elements to be added and deleted dynamically, and is very suitable for the storage and management of real-time data. The average change value is calculated for the first N data in the data linked list. The "first N data" here can be set according to actual needs, for example, it can be set to data within the last minute or the last second. When calculating the average change value, it is necessary to traverse the first N data in the data linked list, sum the change of each data, and then divide it by N to get the average change value. This average change value can reflect the movement trend and stability of the vehicle over a period of time. The calculated average change value is output and can be used for subsequent advanced applications such as posture analysis, data processing, and state judgment.

[0129] Figure 7 The present invention is a flowchart of an implementation method for accurately detecting a vehicle turning right, for performing state determination online based on average value analysis results, in accordance with an embodiment of the present invention.

[0130] like Figure 7 As shown, in one or more embodiments, preferably, the online status determination based on the average value analysis result specifically includes:

[0131] According to the comparison between the calculated average change value and the preset turning threshold, if it is judged that the sixth calculation formula is satisfied, the right turn state or the right turn state is reached; if it is not satisfied, the turning state is considered to be ended; the sixth calculation formula is:

[0132] PB>Y8

[0133] Among them, PB is the average change value, and Y8 is the preset turning threshold.

[0134] In an embodiment of the present invention, first, the six-axis data of the vehicle, including three-axis acceleration and three-axis angular velocity, is collected through an inertial measurement unit (IMU). Then, the collected six-axis data is processed, which may include filtering, denoising and other operations. Next, after processing the six-axis data, a 20-millisecond delay is set, and the processed data is stored in a preset data chain table. Finally, the average change value is calculated for the first N data in the data chain table. According to the comparison between the calculated average change value and the preset turning threshold, when it is judged that the sixth calculation formula is satisfied, it is considered to be in a right turn state or about to turn right; if it is not satisfied, it is considered that the turning state is over. The sixth calculation formula is: PB>Y8, where PB is the average change value and Y8 is the preset turning threshold. This formula is used to determine whether the turning condition is met. The result of the state judgment is output, which can be used for subsequent advanced applications such as posture analysis, data processing and state judgment.

[0135] According to a second aspect of an embodiment of the present invention, a system for accurately detecting a vehicle turning right is provided.

[0136] Figure 8 The present invention is a structural diagram of a system for accurately detecting a vehicle turning right according to an embodiment of the present invention.

[0137] In one or more embodiments, preferably, the system for accurately detecting a vehicle turning right includes:

[0138] The data acquisition module 801 is used to collect information using an inertial measurement unit to obtain online collected information;

[0139] The data transmission module 802 is used to transmit the online collected information to the single chip microcomputer through the SPI interface;

[0140] The data preprocessing module 803 is used to preprocess the data in the single chip microcomputer to form a multi-dimensional data fusion data group;

[0141] The attitude calculation module 804 is used to perform attitude analysis online and make a comprehensive data validity judgment in combination with GIS and turbulence height;

[0142] The data processing module 805 is used to obtain the six-axis data and then perform average value analysis;

[0143] The state judgment module 806 is used to perform state judgment online according to the average value analysis result.

[0144] In the embodiment of the present invention, a system suitable for different structures is realized through a series of modular designs. The system can achieve closed-loop, reliable and efficient execution through collection, analysis and control.

[0145] According to a third aspect of an embodiment of the present invention, there is provided a computer-readable storage medium on which computer program instructions are stored. When the computer program instructions are executed by a processor, the method as described in any one of the first aspect of the embodiment of the present invention is implemented.

[0146] According to a fourth aspect of an embodiment of the present invention, an electronic device is provided. Fig. 9 It is a structural diagram of an electronic device in one embodiment of the present invention. Fig. 9 The electronic device shown is a general-purpose implementation device for accurately detecting a vehicle turning right. The electronic device can be a smart phone, a tablet computer, or other device. As shown, the electronic device 900 includes a processor 901 and a memory 902. The processor 901 is electrically connected to the memory 902. The processor 901 is the control center of the terminal 900, and uses various interfaces and lines to connect various parts of the entire terminal. By running or calling a computer program stored in the memory 902, and calling data stored in the memory 902, the various functions of the terminal are executed and data are processed, thereby monitoring the terminal as a whole.

[0147] In this embodiment, the processor 901 in the electronic device 900 will load the instructions corresponding to the processes of one or more computer programs into the memory 902 according to the following steps, and the processor 901 will run the computer program stored in the memory 902 to realize various functions: use the inertial measurement unit to collect information and obtain online collected information; transmit the online collected information to the single-chip microcomputer through the SPI interface; pre-process the data in the single-chip microcomputer to form a multi-dimensional data fusion data group; perform posture analysis online and combine GIS and bump height to make a comprehensive data validity judgment; perform average value analysis after obtaining six-axis data; and perform state judgment online based on the average value analysis results.

[0148] The memory 902 may be used to store computer programs and data. The computer programs stored in the memory 902 include instructions that can be executed in the processor. The computer programs may constitute various functional modules. The processor 901 executes various functional applications and data processing by calling the computer programs stored in the memory 902.

[0149] The technical solution provided by the embodiments of the present invention may have the following beneficial effects:

[0150] In the solution of the present invention, a comprehensive determination method for multi-dimensional data fusion is proposed. After acquiring the data, multi-dimensional verification and analysis of the data are performed before filtering.

[0151] In the solution of the present invention, the three-axis Euler angle in the six-axis fusion algorithm is judged, and on this basis, the right turn judgment of multi-dimensional data fusion is realized by combining the road bumps and road turning position analysis.

[0152] It should be understood by those skilled in the art that the embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage and optical storage, etc.) containing computer-usable program codes.

[0153] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0154] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0155] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0156] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for accurately detecting a vehicle turning right, characterized in that: The method includes: Use inertial measurement unit to collect information and obtain online collection information; The online collected information is transmitted to the single chip microcomputer via the SPI interface; Preprocess the data in the single chip microcomputer to form a multi-dimensional data fusion data group; Perform attitude analysis online and make comprehensive data validity judgments by combining GIS and bump height; After obtaining the six-axis data, average value analysis was performed; According to the average value analysis results, the status is determined online; The online posture analysis and the comprehensive data validity judgment in combination with GIS and turbulence height specifically include: Using a six-axis fusion algorithm to calculate the three-axis Euler angle, the three-axis Euler angle includes a pitch angle, a roll angle, and a yaw angle; Determine whether the pitch angle, roll angle, and yaw angle have a jump that satisfies a third calculation formula; If it exists, the historical map analysis module is started, and the historical map analysis module analyzes online whether the bump height of the road section passed in the last minute satisfies the fourth calculation formula. If not, it is considered that the current rotation is abnormal, and if it is satisfied, the current data is considered normal; Further judging whether there is GIS auxiliary judgment data for the abnormal rotation, and if not, discarding the current data; If it exists, the GIS data is used to determine whether the three-axis Euler angles satisfy the fifth calculation formula. If so, the data is considered valid, otherwise, the data is deleted; The third calculation formula is: YD>Y2 HD>Y3 PD>Y4 Wherein, YD is the average change rate of the pitch angle in the last minute, HD is the average change rate of the roll angle in the last minute, PD is the average change rate of the yaw angle in the last minute, Y2 is the second contrast margin, Y3 is the third contrast margin, and Y4 is the fourth contrast margin; The fourth calculation formula is: YF>ST Among them, YF is the maximum bump height of the road section passed in 1 minute, and ST is the preset maximum bump; The fifth calculation formula is: YD-GP>Y5 HD-GP>Y6 PD-GP>Y7 Among them, GP is the GIS judgment value, Y5 is the fifth contrast margin, Y6 is the sixth contrast margin, and Y7 is the seventh contrast margin.

2. A method for accurately detecting a vehicle turning right as claimed in claim 1, characterized in that: The collecting of information using an inertial measurement unit to obtain online collected information specifically includes: Online inertial measurement unit information collection, acceleration, angular velocity; Obtain GPS information through the vehicle-mounted GPS; Obtain bump height information through on-board sensors; The acceleration, angular velocity and six-axis sensor acquisition data, GPS information and bump height information are combined as online acquisition information.

3. The method for accurately detecting a vehicle turning right as claimed in claim 1, characterized in that: The online collected information is transmitted to the single chip microcomputer via the SPI interface, specifically comprising: Set the SPI interface channel; All online collected information is transmitted to the single chip microcomputer at preset intervals through serial communication.

4. The method for accurately detecting a vehicle turning right as claimed in claim 1, characterized in that: The data preprocessing in the single chip microcomputer to form a multi-dimensional data fusion data group specifically includes: Obtaining real-time online acquisition information in the microcontroller storage area; Performing online analysis on the online collected information, and calculating the integral deviation using a first calculation formula; Determine whether the integral deviation satisfies the second calculation formula. If so, the data is considered to meet the requirements, and further filter the multi-dimensional data in the same time period and add a timestamp. If the integral deviation does not satisfy the second calculation formula, the data is considered to not meet the requirements and is directly deleted; The time-stamped data is used as raw data, and low-pass filtering and Kalman filtering are performed on the raw data to remove noise; The first calculation formula is: PC= Among them, PC is the integral deviation, P(t) is the absolute value of the deviation of each data corresponding to the historical average value at time t, and n is the total time; The second calculation formula is: {Max(PC)-Min(PC)}÷Max(PC) <Y Among them, Y is the preset contrast margin, Max() and Min() are the maximum and minimum value extraction functions in the data within 10 minutes.

5. The method for accurately detecting a vehicle turning right as claimed in claim 1, characterized in that: After obtaining the six-axis data, average value analysis is performed, specifically including: After processing the six-axis data, the data is stored after a delay of 20ms and stored in the preset data link table; The average change value is calculated for the first N data in the data linked list.

6. The method for accurately detecting a vehicle turning right as claimed in claim 1, characterized in that: The online status determination based on the average value analysis result specifically includes: According to the comparison between the calculated average change value and the preset turning threshold, when it is determined that the sixth calculation formula is satisfied, the right turning state or right turning; If it is not satisfied, the turning state is considered to be ended; The sixth calculation formula is: PB>Y8 Among them, PB is the average change value, and Y8 is the preset turning threshold.

7. A system for accurately detecting a vehicle turning right, characterized in that: The system is used to implement the method according to any one of claims 1 to 6, and the system comprises: A data acquisition module is used to collect information using an inertial measurement unit to obtain online collected information; A data transmission module, used for transmitting the online collected information to the single chip microcomputer through the SPI interface; The data preprocessing module is used to preprocess the data in the single chip microcomputer to form a multi-dimensional data fusion data group; Attitude calculation module, used for online attitude analysis and comprehensive data validity judgment in combination with GIS and bump height; Data processing module, used to obtain six-axis data and then perform average value analysis; The status judgment module is used to perform status judgment online based on the average value analysis results.

8. A computer-readable storage medium storing computer program instructions, characterized in that: The computer program instructions implement the method according to any one of claims 1 to 6 when executed by a processor.

9. An electronic device, comprising a memory and a processor, characterized in that: The memory is used to store one or more computer program instructions, wherein the one or more computer program instructions are executed by the processor to implement the method according to any one of claims 1-6.

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